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                        INDEX
  Welcome and Opening Remarks                         3
    Dr. Steven K. Galson

  Introduction to Program                             6
    Dr. Deborah Norris

  Integrating Neurotoxicology Assessments into       9
  a Pollution Prevention Program
    Dr. John O'Donoghue

  Neurobehavioral Evaluations of Binary and         37
  Tertiary Mixtures of Chemicals:  Lessons Learned
    Dr. Virginia Moser

  The Toxicity Profile of a Mixture  of  Two          60
  Anticholinesterase Insecticides, Chlorpyrifos
  and Diazinon in Adult Male Rats
    Dr. Stephanie Padilla

  Neurotoxicity Chemical Mixtures: Example of       75
  Ototoxicity Produced by Combined Solvents
    Dr. Will Boyes

  Integrating Dose-Response and  Kinetic             93
  Properties of Pesticides During  Cumulative
  Risk Assessment
    Dr. Gregory Christoph

  Aggregate and Cumulative Exposure                 138
    Dr. Mohamed Abou-Donia

  Dose Additivity?:  The Interaction  of  Drugs       155
  on a Behavioral Baseline
    Dr. Anthony Riley

  USEPA Mixtures Risk Guidelines and the           180
  Mixtox Database: Lessons Learned
    Dr. Richard Hertzberg

  OECD Guidelines for Health Risk  Assessment       201
    Dr. Herman Koeter
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 1                      PROCEEDINGS

 2                                           Time:   9:10  a.m.

 3                  MS. SJOBLAD:  Good morning, everyone.  I'm

 4     Beverly Sjoblad from the Office of Pesticide Programs.

 5     We would  like  to welcome  each and every one of  you to

 6     the   symposium  on the   neurotoxicity  of   mixtures,

 7     jointly  sponsored by the   OPP   Technical Training

 8     Committee  and  the  OPP  Scientific  and   Technical

 9     Training  Committee.

10                  A  full day has been scheduled.  So without

11     delay,  I'd like  to begin our program by introducing

12     Dr.  Steven Galson, Director  of  the OPP-TF  Office  of

13     Science,  Coordination and Policy.  Dr. Galson.

14                  DR. GALSON:   Thank you very much,  and  I

15     wanted  to  welcome all of  you  to the  symposium  on

16     neurotoxicity  of mixtures.   I wanted to particularly

17     thank  the  organizers  in   the  Technical  Training

18     Committee of OPPT and the  Scientific and  Technical

19     Training  Committee in OPP, particularly Debbie Norris,

20     Beverly Sjoblad,  Trish Coleman,  Ethel Brown and John

21     Blowen.

22                  It's a very  important event to get this

23     group of  people together.  We need to do  it  more, and

24     I thank you for your efforts to bring us here together

25     on this important topic.

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 l                  I also want to thank the speakers who are

 2     here from industry, from university centers,  and give

 3     a particular thanks  to Herman Koeter for leaving the

 4     springtime  in  Paris.  Where are you,  Herman?   It's

 5     probably raining  there,  too,  but Herman is truly one

 6     of the most patient people in the continent of  Europe,

 7     I'm convinced,  and we're very grateful  to him for the

 8     important  work  that  he  is  doing  on  harmonizing

 9     guidelines  in the OECD.

10                  These guidelines are becoming increasingly

11     important  as the world  and the chemical  regulation

12     world becomes more globalized, and he's  really the one

13     who facilitates the  involvement of  so many EPA  staff

14     and American government officials and  scientists in

15     this important work.   So  thank you,  Herman.

16                  Why is this symposium important?    For the

17     first  thing,  it's   sponsorship.     These  training

18     committees   that   got  together  to   provide   this

19     opportunity  are doing work  that we  don't have enough

20     of.    There  isn't   enough  education  and  career

21     development  for  the  scientists in  our part  of  the

22     agency.

23                 As you probably know,  we're  the largest

24     group of scientists  in EPA outside of  the Office of

25     Research   and   Development,   and   it's   critically

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 1      important for us  to get together  as a group  to see

 2      each other's  faces  and get  to talk about  issues,

 3      particularly pressing issues,  relevant issues such as

 4      this.

 5                  The second major reason this is important

 6      is   that  neurotoxicity  is  a  very,   very  critical

 7      scientific issue.   It's received more and  more focus

 8      over the last few  decades  with the realization that

 9      even small amounts of neurotoxins, particularly during

10      sensitive periods in infancy and childhood and fetal

11      life, can result in profoundly deleterious  effects.

12                  It's  very  important  that  we  get  risk

13      assessment for neurotoxins right and  that  we keep  up

14      with the state of  the  science.   So the relevancy  of

15      this topic is really, really perfect.

16                  The  other  --   third   reason  that  it's

17      important is that the adjectives that were placed  on

18      the  title,  the analysis of mixtures,  the analysis  of

19      cumulative risks, aggregate risks,  these are all areas

20      that, I  think we would  all agree, are  very  important,

21      that we don't know enough about. We don't have  enough

22  ;    focus.

23                  We  know  that  multiple  simultaneous

24      exposures  to  neurotoxins  clearly  have   different

25      €;ffects  than exposure to those chemicals alone.  Yet

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 1     we  don't  have good methods  to  do risk assessment  in

 2     these  areas.

 3                 Under the  Food  Quality  Protection Act

 4     mandate  that  OPPTS   received,  we  have  really  gone

 5     farther than  anybody else in the country and anybody

 6     else  in  the world on developing these  kind of  risk

 7     assessment  methods and  guidelines.   So it's  really

 8     important that  we talk about this and that everybody

 9     is  aware of what's going on,

10                 We  are really on the  cutting edge in  this

11     area.  So it's,  again,  a perfect reason to be  here.

12                 So  I look forward to hearing at least  some

13     of  the day  today,  and  I wish all  of  you  a  good,

14     thoughtful learning  experience, and I'm really happy

15     to  be  here and  happy  that we are doing it.   Thanks.

16                 DR.  NORRIS:   Thank you,  Dr. Galson, and

17     thank you all for being here today.  Welcome and  good

18     morning.

19                 First, I  must tell  you that today  at our

20     symposium on  the  neurotoxicity  of chemical  mixtures,

21     we're not here  in any way to review or establish EPA

22     policy.   We  are here  rather  for  the  pleasure and

23     opportunity of  scientific exchange, which means  that

24     we  can discuss  scientific concepts,  ideas,  and data

25     for the sheer joy of  it,  and it is a great  joy to me

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 1     to do  that.

 2                  As a  self-proclaimed expert on the brain,

 3     I was  talking yesterday with a self-proclaimed expert

 4     on the human mind,  Dr.  Tara Brock.   Dr.  Tara Brock

 5     told me that the mind has about 80,000 thoughts a day.

 6     Knowing  the  brain  and   how  it  functions,  I  added

 7     another  thought to my cumulative total for the day,

 8     and that was that the brain probably processes even

 9     more than  that each day.

10                  Then  Dr. Brock added that 98  percent of

11     these  thoughts  we  have  each  day  are  ones  we've

12     probably  already had.    I  hope  to  change  those

13     statistics today.    I  think  that  we  will  greatly

14     broaden  our concept  of the science of neurotoxicology

15     and the  neurotoxicology  of mixtures.

16                  Today we have the opportunity to hear from

17     some of the world's  experts in neurotoxicology and in

18     the regulatory guidelines that we use and  consider in

19     evaluating  toxicity.      I  have  the  pleasure  of

20     introducing to you this morning's speakers, and I will

21     introduce  them all to you now and then briefly one at

22     a time as  they speak:

23                  Dr. John O'Donoghue who is Director of the

24     health and  Environmental  Laboratories for  Eastman

25     Kodak  Company, and I know him as  the editor of several

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                                                            8

 l     volumes  on the neurotoxicity of industrial  chemicals

 2     which  I  use as references frequently.

 3                  Dr.     Ginger    Moser,     who    is    a

 4     neurotoxicologist from the Neurotoxicology Division of

 5     EPA's  laboratories  in RTF.  Ginger has  received EPA's

 6     highest  scientific  achievement award for her work on

 7     EPA  test methods  and guidelines.

 8                  Dr.  Stephanie  Padilla  is  Chief  of  the

 9     Cellular and Molecular  Toxicology Branch, and  she is

10     also  from  the  Neurotoxicology Division   of   EPA's

11     laboratories at RTP.

12                  Dr. Will Boyes --  have  you arrived yet?

13     No.  The last  I heard, Dr. Boyes' plane had  landed in

14     Baltimore,  and due to  the  weather  or due to some

15     unforeseen event.   So  I've  asked  --  If  Dr.   Boyes

16     doesn't arrive, in an effort to stay  on schedule, I've

17     asked  Dr. Abou-Donia  if he would come forward to this

18     morning's  talks,  in case that happens.

19                  Dr.    Abou-Donia    is   Professor    of

20     Pharmacology and  Cancer and Neurobiology at the Duke

21     University Medical  Center.

22                  Dr.  Greg  Christoph,  who  I'm  so   happy

23     you're here with us today,  because he  is retiring

24     tomorrow as head of  Neurotoxicology at DuPont Company.

25                  So we are very lucky and  delighted to have

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  these speakers with  us  this morning.  Thank you, and

  I will let Dr. O'Donoghue  begin our program.

              DR. 0'DONOGHUE:   Good morning.   I'm very

  happy to be here.  I  have a little bit of trepidation,

  because every time I've been moving my digital slides

  between  PCs,  things  have changed,  and nothing has

  migrated since the last change.

              As Debbie said,  I'm John O'Donoghue with

  Eastman Kodak.   What I'd  like to talk  to  you about

  this morning is how we integrate neurotoxicology into

  our pollution prevention program.

              My  company  is   not  in  the   pesticide

  business.   So  we are not  really going  to  talk very

  much  about  OPs  or  pesticides,   but  rather  some

  relatively common and mundane materials.  Our focus is

  oftentimes,  when  we   find  a  material   that  has

  neurotoxic properties, is  to find what the  limits of

  the effect are.  In many cases,  it's to eliminate it

  from the supply chain.

              So in many cases, we are not trying to do

  a quantitative  assessment.   We  are trying  to think

  through  the   process  of  identifying  materials,

  identifying safe  levels,   and elimination  through  a

  pollution prevention project.

              Now the types of materials I want to talk

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 1     about are constituents of relatively common materials,

 2     paints,  adhesives,  inks  and greases.  We use a lot of

 3     different mixtures  in our everyday  activity,  and what

 4     we  are  concerned about  is  how  these materials  might

 5     interact.

 6                  This is just an overhead of  what  we  would

 7     consider the manufacturing process to be.  So within

 8     the box  you  actually have the manufacturing process.

 9     Then  what  I've  done highlighted  in gold  are  those

10     aspects  of  the  process  where we  are looking  at

11     assessing chemicals.

12                  So  the  process  isn't   just to  look  at

13     products or  co-products.   We  are  looking  at  raw

14     materials  that  come  in.   We  are  looking  at  the

15     manufacturing process,   whether  heat  is supplied  to

16     those materials, whether they become aerosolized,  how

17     they are used,  and  if  the product  is to be reused or

18     recycled,  is there  anything in the  product  that  is

19     going to be  potential  during the  recycling process.

20     Then we  look at what are our potential  waste due  to

21     air  emissions,   water  emissions  or  other  types  of

22     releases.

23                  The main thing  we don't look at is on the

24     left, which  is kind  of earth, wind  and fire, which is

25     going back to actually the mines and looking at  some

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                                                           11

 1     of  the  raw materials.  So  we  are looking at lots  of

 2     different types of materials used in lots of different

 3     ways.

 4                  Now   the  process   we  use  is  a   fairly

 5     straightforward process,  and it actually has a  lot  of

 6     similarities to the  process that's  used  for assessing

 7     materials  under  TSCA.  One  of  the  things we look  at

 8     very  early  on is analogous materials.   Do  we have

 9     chemicals  in our  historical toxicology file that can

10     tell  us something  about  a new  material  or  how a

11     material might interact with the  mixture.

12                  We look  at exposure,  production,  and how

13     people are going to use the  materials.  Based on that,

14     we do a preliminary chemical evaluation.   We decide  at

15     that point  if there is data that indicates  there  is

16     interaction   or   there  is  neurotoxicity.    We  may

17     eliminate the material at that point.

18                  If not,  then the material moves on to what

19     we call a testing strategy. We decide what types  of

20     tests we might run  on those materials,  conduct the

21     test, evaluate it, and then cycle back through.

22                  We also have programs  to look at what I've

23     put down as  process control and  health assessments.

24     We have industrial hygiene  data available to us.   We

25     have  employee  medical  information,   not  for  the

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                                                           12
 1     individual  employees but  for groups of employees,  and
 2     we  also  operate  an  800  number for   customers   or
 3     consumers,  if they have a question.
 4                  So this  is part  of the process.   If  we
 5     were  to detect something post-marketplace, we have a
 6     process  then  to  go back  in  and  reevaluate   the
 7     material.
 8                  Now one of the interesting  -- or latest
 9     aspects of  this is we've been  working with Bill  Law
10     from your office to integrate the pollution prevention
11     framework into our assessment process.   This doesn't
12     have  specific  aspects of  it  that   are  related   to
13     neurotoxicity,  but there are some general pieces  of
14     information  that  are  in  the  pollution  prevention
15     software that we  integrate  into our  process.
16                  So,  for example,  we are  able to estimate
17     chemical  and  physical properties of the materials
18     based on structure activity relationships.   We look at
19     housing and the environment, and how was this material
20     apt  to  be   transformed,  and  is  it  going  to   be
21     transformed into something which is a neurotoxin,-  and
22     we look particularly at  cancer hazard potential.   Is
23     there something about the structure  of  this chemical
24     that would have some  alerts from the point of view of
25     producing cancer  in the nervous system?
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                                                           13

 l                 So  this has  become part  of our overall

 2     assessment  process.   Now the types  of  effects  that

 3     we're  talking about fall in a variety  of different

 4     categories.  Chemicals can have a number of potential

 5     biological  effects.   Some  of  them  are  relatively

 6     direct in  that  they can  cause  irritation to sensory

 7     organs, irritation to the eyes,  to the nose.

 8                 There  can be some general C&S toxicity due

 9     to effects  on,  say, the  liver or the kidney.    Then

10     there are some specific effects that  chemicals have  on

11     the nervous system.

12                 When we're talking about  the  mixtures, I'm

13     talking  primarily  about  pharmacologic and neuro-

14     degenerative  effects  that   chemicals  or   chemical

15     mixtures can  have, not  the  indirect effects through

16     C&S depression or  irritation.

17                 There  are  also potentially  effects  that

18     are neuro-oncologic effects  that can be  caused  by

19     chemicals.   These are  relatively  rare,  but  it  is

20     something that has to be  considered in assessing a new

21     chemical.

22                 Now   the  possible   relationships    that

23     materials   have   to  each   other  with  regard   to

24     neurotoxicity are not that different from other organ

25     systems,  in that  we are  concerned about if you  have

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                                                           14

 l      two materials  that  have the  same effect, they  both

 2      produce peripheral neuropathy, if there is a  combined

 3      exposure,  is that effect additive?

 4                  Are there synergistic effects in that two

 5      chemicals  will have  the  same  effect,  but when a co-

 6      exposure occurs,  the effect is much greater than  what

 7      the additivity effect would be.

 8                  Potentiation is a  situation where we  have

 9      a  mixture  which  contains  a   neurotoxin  and other

10      materials which are not neurotoxic, and whether or not

11      those materials enhance the neurotoxicity of  the  main

12      agent.

13                  Then   inhibition:     Although  we   see

14      inhibition  less  commonly  than  some  of  the other

15      effects, we do see materials  that,  in fact,   inhibit

16      neurotoxicity.

17                  Now what I've  done  on this slide is  put

18      together some  factors  that  affect  how chemicals

19      interact in a  mixture  situation that we have to  be

20      concerned about.  The first is the rate and extent of

21      some  of  the  pharmacokinetic   properties  of   the

22     material.

23                  For example,  absorption:  If  you've got a

24     mixture  and you've got two neurotoxic materials,  if

25     they are absorbed at different  rates,  in fact,   the

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  effects may not impact on each other.   They may not  be

  additive or synergistic.

              For  example,  hydroquinone,  which  is  a

  common black  and white developer, at  high doses has

  effects on the  nervous system that cause tremor and

  convulsions if the dose is high enough.  However, the

  material is very water soluble.   It's absorbed very

  rapidly.   The effects are  produced within  about  15

  minutes, and the material is excreted with a half-time

  of about 17 minutes.

              So  if you have  a mixture that  has two

  materials,  one  is absorbed rapidly, metabolized and

  excreted,  and the other  material  is not  absorbed as

  quickly, what  may happen  is, yes, the effects on paper

  could be additive, but in fact  they are not,  because

  of the pharmacokinetic parameters.

              That's also true of metabolism, the rates

  of metabolism.    It  could  very  well be  that  two

  materials  have   exactly  the  same effect,  but  they

  produce that effect through a mechanism which requires

  metabolism to a common metabolite, and if the extent

  or the rate of that metabolism isn't  similar with the

  two materials, there may  not be  an interaction.

              Another factor which has an impact is the

  exposure route.    We  have  seen  materials  like  3-

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  1      heptanone.  3-heptanone is a material which is present

  2      in many foods as a flavoring agent.  It's in perfumes,

  3      and we've looked at 3-heptanone from the point of view

  4      of trying to produce neurotoxicity with it, because if

  5      you look at the structure,  it  ought to be metabolized

  6      to a  common neurotoxic gamma diketone.

  7                  In fact, with 3-heptanone what you find is

  8      that,  by  inhalation,  you can't  get enough  material

  9      into  the  body  to produce  the gamma  diketone  which

10      would result in  a neurotoxicity.   However, if  you

11      gavage  the animals,  you can produce the  effect.   The

12      reason  we think this  is the  case  is that,  by giving a

13      very large bolus dose, we're increasing the likelihood

14      of a  first  pass  metabolic effect  in  the liver  that

15      results in neurotoxicity.

16                  So  if you  are assessing the material,  and

17      the data you have is  oral,  it may not be  relevant  to

18      the inhalation  situation or  vice  versa.   You may find

19      that,  if  you  test   it by  inhalation,  you  see  no

20      neurotoxicity.  You test it by the oral route, and you

21      do.

22                  Mechanisms of action:  Common mechanisms

23      of action are obviously situations in which additivity

24      is likely to occur.   I put  down -- The last  issue  is

25      the blood-brain barrier, because there are a  couple of

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                                                           17
 l      aspects  of this which are important.

 2                  One is  that  some materials can damage the

 3      blood-brain barrier.    So  you  may have  a material

 4      which,  when tested  alone is not  neurotoxic,  but  if

 5      tested with a  material which somehow or other alters

 6      the blood-brain barrier,  it would  allow that material

 7      to  get   into   the   brain  and,   therefore,  produce

 8      neurotoxicity.

 9                  Now there are also some slight differences

10      in various species  of animals that tell us  that  there

11      are  conditions where  the blood-brain barrier  is  a

12      limiting  factor  in  neurotoxicity;   for  example,

13      iramectin.

14                  Iramectin is a parasiticide, and it's been

15      --It can  be  used  in most  strains of dogs without

16      producing  any  neurotoxicity,  but  in  some of the

17      smaller  collie  breeds  the blood-brain barrier is not

18      as effective as other brains of dogs.  What you end up

19      seeing is neurotoxicity  in those dogs.

20                  It's the same material with the different

21      breeds,  but in  fact, because  there's a defect in the

22      blood-brain barrier in one breed versus the other, you

23      are seeing the  effect.   So there  is a concern  about

24      whether  the chemicals  would  alter the  blood-brain

25
barrier.
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                                                           18

 1                  Now what I've done is I  have put  together

 2     some  scenarios for materials that we've  seen and had

 3     some  concern about how they might interact with regard

 4     to  a  mixture.   So  the  first  one,  I   think,  is  a

 5     relatively simple scenario.

 6                  That is that we have chemicals which are

 7     similar.   They  have the  same metabolites,  and  they

 8     have  the  same molecular  target.   So we would  expect

 9     here  that  the effects that we would see would be of an

10     additive nature.

11                  An example of these materials is  shown on

12     this  slide.   Now  on the upper lefthand corner  is  a

13     chemical diagram of hexane.   In  the upper righthand

14     corner there is a diagram of  methyl N-butyl ketone.

15     These materials are similar except for the carbonyl.

16                  In the body  both  of  these materials  are

17     metabolized  to common metabolites.   In fact, N-hexane

18     can be metabolized to methyl N-butyl ketone.   So  that

19     we end up  with a common metabolite,  2,5-hexanedione;

20     and a 2,5-hexanedione itself is the most neurotoxic of

21     these metabolites.

22                  Now we have tested these  materials in  rats

23     and looked at  them from a comparative point of view.

24     On this slide what I have is the first line shows  the

25     methyl N-butyl  ketone  tested at 6.6  millimolar.

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  1                  Now in comparing these materials,  we use

  2     millimolar doses as compared to milligram per kilogram

  3     doses,  because the body  --  These chemical  reactions

  4     occur on  a  molar basis,  not  on  a  milligram  per

  5     kilogram basis.  So doing the comparisons,  it makes a

  6     lot more sense to  use a millimolar basis for this.

  7                  What we have here is rats with neuropathy.

  8     That  is, they  are  showing some  clinical sign,  some

  9     sort  of weakness or  some sort of  sensory change  or

10     they have some histologic evidence in their peripheral

11     or central nervous  system.

12                  Then we listed the number  of  days it  took

13     for  those animals  to  reach the  endpoint,  and  the

14     endpoint in  this case  was  that at least one of  the

15     legs of the animals was paralyzed by the material.  So

16     it's  a  pretty severe  endpoint,  but  it's  one  that

17     doesn't  change very much.

18                  What  you  find  with  these materials  is

19     that,  as the  animals  are dosed, the  clinical state

20     varies  slightly from  day to day.   So it's  hard  to

21     pinpoint a point in time  when  you can say the effect

22     is quite similar.  By  choosing this endpoint,  which is

23     stable,  we can do a better  comparison.

24                  Then we've calculated what I refer to  here

25     as a neurotoxic index.  This neurotoxic index is based

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 1      on the  number of  days  that it  took  for the  MNBk

 2      animals to be  paralyzed.   So because  it took  55.8

 3      days,  any group  that had its endpoint  at that  same

 4      time would have a neurotoxic index of one. Any group

 5      that  took less time  would have  an index  of  greater

 6      than one or be more neurotoxic.   So  it's  a basis for

 7      comparing the relative effects here.

 8                 Now with these  two  materials, you might

 9      expect  that the effect  would be additive,  but  we  also

10      dosed  these  animals  with an equi-molar  dose of  Ki-

ll      hexane,  and what you find out with N-hexane is  you see

12      no neurotoxicity  with this material.

13                 Now  these  dose  levels  correspond  on  a

14      milligram per kilogram  basis to somewhere  around  600-

15      700 milligrams per kilogram.  So  the N-hexane  dose

16      here is quite a substantial  dose.   It's not a minor

17      dose  but,  in  fact,  there   is  --  Even  though  the

18      metabolites are the same and you're giving the  same

19      molar amount  of the material, the effect you're going

20      to get  here is  not  additive.

21                 Now we doubled  the  dose  of  N-hexane  to

22      13.2 millimolar,  and  that wasn't effective.  Then  we

23  i    raised  it  up  to 46.2, and 46.2 is as much material  as

24      we could get  into the animal.  That was the physical

25      limitation here.   It's  about four grams per kilogram

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 1      of  the material.

 2                  when  you give  that  dose,  you  do  get

 3      clinical  effects.   Here  we got  three  of  the  five

 4      animals  had  effects,   and  four  of   the   five  had

 5      histologic  effects;  but it took almost twice  as much

 6      time  as with MNBk.  So  the  effects  here -- You have

 7      the same  metabolites.   The effects are not  additive.

 8                  We also looked at  a  material  called

 9      practical grade  hexane.   Practice  grade  hexane  is

10      actually  what  most people  are familiar  with.  This is

11      actually  a mixture of hexanes.  It's about 40  percent

12      N-hexane.  It  has  cyclohexane in it,  methylpentane in

13      it, and a variety of other materials.

14                  Our original concern with this mixture is

15      we  were concerned that this mixture would,  in  fact,

16      increase   metabolism   of    N-hexane   and   be   more

17      neurotoxic.    In  fact,   what we found  was  that  the

18      neurotoxicity  was  no more  than with hexane alone.   So

19      these mixtures of hexanes didn't have any interaction.

20                  Now there is another way to look at  these

21      materials.     Hexane,   MNBk,   2-hexanol   and   2,5-

22      hexanedione are all commercial  -- have been commercial

23      materials.  N-butyl ketone now, there's a significant

24      new use rule on,  and 2,5-hexanedione itself has  had

25      relatively minor  use.   But  in fact, these materials

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  1      could be used in combinations in some products.

  2                  So we  looked at  how we would go  about

  3      trying to compare  these  materials  and how  to  figure

  4      out  on  a  relatively simple  basis  how  to  judge  the

  5      relative activity  of  these   materials  if  they  are

  6      combined.

  7                  Now this data is  very similar to the data

  8      that  I showed you before on the neurotoxic  index,  in

  9      that  for each of these materials we  gave them the same

10      millimolar  dose,  and we  then looked at the onset  of

11      neurotoxicity.   We divided that  by the time it took

12      for the MNBk animals.

13                  So MNBk  here has a  neurotoxic index  of

14      three.   N-hexane, because it  took a higher dose and a

15      much  longer period of  time to create neurotoxicity,

16      has a neurotoxic  index  of --  I think it was  less than

17      .1.  Then 2, 5-hexanedione, which  is the last material

18      at the top  of the  screen, it  took  a third  less time

19      for  the animals  to develop  neurotoxicity.   So,   in

20      fact,   it   was   three  times   more   neurotoxic  than

21      hexanedione.

22                 The other part of  this relationship is the

23      serum  concentration of 2,5-hexanedione,  because you

24      can see  that  there  is a relationship between how the

25     neurotoxic index in the  peak concentration -- the more

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 1     2,5-hexanedione was formed  by these  materials, the

 2     more neurotoxic it  was.

 3                  So this allowed us a  basis for actually

 4     combining these exposures and for looking at exposures

 5     to  other materials,  because  we   now  knew  that the

 6     effect was  based on the  serum 2,5-hexanedione level.

 7     We could measure that  in blood.   We could measure  it

 8     in  serum,   and  we  could  measure   it  in  urine.

 9                  Therefore,    it   became   a   convenient

10     biomarker of exposure,  and also a convenient biomarker

11     of effect,  because you  can actually quantitate at what

12     level in the animals you will see neurotoxicity based

13     on a 2,5-hexanedione level.

14                  This parameter is  one  that's recommended

15     now by  ACGIH as  a  biological  index for exposure  to

16     hexane and  related materials.   So it  is possible  to

17     calculate how these similar materials are going to add

18     together,  but they  don't add together in  a  simple

19     fashion.   It's  not  a  matter of  adding one mole  of

20     hexane to one mole of methyl N-butyl ketone and coming

21     up with two.  It's a matter of understanding what the

22     metabolic pathways  are and how they can be combined

23     based on that  common pathway.

24                  Now  the second scenario I put together  is

25     one in which there  are similar classes of chemicals,

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 1     and  in this  particular case  there are  ketones  and

 2     other  similar  alkane solvents  that  have different

 3     metabolites.   They  are  not metabolized  in the  same

 4     way, but  there  are metabolic interactions that occur

 5     with the  mixture.

 6                 This  is a mixture that  was proposed  for

 7     use in paints and for plastics.   This mixture was  sent

 8     to us for evaluation, because there was a  low level of

 9     MNBk in  it.   It's  less  than one percent MNBk.    Our

10     first thought with this was,  well, that's  probably not

11     going  to  be  enough  to  cause   the  solvent  to  be

12     rieurotoxic.   There  is  also some 2-heptanone  in  it,

13     which was a little bit of concern, because there are,

14     at least  in  theory, ways in which  2-heptanone could

15     form a gamma  diketone.

16                 So  our initial  read  on  this  mixture  was

17     this  probably  isn't  a problem,  but we  did  some

18     screening level tests on it  and found that one of  the

19     components,  5-nonanone  which  was  in there  at   12

20     percent, when we actually tested  the  mixture, we found

21     that 5-nonanone was metabolized  to methyl  N-butyl

22     ketone.

23                 I  haven't  shown you  the  whole  pathway

24     here.  The methyl  N-butyl  ketone then is  metabolized

25     to 2,5-hexanedione.  So the potential pathway here

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 1     between  this  and  MNBk   is   very  similar.     The

 2     biochemistry is very similar except in this particular

 3     case  we're getting decarboxylation,  which makes  the

 4     material  a lot more neurotoxic than you might  expect

 5     otherwise.

 6                  So before  we  had  discovered  this,   we

 7     started off looking at  the  components  of the mixture.

 8     This table  shows again the chemicals that we  looked at

 9     and  the mixtures  on  the left,  the day of  onset  of

10     neuropathy, the number of days it took before  we  saw

11     any evidence of clinical neuropathy, the most  severe

12     clinical  neuropathy,  and a +3  here  is the animal  is

13     dragging  one feet.  A  +1 is that the animal doesn't

14     completely extend the foot, and  a +2  indicates that

15     the animal is having some  problems  with placing  the

16     foot.   They tend  to  trip or misplace  the foot,  but

17     they are  able to  walk.

18                  Then  the next  column  is  the number  of

19     animals  with  clinical  neuropathy at  the  end  of  90

20     days,  and the last column  is  the number  of animals

21     that had  histologic changes at the end of  90 days.

22                  So with   the  original  material,   this

23     commercial  grade methylheptylketone --it was given at

24     2,000 milligrams  per  kilogram  --  we got a  neuropathy

25     at about 60 days.  It was severe, and all the animals

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 1     eventually had  clinical signs or histopathology.

 2                 Now we also looked at the main ingredient

 3     of this material, which was 5-methyl-2-octanone, and

 4     we tested it at 2,000  milligrams per  kilogram.   In

 5     fact,  we saw  no effect  with this  material  from a

 6     neurotoxicity point  of  view.

 7                 The other material that we  looked at here

 8     was  5-nonanone.  It's  shown  here as N-O-N,  and the

 9     high  dose that we  used  was 2,000  milligrams per

10     kilogram, and we  got neuropathy in 11  days.   It was

11     severe, and in  this  case  the  screening level animals

12     all had it.   They all had  -- Both of them had clinical

13     signs, and both of them had neurotoxic effects.

14                 Now we dropped the dose  down,  and we got

15     a similar effect.    We  dropped  the dose down  to 233

16     milligrams per  kilogram.   This equates to the amount

17     of 5-nonanone that was in  the  original mixture. So the

18     12    percent    5-nonanone   in    the    commercial

19     methylheptylketone   equates   to  233  milligrams per

20     kilogram,  and  here we  saw  no  neuropathy  in the

21     animals,  even   though   when we  looked   at   them

22     histologically  there was  a very  slight neuropathy.

23                 So  there wasn't enough 5-nonanone in this

24     mixture to account for the neurotoxicity that we were

25     seeing.   There  wasn't  enough MNBk in  the  mixture  to

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 1     account  for that or enough of the 2-heptanone.

 2                  So what we  did is we went  back, and  we

 3     reconstituted the mixture  with  the  two materials  we

 4     had the greatest concern about, and that  was 5-methyl-

 5     2-octanone   and  5-nonanone.    So we recreated the

 6     mixture  and dosed  the animals  at 2,000 milligrams per

 7     kilogram.

 8                  In fact, what we saw was  that we produced

 9     a  neuropathy  that  was  similar  to  the  commercial

10     mixture.  All  the  animals had  histologic changes, but

11     they didn't all  have clinical  signs.  So by combining

12     the two  materials, we  got an effect that was similar

13     to the mixture, but wasn't even quite  as  severe as the

14     mixture.  It was slightly less.

15                  So there  are  other  components  in  this

16     mixture  that  are  probably contributing  to this.

17     Probably the MNBk and the  2-heptanone  are adding  --

18     have an  additive  effect,  but  the effect  that  we're

19     seeing here is  a  promotion of the metabolism  of  5-

20     nonanone to its ultimate neurotoxic endpoint,  which  is

21     again a  diketone.

22                  Now  because  5-methyl-2-octanone would  do

23     this, we were  concerned about  other mixtures.  Having

24     studied  other  mixtures of ketones, we were aware that

25     methylethylketone, which is a  very common solvent  --

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 1     methylethylketone will potentiate the neurotoxicity of

 2     methyl N-butyl ketone.  It enhances the production at

 3     2.5 hexanedione.

 4                 So our thought here was, well, gee, would

 5     methylethylketone  potentiate  the  effects  of  5-

 6     nonanone.   In fact,  what we  found,  it didn't.   We

 7     dosed these animals in a comparable way to the animals

 8     that got 5-methyl-2-octanone,  and it didn't.

 9                 The reason we believe  this is the case is

10     because what the 5-methyl-2-octanone is doing is not

11     promoting the metabolism  of  MNBk,  but  it's promoting

12     the metabolism of 5-nonanone to MNBk.  It's affecting

13     the decarboxylation reaction but not the oxidation of

14     2, 5-hexanedione; and because MEK doesn't do that, you

15     don't see the potentiation in this particular case.

16                 Now this  is a summary of a  number of the

17     mixture studies that we've done.  We've looked at MEK,

18     and it potentiates N-hexane.   It potentiates methyl N-

19     butyl ketone.  It does not potentiate  5-nonanone.

20                 We've looked at 5-methyl-2-octanone which

21     potentiates the effect of 5-nonanone, but it doesn't

22     potentiate  the effects of  another  material  which

23     produces  a  gamma  diketone,  which   is   ethyl  N-

24     butylketone.

25                 Others  have  looked  at  the  effects  of

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 1     toluene,   and   toluene,    in  fact,   inhibits   the

 2     neurotoxicity of N-hexane.   This  is  an interesting

 3     inhibition,  because  I'm  sure Dr.  Boyes  is going  to

 4     talk about toluene.  Toluene itself is  neurotoxic.   It

 5     produces  auditory problems.

 6                  Here you have  two  neurotoxic materials,

 7     one that  produces an auditory change,  N-hexane which

 8     produces  a  peripheral neuropathy.   You  give them

 9     together, and you don't get the peripheral neuropathy.

10     So you might think that, because they are neurotoxic,

11     they would  have some kind  of interaction.   In fact,

12     they  do,  but  it's  the opposite  of what  you would

13     actually  expect.

14                  Now this scenario I put together involves

15     significantly different  chemicals.   On  a molecular

16     basis, the  chemicals are very different.   They have

17     different  metabolites.    They   have  a  different

18     molecular target.   They don't operate by exactly the

19     same mechanism.   Yet they produce the same effect  in

20     the animal.

21                  I'm sure later in the day people are going

22     to talk about organophosphates .   I put this  cartoon  up

23     here   in   case  you're   not   familiar   with  the

24     neuromuscular  junction.   This  is  a cartoon  of  the

25     neuromuscular  junction.

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 1                  This is the presynaptic area.  This is the

 2      synaptic cleft and the postsynaptic  area.  Now  what

 3      happens  here is  the action potential  comes down.   You

 4      get  calcium influx into  the  neuromuscular junction.

 5      You  get  fusion  of  the  synaptic  vesicles  with  the

 6      presynaptic membrane and release of acetylcholine into

 7      the presynaptic  cleft, and then you get binding on the

 8      postsynaptic membrane, and you get an  action potential

 9      in the muscle.   The  muscle contracts.

10                  Now   what  causes  this   to   reverse   is

11      acetylcholinerase, which breaks down the acetylcholine

12      and reduces its  effect  on the postsynaptic membrane.

13      Now these membranes  then are  --  These vesicles then

14      are recycled back to  the  cell body.

15                  Now  with this next material,  this is  4-

16      nitropyridine-n-oxide.  It was  an R&D  material that

17      people   were  pretty  excited  about,  and  we  were

18      concerned  about  it,   because  pyridines  can  have

19      neurotoxic  effects.

20                  So we did  some  screening  level  studies

21      with  this material,  and  this is the acute toxicity

22      data that we got from this material,  which caused  us

23      to be very  concerned.   You see the dermal LD£0?  The

24*!    dermal LDSO  is a  gram per kilogram.   The  oral  LD50  is

25      50-100.

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 l                  Now if you put it in the eye,  and the eye
 2      is   unwashed,   the  LDSO  here   is   greater  than  10
 3      milligrams per kilogram.  You,  in  fact,  see clinical
 4      effects in the animals, but none of the  animals die.
 5      The   effects  that  you  start  to  see   are  muscle
 6      twitching,  and then you  see massive vesiculation in
 7      the  skeletal muscle, seizures, and  then if the animals
 8      get  a high enough dose of  this material,  they stop
 9      breathing and die.
10                  What you're seeing is an acute cholinergic
11      effect  in these animals due to application to the eye.
12      Now  if  you put this material in the eye and you wash
13      it out,  it has an LDSO, we  say,  between one  and ten.
14      We've never actually been able to calculate precisely
15      what it  is,   but we've  had  animals  die  with  one
16      milligram per kilogram of this material in the eye.
17                 We think what happens is that, when you go
18      to wash it  out, what you in  fact do is solubilize it,
19      and  it's  very rapidly absorbed through  the eye  and
20      into the  CNS.
21                 Now   this   material,   which   is  a  test
22  I   material   on   the  left,  is  very  similar   to   4-
23      aminopyridine.       These    pyradines    are    used
24     pharmacologically  to  enhance   the   release   of
25     acetylcholine.     People  that   have  Eaton-Lambert
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 1      syndrome,  for example, have very weak muscle activity,

 2      and one way in  which to enhance  the  activity  is  to

 3      provide them 4-aminopyridine.

 4                  4-aminopyridine affects the potassium pore

 5      in  the  membrane.     What  happens  is  the  action

 6      potential,  when  it  comes down  to the  endplate,  is

 7      prolonged.   So you get a  long action potential, and

 8      the prolongation of  the  action  potential  allows

 9      release of significantly more acetylcholine.

10                  So what  you  end up with is a cholinergic

11      crisis.  Now our concern  with  these materials,  if they

12 •     are in  a  mixture with  a material  which  is  also  a

13      cholinesterase inhibitor, you would have two materials

14      that  produce a  cholinergic  crisis by  two different

15      mechanisms.    In one  case  it  enhances the  action

16      potential.   It releases  more  acetylcholine.  In the

17      other   case,   it inhibits  the  breakdown  of   that

18      acetylcholine.

19                  So you get an enhancement of the effect.

20      This  enhancement occurs by  two different  --  at two

21      different molecular targets.  The effect in  the animal
    i
22  !    is  similar, and the effects are additive, even though

23      the mechanism  of  action  isn't additive.

24                 Now  the  fourth scenario I  put  down here

25      are we have  significantly different  chemicals.  They

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  1     have  different  metabolites,  but  they have the  same

  2     molecular target.

  3                  This cartoon  is  to  help you  understand

  4     some  of  the  neurobiology associated with materials

  5     that  we're  concerned with  here.   The  axon  is  the

  6     mechanism --  the  pathway by  which the  cell  body

  7     communicates  with   the   periphery,   whatever   that

  8     periphery happens  to be,  whether  it happens  to  a

  9     neuromuscular junction  or happens  to be  a sensory

10     endpoint.

11                  There is  movement of some relatively large

12     structures.   The organelles  that are formed in  the

13     cell  body  are   transported  to  the  neuromuscular

14     junction.  So the synaptic vesicles,  for  example,  are

15     moved  from the  cell  body to  the endplate and  back

16     again.

17                  This cellular movement is very important

18     to the  life  of that axon.   If  there is anything  that

19     interferes with  that  intracellular movement, you,  in

20     fact, get some form of axonopathy. The materials  that

21     we have been  concerned with  have been materials  such

22     as acrylamide, such as MNBk,  such as N-hexane.

23                 We've looked at the mechanism of action of

24     these materials.  This schematic  is a  schematic I put

25     together about how acrylamide,  in fact, can result  in

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 1     a peripheral neuropathy.  In this particular case,  the

 2     acrylamide is  absorbed and distributed.

 3                  We know it undergoes hepatic metabolism.

 4     We're  not  totally sure  whether the  metabolites  in

 5     acrylamide itself  can have a neurotoxic effect,  but we

 6     know that from binding acrylamide to various targets

 7     in  the  axon  you  get   interference  with   axonal

 8     transport.    Then you  get  some  degree   of   axonal

 9     degradation.

10                  Now depending on  the  dose level,   you

11     actually  also  get  axonal  regeneration.   So  there is a

12     balance here between very low levels  of exposure axons

13     being  damaged  and axons  regenerating.   If, in fact,

14     the damage  occurs  at a faster rate than regeneration,

15     you get clinical  deficits.

16                  Now what we've  been concerned about  is

17     that   these materials  all  have  some  very   common

18     similarities   in   that   they  all   have  effects   on

19     glycolytic  enzymes.  They have effects on glycolytic

20     enzymes which  has an impact on  energy metabolism  in

21     the axon  and can  affect  axonal transport.

22                  They  have  effects on the microtrabeculae

23     system in  that they  tend to  stabilize the   axonal

24     proteins, and the  stabilization can result  in reduced

25     axonal  flow, and  that  will slow down transport.

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 1                  They can also  interact  with the  active
 2     transport  proteins,  and  they   can  interfere  with
 3     movement, having an impact on retrograde flow,  and in
 4     the end they all result  in this dying  back neuropathy.
 5     They cause a neuropathy by  impacting axonal flow.
 6                  So  our concern has been  that there  is  a
 7     common  mechanism  of  action.    Even   though  these
 8     chemicals are very different, there is no reason  that
 9     we see  that  these materials should not  interact  and
10     cause peripheral neuropathy  at  --  I won't  say  low
11     doses,  but   our  concern has  been that, if  we  give
12     subneurotoxic doses  of  some of these materials,   are
13     those effects additive?
14                  From what we  have seen, we  think  they
15     probably  could  be  additive,  although  for  the  most
16     part, we have not had  a reason to  actually create  such
17     mixtures.
18                  So  in  summary,  there  are  interactions
19     among  chemicals  that are  affecting neurotoxicity.
20     These interactions are  modulated by  pharmacokinetic
21     parameters,   absorption,  metabolism excretion.  There
22     are metabolic interactions  that  occur outside of  the
23     nervous system -- we see  them primarily in the liver  -

24     - that affect neurotoxicity.
25                  There  are also  nervous  system specific
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 1     vulnerabilities,  either with the blood-brain barrier

 2     or  with the transport  system in the nervous  system,

 3     that  offer unique  opportunities for interactions  of

 4     chemicals.

                    We see mechanisms  of  action  that are

 6     common for  a number of  neurotoxic chemicals,  and thus

 7     offer the opportunity for an additivity effect.

 8                  So in summary we  can increase or decrease

 9     the likelihood of  observing  neurotoxicity  with   a

10     particular  mixture  through  any  of  these   various

11     parameters.  Thank you.  Do you  want  to take questions

12     now,  Debbie,  or later?  Questions?

13                  (Applause.)

14                  (INAUDIBLE QUESTION)

15                 DR. O'DONOGHUE:   The way we've determined

16     additive  effects is to go back in and reconstitute the

17     mixture at various concentrations and try to see if  we

18     can reproduce  that additive effect with the mixture.

19     That's  what we tried  to do with some of  the ketone

20     solvents.

21                  (INAUDIBLE QUESTION)

22                 MR. O'DONOGHUE:   No.  We tried to --  He

23     was  asking  how we went  about doing  the  additive

24     studies.   What we  actually  do  is try  to understand

25     what  the  chemical mixture is and  then  recreate  that

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  mixture and then retest the mixture .   We are not doing

  it by a modeling program.

              Does that  answer your question?

              I  think,  actually,  when you  talk about

  cholinesterase  inhibition  later on, we'll probably

  talk more about how those things are actually modeled.

  The reason we don't do  a lot of  that is because when

  we find one of these materials, our main objective is,

  frankly, to get rid of  it.   It's not to figure out a

  way to use it .

              We are not intentionally using neurotoxic

  materials. We're trying to  understand what  the limits

  of their safe use  are and,  in many cases,  get rid of

  the materials,  if  possible.  So our  goal  is quite a

  bit different.

              DR. MOSER:  I wanted to  thank Debbie and

  the organizers  for inviting me to be here  today.   I

  appreciate this opportunity.

              I'm going  to switch  gears  a little  bit

  from what  John  was covering,  and I'm  going to  talk

  more specifically  about one study and  also get  into

  some of the more statistical  analysis on that study.

              As we all know,  and the reason that we are

  all here,  I think,   is that most environmental chemical

  exposures do occur  in multiple.  So we are not exposed

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 l      to just single chemicals.  So a lot of  the information

 2      that  we have  about single  chemicals  may  not be  as

 3      appropriate to understanding mixtures.

 4                  For now, there  is  just simply no way  of

 5      predicting when or  what kind  of  interaction may  be

 6      produced when there is exposure to  multiple  chemicals,

 7      and   trying  to  model  these  interactions   in  the

 8      laboratory  requires a  lot  of different   types  of

 9      considerations,  and also the statistical analysis  of

10      these  interactions  becomes   very  difficult   very

11      quickly.

12                  What I want to present here today is the

13      data  from  a  large,  rather  a  huge study  that we've

14      conducted  quite a few years back now.   I want to try

15      to present  some  data that you may  find interesting  on

16      the mixture, and I also  want to try to illustrate the

17      complexity of this type of study, both  in terms of its

18      statistical analysis and the data  interpretation.

19                  In  this  particular study we wanted to  go

20      past  binary combinations, which are typically  the way

21      that  people  look  at  chemical   mixtures  in  the

22      laboratory.  We  wanted to  go all  the way from two  to

23      three chemicals.

24  I                The  project was  initiated as  part  of  a

25      Superfund project.  We wanted to study chemicals that

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were  frequently  found in  Superfund sites  and  were
found to  co-occur.
            We  used a full factorial design to  study
these  interactions  of  these three  chemicals,  which
means we  had  three  chemicals and five dose levels  of
each chemical.  One of those dose levels was zero, and
then there were four other dose levels.
            We  looked at  all possible combinations.

so  it  was a  five to the  three study.   Five  to the
three gives you 125 treatment groups.  In the study  we
used ten  rats for each treatment.  So you can  see why
I say it's such a huge study.
            The  study  was  actually  conducted   in
collaboration with  some other people  at EPA,  and  we
did neurotoxicological evaluations on the animals.  In
the same animals we did general toxicity studies, and
we  also  at  the  end of the  study took  organ  -- did
organ histopathology and clinical chemistries.
            The study was run in conjunction with the
developmental toxicity study using the same chemicals
and  similar  dose   levels.     The  results  of  the
developmental tox study  have  been published already,
but this  is the debut for the neurotox data.
            I'm  going  to  briefly   describe  what
response surface modeling is.  It's a method that aims
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 l      to   mathematically   describe   the  surface   of   the

 2      response,   the   response  to   the  combination   of

 3      chemicals,  all combinations of  chemicals.

 4                  It uses a factorial design where chemicals

 5      are present -- all the different chemicals are present

 6      in  different  levels,  dose  levels.    Some  of  the

 7      advantages  are that  it does use all the  experimental

 8      data.  There is no mathematically derived numbers like

 9      ED50.

10                  It does estimate the response surface over

11      a range of doses.  It provides  estimates  for both  the

12      individual  chemical   as  well   as  the   interactions

13      between  the chemicals.   Theoretically,  there is  no

14      limit  to  the number of chemicals that can be studied

15      this  way,  even  though you  can only  picture --  you

16      know,  draw a graph for combinations of two chemicals,

17      mathematically you could have  an infinite number  of

18      chemicals.

19                 The   other advantage  to  this   type   of

20      modeling  that  we  did was  that there would be data

21      available to take  out subsets of the data, so that  we

22      could  analyze  the subsets of data and see whether  we

23      could  predict  the  same result using a reduced dataset

24      compared  to the  full  dataset.

25                 Now    as   I    say,    we    did    the

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neurotoxicological evaluations in these animals.  We

used  the functional  observational  battery  or FOB,

which  probably most  of  you  all  are  familiar with.

It's  a  battery  of  tests  that  rapidly  evaluates

neurological function of  the  animals.

            We  also  used  motor  activity using  an

automated assessment  of locomotor activity.  The FOB

is   a   neurological   exam.      It   does   evaluate

neuromuscular dysfunction, sensory deficits, autonomic

changes, and changes  in activity levels and reactivity

or excitability of the animal.

            The standardized  series of open field and

manipulative tests that are well standardized are used

widely  now  to screen for hazard identification for

potential neurotoxicity.   So these were the tests that

we used  in this study.

            Protocol here  shows that we used Fischer-

344 female rats.  The reason for this was, as I said,

we   did  this   study   in   conjunction  with  the

developmental tox study.   So,  of course,  we had  to use

females.  We used the Fischer-344, because that was

the standard strain that's used in the Chernov-Kavlok
assay.
            We did  neurobehavioral testing,  as I've
already  said.   We tested  the animals  before dosing
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 1     began, four hours after the first dose, 24 hours after

 2     the  first  dose.   We dosed the animals repeatedly for

 3     ten  days,  and  then  we  also tested them at four and 24

 4     hours  after  that tenth dose.

 5                  Because of the number of treatment groups,

 6     obviously,  we couldn't  test all  the  animals at  one

 7     time.  We  had  to split up the study into  replicates.

 8     We had the statistician  draw up  the replicates for us

 9     so that we had five replicates of  250  animals in each

10     one.   Of course,  we can't test 250 animals at a time

11     either.  So  we had  to break down the replicates into

12     blocks so  that essentially we tested 50 animals at a

13     time.
          »
14                  The  chemicals that we chose to study are

15     listed here.   The  first one is heptachlor.   It's  a

16     cyclodiene pesticide  that's very  persistent in  the

17     environment.   It does act on the nervous  system.   It

18     blocks  the  GABA-ergic   system.      So   it   causes

19     excitation.

20                  The next chemical is diethylhexylphthalate

21     or DEH.  It's  a liver  toxicant,  and it's not  known to

22     act,  on the nervous  system at all.

23        v.         Then the  last  one  is trichloroethylene,

24     which  is a volatile organic solvent.   The mechanism

25     for  trichloroethylene isn't exactly known, but  it is

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 l     known  as  a general type of CNS depressant.

 2                  i want to point out that only two of these

 3     chemicals did  act on  the nervous system,  and  the

 4     mechanisms of actions are very different for the three

 5     chemicals.

 6                  The doses that we used are listed  here.

 7     For  heptachlor -- or  for all the  compounds that  we

 8     tested, we chose the doses in order to  span  the  range

 9     of effectiveness.  We wanted to be  sure that the high

10     dose   was  effective   and  that  the  low   dose  had

11     essentially no  effects,  and we wanted  to be able  to

12     construct a good dose response curve with these four

13     doses.

14                  One thing to point out is  that with  the

15     response  surface analysis you don't need to  have your

16     dose basing be  any specific way.  So you can see that

17     the dose  basing was very different  for the  different

18     compounds.   We could select  our doses in  order  to

19     construct the best dose  response  curve.  We were  not

20     restrained in any  way.

21                  The other thing  to point out is that  the

22     components   were   mixed   together  into  the  dosing

23     solution,  so that  the rats only received one dose a

24     day instead of  getting the three different doses.   So

25     we had basically  at  any one time 50  different  doses

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 1     being given  in a day,  and they were all dissolved  in

 2     corn oil, which was our vehicle.

 3         -         Now for  the  data  analysis.    This was

 4     really the trickiest part of the whole study.  The FOB

 5     produces  different kinds of endpoints.   Some of the

 6     data are  binary or just simply yes/no data.   Some  of

 7     them  are continuous  like  body  weight  or activity

 8     count, and some of them -- most of them, actually, are

 9     ordinal.  They are ranked scores.

10                  There   are published methodologies for

11     analyzing binary and  continuous  data using response

12     surface  analysis.    So  far,  even  now,  there  is  no

13     analysis  that  is developed for  ordinal types of  data.

14     So  for those  data  we had  to  make  transformations

15     before  we could  actually put  the  numbers  into the

16     response  surface analysis.

17                  With the data analysis  we get parameters

18     that will -- you  can  get parameters  for an overall

19     test of interactions.   So it will test whether or not

20     there  is a  significant  deviation  from  additivity,

21     significant  less than  probability value .05.

22                  You can also get  individual parameters for

23     the effects  of the chemicals by themselves.  You can

24     look  at  the   binary   combinations  of  each   three

25     chemicals.     So  you   could  see  whether   there   is

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 1      interactions between just two  of the chemicals,  and

 2      then you  can also look at the tertiary interaction or

 3      the three-way  interaction.

 4                  We could also analyze  -- Because of  the

 5      way  that we set up the replicates, we  could also

 6      analyze whether there were  replicate effects or not.

 7      We  did do  a reduced design  --  a  reduced analysis.

 8      What we did was take the zero level and the data from

 9      the next  to the low dose and the high dose.  So that

10      the same type of analysis was run using just two doses

11      of  each  compound instead of  the four doses  of each

12      compound.

13                  We  were  hoping   that,   if   this   was

14      predictive  of the full factorial design, this would be

15      a more efficient way to  do  the  study.

16                  Then because of the  time restraints,   we

17      had to limit the analysis to just one time point,  and

18      the time  that  we chose was the four-hour data after

19      the tenth dose.

20                  First,  I'll  go  over the effects  of  the

21      individual  compounds, what's listed here are the --

22      The measures of the functional  observational battery

23      are listed  here.  As you can see, trichloroethylene,

24      being  a   CNS depressant, does  tend  to  decrease   or

25      depress most of the nervous  system functions.

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 1                  We get decreases in grip strength.  We get
 2     gait changes,  decreases in activity levels.  Kind of

 3     interesting,  we  did get  increases in alertness,  but
 4     basically    decreases   on   most   of   the    other
 5     responsiveness tests like sensory motor responses.
 6                  We saw some  general  health changes  with

 7     piloerection and lacrimation,  and we saw body weight

 8     decreases and a  small amount of lethality at the high

 9     dose.

10                  Heptachlor, as I've already said, inhibits

11     the inhibitory transmission.  So it actually produces

12     increases in some of the nervous system functions. We

13     get  increases in grip strength,  gait  changes again.
14     We  do  see  decreases  in  locomotor  activity   with
15     heptachlor  and these types of  chemicals,  but we  get
16     increases in responsiveness to both being handled and
17     some of the  sensory  tests.

18                 We also got lacrimation, some salivation,

19     decreases  in  body  weight,  and  a  small  amount of
20     lethality.  As I've already said,  DEHP  does not act on

21     the nervous  system,  and we got  absolutely no effects

22     on these tests with  that  chemical by itself.
23                 This is the  results  of the  statistical
24     analysis.  There is actually a lot on this slide.  So
25     I may need to  take a minute.
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 1                  Again,  these are  the endpoints  that we

 2     tested.   This  first  column is  just whether  or not

 3     there  was a  significant interaction  or significant

 4     deviation for additivity.  As you can see, for almost

 5     all the endpoints there was a significant interaction.

 6     In fact,  just about every single one of them showed a

 7     significant  interaction.

 8                  Now what's listed here  is  the algebraic

 9     sign of the parameters, where there was a significant

10     parameter.   So  that means that,  if  the parameter is

11     significantly different from additivity, the algebraic

12     sign is listed.

13                  What  that  means is  a positive sign shows

14     that the  effect is increasing with dose.  So you have

15     a  positive  going  to  this  response   curve,  and  a

16     negative  sign is  a  decreasing dose  response curve.

17                  So  for  instance,   with hind limb  grip

18     strength  we  decreased  grip  strength with  TCE  by

19     itself, and we increased grip strength with heptachlor

20     by itself.  Now in cases like gait score -- I want to

21     point  this  one  out --  we  did  see gait  changes with

22     both  TCE  and  heptachlor.    So  we had  a  positive

23     parameter for both of  those, but then the interaction,

24     the trichloroethylene by heptachlor interaction had a

25     negative  parameter.

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            That means that the dose response curve is

either decreasing or else it's not increasing as much,

and  that's  interpreted  as  being   antagonism.     So

whenever the  sign  is opposite  from the signs of  the

individual parameters, that's antagonism; whereas,  if

it  was  the  same  direction,  then  that  would   be

considered  synergism, because the  effect  would  be

greater than  the two compounds alone.

            As  you   can  see,   for  most  of  these

characterizations    we    did    have    antagonism.

Interestingly, with  lethality we did see significant

effects of both individual compounds. When you looked

at trichloroethylene and heptachlor together, we had

antagonism.

            We also  had  antagonism  with the DEHP and

the heptachlor as a binary combination.  Remember that

DEHP had no effect on its own.   But then if you look

at  the  three-way  interaction,  the three  chemicals

together actually  produce synergism.

            So  this  was  kind  of  the  worst  case

scenario where for most of the neurological endpoints

we got antagonism,  and then for lethality,  which  is

probably   the  most  important  toxicity,   we   got
synergism.
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 1      really characterize what kind of interaction  it  was,

 2      because none  of  these  interaction  parameters  were

 3      significant.   So  you  couldn't  really say whether  it

 4      was   antagonism  or  synergism,  and  that  actually

 5      occurred for  half  of  the  endpoints  where  we got

 6      effects.

 7                  So we compared the  results of  the  overall

 8      analysis,  the  full  factorial design, with the  data

 9      from the reduced analysis.  So this is just the  same

10      column that I just showed with the number of yeses and

11      noes,  showing whether or not  there was a statistically

12      significant  interaction,  and  then  compared  to the

13      reduced dataset analysis.

14                  For the most part,  they did not match up.

15      In fact,  there were very, very  few  cases  where  they

16      did  give the  same  result.   So it's obvious that,

17      looking at just the two doses the way that  we did, you

18      could not predict the  outcome  that  you  would  have

19      gotten when you  looked at  the  full dose response

20      curves.

21                  So now I'm going to try to show a little

22      bit  of the  data.  This  is the  data  from gait score.

23      Gait   score   is  a  subjective   assessment  of  the

24      abnormality of gait by the animals moving around in an

25      open  field.    It's scored from one  to four, with one

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    1     being  normal  and   four  being   the  most   severe
    2     abnormality.
    3                  What   I've  got  plotted  here   are  the
    4     individual  compounds,  and the  circles  indicate  the
    5     average gait  score.  So this is the average rank.  You
    6     can see,  with the TCE and the heptachlor, that there

*   7     is  an increasing  severity of gait.
    8                  Then  the triangles indicate incidence or
    9     the number  of rats that show an abnormal gait.  What
1 10     we  had to do,  because there is no statistical model to
  11     handle  rank data, we had  to  convert all the data to
  12     incidence.  So either the animals had abnormal gait or
  13     they didn't.
  14                  So you  can see  what  the  dose  response
  15     curves    look    like    for   the    incidence    for
  16     trichloroethylene   alone,    heptachlor,   and  DEHP
  17     basically didn't  have any effect on gait.
  18                  So the first place to  look at is to start
  19     looking at  the binary combinations.  One of  the best
  20     ways to look at binary combinations is to construct an
  21     isobologram.    What   you   do  is you  construct dose
  22     response  curves for,  say,  chemical A in the presence
  23     of  many  different doses  of  the  other  chemical   or

«. 24     chemical  B.
  25                 You  can  actually  then  derive   ED50  for
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 1     chemical  A,   and  there   will  be  different   ED50,

 2     depending on  the dose,  of  B --  depending on what dose

 3     B was.   Then  you can plot  it.

 4                 So this line  shows the EDSO  for chemical

 S     A,  the ED50 for chemical  B by itself.   Just draw a

 6     line,  and that's the theoretical line of additivity.

 7     If  you give  a  dose,  say,   of four  of chemical  B and

 8     then do a dose response with chemical  A, if the ED50

 9     falls  right about here, then it falls on the line of

10     additivity, and that's taken to be a statistical --

11     That's  interpreted  to be --  Statistically, that's

12     interpreted to be additivity.  But  if the ED50 is much

13     less, it  falls in this area which is synergism, and if

14     it's much higher  than the ED50 would have been,  it

15     would be antagonism.

16                 This is  just a standard way  of looking at

17     binary  combinations.   Then  this shows  the  analysis

18     with gait score.  What I   did  was  calculate the  EDSO

19     for  heptachlor  alone,  which was  about  14,  ED50  for

20     trichloroethylene alone,  which  was about  1,000  --

21     these are milligrams per kilogram -- and  connected the

22     line,  and  then  looked  at  the dose   response  for

23     heptachlor in the presence of all the different  doses

24     of  trichloroethylene  and  the  dose  response  for

25     trichlorethylene in  the presence of  all  the doses of

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 1     heptachlor.

 2                 That's what these EDSO points are that  are

 3     plotted.  You  can see that they all fall in  the area

 4     of antagonism,  and  that was kind of  a relief to  me,

 5     because that's what the response surface analysis  had

 6     said also, that there was antagonism between these  two

 7     chemicals.

 8                 Now  the  problem with the isobolograms is

 9     that it doesn't  show all  the data.   You have no idea

10     what the  dose response curves  look  like,  and these

11     EDSO are mathematically derived numbers.

12                 So this  3-D graph actually shows all  the

13     data   for   the   combination   of   heptachlor    and

14     trichloroethylene.  Note that this all in the presence

15     of  zero  dose  of DEHP.    So  this  is just  a binary

16     combination right now.

17                 This  kind of purple looking bar  showed  the

18     dose response for trichloroethylene in the presence of

19     zero heptachlor.  So that's trichloroethylene alone,

20     just like you  saw  a  couple of graphs back.   Then

21     heptachlor alone  is  this  set of bars right here.

22                 Now if you, say, look at the high dose of

23     trichloroethylene by itself and then you start adding

24     heptachlor  to it,  you  actually get  less abnormal

25     gaits,  especially at  this  mid-dose  of heptachlor.

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That's where  the antagonism is showing up.
            In the same way,  if you look at the high
dose of heptachlor, you've got about 50 percent of  the
animals  showing  abnormal  gait, but as you  add  low
doses  of  trichloroethylene,   that  percentage drops
down.   Then  even  at  the  highest  dose  of  the  two
combinations, the two chemicals in  combination, you're
not getting much more  effect than  you got with  the

individual  compounds.
            So this kind of dose response here where
you see all the data, you can actually see where  the
antagonism  is.
            Now you  can get  into  the really hairy
data.  This is the three-way interaction, and  this is
the  data for  lethality.   As  I  said,  there  was a
significant interaction between trichloroethylene  and
heptachlor,  between  DEHP and  heptachlor,  and also
between all three compounds with lethality.
            So  this graph here is the same as what  you
saw with the gait score with a zero level of DEHP.   As
you can see, the chemicals by themselves produce very
little lethality at the high  doses.  In the low dose
range here, we've got low doses of trichloroethylene,
the incidence of lethality in  the heptachlor high dose
was  actually   less.    So  you  can see  where   the
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 1     antagonism  is  coming from.

 2                 Again, this is all dose dependent kinds of

 3     interactions, which makes interpretation even harder.

 4                 Now   if   DEEP  had  no   effect  on   the

 5     interaction between trichloroethylene and heptachlor,

 6     then all five  of  these  graphs would look the same  or

 7     essentially the same.   If  you just kind of jump from

 8     one to the next,  you can see that they don't  look the

 9     same.

10                 So we've got the  low dose of DEHP.  Then

11     it goes up a dose, and then this is the highest dose.

12     Now you  recall I  said  that  when  you looked at the

13     three chemicals together, you actually had synergism,

14     and the best way of looking at that is to look at this

15     high dose combination.

16                 This  is  the highest level of  DEHP,  the

17     highest dose of trichloroethylene and the highest dose

18     of heptachlor,  and we  got  100 percent  lethality  in

19     those animals.   So this shows where the synergistic

20     responses actually are.

21                 So this just kind  of  summarizes the study.

22     Basically,  we  had deviations from  additivity  in  77

23     percent of  the endpoints.   So a great number of the

24     endpoints did show significant interactions.

25                 The reduced dataset predicted this kind of

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 1     level  of  interactions for only three of the  ten.   So

 2     it was not very efficient  in being predictive.   The

 3     interactions  mostly  involved  trichloroethylene  and

 4     heptachlor, which kind of makes sense; because  those

 5     are  the two that  act on the  nervous  system, in  the

 6     first place.  But some of the interactions  did include

 7     DEEP,  which has no effect on its own.

 8                 Some   of    the   interactions   were

 9     characterized  as antagonism.  Other ones could not be

10     characterized.    Then,   of  course,   lethality showed

11     synergistic effects,  and some  of  these effects were

12     dose dependent.  The interactions were dose dependent.

13                 So the bottom  line is  that  the outcome

14     could not be predicted on the basis  of the functional

15     effects  of these  chemicals,  and  they could not  be

16     predicted based on the  mechanisms of action.

17                 So,  basically,  there's  just no substitute

18     -- Based on these data,  there's  no  substitute  for

19     experimental testing,  if you're looking at compounds

20     with different  mechanisms of action.

21                 Some of the lessons learned from the  study

22     is that these  kinds of factorial  designs  are  not

23     practical,  I   don't  think,   for  general  laboratory

24     testing.   It  took  1,250 rats to generate all  these

25     data, and it was about a year and a  half to two  years

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 l     to collect  the data and then another year and  a half

 2     to two years to actually analyze the data.   So  it's a

 3     tremendous  drain  on resources.

 4                 There's still analyses  that  could be run.

 5     There  are other  time  points.    As I said,  we only

 6     analyzed  that  one time point.  We actually should  go

 7     back and analyze the data using  the  pre-dosing data  as

 8     a  covariate,   because  some  of  these  endpoints,  the

 9     response  does  depend on what the baseline data were.

10                 Then  there is still a method  that needs  to

11     be developed to analyze the ranked data.  Converting

12     the ranked data to  incidence  data is probably not the

13     best way, but  it's  the only way we have of doing it.

14                 Then  just  to end,  I need  to acknowledge

15     that this work was conducted under contract to EPA.

16     The  laboratory  work   was  conducted   by  ManTech

17     Environmental,  and that was  a  contract  to  the HERL

18     back  when  we   were HERL  back  before  we became   a

19     national  lab.

20                 Joe Elder  and Bob Dyer were the  contacts

21     at the EPA  and helped  us a  lot  with setting up these

22     studies and keeping them going,  and  Billie did most  of

23     the testing for the neurotox testing, and Mike did all

24     the general tox testing as well  as preparing all these

25     125 dosing  solutions  three  times for  each study.   So

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 1     that was  quite a feat in itself.

 2                  The data analysis  were all conducted at

 3     ~he Medical  College of Virginia under the  supervision

 4     of  Chris  Gennings, and  she had a graduate  student,

 5     Carol, who did all this  work and,  I   think,  finished

 6     her whole thesis  and probably  killed herself  after

 7     finishing all  this analysis.

 8                  That's it.   So if we have time,  I'd like

 9     to answer some questions.

10                  (Applause.)

11                  DR.  NORRIS:  Can I remind people to please

12     use the microphone if you have  questions,  and we have

13     just a few minutes.

14                  QUESTION:   Could you speak  a  little bit

15     more about  some of the  interactions  you  saw at  the

16     lower doses,- you know, the very lowest doses that you

17     tested.

18                  DR.  MOSER:   For the other endpoints,  you

19     mean?

20                  QUESTION:  Yes.

21                  DR.  MOSER:   Well,  for the most part,  if

22     you actually graphed  out all the data and looked at

23     it, which generates millions of these 3-D plots,  it

24     looked pretty  much the  same kind of pattern  that you

25     saw with  lethality and with gait score.

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 l                  If you  started adding low  doses of  the

 2     second chemical, the  effects of  the  first  chemical

 3     tend to go away.   So it looked more  like there  was

 4     antagonism for most of the endpoints.   Then  when  you

 5     start  getting up to higher levels again,  that goes

 6     away,  and you start getting more effects.

 7                  It just is a clear case of the antagonism

 8     being  dependent  on the dose of the  chemicals.   I don't

 9     know if that's enough  to address, but we don't have

10     any  other characterization of it besides that.

11                  QUESTION:   I wanted to ask you a  question

12     in terms  of synergism  of lethality.  Number one, were

13     the deaths very shortly after you gavaged them or were

14     they more long term, and how many --  I actually have

15     three  questions.   The second  question  is  how long

16     after  the tenth day of dosing did you hold the animals

17     or did you  sacrifice them  right  after  you  did  the

18     neurological  testing?

19                  Third,  in terms of the lethality issue,

20     would  the volume of the dose being important?

21                  DR.  MOSER:   Okay.   If  I  can remember  all

22     three,  the  first  one  was,  no,  we  never got death

23     immediately  after dosing which,  of course,  is a very

24     important thing when you're talking about this number

25     of animals and gavaging.  But the technicians that we

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 1     had doing  this  were very good and never did lung any
 2     animals.
 3                 The death  actually occurred  during  the
 4     last couple of  days of  dosing,  and we found and  have
 5     shown with heptachlor other times, too, that when you
 6     dose heptachlor repeatedly,  it tends to build up and
 7     you  start to  see  toxicity after  days  of repeated
 8     dosing that you don't see on the first day.
 9                 So  all the  deaths  occurred  on about  the
10     seventh, eighth and ninth and tenth day of dosing,  and
11     usually what would happen is we would come  in the  next
12     morning and find them dead.   We rarely ever saw  them
13     die during the  day.
14                 Because the study was run with the general
15     tox, what we did was we tested  them at 24 hours after
16     that tenth dose, and then immediately after  that there
17     were  sacrificed  and we  took  the  organs out,   and
18     weighed the organs, prepared them for pathology  and
19     took blood for  clinical  chemistries  and that sort of
20     thing.  So we never held them past that eleventh  day.
21                 Oh,  the volume.   I'm trying to remember.
22     I think the volume that we used was 5 milliliters per
23     kilogram,  which is a very reasonable level for  oral
24     dosing.  We had to kind of hold  some of  these doses
25     down to make  sure we could do that, but I think that's
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 1     what  it  was,  5 mis per kilogram.

 2

 3                  Any other question?  Thank you.

 4                  DR. NORRIS:    Thank you,  Ginger.    I'm

 5     trying  to be quick with  questions  at this point  and

 6     stay  on  schedule.    We  will  have  time  for more

 7     questions during the panel discussion.

 8                  Our next speaker is Dr.  Stephanie Padilla.

 9     Thank you.

10                  DR. PADILLA:    Let's  change  gears  yet

11     again.  This is a group of studies  that Wendy Haines,

12     who  is  the  second  author here, who is  a graduate

13     student  in my  laboratory, started about a year  and  a

14     half  ago.   Basically,  her doctoral  dissertation is

15     going to  be looking at mixtures  of  OP pesticides,  OPs

16     and carbamates, and we'll probably also do an OP  and

17     a pyrithroid pesticide.

18                  We  first  started  out with  these  two

19     compounds, chlorpyrifos and diazinon.  I'm sure  these

20     structures are probably familiar to many people  in  the

21     room.  We chose them, because  we thought we would  get

22     a more than additive interaction on  the basis of  their

23     structures,  their  metabolism,  and their mechanism of

24     action.

25                  They   are    both    anticholinesterase

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  inhibitors  or   they  are  both   anticholinesterase

  compounds.  So they both inhibit acetylcholinesterase

  as their mechanism of action.  They are both converted

  from  their  parent  compound here.   This  sulphur is

  replaced by an oxygen,  and  this becomes chlorpyrifos

  oxone, and this one becomes diazoxone, which are very

  potent cholinesterase inhibitors.

              This conversion takes  place by the P-450

  enzymes, mostly in the liver.   So  they could interact

  at  that  level.     They  are   both   detoxified  via

  carboxylesterases,   stoichiometric   binding  by  the

  carboxylesterases, and  they are also both substrates

  for the A-esterases  and can be hydrolyzed  by the A-

  esterases.

              So their metabolism is extremely similar

  in the animal.  What  we wanted to do was not construct

  an isobologram,  but what we wanted to do was to look

  at  the  interaction of  these  two  compounds  using

  multiple endpoints,  and I'll  get  into  that  a little

  bit here.  Well,  I'll go back.

              Our quality -- Our hypothesis was that the

  interaction was  going  to  be  more  than  additive,

  because  we  thought  that the   interaction  with  the

  detoxification enzymes, that  the  presence of  one or

  the other  would inhibit  the detoxification  of  the

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 1     other  enzyme and,  therefore, create more toxicity of
 2     the mixture  than you would predict from looking at the
 3     two  by  themselves.    So  we  predicted  that  their

 4     interaction  would be supra-additive.
 5                  We used a  dose additive model.   Let  me
 6     take a little bit of time with this, and I also have
 7     some examples graphically after this.
 8                  We spent some time reviewing the mixture
 9     literature,  and it seemed to be that, if you wanted to
10     look  at  compounds that  had  the  same  mechanism  of
11     toxicity, that  are homergic --is what they are called
12     in  the literature  --  that you needed  to employ  the
13     dose additive model.
14                  To do this, what you do is you determine
15     the ED50 for  whatever endpoint you've decided to look
16     at.  In our  case, we looked  at two dose levels.   We
17     looked at what  I would consider a very low dose level,
18     which  was the amount of the compound that would  cause
19     50   percent   inhibition    of   red    blood    cell
20     acetylcholinesterase.
21                  Then we looked also --  I did another  group
22     of experiments that looked at a higher dosage level,
23     which  was the amount of the compound that would  cause
24     50 percent inhibition  of  brain acetylcholinesterase.
25                  So we looked at it at two different dosage
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 1     levels,  but  in general what you  do is you determine

 2     the ED50  for whatever your target  endpoint  is  for both

 3     of the  compounds.   Then you take half that dose  for

 4     each one of the compounds and put  that  in your mixture

 5     and look to  see what  the effect of the mixture  is.

 6                  Your results can look like  this, depending

 7     on what  the interaction is.  This is completely made-

 8     up data.  That's why  it  looks  so good.  If you go up

 9     here at  the very top,  this is what your results would

10     look like if you had  an  additive interaction.

11                  So in this case, if we had -- This is the

12     dose of chlorpyrifos that causes 50 percent inhibition

13     in whatever tissue we're looking at.  This  is  the dose

14     of diazinon that causes 50 percent inhibition of that

15     same tissue.

16                 Then if you  take half of those doses and

17     mix them together and dose the animal,  you should get

18     --If the interaction  is additive,  you should get 50

19     percent  inhibition.

20                 Now this is what it would  look like  if it

21     was  more   than   additive  or  supra-additive  or

22     potentiation or synergy,  depending on who you read and

23     how they define it.  But this again would be the dose

24     of chlorpyrifos  that  caused 50  percent  inhibition.

25     This would be the dose of diazinon,  but  together you

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 1     would get more  inhibition  if  you used  half of  the

 2     dose.   This  down here, of course, is if you  actually

 3     had  antagonism or infra-additivity.   There  would be

 4     less  inhibition in the groups.

 5                  So this was the model that we chose to use

 6     with  these two compounds.  The first thing you have to

 7     do is construct a very good dose response.   You  have

 8     ~o know what  dose of  chlorpyrifos or what  dose  of

 9     diazinon causes 50 percent  inhibition in  the  brain or

10     50 percent inhibition in  the red  blood cells. That's

11     what  you've  got here.

12                  The x axis here  is approximately  the  same

13     for both of them, and you can see that there is a  very

14     different  pattern here  for the  two  compounds.    The

15     upper is chlorpyrifos.  The  filled symbols  are brain,

16     cholinesterase.   The  open symbols are red  blood  cell

17     acetylcholinesterase.

18                  You  can  see   here   that  chlorpyrifos

19     inhibits red blood cell acetylcholinesterase  at lower

20     doses than it  does brain acetylcholinesterase.   This

21     is not news.

22                  The same  thing  is true for diazinon.   The

23     various symbols,  the various  shapes are different

24     experiments  that  we did.  This is just to give you --

25     We had  to  repeat  -- We  had some  data  from other

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 1     studies.   Then we did newer  studies to combine  with

 2     that  data,  and the same thing  is  true down here for

 3     diazinon.

 4                  This  line right  here  is  the  50 percent

 5     line.   So if you look at diazinon here,  you can see

 6     that at this dose of about 10 milligrams per kilogram,

 7     you get about  50  percent  inhibition of the red blood

 8     cell  acetylcholinesterase,  but at  a dose at about  --

 9     this  is about  75  milligrams per kilogram,  you get  50

10     percent inhibition of the brain acetylcholinesterase.

11                  So there's a lot  of work to  be done  up

12     front with  these  type  of  studies,  because you've got

13     to have a really good dose response curve.   Even  if

14     you had a really good one, it doesn't always turn out

15     like you  want  it  to.

16                  So  these   are  all   the  compounds   by

17     themselves.  These are all acute dosing in  corn oil  in

18     adult male  rats.  The  animals were sacrificed at the

19     time of peak effect, which conveniently for these two

20     compounds is about the same  time, which is about three

21     to four hours  after dosing.

22                  Our  endpoints:    We had  three different

23     flavors   of  endpoints.     We  basically  looked  at

24     cholinesterase inhibition, of course, in the brain,  in

25     the   red   blood  cell,   but   we   also   did   other

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 l     cholinesterase determinations in other tissues.

 2                  We did toxicokinetics.   We're set up  to

 3     look  at the  toxicokinetics of  chlorpyrif os.   So  we

 4     looked  at the distribution of  chlorpyrifos in various

 5     tissues to  answer the  question  of whether co-dosing

 6     with diazinon would cause  a different distribution  of

 7     chlorpyrifos and/or its metabolites in the liver and

 8     the brain.

 9                  Then in collaboration with Ginger Moser,

10     she did behavioral assessment on  these animals.    So

11     she  ran an  abbreviated  functional  observational

12     battery on  the  animals.

13                  So  in the first experiment, which is the

14     lower dose  experiment,  we  used  male -- adult hooded

15     male rats,  and our target  endpoint was red blood cell

16     inhibition   --  red blood   cell  acetylcholinesterase

17     inhibition  of  approximately 50 percent.

18                  We determined  the ED50 for chlorpyrifos  to

19     be  approximately  one  milligram  per kilogram,  and

20     that's  the  dose of  chlorpyrifos  that produces   50

21     percent  inhibition in the  red blood cell  by itself,

22     and diazinon to  be  ten  milligrams per kilogram.

23                  So  what  we  did was  we  --  in  our mixture

24     group -- the mixture group  consisted of .5 milligrams

25     per kilogram of  chlorpyrifos and  5 milligrams  per

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 l     kilogram of diazinon.  We had five dosage groups.   We

 2     had a  control  that  got  nothing but vehicle.

 3                 We had  the  two groups by themselves.   We

 4     had  chlorpyrifos  by itself.    We  had diazinon  by

 5     itself.  We  had the mixture of  the  two at half, but

 6     then   we  also  --   In   order   to   compare   the

 7     pharmacokinetics, we  needed to have the chlorpyrifos

 8     at  half the  dose,   at  .5  milligrams  per  kilogram,

 9     because we needed to have  that  group  to  compare to the

10     group,  the mixture group,  to see  if  the  amount  of

11     chlorpyrifos and metabolites in those two groups were

12     the same or had been  altered.

13                 You can't just  assume that the amount  of

14     chlorpyrifos   in  the animal  at one  milligram  per

15     kilogram is going to be twice that as what you should

16     see at five.    So  we needed --  I mean the  .5.    We

17     needed the  .5  to  compare  with the one.   So that's a

18     general outline.

19                 So this is  just to show you where we are

20     in the dose response curve with regard to those doses.

21                 So we had about  one milligram per kilogram

22     for chlorpyrifos and the mixture  group.  So that's the

23     dose that we predicted would produce about 50 percent

24     inhibition of acetylcholinesterase in red blood cells

25     and about ten  for the diazinon.  So you can see where

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 1     we  are  here on the dose response curve.

 2                  So here are the  results,  same bars  as  I

 3     was showing you guys before,  only this is  real  data.

 4     This is chlorpyrifos by itself  at  one milligram per

 5     kilogram.     We   didn't   get   exactly  50  percent

 6     inhibition,   and  diazinon  at  ten  milligrams   per

 7     kilogram,  again a little  bit higher than 50 percent.

 8     But you can  see here that  the  mixture  is right in

 9     between the  two,  which would  indicate  that  we have an

10     additive interaction at  this low dose.

11                  Same thing  is  true for plasma.  We had  a

12     bit more inhibition by the  two compounds in  the plasma

13     by  themselves,  but the mixture here produced a value

14     that  was in between these two,  not  higher or lower.

15     We  did  not  get  any significant  inhibition  in  the

16     brain,  which  is  what  you   would   expect  from  our

17     original dose  response curves.

18                  Now  if  you look at  the  toxicokinetic

19     results, we're looking at --  At this  low dose, we  were

20     unable   to   see  any chlorpyrifos   or  any  of   its

21     metabolites   in   the  brain,  but   we  did    see
    j
22     trichlorpyridinol,   which  is  a  metabolite  TCP of

23     chlorpyrifos,  in the liver.

24                  if  you  look  here,  this  is  how  much

25     trichlorpyridinol is  in  the  liver.     This  is an

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 1     nanograms  per  gram of  tissue.   This  is  how much
 2     trichlorpyridinol  is in the liver of the animals that

 3     were dosed with one milligram per kilogram, and this
 4     is how much, and it's just about, conveniently, half.

 5     You couldn't have  predicted that.
 6                 This is how much  is in the  liver at  .5

 7     milligrams per kilogram, and this bar here is how much

 8     trichlorpyridinol  that was in the liver of the animals

 9     that received the  mixture.
10                 Now this is  a  different  paradigm here.

11     What you're looking at  here is how closely these two
12     bars resemble each other,  because these animals got  .5

13     milligrams per kilogram of chlorpyrifos, but they also
14     got 5 milligrams per kilogram of diazinon.  But you
15     can  see here  that these bars  aren't  significantly

16     different, and having that amount of diazinon on board

17     did   not    change   the   distribution    of   the

18     trichlorpyrydinol, presumably, and the chlorpyrifos  in

19     those animals.
20                 There  was no behavior to measure in those

21     animals.  They  were not  showing enough over-toxicity

22     or behavioral alterations at  those dosages for there

23     to be any  assessment in  those animals.    So I don't
24     have any behavioral results for those  animals.
25                 Now in the  second experiment -- This was
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  1     basically the same type of animal, Long-Evans hooded
  2     adult  male rates.   Our endpoint  here is 50  percent
  3     inhibition of cholinesterase in the brain, not the red
  4     blood  cell.
  5                  It  was determined that the  EDSO  in  this
  6     case   was  20   milligrams  per   kilogram  for   the
  7     chlorpyrifos, which is 20 times higher than it was for
  8     the red blood cell, and 75  milligrams per kilogram for
  9     the diazinon, which is about 7.5 times higher than it
10     was for  the  red blood cell.
11                  Again,  we had  five dosage  groups.   We had
12     a  control.  We  had each one by itself.   We  had  the
13     mixture,   and   then  we  had  animals  that  received
14     chlorpyrifos  at half  the dose of  it by itself so  we
15     could  compare the  distribution.
16                 Again,  our dose response curves  here  were
17     at  about  ten  for  the  chlorpyrifos  for the brain
18     acetylcholinesterase, and we're down  here at 75  for
19     the diazinon and for  the brain acetylcholinesterase.
20     Here are  the results  from  that group of studies.
21                 This is cholinesterase inhibition on  the
22     y axis.   This is chlorpyrifos  by itself.  We  did not
23     have 50 percent  inhibition in the brian.  We only had
24     about 90 percent -- 80 percent -- Sorry.  We only had
25     about 15-20 percent inhibition in  the  brain.
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 1                  The diazinon, we did have about 40 percent
 2     inhibition in the brain,  and the mixture was between
 3     the  two.   So,   again,  the  interaction  is additive.
 4     We've  also looked  at  the retina.   The chlorpyrifos
 5     produced about  60 percent inhibition in  the retina  of
 6     those  same  animals.   Diazinon  produced about   40
 7     percent  inhibition,  and again the  mixture group  is  in
 8     between   them,    indicating   that   the   quality   of
 9     interaction here is additive,  even though we've used
10     a much higher dose.
11                  Diaphragm for peripheral tissue --we just
12     decided  to  look at this  -- is  basically the  same
13     thing.  We had a lot less  inhibition in the brain than
14     we did in  the  diaphragm of  the  chlorpyrifos  dosed
15     animals  than we  did  in the diazinon dosed animals all
16     by themselves,   but  again the  mixture  was right   in
17     between.
18                  So  we  don't  see  anything  here  in the
19     biochemical results that would indicate that we've got
20     anything  more than  just  an  additive interaction   of

21     these two,  either at a low or a higher  dose.
22                 Now if you look at the toxicokinetics,  we
23     were able  in  the liver to see both chlorpyrifos, the
24     parent    compound,    and   trichlorpyridinol,    the
25     metabolite.  Here again,  you're looking at --  You're
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                                                           72
 1     comparing these  two bars.

 2                  In     the    liver    the    level     of

 3     trichlorpyridinol  in the mixture animals was basically

 4     not  any different than if  you  had given  the same

 5     amount  of chlorpyrifos  by  itself.  These two are not

 6     significantly  different statistically, but  it does

 7     look  like,   if   you did  this  maybe  with  200  more

 8     animals,  you  might be  able  to  get  yourself  a

 9     statistical difference  between here.  But all this  is
          t
10     telling you is that maybe the presence  of diazinon  at

11     this  dose   is   inhibiting  the  conversion  of  the

12     chlorpyrifos to  its metabolites.  But right now this

13     is not  significantly different and,  therefore,  we've

14     got no reason to suspect anything more than additive.

15                 We actually did see some trichlorpyridinol

16     in the  brains  of these animals,  and again these two

17     bars are not significantly  different. So the presence

18     of  diazinon  did  not  change  the  distribution   of

19     trichlorpyridinol  in the animals.

20                 Here  are the  behavior results.  We saw

21     something a  little bit  different  here.   This is the

22     motor  activity  in  these  animals,   same  method   of

23     presentation here.  The  chlorpyrifos  animals alone saw

24     about a 40 percent inhibition in motor activity.  The

25     diazinon animals you saw a slight inhibition in motor

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                                                           73
 l     activity.
 2                  The mixture  group is  basically between
 3     these   two  bars,   and   so   you   have  an  additive
 4     interaction.  The mixture is where you would expect it
 5     if you  gave  half of the dose of these two bars.
 6                  Up  here,  this is the ataxia ratings that
 7     Ginger's  group  does.   You  can   see  here  --  We've
 8     actually  done   this  twice,  because  it  was  quite
 9     interesting  the  first  time.   This  is  the control
10     animals.  These are the  chlorpyrifos by itself.  These
11     are the diazinon by itself.
12                  In  each case  here you  had one animal that
13     showed  a  strange -- You've got a  four or five point
14     scale  here.    So   this  is  slight ataxia  in  these
15     animals.    But  in the  mixture  group  we  had four
16     animals,  five  animals,  that  showed this effect,  and
17     each time we saw this.
18                  I don't want to make anything too much  out
19     of this except  that it  was repeatable.  so we've  got
20     here is an interaction at either  a low  or high dose
21     that shows an additive interaction, the low dose being
22     the dose  that  inhibited the  brain --  I  mean the  red
23     blood cell acetylcholinesterase,  and the higher dose
24     being the  animals  that had approximately 50 percent
25     brain acetylcholinesterase inhibition.
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 1                  In  neither one did we see any type  of  --
 2     any evidence that would lead us  to believe that the

 3     interaction  could be considered anything but additive

 4     except for some aspects of the behavioral testing.
 5                  These  are  the people  that did  all the

 6     work.   Debbie  Hunter  does  all  the pharmacokinetic
 7     analysis.  Lynn Lassiter and Renee Marshall help out

 8     in  the  lab  with  assessing  the  animals  and  also

 9     collecting tissues.   Kathy McDaniel, Ginger Phillips

10     and -- I mean Pam Phillips and Ginger Moser work with

11     the behavioral  assessment  of the animals.
12                  I'll  be glad  to take  any  questions,   if

13     anybody has  got any questions.   Nope?  Okay.

14                  (Applause.)
15                  DR. NORRIS:  Thank you, Stephanie.   We'll
16     take  a  quick 15 minute break,  and we'll reconvene.

17     Thank you.

18                  {Whereupon,  the  foregoing matter went off

19     the record at 10:49 a.m.  and went back on the record

20     at 11:14 a.m.)
21                  DR.  NORRIS:  Okay.   We keep  getting off

22     and on  schedule.   We'll try and stay closer  to  on

23     schedule.
24                  I'll introduce our next speaker who made
25     it from Baltimore,  thank goodness.   In spite of the
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                                                           75

 1      weather?     Dr.   Will   Boyes   is   the  Chief   of

 2      Neurophysiology and Toxicology Branch and the Acting

 3      Division Director  for the  Neurotoxicology  Division

 4      right  now at RTF in North Carolina.

 5                  I see  folks still  coming  in.    I did

 6      major   introductions  before  you  arrived   to  this

 7      morning's session,  but I'll let you take it from here,

 8      Will.   Thank you.

 9                  DR.  BOYES:   Well, thank you,  Debbie,  and

10      I'm  very  happy  to be  here,  especially  given  our

11      episode on the plane this morning.  We approached the

12      Washington airport runway twice,  and  then the pilots

13      said they couldn't see the runway.  So we were going

14      to Baltimore.  I was looking  at my watch  wondering if

15      I was going to make  it or not, but they put us in cabs

16      and we  got here  in good shape.

17                  I haven't heard the morning's  introductory

18      talk.   So if I repeat something,  please  forgive  me.

19      One more apology:   I'm sorry  I didn't  put a biography

20      in the  thing.   I didn't  realize that  it  was  going to

21      be  published  like  that,  but I  work  in  the  same

22      division  as  Stephanie  and Ginger and,  if  you want  to

23      contact me,  I'm  in the  EPA directory.

24                  I am Acting  Director for  the Division  at

25      the moment while  our  division  director  is  up here

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 1     learning what risk assessment is all about.  So if I'm
 2     a  little  bit  confused,   I  have  a  lot  of  excuses
 3     already.
 4                  Let's  go  ahead  and  get   started  now.
 5     People  are sitting down.   This is  a  set of  studies
 6     that was  done almost a decade  ago,  like what  Ginger
 7     presented  to you.   At that  time the  EPA  Superfund
 8     office  was very interested  in mixtures and  funding
 9     research  in  the Office of Research  and Development,
10     and this is one of the projects that we  ran  with  that
11     funding.
12                  This one was  done with me as the project
13     officer  in  collaboration with Chuck  Rebert  at  the
14     Stanford  Research Institute.   All of  the  data  were
15     collected  out in Stanford at  SRI.
16                  We were interested in solvents.   Before we
17     designed the studies,  though, we  started looking at
18     the  literature.   At  that time,  and  I think  it's
19     probably still true today, the literature on mixtures
20     in neurotoxicology is very sparse.  There are very few
21     studies published.
22                  The  ones that have been published  have
23     used largely in vitro preparations and,  because of
24     some of the  complexities  that I think you've already
25     heard about,  it's  more practical  to do it  that  way.
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                                                           77

 1     But  it  leaves  a  lot to  be  desired  in  terms  of

 2     applicability to in vivo situations.

 3                  Those in  vivo  mixture  studies that  you

 4     find are largely, if they are not cancer studies,  they

 5     are hepatotoxicity studies.  There's very  few  in the

 6     nervous   system,   and   there's  even   fewer   with

 7     environmentally relevant compounds.

 8                  So we  wanted to  address  some of those

 9     problems,  but  first  why is  that the  case?   It's a

10     difficult  thing  to  do mixture  studies, and  it's a

11     difficult  thing to study the nervous system.

12                  The nervous system is a very heterogeneous

13     structure.    It  has  multiple  target  sized complex

14     systems.   You can have many  outcomes.   I think   you

15     saw this  in Ginger's  presentation.   In some  cases,

16     there are  very steep dose response curves,  which can

17     make the analysis difficult.

18                  The difficulties  with  chemical mixture

19     studies we're  all hearing about  today,  that there are

20     many  chemicals,  each  --  If  you  look at complex

21     mixtures, each complex site, each Superfund  site,  each

22     source  if  you're talking about  air pollution, might

23     have a unique  mixture, and how do you generalize  from

24     one complex  mixture to another?

25                  The designs, when  you  start  looking  at

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 1     multiple chemicals and multiple interactions,  get very

 2     complex,  and so  do  the analyses.   So  this  is what

 3     you're faced with when you try and do these kinds  of

 4     studies.  But we  forged ahead bravely.

 5                  We   wanted   to   test  the  additivity

 6     hypothesis,   because   in  the   absence   of   other

 7     information,  the  EPA risk  assessment  assumes that

 8     compounds are additive.

 9                  We wanted to do this in whole animals with

10     relevant compounds .  So based on the interest  from the

11     Superfund program, we  looked at a series of volatile

12     organic solvents.  They are not just  of interest,  of

13     course, to  Superfund.   They are  major components  of

14     EPA's  portfolio  for  just  about  all  the  program

15     offices.

16                  With  organic   solvents  in  the  nervous

17     system,  in  humans the  primary  concern  is  usually

18     cognitive function, but there has  not been good models

19     for  cognitive function in  animal studies.    So  we

20     thought we  would  focus on  a very definitive outcome

21     that has been reported.  It  was actually discovered  by

22     Chuck Rebert in  his  collaborative work  prior in the

23     mid-eighties.  That's damage to hearing,  ototoxicity.

24     I'll talk more about  that  in just a minute.

25                  We  wanted  to  use   whole   animals   by

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                                                           79
 l     inhalation  route,  and to do this, though, we started
 2     with   an   abbreviated   experimental   design  that's

 3     similar, in fact,  to what Stephanie presented in the

 4     last talk.
 5                  This is  a scanning electromicrograph from

 6     several   rate  cochlea   that   I  stole   from  the

 7     dissertation of Ann Christian  Johnson.    These  are

 8     stereocilia on three  rows of hair cells.   This  is

 9     after  just  a couple of  days  of  exposure to toluene.

10     I think the exposure for 18 hours a  day  for three days

11     to 1400 ppm high dose and extended exposures.

12                  This is  a couple of  days  later,  and you

13     can see that here  they  look  normal and healthy,  and
14     here you can see  the stereocilia beginning to break

15     up,  fall  apart.    Some  of  the  cells are  actually
16     missing.

17                  This   is a   couple   of  days  after  the

18     exposure stopped.  You  can see  there  are whole areas

19     where  the  hair cells,   these outer hair  cell,  have

20     disappeared, and there are other  cells infiltrating  to
21     take their  place  a couple of weeks later, and there

22     are whole regions  of the cochlea that  are devoid  of
23     outer  hair  cells.    This  will   produce   a  profound

24     hearing loss in those frequency bands in the cochlea.
25                  This is  from a review  article by Gordon
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                                                           80
  l      Pryor.   A lot of  solvents  were  tested by Rebert  and
  2      Pryor through the eighties and nineties, and  this is
  3      from a review article that he published.
  4                  It's interesting.  There is a very  unusual
  5      and  so far  not  well figured  out  structure activity
  6      relationship.   The compounds that do produce  hearing
  7      loss,  and these are high dose, again, phenomena,  but
  8      a lot of substituted benzenes, methylbenzene which is
  9      toluene,  ethyl  and  propylmythoxybenzene  and mixed
10      xylenes.  There are three xylene  isomers, but when  you
11      test  the  isomers separately,  only the para-isomer is
12      ototoxic.  The ortho and the meta- isomers are not.
13                  Styrene and other substituted benzene is
14      ototoxic,  and then some  -- and  then monopyribenzene
15      also, carbon disulfide, a little bit different  solvent
16      from these,  and then some alkanes.  Trichloroethylene
17      is ototoxic  and N-butanol.  But  then interestingly,
18      these did not produce noticeable ototoxicity.  Benzene
19      itself, even though all the substituted benzenes --a
20      lot  of substituted benzenes  do,  these  substitutes
21      don't,    isopropyl,   1,2-dimethyl,   1,3,  the  xylene
22      isomers.
23                  Then  comparing  with  trichloroethylene,
24      dichloromethane,  trichlorethane,  tetrachloroethylene
25      or perf doesn't.   N-butanol is.    2-propanol  is not,
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                                                           81

 1     and  ethyl alcohol and N-hexane  is not.   N-hexane  is

 2     the  well  known  compound  for  producing peripheral

 3     neuropathy,  like  I  imagine  John  O'Donoghue  talked

 4     about,  but  I  didn't  hear him,  because  I  was  in

 5     Baltimore.

 6                  Okay.   So I mentioned that  we wanted  to

 7     use an abbreviated experimental  design.   This sort  of

 8     schematically  illustrates  what  I'm  talking  about.

 9     It's very important when you look at chemical  mixtures

10     to do a dose response  curve for your constituents  of

11     the mixture.

12                  That's because, if you think of  compound

13     A here as just  a  typical dose response curve,  if you

14     do a mental experiment of combining a dose level like

15     this of compound A with  the same  thing of compound  A,

16     a mixture of the compound A with  compound A,  and then

17     that would be this dose  plus  this dose.   So  it would

18     put you out  here,  and you  could get  a very profound

19     effect by mixing compound A with  itself, much greater

20     than you  would ever expect,   and it's  only because

21  j   you've moved into a  steep part  of the dose  response
    I
22     curve.

23                  So  if  you  don't understand  the shape  of

24     the dose response curve,  then you can fallaciously  or

25     spuriously conclude  that you  are getting  a greater

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                                                           82

 1     than additive  effect,  when in fact you've just moved

 2     into a nonlinear part  of the dose response curve.

 3                  So if you  don't understand where you are

 4     on  the  dose  response  curve  for  your  individual

 5     compounds,   then  it  becomes  almost  impossible   to

 6     interpret  a  mixture of different  compounds.

 7                  This is the design that Stephanie talked

 8     about  where  she  took  a  dose  response  curve  for

 9     compound A and for compound B,  took an equal effective

10     dose of these,  and then you add proportions of those

11     doses together.   If you add this  EDa to EDb,  then you

12     would expect to  get a very  large effect.   You can

13     predict that based on  the shape of the dose response

14     curve.

15                  We actually used these as our target dose

16     in  our  experiments  where  we added fractions  of the

17     effective  dose  of  compound A  and fractions  of  the

18     effective  dose  of  compound B  together,   because we

19     wanted a target on the  steep part of the dose response

20     curve so  that we  would be able to see changes that

21     were both  greater than or less than additivity.

22                  So this is  our basic strategy.  This is a

23     typical  isobologram which you would  take  relative

24     proportions  of  the compound A and compound B and add

25     them together  at different dose levels,  so  that you

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                                                           83
  1      have these different rays coming out, and then if they
  2      are additive,  you would get  a straight line  between
  3      them at  the  point which produces an equal  effect all
  4      along  here.  This is an additivity isobole.
  5                  If you  get --  if  it  takes  larger  and
  6      larger doses of these  compounds  to produce the  same
  7      effect,  then   that's   considered  to  be   less   than
  8      additive  or   some   people   have   used   the   term
  9      antagonistic.   If  it takes lower  doses to produce the
10      same   effect,   then   that's   termed   greater   than
11      additivity or  synergism.
12                  We didn't have the ability to do that  with
13      these  large  animal studies.   So what we've done is
14      focused  on the line  of  additivity,  which you  can see
15      here,  and  take a 50/50  mixture of  the  doses   that
16      produced these effective doses,  75:25 and 25:75,  and
17      then 100/0 and 0/100 or vice  versa, and add these --
18      use these dose combinations to test our additivity.
19                  So  if  the  compounds  are,   in  fact,
20      additive,  then  they  should   all  produce   the   same
21      effect.    If  they are  greater  than or   less   than
22      additive, they  should be statistically different  from
23      this linear  extrapolation between the effective  dose
24      of A and the effective  dose of B.
25                  So  that's   the general  strategy for our
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                                                           84
 1     experimental  design.
 2                 This   just   sort   of   differentiates
 3     traditional  from our  simplified isoboles.   Many  of
 4     those levels for each compound when you apply  the iso-
 5     effective   concentration   combinations,    and  it's
 6     practical  in  vitro,  but it's very difficult to do  in
 7     vivo unless you have  very simple measures.
 8                 So  we  used  our  simple  dose group   at
 9     relative proportions,  and then we're plotting them  to
10     see  if  the  effects  are  different  from the  iso-
11     effective.  You can do this more practically with the
12     kind of studies we  want to  do.
13                 Nonadditivity  will  be  demonstrated  by
14     whether or not  the  effects  we see are different from
15     that linear prediction.
16                 This  is kind of a repeat  of  just what I
17     said.   Dose response  curve for each  compound.   Our
18     mixtures  are  0:100,  75:25,  50:50,  25:75  and 0:100
19     proportion of the iso-effective concentrations.
20                 We  picked five  solvents from the list of
21     positive    ototoxic    solvents:        Styrene,
22     trichloroethylene,  toluene, mixed xylene isomers, and
23     chlorobenzene.  We did the same  solvents on this side.
24     When you do binary combinations of these,  you come up
25     with ten experiments  that you can run.
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                                                           85
  1                  We ran all ten  of these combinations in
  2      this  set of  studies, first doing  dose response curves
  3      for the individual solvents,  picking the iso-effective
  4      levels, and  then going back with a second  study with
  5      five  dose  groups for each of these solvents.
  6                  We had about  eight rats in each of these
  7      dose  levels  or -- I'm sorry, about 40 animals  plus  a
  8      clean air  control for about 50  animals,  48 animals,
  9      for each of  these ten experiments.   So you can see,
10      even  with  these simplified designs, when you look at
11      multiple chemicals,  it becomes a very large endeavor.
12                  The animals were inhalation exposed five
13      days,  eight  hours  per day,  Monday through Friday.
14      It's  enough  to produce permanent  ototoxicity.   We
15      waited about ten days before we started testing their
16      hearing.

17                  This  is how we tested hearing.  We  didn't
18      dissect    out    the   cochlea    and    do   scanning
19      electromicrographs  like you  saw  in that  beautiful
20      slide of Ann  Christian Johnson's.  We have a much more
21      efficient  way  to  do   this,   and  this   is   the
22      electrophysiological  method  called the  brain  stem
23      auditory evoke potential or some people  refer to it as
24      the auditory brain  stem response, ABR.
25                 To  do this, you anesthetize the animal so
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                                                           86
 1      that  it doesn't move.  That's the main thing you want.
 2      You   can  put  needle   electrodes  under  the  scalp,
 3      maintain body temperature so that's not a concern,  and
 4      then  stimulate  the ears with tone pips.
 5                  You can make  the sound  loud or you  can
 6      change  the  frequency from  low  to high.  Now what  we
 7      report  is a  response  that's  the mass discharge  of
 8      cells in  the auditory system.  There's a lot of work
 9      that's gone into the generators of this potential,  and
10      we know that the first peak we measure is  generated --
11      There's a little  response in the cochlea.  The  first
12      major peak is in  the auditory nerve.
13                  Here  you can see the eighth nerve or  the
14      acoustic  nerve.    Then  the next  peaks are generated
15      along the line of  the ascending auditory pathway, and
16      the neural  generators  for  each  of  these are pretty
17      well  identified,   the  cochlea   nucleus,   superior
18      auditory complex,  lateral aniscus, medial tinnicular.
19                  So  we can  follow the ascending auditory
20      signal  as it  goes  up  the auditory pathway.   This
21      basically  says  what  I  just  said.    Anesthetize the
22      rats.    Stimulate  varying  and  loudness  in  pitch,
23      recording the electrical  activity.
24                  Then  we needed a simple measure.   There
25      are a lot of measures you  can  take  off of this, but
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                                                           87

 1     because  of our  complex design,  we wanted a  single

 2     dependent  variable.    What we  settled  on  was  the

 3     integrated amplitude of the response between 55 and 85

 4     decibels.   I'll  show  you this in just a minute.

 5                 This gives  us a  very nice  and  regular

 6     measure  that's  sort  of  a global  indicator  of  the

 7     neural function  in the  auditory system.  Let  me  show

 8     you the next  slide, and that will become clear.

 9                 These  are examples from groups  of  animals

10     averaged together  at  25 to 95 decibels sound  level.

11     In the control animals you can see  that it  starts out

12     at  25  decibels  with  a very  small response,  and  it

13     grows in amplitude -- this is voltage on this  scale,

14     and this is time on this scale -- and becomes  earlier

15     in  latency as the  response  -- as  the  stimulus  gets

16     louder.

17                 PI is  the peak generator in the auditory

18     nerve, and you can see  that  it follows  up and  down

19     with a nice  relationship to intensity.   What we  did

20     for the dependent measure was  to take between here and

21     here, rectify it so that it was all positive, and  then

22     do  an  area under  the  curve.    It gives us  just a

23     summary  measure for  the  function  of  the auditory

24     system.

25 [                Chuck  decided  to  it.   I  think  it   was

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                                                           88

  1     between 25 and 85  decibels.   So he summed all of these

  2     up.   I think he left off  95  decibels,  because of the

  3     possibility of auditory recruitment, which happens in

  4     some animals with hearing loss.

  5                 What's interesting here is the effect of

  6     styrene.  Here you can see after 2000 PPM styrene for

  7     five days many things.  First of all,  the  amplitudes

  8     are  much smaller,  but more importantly, the threshold

  9     for  eliciting response is tremendously changed.

 10                 What you  see, about 25 decibels in  the

 11     control, you're seeing at around 65 decibels in  the

 12     exposed animals.   This is a profound hearing loss,

 13     about a 40db loss of hearing.  So that shows you  the

 14     kind of an effect that we're talking about.

 15                 Now I  didn't bring all the data.  I wasn't

 16     sure how much time it would take me  to go through that

 17     part of  it.    But this  just samples  dose response

 18      curves  for the individual compounds alone.  This  is

 19      chlorobenzene,  and this is toluene.  This  is kind  of

 20      the  best and the worse.

 21                  The toluene looked very nice.   We had a

 22      nice linear response curve.   Chlorobenzene  --we had

 23      a little trouble with this particular compound,  not

 24      the  others,  in  that  at  high doses the animals lost a

25      lot  of weight.  So we couldn't go much  higher, and we

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                                                           89

  1      had a couple of uneffective dose levels.  So we didn't

  2      have a  lot  of  room on the chlorobenzene dose response

  3      curve  to work out things.

  4                  Let me show you the next slide now.  This

  5      is  sort of  -- We ran ten experiments.  One of them,  we

  6      had to throw  out  because  the  solvent  generation

  7      apparatus  basically failed,   and  the  animals got

  8      exposed to what  they weren't supposed to.  But the

  9      other  nine all gave us nice data,  and we  felt  very

10      confident about that they were  exposed to  what  they

11      were supposed to be.

12                  We did have some  trouble with some  of the

13      experiments,  and I'll talk about that  in a minute.

14                  This  is what we expected to see. This  is

15      trichloroethylene and  toluene.   The control animals

16      had a  very  large integrative amplitude,  about  200.

17      The trichloroethylene  alone produced about a half  a

18      maximal effect.    This was   exactly  what  we were

19      shooting for,  and the toluene produced  about the  same

20      thing.

21                  You can see that the 25:75,  50:50, and

22      75:25   combinations  produced  effects   that   were

23      equivalent  to  those  of  the  compounds alone.  This  is

24      exactly what we expected to see with additivity, and

25      this is the equivalent  to the  graphs that  Stephanie

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                                                           90

  1      was showing in the last talk.

  2                  The problem that  we had was that  we saw

  3      drift in the dose response curves  for  the  individual

  4      compounds alone.   Here, xylene produced about what we

  5      wanted to by  itself,  but  here trichloroethylene  at

  6      2800 PPM is less  effective than it was here at  2600

  7      PPM.   But still,  the  combinations between these two

  8      produced a linear relationship.

  9                  Dave Sensgaard did a lot of statistical

10      analysis with us,  and demonstrated that, even  though

11      our individual compounds were  not iso-effective,  that

12      the linear relationship between them was indicative of

13      an  additive  effect.

14                  Here  you can  see a similar effect.   Here

15      the xylene was not  as effective as  we thought,  but

16      again in  combination  with  chlorobenzene,  it  was a

17      linear  relationship between  the effects of  the two

18      compounds.

19                  Similar  here,   this is  chlorobenzene  at

20      2000.  This  was chlorobenzene at 2400.   So again you

21      can see  there's  quite a drift  in the  different

22      attempts  to  ascertain  the dose  response curve for the

23      individual  compounds.    But  the simultaneously  run

24     mixtures  here  with toluene showed  again  an additive

25     interaction.

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                                                           91
  1                  This is four of the ten experiments,  but
  2      without showing you all  the  data,  what we  found  was
  3      essentially in all the experiments  there was a linear
  4      relationship between the different combinations.   so
  5      we did not detect any nonadditive interactions.
  6                  Now let me go back and make one more point
  7      from that slide.  One  of  the  things  that I think  is
  8      our lessons learned is that,  obviously, you need  to
  9      have --It would be nice to have stable  dose response
10      curves to do this kind of work.
11                  Another thing  is that it's not clear to me
12      how big a difference  from additivity  we would have
13      been able to detect.   So  it would  have been nice  to
14      have compounds that we knew were nonadditive  to see  if
15      our design was  sensitive  enough to  detect them.
16                  We did some mathematical calculations, but
17      it  would have been nice to have some positive control
18      compounds.

19                  So  a  couple   of  conclusions:    For  the
20      ototoxic   solvents  we  saw   no  deviations   from
21      additivity.   This  suggests that  the  compounds  are
22      substitutable for each other,  that they  cause the same
23      thing.  And  given  that they are all organic  solvents
24      and they  produce a fairly similar  hearing loss,  one
25     possibility  is that this  suggests a common mechanism
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                                                           92

  l      or  mode of action.  It doesn't prove  it,  but that is

  2      a possibility.

  3                  Lessons for  the  experimental design:   I

  4      think that our simplified analysis of the isobolograms

  5      was effective at allowing us  to test binary  mixtures.

  6      We  had,   as  I   pointed  out,   problems   with   the

  7      instability  of  our  dose  response  curves  for  the

  8      individual compounds and,  as I  mentioned before,  it

  9      would have been  nice  to have a  positive control  to

10      differentiate between our ability to detect additivity

11      and nonadditivity.

12                  We  weren't able  to  follow these  studies

13      beyond  this.  But  one of  the  things  that  I would have

14      liked to  have been able  to do was to look  at noise.

15      Noise is  a  very  common  cause  for hearing loss  in

16      occupational  settings, although  it's  not  currently

17      something EPA is concerned about.   But other people

18      have  looked  at  workers  exposed  to solvents  in  the

19      presence  of  noise.

20                 The  conclusion has been  that  it  does tend

21      to  cause  a  greater than  expected  hearing  loss  for

22      noise when you  are exposed to solvents.   So I think

23      that's  something that  is a very  interesting outcome

24      from  this work.

25                 I'd be happy to take any  questions.  Thank

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                                                           93
  1

  2

  3

  4
you.
  7

  8

  9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25
drifted?
(Applause.)
QUESTION:  Why do you  think the baseline


DR. BOYES:   I don't  have the  answer to
 that.  We did take  simultaneous blood levels from all
 of the animals during all the exposures,  and they were
 fairly constant.  It doesn't look like it was exposure
 of the animals  to the solvent or absorption into the
 blood.
             It was  something in the different batches
 of animals  or the different time of year or whatever
 it  was  when we  ran  the studies  that  the animals
 differed in their sensitivity to  the  solvent.  I  wish
 I knew that, because I don't.
             DR.  NORRIS:  Thank you,  Will.    Our  next
 speaker, Dr. Greg Christoph.
             DR.  CHRISTOPH:    Thank you,  Deborah,  and
 thanks to the organizers for inviting me  here to talk,
 as I'm just preparing to leave the industry.   It  was
mentioned  earlier  that  I am retiring tomorrow,  and
 actually I'm not going  to  retire.    I'm going to  --
 Well,  I  am  retiring,  but  I'm going   to  start a
 different career with a different kind of industry and
a different kind  of regulatory agency.
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                                                           94
  1                  So this  is my  opportunity  just to  say
  2      exactly what  I  think.   Well,  indeed,  I think  I've
  3      always  said exactly what I thought.  At  least I  hope
  4      I have, and perhaps that's one reason why I'm retiring
  5      as a director instead of a senior vice president.   But
  6      I do work for the DuPont Company  as  of  today,  and I'm
  7      sure all of you are aware that DuPont is  a very,  very
  8      large chemical company and makes a number of  the kinds
  9      of chemicals we've  been talking about here  today.
10                  In particular,  since I  am  going to be
11      talking  about  pesticides,  DuPont  does  have  an
12      agricultural chemicals business, and they do  make  lots
13      of different kinds of pesticides.   In particular,  they
14      make  an  organophosphate  cholinesterase inhibitor.
15      They make carbamate  cholinesterase inhibitors.  They
16      make a  pyrethroid sodium  channel opener that is an
17      insecticide,  and  soon  they  will   be   producing  a
18      pyraziline  insecticide which is  a new and different
19      kind  of  mechanism  of  action  which,  nonetheless,
20      affects sodium channels.
21                 So we make a lot of  different chemicals
22      that are  sort  of,  by definition,  neurotoxic or have
23      neurotoxic  potential  in  animals  anyway because of
24      their known mechanism  of action.
25                 I'm going  to be talking primarily about
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                                                           95
  1      theory,  very little data here,  in  fact,  no data that
  2      I've produced myself,  some tiny amount  of  data from
  3      the  literature;   but   it's really  the  ideas  about
  4      cumulative risk assessment that I would  like  to talk
  5      to you about,  particularly in the  context of the Food
  6      Quality   Protection  Act   and  how  cumulative   risk
  7      assessment is envisioned  in  the context of the Food
  8      Quality  Protection Act.
  9                  Basically, the steps  in cumulative risk
10      assessment are fundamentally  no different, I think,
11      from those in any  kind of a risk assessment  analysis.
12      There's  four fundamental categories of activity.
13                  First  is hazard identification.  There's
14      dose response  characterization, exposure  estimations,
15      and finally the risk assessment  itself where all that
16      information is pulled together.   The nature of the
17      events or the specific events within each of  those
18      categories are a little bit  different,  however, for
19      the cumulative assessment  process.
20                  First  of  all,   the  hazard identification
21      step -- really, the  analog  of that in cumulative risk
22      assessment has to do  with deciding what the common
23      mechanism of  action is for  the  chemicals.   It is
24      generally decided  that something  like  inhibition of
25      the  cholinesterase enzyme is a common mode of action,
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                                                           96

  1      and that itself poses a hazard.

  2                  So  that  kind  of  hazard determination,

  3      while  there's  a lot of discussion about  that and what

  4      needs  to do into  that,  is  typically  --  When  that

  5      determination  about the common  mechanism of action or

  6      common mode  of  action is made,  that's essentially the

  7      hazard identification step associated with  the  group

  8      of  chemicals.

  9                  The dose response  characterization  step

10      really becomes an issue in deciding what  the  identical

11      critical endpoint   in  the  same  species   for  all

12      compounds  is,   and  those  are kind of ideal kind  of

13      statements.   It would be  very, very  -- It would  be

14      best is,  for all the compounds in the risk cup, if the

15      same endpoint were used.  So red cell, cholinesterase

16      inhibition  in   female  rats,  for example,  is common

17      critical endpoint.

18                  That's not always going to be possible for

19      certain  groups  of  compounds,  for pyrethroids,  for

20      example.  One may have a more sensitive endpoint which

21     might  be muscle fasciculations in one  case,  and  it

22     might be a mild limb tremor in  another.   You have  to

23     decide whether  those  kinds of  endpoints are similar

24     enough to  be counted  as  the same thing and do the

25     modeling and the risk evaluation activities  off  those

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                                                           97
  1      kinds   of   qualitatively    similar   or   probably
  2      mechanistically related sort  of  data points.
  3                  Dose  normalization   is   something  that
  4      happens,   is  absolutely   necessary  to   do  within
  5      cumulative risk  assessment,  and it's blue here because
  6      that's what I'm  going to spend most of my time talking
  7      about today or at least one of the things I'm going to
  8      spend most of my time talking about.
  9                  Exposure estimation  is  a huge area.   In
10      fact,  all these things are  huge  areas and require a
11      lot  of discussion,  and I'm only going to talk about a
12      few  of them today.   But  basically,  the way  exposure
13      estimation works is  some kind of a Monte Carlo routine
14      which assembles  data associated with the foods people
15      really eat,  the  kinds of pesticides  that  are used on
16      those foods,  the  kinds of  quantities of pesticides
17      that potentially occur on those foods, and the kinds
18      of  other  activities  that people  get  involved  in,
19      because  the  cumulative   risk  assessment   process
20      involves  both  an  aggregate  and  a  cumulative  risk
21      analysis,  all  combined into one thing.
22                  Then,  of course,  there is the  addition of
23      the normalized dose units, and I'm going to spend  some
24      time  talking about  that process.
25                  There's some knowledge of how we are going
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                                                           98
  1      to apply uncertainty  factors to  the analysis.   I'm
  2      sure everyone is  familiar with  the way uncertainty
  3      factors are used in risk analysis or risk  assessment
  4      for a  single  compound.   The way those uncertainty
  5      factors are used,  I  think,  is a little bit  different,
  6      and certainly  it's more complicated  in the  context of
  7      a   group of  compounds  that  share   a   mechanism  of
  8      toxicity.
  9                  I'm  going to spend some time  talking about
10      the time frame,  the  appropriate time frame  over which
11      exposures  ought  to  be  considered for  addition,  and
12      certainly,  the kinetics of the individual compounds in
13      the risk cup matter  in  that context.
14                  Finally,  the  risk  assessment  itself  in
15      which  all  the data  are assembled together and  some
16      kind of an  outcome is handed over to a risk manager.
17      Now my personal   opinion  is   that  the   kind   of
18      information that the risk manager should get is  the

19      frequency   of   their   expected   or  potential
20      potentially expected frequency  of adverse effects  in
21      the  population.

22                  So many people out of  a large number, like
23      265  million,  might be expected to  experience   an
24      adverse effect due to the presence  of this combination
25      of pesticides used in the  marketplace.
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              While I  think that's  kind of  where  we

  should be driving, the analysis,  as  I understand where

  it's going,  is a  little  bit different  than that  in

  that it basically  is a more sort of a point analysis.

  That  is,  well,   this  group  of pesticides has   an

  unacceptable  margin of exposure.   So we  have  to  do

  something about that.

              I  think we can  make a more intelligent

  decision  if  we  knew  something  about  the  actual

  frequency of adverse effects and are actually worried

  about calculating  that  kind  of value.

              As  I  said,  all  these  are  very  long

  conversations in  themselves,  and I'm simply going  to

  focus on the ones  highlighted in blue  here.

              First   of   all,   dose   normalization:

  Essentially, this is the theory underlying justifying

  how we are going to be adding doses together.  So it's

  worthwhile to talk about  that  theory.

              Essentially,  on  the top figure  there on

  the right we have  two pesticides, and  let's  say they

  are cholinesterase inhibitors and the experiments are

  similar,  say  90  day  rat  studies in  which we  have

  sampled  red  cell   cholinesterase inhibition, and we

  have  a  dependent  variable   there  listed   on  the

  abscissa,  and  --  no,  let's  see,  ordinate.    The

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 1     dependent variable  on  the ordinate  is -- let's  say

 2     that's  cholinesterase  inhibition.    This is  clearly

 3     totally  invented  numbers  that  fit   a  very  pure

 4     mathematical  function,  in  this  case  the  logistic

 5     function,  which is fit to them.

 6                  The two compounds have different  potency

 7     in this regard.  The compound on the left is ten times

 8     more  potent than the compound  on  the right.   So  how

 9     are   we  going to   add  exposures  of   these   things

10     together?

11                  Well, we have to normalize the doses --or

12     we have to normalize them  somehow to make  them appear

13     as though they had comparable potency.  So there is an

14     adjustment  that's  done.

15                  The one that's  shown  here  on the  lower

16     panel, the left figure in  the lower panel, shows what

17     happens if we  compute the EDSO for the compound on  the

18     left  and divide  all the  doses used in  the  actual

19     experiment by  that  EDSO.   We can express the dose  for

20     that  chemical  in EDSO units.

21                  So, two, we  take the ED50 of the compound

22     on  the  right  and  divide  all the  doses   in that

23     experiment by  the EDSO,  and then we express the  doses

24     then  in ED50 units.   So then  we can pull those  two

25     things  together,  and  in  the  lower  lefthand  figure

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o
 l      there you see what happened.
 2                  The  data  points  are   probably  maybe,
 3      unfortunately,  too  small to see,  but the  four data
 4      points for the  two chemicals making eight data points
 5      altogether now  appear on a single merged superimposed
 6      function,   when the  doses  are  expressed  in  common
 7      xinits,  in  this  case common  ED50  units.   The  dose
 8      response  functions are perfectly superimposable.
 9                  Indeed,  this  is   an assumption  of  the
10      analysis,  that  the dose  response  curves are  parallel.
11      parallel  is really the  wrong word,  because  they  are
12      not linear  functions.  They are sigmoidal functions or
13      curvilinear functions, and in  this case  the best  way
14      to  -- more accurate  way to describe it is  the  dose
15      response functions share the same slope parameter,  but
16      I  think you  know  what  I mean.   The dose  response
17      functions  are parallel.
18                 Under  those  conditions,  expressing  the
19      dose  according  to a common effect level,  in  this case
20      ED50,   has   the   effect  of  superimposing   the  dose
21      response curves.
22                 Now in this case, what happens is that  the
23      -- one can  take any number of EDSO units of any of  the
24      chemicals in  the mixture  and add  them to  another one,
25      and that result,  that  sum, will lead  to a predictable
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                                                          102

 1     effect.   So for example,  one could take  .1 EDSO units

 2     of  chemical A and  .1 EDSO  units of chemical B  and .1

 3     plus  . 1 is  .2,  and we can simply  march up the dose

 4     response function in the lower lefthand panel and say,

 5     okay, where does .2 ED50 dose units bring us in terms

 6     of  the  predicted effect.

 7                  Perhaps it would be better to deal  with a

 8     slightly larger set of numbers like, let's say,  we had

 9     --  If we had .5 ED50 units  of compound A plus  .5  ED50

10     units  of compound  B,  the total  is 1 EDSO  unit.   We

11     would expect 50 percent inhibition in that case.

12                  This is true  regardless of the number of

13     the chemicals in a  risk cup.   If there are ten or 34

14     chemicals in the risk cup,  if we're adding up and they

15     all have the same slope and all have the same parallel

16     dose response functions,  when we normalize the doses

17     according to a common -- the dose  associated  with a

18     common   effect  level,   that  has   the   effect   of

19     superimposing the dose response curves and making it

20     justifiable that we're adding  doses  to lead to  a

21     common  effect.

22                  This is fundamentally what we're doing in

23     any kind of a dose addition cumulative risk analysis.

24     We  don't  always  say  we're  marching up  the  dose

25     response  curve like that,  because  very often  we  are

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 l     looking  for some kind of  a  point threshold,  whether

 2     you  exceed it or not, that  might be important.   But

 3     essentially what  we're  doing  is  adding  doses  and

 4     tracking up the dose response curve.

 5                  There is a tendency to say,  well, we can't

 6     really express doses on EDSO because the data  aren't

 7     good enough  to actually  get our  handle  on a  good

 8     estimate of  the ED50  for all  the  compounds in the risk

 9     cup.   That's  probably  true.   Probably  the  actual

10     regulatory data that you have in your hands  that are

11     submitted  to you  by  companies probably is  not  always

12     good enough to produce  an accurate  estimate of  the

13     ED5C  or an  ED10, for that matter.

14                  So it is tempting and probably necessary

15     as a practical matter to, instead  of using a  common

16     effect level like ED10 or EDSO and  to normalize  dose

17     units, rather instead to use  the no effect level of

18     the  two  compounds.   That's shown here.

19                  So the experiment  here has  a  no  effect

20     level of .1 mgs  per  kilogram.  The  compound on  the

21     right has a no effect level of, looks like,  3 mgs per

22     kilogram,  and then we could normalize the doses by

23     dividing each of the doses in the experiment by the no

24     effect level dose.   Now  we're  expressing doses in

25     terms of no  effect units,  NOEL units, basically.

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 1                  So one NOEL  unit in both  cases is  a no

 2     effect  level  for both  compounds.   Note  that  what

 3     happens,  because  of  the arbitrariness of  how  the

 4     experiments are conducted and dose selection and all

 5     the different kinds of rationales that go into exactly

 6     how  the experiment  is conducted  and statistics  and

 7     everything  else, when such a transformation of dose is

 8     made,  the  dose response  curves  are  no longer  --  are

 9     not superimposable.

10                  In essence,   when we're  doing  that,  now

11     when  we're adding  up NOEL  units  of compound  A  and

12     compound B,  we don't  really know which line to track

13     up to know  what the predicted level  is going  to  be,

14     and that's  simply a consequence of the fact that  the

15     functions are not superimposable like they  are  on the

16     left.

17                  So   for  this  reason,  it's  technically

18     inappropriate   to  use  something  like  a  NOEL,   but

19     obviously,  until we get  better data to the agency,  I

20     think we  are  probably  going  to be   forced   as  a

21     practical matter to do that.

22                 Now on the positive side of doing  that,

23     probably the errors are  not going to  be too  great,

24     because where we are located down here  on the low end

25     of the dose response  curve is -- things are going to

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                                                          105
 l     be  reasonably similar  to each  other most  --  well,
 2     we're pretty sure that doing such an action  certainly
 3     would not  put the public at risk.   It's simply  not
 4     totally  scientifically  appropriate to do it.
 5                 There are other issues pertaining to non-
 6     parallel dose response functions.  I was  in  a  hallway
 7     conversation  earlier:    What do you  do  in  the case

 8     where the  dose response  functions  are not parallel?
 9                 It's  an interesting problem,  because  --
10     Well, technically, we really shouldn't be adding  the
11     doses  together   in  that  case.     As  a  practical
12     resolution to  that,  instead  of  comparing   --   or
13     normalizing doses around  EDSO  for example,  what  one
14     might do there  would be to normalize  doses around a
15     lower effect  level such  as  an ED10, and  now  express
16     the effects in terms of  ED10 dose units.
17                 The reason for doing that is because, when
18     non-parallel  dose  response functions  are normalized
19     around an  EDSO,  essentially they become superimposed
20     at the inflection point of the sigmoidal dose response
21     function around the EDSO  point.   So one could  imagine
22     one compound having that  slope and then the normalized
23     function intersecting  with that, but  following that
24     sort of trajectory.

25                 What that does is  displace things  on  the
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                                                          106

 1     low  end a  lot more  than if  one were  to normalize

 2     around  an   ED10  in  which  case  the  compounds   of

 3     dissimilar  slopes are now fairly close together on the

 4     low  end where we  are  actually  going to  be  adding

 5     things up where things really matter,  and are more

 6     disparate  at  the  higher dose  levels  where  people

 7     aren't really  exposed to those kinds of levels anyway.

 8     If we  get up to 50-60 percent inhibition, we know we

 9     are  close to that  in human beings.   So that would be

10     how  to handle  that  problem.

11                  Well, in kind of the  real world, how does

12     this work,   this  idea  of normalizing dose response

13     functions  to  something like an  EDSO?  Here  we have

14     seven  organophosphate  cholinesterase  inhibitors.    I

15     just labeled them A,B,C, and so on here.   But in fact,

16     you  know, they are  things like chlorpyrifos and your

17     basic  list  of  typically used organophosphates.

18                  These are dose response  functions for  red

19     blood  cell  cholinesterase inhibition,  all taken from

20     90-day rat  dietary studies.   You see here that  the

21     seven  compounds have  -- They vary almost a factor of

22     100  in  potency.     In   general,  the  dose  response

23     functions are  fairly  similar in slope.

24                  They  are not so markedly  different than

25     one would say  those are really different.  Certainly,

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 l     they   are   not  statistically   different,  although

 2     interestingly,  there are two groups  of disparate  --

 3     two groups of different slopes here, but they are  not

 4     that different.  Actually, I think it's the ones with

 5     the  white  data points  that mathematically  end  up

 6     having slightly less steep slopes than the others.

 7                 Let's  go  through that  exercise  of  now

 8     determining the EDSO for each of  these compounds and

 9     expressing  the doses of  that  compound in EDSO units

10     and going on through that  entire process for all seven

11     compounds, and then  assembling  all the data together

12     in the same graph  and seeing what happens.

13                 That's what's going  on in panel  B down

14     here   in  which   we  have  percent   inhibition   of

15     cholinesterase as  a function of dose expressed in EDSO

16     units.   You can see that the  data points  from the

17     seven  different  experiments  all  pretty much  fit

18     reasonably nicely  a logistic curve function, and here

19     are  the parameters   associated   with  that  logistic

20     function.

21                 One can compute a 95 percent confidence  --

22     set of 95 percent confidence intervals associated with

23     that  logistic  function,  and  from that   95  percent

24     confidence interval  one can  determine  that an ED20, a

25     dose  that  causes  20  percent inhibition of  red cell

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                                                                      108
o
 l      cholinesterase,  is  0.34  EDSO units,  and that's  the
 2      intersection of  the upper  bound of  the 95  percent
 3      confidence limit  with the 20 percent inhibition level.
 4                  So regardless of the OP that we have here
 5      that differ by a  factor of  100,  in general 0.34  ED50
 6      units,  whatever  the EDSO  of  the particular compound
 7      is,  is  going to produce about 20 percent  inhibition.
 8                  If one does not like ED20  as some  kind of
 9      a point of departure for risk analysis, you might like
10      ED5  or  ED10 or  some other  number.   What  I'm  just
11      showing you is  simply a matter of whatever number you
12      like.   If you  like  ED10,  you  can determine what  the
13      intersection of  the upper  bound of  the 95  percent
14      confidence limit  with the ED1D is, and determine that
15      that is 0.2 ED50 units.
16                  Once  you have that  number,  then  one  can
17      sort of very readily move forward with a dose addition
18  j    process.   So when you  know that an  apple has X  EDSO
19      units on  it,  then you  could  add  that to Y EDSO  units
20      of  a pear --  of a  pesticide that's on  a  pear,  a
21      different  pesticide  that's on another piece of  food,
22      for  example.
23                  So  I  just mentioned the process or the POD
24      --   that   is,   the  point  of  departure  from  the
25      experimental  data.  I simply want to  define that  for
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                                                          109

 1     you.    That's  the  number  from  which  all  further

 2     analysis  is going  to occur,  and whether that point of

 3     departure is a NOEL or an ED10 or an ED20,  we're simply

 4     going  to  call  that a point of departure  to  have  some

 5     language  that's  useful  in the analysis.

 6                 We need to have  some kind of a measure or

 7     assumed   or  assigned  values  associated   with   the

 8     pesticides that  people  consume.   So  we're  going  to

 9     have to know the  amount  of  pesticide residues on foods

10     or be  able to  estimate  that  in some way.

11                 We need to know  how much food is consumed

12     by an individual and the kinds of foods that  they  eat,

13     and when  they  eat it; and we  certainly need  to  know

14     their  body weight.

15                 Basically,  these kinds of  things occur in

16     the context of a Monte Carlo  algorithm that  assigns

17     them based on distributions of  their occurrence in the

18     real world.  Then we're going to go through  a  process

19     of computing the  normalized units of exposure for  each

20     of the compounds.

21                 So a person might eat an apple that has 12

22     tnicrograms per kilogram of  chlorpyrifos on it,   and

23     we're  going  to  have  to  convert  that  amount  of

24     chlorpyrifos to  normalized  exposure  units  and  then

25     determine what the  dose  in normalized exposure units

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                                                          110

 l      for  that  compound  on  that  apple  are   for  that

 2      individual.

 3                  This whole process can -- You know, now we

 4      have  to express the cumulative exposure,  and there are

 5      a  number  of  different ways  of getting  there.   The

 6      mathematical  --  Well,  the  arithmetic  --  It's  not

 7      mathematics,  really; it's arithmetic -- by  which one

 8      expresses these cumulative exposures can look really

 9      different, depending on  which kind  of metric  one

10      chooses.

11                  One way to do it  is to calculate a margin

12      of  exposure.     Another   way  is   to  calculate  the

13      milligram per kilogram equivalents of  a particular

14      index chemical,  and  there's a lot  of  interest  in

15      saying  that chlorpyrifos,  for example,  is  an  index

16      chemical.    It's  certainly  a  widely used   chemical

17      that's been fairly well  studied,  and  we can  express

18      all other organophosphate cholinesterase inhibitors in

19      the  same  -- as  milligram per  kilogram chlorpyrifos

2 0      equivalent s.

21                 Alternately,  we can say how that  exposure

22      fills up the risk cup.  So we  have  a risk cup which is

23      allowed   to  hold 100  percent  of  one's  permitted

24      allotment of organophosphate cholinesterase inhibitor,

25      and as one eats more foods that  contain more different

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                                                          Ill

 1     kinds  of  pesticides,  more  different  kinds  of  OPs,

 2     we're  filling up that risk cup.

 3                  Arithmetically,  the calculations look like

 4     quite  different.  Mathematically,  they are absolutely

 5     identical.   So it  doesn't  really matter  if we're

 6     talking margin of exposure or relative potency factors

 7     in milligram per kilogram equivalents or we're filling

 8     up a certain percentage of the  risk cup.  The numbers

 9     are quantitatively exactly the  same, because  there is

10     a   mathematical   identity   underlying  all  those

11     computations.

12                  The reason  that  the  answers  might  be

13     different  is if one is not careful  about handling the

14     uncertainty  factors that figure into the equation.  If

15     one handles  the uncertainty factors in the same  way,

16     all the values  will  come  out exactly the same.   They

17     are  all directly  translatable  to  each  other  in a

18     precise quantitative  way.

19                  Well,  let's  go  through  a hypothetical

20     exposure  scenario here,  just  to  show you how  this

21     might work in the context  of  a margin of exposure  kind

22     of analysis, and  I've just  made  up  these  numbers.

23     These  numbers have no meaning at all.

24                  First of  all, we are going to define  the

25     margin  of  exposure as the rat POD,  the normalized rat

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                                                          112

 1      POD,  divided by  the human  exposure, the  normalized

 2      human exposure,  sort of expressed in the same kind of

 3      unit.

 4                  So a rat  POD might be  one  - -  or  . 1  EDSO

 5      units.   Actually,  I said it was .34  EDSO units before

 6      when  I  was going through my exercise, and  that would

 7      be  a  .34 ED50  units  provides  20 percent red  cell

 8      cholinesterase inhibition in the  rat.

 9                  Well, what is the actual human exposure on

10      that apple, for example?  What is the normalized human

11      exposure? What is the ratio of those two?  That's the

12      margin  of exposure.  Clearly,  the  higher that  number,

13      the larger the difference is between what it takes to

14      affect  a rat  and what the human receives.

15                  We  can  calculate  an  MOE for  the  entire

16      chemical,  and  that's   simply  done  by adding   the

17      reciprocal MOEs and taking the reciprocal of that.  In

18      this particular  example, I've had a  person eating --

19      On  a  given day,  I  had  them eat  one kiwi  fruit,  12

20      grapes,  four slices of bread, two slices of  pizza,  ten

21      ounces  of cornflakes,  16 ounces  of  green salad,  l

22      ounces  of  spaghetti with tomato  sauce, and  14  apples.

23                  Then  we have chemical A, B,  C  and D.   So

24      if we want to determine  what the MOE  for a  particular

25      chemical is,  we  compute what the MOE is  for the  kiwi

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 1      fruit,  what  the MOE is for the apples, what the MOE is

 2      and so  on.   Then we can  figure  out what  the total MOE

 3      for the chemical  is.

 4                  In this case  in  my hypothetical  example

 5      here, the person  has  consumed -- has a  386  margin of

 6      exposure. That would -- Whether that's safe or not or

 7      harmful  or  not  depends  on  where   the uncertainty

 8      factors are associated  with  that and  whether  there

 9      will be some kind  of a threshold above which or below

10      which we would no longer regard that  exposure as safe.

11                  In general,  at this point in  time  and

12      probably it  will change soon,  is changing as we speak,

13      right now an acceptable margin of  exposure might  be

14      regarded as  100.   That  incorporates two uncertainty

15      factors,  animal to human, uncertainty associated with

16      the animal to human extrapolation,  and a factor of ten

17      for variation  in  sensitivity  within  the   human

18      population.

19                  So ten times  ten  is  100.   That   number

20  !    might  go  up if,   one  has  more uncertainties  about

21      things  like  unstudied  effects in developing animals,

22      but for now let's just  say it's 100.   So  386  is a

23      larger  value than 100.  So one  would say,  well,  the

24      MOE for that chemical, that particular chemical A,  is

2 5      okay.

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 1                  So we go through  the process and  simply

 2     now  look  at  the  exposures  associated  with  these

 3     different  foods which have chemical B,  C,  D  and E and

 4     so on,  and indeed this  would actually extend quite  a

 5     bit  further.   There are 34 OPs registered for  use in

 6     the United States,  and  one could  imagine --  You know,

 7     one has to deconstruct things like pizza. Well,  pizza

 8     has  tomato sauce.   It  might have  green peppers  and

 9     onions  on  it.   It  has  dough,  flour,  which is  grain

10     which is --  pesticides  are used on that.

11                  A slice of  pizza might have five or  six

12     different  pesticides on it altogether.  Spaghetti is

13     made with  flour as well.  It's got tomatoes in it.  So

14     a particular food  -- and the  Monte  Carlo  algorithm

15     does  all  this  deconstruction  of foods   based  on

16     standardized data that comes  out  of the  -- I  think

17     it's USDA.

18                  So  once one  figures  out how  the values

19     ought to be assigned --  and here  the  exposures  are

20     expressed  in MOE units -- one gets an  MOE  for each

21     chemical.   Then one has to  determine  what the  total

22     cumulative margin of exposure is,  and that's  a similar

23     kind of equation except now we're  adding up the MOE

24     values or adding up the  reciprocals of  the MOEs  to get

25     the cumulative  margin of  exposure.

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 1                  In this particular example, the cumulative

 2     MOE  is  54.   That is less than 100 or we've  filled up

 3     184  percent  of  the   risk   cup.    This  particular

 4     combination of events  would  probably be regarded as

 5     unsafe.

 6                  That is  really just  one  iterative step in

 7     the  Monte Carlo routine  that assigns pesticides  and

 8     pesticide levels to foods that  are  also assigned to

 9     individuals.   That  iterative  process  is   executed

10     10,000  times or more,   and from  that,  the total MOE,

11     one  obtains then the distribution of MOE values.

12                  Here I've  simply invented a distribution

13     of  MOE  values to  show you  how  this might  work,   in

14     which case  we have the average,  in  this case, total

15     MOE  is  on  the order of  900  or  so.    So the average

16     person is probably pretty safe.  But there is a small

17     group of people down here --in fact, it's  .82 percent

18     of the population -- that might be expected to have an

19     MOE  less  than 100.

20                  The  risk manager getting this information

21     would then  want  to decide whether something  needs to

22     be done about this.  He can either look at the events

23     that went into  that  .82  percent  of  the population,

24     what was  it that they were doing?   Were they eating

25     apples?   Was  it  pizza that  did it?   You  need  to

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 1     maintain the linkages in  the Monte Carlo routine to

 2     get back to  the specific chemical  events that led one

 3     there.

 4                  So that's kind  of  how the process  works

 5     now,  as we  understand it.   I  want  to  take a  short

 6     aside  here and mention that the  -- about getting to

 7     the frequency of  the population that's affected.

 8                  This  is right  now what  would  go to  the

 9     risk manager.  .82 percent  of  the population has  an

10     MOE of less  than 100; what  do we need  to  do  about

11     that?   I think it might be worthwhile if  we  take that

12     extra  step and try and figure out what the  frequency

13     of the  affected individuals in the population is.

14                  To know that,  it's  not  .82 percent of  the

15     population that's affected.   I mean,  that's a lot  of

16     people  in a  country  the  size  of  the United States.

17     Let's remember that the underlying assumptions that go

18     into the risk factor analysis  --  or  the uncertainty

19     factor  analysis in this  -- One of them,  for example,

20     there's a tenfold variation in human sensitivity. How

21     frequent is  that  sensitive person in the population?

22                  The bit of thinking I've done about that

23     problem, it's actually a pretty rare individual.  It's

24     certainly  as  rare as one in 1,000 persons is going  to

25     be ten  times  more  sensitive  than  the average person.

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 1      It's  probably more  like one in  a  million people  is

 2      going to be  ten times more sensitive  than  the average

 3      person.   That's  based  on  an  analysis of  a lot  of

 4      pharmacological data, because the drug companies have

 5      a lot of  information on that.

 6                  So given your standard drug, what is the

 7      variation  introduced --  or in human sensitivity  to

 8      drugs? Those  numbers are on  the  order of  a  factor  of

 9      ten  has a  probability  of  something  like  one  in  a

10      million or  certainly more than one in 1,000 or  less

11      than  one in 1,000, probably something more  like one  in

12      a million.

13                  So when we're looking  at .82  percent  of

14      the population, that's  .82  percent of the population

15      that's one in  1,000 or one in a  million in  itself.  So

16      it's  a very  small fraction  of a very small fraction.

17      We're not  talking  about  millions  of  people being

18      affected by  this  .82 percent.  It's quite a bit  less

19      than  that.

20                  Okay.  In a few minutes  I'll say something

21      about the kinetics and trying to get to an analysis  of

22      the time  frame that one needs  to go  through in  this

23      process.

24                  If our  data were  based  on that 90-day

25      study --  remember,  that's  a repeated  dose,  90-day

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 1      study;  the animals  receive that dose every day Cor 90

 2      days, and we determine what the POD or the NOEL was in

 3      that  experiment.

 4                  What  I just went  through with the  Monte

 5      Carlo analysis really involved just a single day --

 6      Okay?  -- analyzing the dietary life in a  single  day

 7      of  this  hypothetical  individual  and generating  a

 8      normalized distribution in that single day.  We  ended

 9      up  concluding that .82 percent of the population in

10      the hypothetical  example might have had a  problem.

11                  Okay.    Does  that  .82  percent  of  the

12      population  really  have  a  problem,  because  in fact

13      that's  just  happening  on  one  day?   Remember,  that

14      individual was eating  12  apples  or 14  apples,  was

15      eating a ton of spaghetti,  very, very large  salad,  and

16      I guess,  just sort of apply some  common  sense  here.

17      How many days in a row are you  going to  eat  14 apples?

18      I submit, not  many.

19                  So what we  really  need to do is to  start

20      thinking about how those daily exposures really  occur

21      in  time.   If  one  is at  the  99.9 percentile of  the

22      exposure distribution on one day because they eat  14

23      apples,  is it likely they are going to be there on  the

24      next day and  the next day and  the next day; because,

25      let's face it, in the context of a long acting set  of

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 1      compounds like organophosphates what's going to happen

 2      is  that  it's  the  daily exposure that accumulates over

 3      time  that builds up to that sustained daily  exposure

 4      that  causes the cholinesterase inhibition to build up

 5      to  a  particular level.

 6                  I've modeled  this process   extensively.

 7      The modeling --  The  details of  the  modeling  are

 8      important if you  were  to  accept my   quantitative

 9      conclusions,  and  I really  don't  have  time  to  talk

10      about sort of the detailed model here.  But again just

11      sort  of  --  It's  the  qualitative  conclusion which  is

12      the take-home message here.

13                 So I  think what we really need to do here

14      is  to   just  ask,  well,  what  is  the  accumulated

15      cholinesterase inhibition?    Here we  have   percent

16      inhibition  as a function of  time  in hours.   Zero  to

17      1500  hours  is two months.

18                 So a  compound like chlorpyrifos which has

19      an  acute half-life  of  about  175 hours, one  consumes a

20      little  bit  one  day  one.    Then  that produces  some

21      cholinesterase  inhibition which decays  with a  half-

22      life  of  175  hours.   On day two,   another amount  of

23      chlorpyrifos  is added to the diet.  That is going  to

24      increment the  amount  of   residual   cholinesterase

25      inhibition that occurred from the day before,  and  so

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 1      on and so on and so on.

 2                  Clearly,   you  can  model  this  process.

 3      That's what I've done  here  to generate this  kind of

 4      function.  This basically describes the progression of

 5      cholinesterase  inhibition  in  a  rat  administered

 6      chlorpyrifos every day over a period of two  months.

 7                  One  can   see  that  it  takes about

 8      actually,  about a month  to  get to 95 percent of the

 9      steady state.   So that's an animal that's  eating one

10      POD unit  --  That  rat is eating one POD  unit to get --

11      and POD is defined as the amount it takes to get  to 20

12      percent inhibition in a subchronic study.  That rat --

13      By eating that  one POD  unit  every  day for  two months,

14      in fact,  that's  what  it takes to build  up  to  that

15      level.

16                  Now what if the  person were to eat -- not

17      person -- that  rat  were  to eat  one  POD  unit  on a

18      single day,  never had  any  on any  other day?  Well,

19      that's shown right here.   In fact, what that  does is

20      produce  a  very  small  two  percent  change   in  the

21      dependent variable.

22                  Basically,  the way  the risk analysis  is

23      set  up now,  as  I understand  it,  we're basically

24      treating  that  two  percent  effect as  if   it's  a  20

25     percent effect,  because we  are  only  focusing on  one

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 l     day of exposure.  We need to focus on the multiple day

 2     to day  to day exposure  scenario,  because that's  the

 3     experiment  that's driving our analysis,  not  a  single

 4     day of  exposure.

 5                  So we need  to  open  up that process.   In

 6     fact, if  you compute what it would take  for  a  single

 7     day  exposure  to  get  to  20 percent cholinesterase

 8     inhibition,  which is kind  of where our  hypothetical

 9     point of  departure is for  this  experiment,   it  takes

10     four and  a  half POD units.

11                  So one POD  unit might be  the amount  of

12     pesticide residue that's,  say,  on 20 apples.   If  you

13     are never exposed to chlorpyrifos, you could  probably

14     eat 4.5 times 20, some 90 apples,  on a particular day,

15     and you would  still be below the threshold that might

16     be regarded as adverse.

17                  So it's simply by examining  the  kinetics

18     and modeling  that  process,  one  can  get  at  least a

19     qualitative flavor that  we need to focus on, land on

20     more than a single day of exposure.

21                  Clearly, the individual kinetic properties

22     of the  compounds in the risk cup make a difference.

23     It takes  a  different  amount of  time to get to  the

24     steady state level.  Here we have  three different OPs.

25     A compound  like azinfosmethyl has a  half-life  of  35

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 1     hours.   Chlorpyrifos is  175.   Chlorthoxifos  is 125

 2     hours, and you can  see that  these different kinetics

 3     mean that steady state is established after different

 4     periods of time.

 5                 For chlorpyrifos it  takes about 30 days.

 6     Other compounds with quicker  half-lives will get there

 7     quicker.  In a cumulative analysis the time frame that

 8     we need  to  sort  out is  equal to that which has the

 9     longest -- well, the compound with the longest half-

10     life in the analysis.

11                 So probably what we would be needing to do

12     in the case of organophosphates is to focus on 30 days

13     of dietary  -- daily dietary consumption  and try and

14     figure out what the process  over 30 days  looks like.

15                 What would happen there? What's the right

16     metric.   Once we've determined  that the  right  time

17     frame for OPs is  something on the  order  of  30 days,

18     what kind of  metric do we do within that  30 days?

19     Do we choose the very,  very highest value that  occurs

20     in 30 days or do we choose the mean value?

21                 Well, the  mean value is certainly a lot

22     closer to the one we should  be using rather than the

23     P.  Here is an example of that.   I've gone ahead and

24     now computed  what the  cholinesterase inhibition, the

25     expected cholinesterase inhibition would be over time.

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 1     Here we have cholinesterase inhibition over two months

 2     of continuous  administration or daily administration

 3     anyway.

 4                  Here on the graph on the right we have the

 5     exposure  units that go into  this.   Again, I've  just

 6     programmed my  computer to make up values  to introduce

 7     some variation in them.  The average over this 30 day

 8     period is  about  .39 POD units.  So that's like .39 of

 9     a NOEL is  administered to this rat here.

10                  Now  under those  conditions where   one

11     receives  kind  of an average of  .39, you see  that we

12     build  up  to a steady state  which looks like about

13     three percent  inhibition here under those conditions,

14     with even  a  spike of activity I forced in here of two

15     and  half  POD  units.   That would  be  like going  from

16     the 20 apples  to  50 apples.  So eating 50 apples  on a

17     day, one  still  does  not get  anywhere close to  the

18     level  of  cholinesterase  inhibition  that would be

19     regarded as  adverse.

20                  Now turning  this  kind of analysis to the

21     more cumulative  case,  in  this case  we  have three

22     different  OPs, A,  B,  and C.   Here is  the exposure

23     scenario.    Everything is  randomized,   so different

24     amounts of compound A on every day of the two months,

25     different  amounts of  compound C on every day of  the

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 1      two  months,  and  different  amounts of  compound B  in

 2      every day of the two months.

 3                  During this two month exercise of  daily

 4      diet,  you can see here -- Let's just focus on the last

 5      month of exposure  here.   We  have one,  two, three,

 6      four,  five,  six, seven, eight,  nine -- nine out of the

 7      30 days  in which  this  animal in this  -- While  this  is

 8      modeled  after animals, one  could think about  this  as

 9      the one in 1,000 person that's  more sensitive than the

10      average  person  and also assuming that  the  average

11      person is ten times more sensitive than the  average

12      rat.   Under those kind  of  conditions,  you can  think

13      about these  data as being a human exposure scenario.

14                  So  nine out  of  these last 30 days  contain

15      an exposure  which is --  in  the present context,  would

16      be regarded as excessive, but the degree of cumulative

17      inhibition is just  getting  to  the point where  it's  19

18      percent  inhibition.  It  does not exceed our threshold

19      of 20 percent.

20                  So  even though we've  busted  the line  on

21      exposure, the process  of actually modeling the effect

22      according to using  more  of  an  average like  .8 -- the

23      average  of  these values are less,-  30  days is .8 POD

24      units, which brings us right there.

25                  So,  clearly, I  don't  think  we should  be

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 1     using an individual daily peak which is driven by rare

 2     and  unusual  events and  with long  acting  compounds

 3     don't have the ability to have that immediate punctate

 4     reaction  or  impact on the system.

 5                  On the other hand, using the mean itself

 6     is probably  a little bit  too --  not conservative  or

 7     not  conservative enough, because if we look  at  this

 8     for  our mean over  this last month  is  .8,  and we're

 9     really  driving  right  up  to a  level  that  we would

10     regard  as  -- we would start regarding  as dangerous.

11                  Certainly,  if the mean were 1,  we would be

12     over  that danger level, and  we probably wouldn't  want

13     to do that.  So the mean isn't quite  the right answer.

14     The mean exposure,  the  mean daily exposure over  that

15     30 days is not quite the right answer.  It's  got to be

16     something  less  than that to be safe.

17                  Exactly how much less  it  should be, I

18     think, kind  of relates to the inherent variation, how

19     often excursions and large spikes and unusual events

20     can  occur,-  and  we've  got  some  ideas  about  how  to

21     approach that.

22                  My point in bringing this out here is  to

23     stimulate folks in the agency to think about the right

24     way  to  solve this problem.   The  analyses  I've  done

25     suggest that the right  answer is  not to use  the  mean

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 1     but  rather the mean plus  a  fraction of the  standard
 2     deviation,  and the fraction  of the standard deviation
 3     --  the  amount  of  the  fraction depends upon  the
 4     individual kinetics,  the individual half-life  of  the

 5     individual compounds in the risk cup.
 6                  In fact, that  sort of analysis works,  and
 7     it would be a useful way to go about  protecting the --
 8     both protecting the public and preventing the  analysis
 9     from being more conservative than it needs to  be.
10                  So to summarize here,  we've talked about
11     the process  of dose normalization.   I've urged  you to
12     recognize  the  kinetic difference among  chemicals in a
13     risk cup can be as important as  potency differences.
14                  Simply because we' re normalizing compounds
15     according  to potency doesn't mean they are equivalent
16     compounds.  Very large kinetic differences can  occur.
17     That's especially true if we're dealing with something
18     like carbamates which have a half-life on the  order of
19     30 minutes or  45  minutes,  and  adding them  in with
20     something  like OPs which have half-lives on the order
    i
21  !   of 100 or  150  hours.
22                  So we  need to  be  very aware of   those
23     kinetic differences and factor them into the analysis.
24     We need to  select the  correct  time  frame  for  the
25     compounds in the risk cup,  and my suggestion is  to  use
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 1     something like  four half-lives of the longest acting

 2     compound in  the risk cup.   For  a group of compounds

 3     like OPs, that  would be  on the order of four weeks.

 4                  Finally,  use a realistic exposure metric

 5     that accumulates  temporally separated exposures in a

 6     manner consistent with the kinetic properties of those

 7     chemicals.   Thank you very much.

 8                  (Applause.)

 9                  DR. NORRIS:   Do you  have some questions?

10     The other  thing  we can  do  is briefly have  all  the

11     speakers come up  and start the panel discussion now,

12     since we are running a little bit behind.  Then if  you

13     would  just  stand  right  there,  you  can  ask  your

14     question when they get here.

15                  Okay,  we are ready.   Thank you.

16                  QUESTION:  The example you used where  you

17     were looking at the cumulative risk from the OPs,  you

18     used  red blood  cell cholinesterase  inhibition.   I

19     think   that   people   would  generally   agree   that

20     cholinesterase  inhibition  and  toxicity is  perhaps

21     measured  by other  parameters,  signs;  and  other

22     anticholinesterase type  --or cholinergic symptoms is

23     not necessarily correlated  with the red  blood cell

24     cholinesterase  inhibition.

25                  My question  to you is:  You had compounds

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 1     there from your company,  and I'm just wondering  when

 2     you  ranked -- You ranked  the  potencies -- With  what

 3     you   did,  you  ranked  potencies  by  cholinesterase

 4     inhibition.   But  if  you looked at the same chemicals

 5     in  terms  of  what you  know about their  toxicity  in

 6     terms  of  clinical   signs  and   cholinergic   type

 7     responses,  were the  rankings similar or did you  find

 8     big  differences  in  potency  if  you  would  look  at

 9     toxicity by that  other way?

10                  The reason I'm asking that is because,  of

11     course,  the agency is going to have  to look at  risk

12     mitigation issues, and we are  going to  be  getting

13     chemicals from  different  companies.   So one chemical

14     could emerge from a  process  such  as what you  have

15     looking very bad in terms of cholinesterase inhibition

16     but not necessarily seeming to make any sense by other

17     criteria of toxicity, i.e., clinical  signs and  that

18     sort  of  thing.

19                 DR.  CHRISTOPH:   Yes.  I  focused on red

20     cell  cholinesterase  inhibition,  because that's where

21     the agency seems  to be going.  My personal opinion  is

22     that   we  should focus   on  brain   cholinesterase

23     inhibition,  but  that's just  what  I think.   It  just

24     makes  more  sense to  me,  because  basically  we're

25     talking  about red cells, and essentially the red  cell

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 1     cholinesterase  inhibition  is  really  a biomarker  of

 2     exposure,  to  me,  more than an adverse  effect.

 3                 The  rationale,  of  course,  is  that  it's

 4     correlated with  an adverse effect elsewhere and  is a

 5     surrogate  for peripheral  cholinesterase inhibition.

 6     I think Stephanie would be a more appropriate person

 7     to comment on that.

 8                 I really think -- You know,  there was,  of

 9     course,  a huge  discussion  about  what's  adverse  in

10     terms  of  cholinesterase inhibition, and I think the

11     agency has just  decided  that  it  is  cholinesterase

12     inhibition itself that  is.  In  my mind,  the thing

13     that's  adverse  about it  is not  inhibition  of  the

14     enzyme.   It's the consequences of  inhibition of the

15     enzyme, the consequences of the kinds of things you

16     were talking  about,  signs.

17                 Well,    in   fact,   those   are   fairly

18     insensitive.  The things we use to measure those are

19     our eyeballs,  for  the  most part,  and  the  kinds  of

20     things that would occur that are pretty easy  to see  up

21     to  really high   concentrations  in high levels   of

22     cholinesterase inhibition,  things  like tremor and  so

23     on,  are simply not particularly sensitive instruments

24     to measure those  kind of things.

25                 In my mind, the real adverse  effect  is

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 1     disruption  of   the   fidelity of  neurotransmission.

 2     That's kind of the bottom line on this.  At what point

 3     is there so much acetylcholine in the synapse that the

 4     fidelity  of neurotransmission is degraded?

 5                  That endpoint,  no one  is measuring, to my

 6     knowledge.   I guess that's -- I would like to see work

 7     focused on that kind of problem.

 8                  So  the nature of your  question was how do

 9     these other kinds of  behavioral  things correlate?  In

10     fact,  we  really haven't studied  that  very much  or

11     studied that particular question very much,  because,

12     frankly,  it's not important  to us  from the context of

13     regulations. The agency drives the  regulations,  and

14     the  agency has  decided  that red cell cholinesterase

15     inhibition is where  we're going to be driven.

16                  So  I'm sorry that I  can't  offer  a  more

17     detailed  answer than that.

18                  (INAUDIBLE QUESTION)

19                  DR.  CHRISTOPH:   I really can't answer the

20     question  in detail.   I'm sorry.   I don't  know.

21                  QUESTION:   Yes.   I think,  for the  most

22     part, those of us who are in the business  of the risk

23     mitigation  part  of it, and not to cloud the issue,  but

24     when you  speak  of that  one  in  a  million individual

25     with higher levels of sensitivity  or one in 1,000,  if

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 l     we break  it  down even further and we correlate  or we

 2     use an example of children one to six,  I think for the

 3     most  part for us, we try to  regulate on the  side of

 4     safety.

 5                  I think our greatest  challenge is to make

 6     sure  that we're  using  sound science.   But by  doing

 7     that,  if  you take that one  in  a  million individuals

 8     and,  you  know,  we use that age group of one  to six,

 9     just  for  example, I think we are probably  in  a --  you

10     know,  it's safer to make  sure that we are regulating

11     on the  side  of  safety for that particular age  group

12     for the various reason of,  in  general,  the  chronic

13     effects over  the  long term that they could be  exposed

14     to various chemicals,  OPs in particular.

15                  So  that  is one  of  the things that,  you

16     know, we primarily try to  keep in  mind when we look at

17     mitigation for  various  OPs.

18                 DR.   CHRISTOPH:    If   I  can  could  just

19     comment  on  that,  companies  are  not  interested  in

20     poisoning people.  We  have  the  same interests you do

21     in terms  of  ensuring the  safety of our products.

22                 Throwing  around  figures  like  one  in a

23     million --  I actually believe  we  are  talking  about

24     those  kind  of  risk  factors here with  the  use  of

25     pesticides.   That's my personal belief, as I've kind

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 l      of gone through and looked at it.  There are certainly

 2      people who would disagree and think that the risks are

 3      much higher than that.

 4                  You are absolutely right that the highest

 5      risk group that always falls out of all these analyses

 6      are  children one to six.

 7                  QUESTION:  I have got an  anecdote.   In a

 8      former life I was  a Food and  Drug inspector, and a

 9      bunch of us were  sitting around  sniggering one  day

10      about how we were going  out and  looking for swordfish

11      based on numbers, mercury numbers, that had to do with

12      eating   swordf ish    every   day.      That   was    in

13      Massachusetts.   Swordfish was a luxury good.

14                  Then one of  the women  in the  group looked

15      sort of stricken.   She was on some kind of  diet.   She

16      was  eating tuna every morning -- every evening.  That

17      was  her big  thing,  was tuna.

18                  My  guess is  you're sort of assuming that

19      the  12 apple person  -- eating 12 apples is  like being

20      struck by lightning,  and I  think that's  wrong.    I

21      think that people who eat 12 apples  one day are going

22      to eat 12 apples the next day.   This is someone  who

23      has  a serious  apple thing, and  you  do have to think

24      about that  person  in   the  course  of   that  risk

25      assessment.

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 1                  DR.  CHRISTOPH:   I  agree with  that.    I

 2     would  just  like  you to  think about  the transition

 3     rules.   You know,  there  may be  an individual  there

 4     that  eats 12  apples day after  day after  day.    How

 5     frequent  is  that individual?  You know, think  about

 6     that when you  build a Monte Carlo is my point.

 7                  Sure, build that person into the analysis,

 8     and  build  that  person  into the  analysis  with  a

 9     frequency that they actually occur in the population.

10     I don't  know exactly what  the day to day transition

11     rules  are  in  the   Monte   Carlo.    I  mean,   it's  a

12     complicated  question, and  there are some  data from

13     three consecutive days of people,  real people, eating

14     food.  That  can certainly  help  in building that  one

15     month of  exposure.

16                  Something like  the three-consecutive  day

17     data in the  database would  help  us get to understand

18     how  often  that  person  eats  --  how  frequent  that

19     individual is  that  eats 12  apples a day.

20                  DR.  NORRIS:   One  more  question.    Thank

21     you.

22                  QUESTION : Okay.   I  had a question  about

23     your normalization  of data.   If you were  using  two

24     compounds that were  not equally efficacious, would  you

25     use the  same methodology  to normalize  data or  would

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 1     you  have  to  adjust  it,  since  the  EDSO  would  be

 2     somewhat different?

 3                  DR.  CHRISTOPH:   Well,  I think  the  whole

 4     point  of  normalization  is  that they are not  equally

 5     efficacious.   They differ  in potency  or differ  in

 6     efficacy.   Is  that what you mean by efficacy  here?

 7                  QUESTION:   Well, in terms of efficacy,  I

 8     mean  their ability  to induce  a response.   When you

 9     presented  your  example of  how you  would  normalize

10     data,  both of the  compounds appeared to produce the

11     same amount  of effect.

12                  DR.  CHRISTOPH:   right.

13                  QUESTION:   See what I'm saying?  So if you

14     were using two compounds that did not produce the same

15     strength of  effect,  how would you normalize the data

16     then?

17                  DR.  CHRISTOPH:  So your idea, one compound

18     would  be from  zero to  100,  another compound might  be

19     zero to 50 and not go  any higher than that?

20                  QUESTION:   Correct.

21                  DR.  CHRISTOPH:     In  that  case  we are

22  :   dealing  with  a  case  of non-parallel  dose response

23     functions,  which   bring us   into  a  whole   special

24     category of  events.

25                  I  think the first response when you end  up

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  1      with non-parallel dose response  functions,  you should

  2      ask are these really compounds that belong together in

  3      the same risk cup?  Are  they truly common mechanism

  4      chemicals or is there something different about  them

  5      that makes them work real differently?

  6                  There may be mathematical ways to handle

  7      that.   One  way to think  about  it is,  since we  are

  8      really concerned  about  the  low  dose side  of  this

  9      equation  anyway,  is  the  lower  half  of the  dose

10      response curves  --  are  they pretty  similar   to  each

11      other? And  if those are pretty  similar to each other

12      --We can forget the  stuff  that happens above  50

13      percent  cholinesterase inhibition,  because we are not

14      going to  drive any human to 50 percent cholinesterase

15      inhibition by  this process,  by eating food.

16                  So you know,  if it's  that  case, let's  deal

17      with the lower half of the  dose response function,  see

18      what's going on down there.   Can we somehow  convince

19      ourselves that  the lower  halves  of the functions  are

20      superimposable  after a normalization step?

21                  DR.  NORRIS:   Greg,  I didn't mean  for  you

22      to  end up  having to  answer all  the questions,  if

23      anybody else has anything to say.  I would like to add

24      that  I think, if I ate 90  apples in one day,   I would

25      have other problems.

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 1                  Well,  let's go  to lunch.   If anybody is

 2      not from around here and needs to know where to go to

 3      lunch,  apparently,  I've been told there's restaurants

 4      all over.  I can see the pesticide people saying, yes,

 5      there are restaurants everywhere.   I hope we'll find

 6      them.

 7                  Thank you.   We'll see you back here at two

 8      o'clock.

 9                   (Whereupon, the foregoing matter went off

10      the record at  12:41 p.m.)

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                                                   137
          AFTERNOON   SESSION
                                     Time:  2:05 p.m.
            DR.  NORRIS:   All right.   Most of us  are
back.  Others will  probably come trickling in.  If  I
can  have  your  attention,  please,  we'll  begin  the
afternoon  session of  our program.
            Beverly and  Ethyl,  I've  been  asked  to
change the name of  our program  by management again.
They would like to make sure it's the neurotoxicity of
chemical   mixtures.    Perhaps  the   management  is
concerned  that  there  will be the  neurotoxicities of
the mixtures of  the colors  in my dress or something.
I'm not sure.  We aim to please.
            We have four speakers this afternoon,  and
I'll introduce them all at this time and then briefly
as they get up to speak.
            Dr.    Abou-Donia    is    Professor    of
Pharmacology  and  Cancer Biology  and Professor  of
Neurobiology at  the Duke University Medical Center.
            Dr.  Anthony  Riley  is Director  of   the
Behavioral Neurosciences and Professor and soon to be
chair  of  the   Psychology  Department  at  American
University.
            Dr.  Rick  Hertzberg  initiated the   EPA
Mixtures Risk Assessment Research Program  here at  EPA
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 1     and chaired the  initial  Mixtures Risk Guidelines.  He

 2     has also developed the first version of EPA' s mixtures

 3     database, Mixtox;  and I  hope you're finding some more

 4     data  to add to your database today, Rick.

 5                  Dr.  Herman  Koeter:   Dr.  Koeter is  the

 6     Principal   Administrator  at  the  Paris  based   OECD

 7     Environmental  Health and  Safety Division.   He  is  in

 8     charge   of  test   guidelines,   harmonization,    the

 9     endocrine disrupters program,  and the  animal welfare

10     policies.

11                  We are very  proud  and pleased to have all

12     of you with us this  afternoon.   Thank you.  I  won't

13     take  anymore of your  time.   I will let  Dr. Abou-Donia

14     begin.

15                  DR.  ABOU-DONIA:   Thank you,  Dr. Norris.

16     My  talk  this  afternoon  is   on  chemical/chemical

17     interactions which we  have been hearing  about  all

18     morning.  As we know, most individuals  are exposed to

19     multiple chemicals.   We are not exposed  to just  one

20     chemical.

21                  FDA   requires  testing    of   drug-drug

22  ;   interactions.    The  testing that  the FDA  requires

23     actually   is  very   intensive,   and   they  require

24     particularly  the    metabolic   profile   of   each

25     interaction,   including  the   specific  enzymes   of

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 l      cytochrome P450 and the effect of each chemical on the

 2      induction or inhibition of  the  enzyme as well  as  of

 3      the MDR peak glycoprotein, the drug transporter in the

 4      body.

 5                  Unfortunately, there is no requirement for

 6      drug-pesticide  interaction  or  pesticide-pesticide

 7      interactions neither by the EPA or the FDA.  When  we

 8      think  about  it,  chemical companies  discovered many

 9      years ago that combined exposures to chemicals is more

10      toxic.   That's why it's very rare that anybody  would

11      use one  insecticide in  the field.  Usually, it's used

12      much more  than one insecticide.

13                  On  many  occasions  I  have  asked the

14      questions  to  some  of  my friends  in the   chemical

15      industry,  do  you have  any  data  on  the effect  of

16      combined exposure and health effects?  They  said no.

17      Why?  Because this is not required.   So I think this

18      is an area  that  should really be -- should be looked

19      at.
    I
20                  The    other    thing   is   drug-pesticide

21      interaction,  which would be  a joint  venture between

22      the  FDA and the  EPA.    All  of us  are  exposed  to

23      pesticides,  one  way  or the  other,  and we  are also

24      exposed  to drugs.   We have prescription drugs and

25      over-the-counter  drugs, and  we get exposed to both  of

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 1     them  at the same  time,  but we don't  really know if

 2     this  would cause any interaction.

 3                  That's why our specific -- our  study was

 4     to  investigate  the interaction between a drug,  which

 5     is pyridostigmine  bromide  and insect repellant  called

 6     a DEBT,  and an  insecticide,  permethrin.  These three

 7     chemicals  were  presumably present in the environment

 8     during  the Gulf War, and they might be  involved in the

 9     Gulf  War diseases  or illnesses.

10                  The pyridostigmine  is a carbamate that has

11     positive charge.   So presumably,  this carbamate does

12     not cross  the  blood-brain barrier.   It only acts  on

13     the peripheral system.  Pyridostigmine bromide is used

14     - - is actually approved for  use for myasthenia  gravis

15     at very, very  large doses that range  between  200  to

16     almost  1,000 milligrams per  kilogram dose.  However,

17     it  is used --  It  was  used during the  Gulf  War  as a

18     prophylactic treatment against possible death by nerve

19     gas.

20                 The  way  it acts,   as we  know,   it  will

21     shield  the acetylcholinesterase   in  the  peripheral

22     nervous  system  so that when  there  is exposure  to

23     certain of the nerve gases, there will be protect ion ,-

24     and then when  the gas  goes away,  the enzyme  will

25     eventually spontaneously recover, and the person will

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survve .
 1

 2                  This chemical is fairly  toxic.   It has a

 3     LDSO of 61.6, which  is  less  than half of that  of the

 4     chlorpyrif os which you have  been talking -- hearing

 5     about  today.  It's about  150.   Of course, this  is a

 6     reversible inhibitor of the cholinesterase .

 7                  The  other  chemical  which we  have  been

 8     using  is  called DEBT, which is -- This was developed

 9     in  1940s  by the  military to  be used  for military

10     personnel  when  they  go  in  the   tropical   areas.

11     However,  it's  now available  in  the  market,  and  an

12     estimated 50 million Americans use it every year.

13                  This  chemical  is  used as  an   insect

14     repellant and has very,  very low acute toxicity, 3,000

15     milligrams per kilogram.   However, in the literature

16     there  are  several reports  of death resulting  from

17     exposure  to DEBT.   So even  though  it has  very  low

18     toxicity,  it is still lethal at certain dose levels.

19                  The third chemical we have been using is

20     permethrin,   which  is  --   This   is  a  pyrethroid

21     insecticide,  and  it  acts by disrupting  the  sodium

22     channel and the  axon.  Now this chemical is even  less

23     toxic.  The LD50  is 9,000  milligrams  per kilogram in

24     rats.  However, the permethrin is  very widely used for

25     human use.

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 1                  It is used in many shampoos for treatment

 2     of  lice,  for control  of lice.   It is also  used to

 3     impregnate  carpets, mattresses,  many  of  the linen

 4     blankets.  For those people who are actually sensitive

 5     to  mites  and other  insects,  that would  reduce some

 6     kind of allergy.

 7                  So  this chemical is  very widely used.

 8     During  the  war  it  is  estimated  several  hundred

 9     thousand American  personnel used uniforms  that were

10     impregnated  with permethrin to control insects.   So

11     potentially, a soldier that was in the Gulf War might

12     have  been  exposed  concurrently  to  pyridostigmine

13     bromide, DEBT and permethrin  at  the same  time.

14                  Some years ago, 1994  or '95,  we did some

15     study by  actually  trying  the effect of  each one of

16     these chemicals alone at a very high dose and then in

17     combination.   With  the high dose,  that  caused  no

18     effect.  When we used them  in binaries, unlike single,

19     there was minimum toxicity.   Binary combinations was

20     greater  toxicity.    When  we used  three  chemicals

21     together, they produced paralysis and sometimes death.

22                  Later  we applied for a  grant from  the

23     Department  of Defense,  and  we  are  using the same

24     combinations  to  study their  action  at  a  real life

25  I   constant dose level that presumably the soldiers were

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                                                     143

  exposed to.

               In  this  study we  are  using --  we  are

  actually  studying both stress  as  well as chemicals.

  the  chemicals that  we used  are  the pyridostigtnine

  bromide,  DEET and permethrin.

               The  dose that we are  using  -- These  are

  the doses that we obtained from DoD.   The  dose was  1.3

  milligrams per kilogram.  This  is  the exact dose that

  the soldiers were taking.   They  were given -- They

  were supposed to take three 50-milligram pills a day.

  That's 90 milligrams, which divided by 70  would be  1.3

  milligram per kilogram per day  orally.

               DEET,  they  told   us  the  dose  was  40

  milligram per kilogram dermally.    So we used that

  amount.   Permethrin,  .13  milligram per  kilogram in

  ethanol  dermally.   We  treated the animals,  rats,

  Sprague-Dawley rats,  for  28 days.

              Also  another  group of animals was  given

  stress by simply  placing  the  rat  in a  plexiglass

  restraint for five minutes every morning.  The design

  of  the experiments  was  like   this.   We have  four

  groups.  One was chemicals, the three chemicals, the

  same doses I  just gave, and stress,  chemicals  under

  stress, and  the  control.   Controls  were  given  water

  orally as well  as ethanol  dermally.  The water was

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 1     given  at  the  one  milliliter per kilogram.
 2                 So what we did,  we had male Sprague-Dawley
 3     rats treated 28 consecutive days. Animals were weighed
 4     weekly.  We had subgroups of  five rats that were used
 5     for   enzymatic   analysis,   to   analyze  for  brain
 6     acetylcholinesterase and plasma acetylcholinesterase.
 7                 We determined the binding of  m2 muscarinic
 8     acetylcholinesterase muscarinic receptor to  [3HJAF-
 9     DX384.  Now  we determined that the  integrity of  the

10     blood-brain barrier, which we heard some  about it this
11     morning from  Dr.  O'Donoghue  --we determined that a
12     similar way --a  couple  of  ways.
13                 One,   we   determined   the   uptake   of
14     triturated hexamethonium iodide,  a chemical that  has
15     positive charge,  is not  supposed  to  cross the blood-
16     brain barrier.  The horseradish peroxidase as well  was
17     used  in  this  experiment.    Then  we  looked at   the
18     histopathology in the  brain as  well  as the liver.
19                 Well,  this treatment for 28 days resulted
20     in  --  There   was  not   clinically different  --   The
21     treated animals are not much clinically different than
22     control.  As a matter of fact, they might have done a
23     little better, because they were a little bit lighter

24     or gained less weight.
25                 The weight gain was -- All of the animals
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 1     The diesel here was not -- let's see.  It was not used

 2     in the  calculation of the line.  It's an actual data

 3     point,  but  the line is based upon the other mixtures

 4     Of PAHS.

 5                  So in this case,  it  looks like  there  is

 6     some  consistency,  some proportionality between  mouse

 7     skin  tumors  and human lung cancer risk.  That's kind

 8     of the basic idea here.  If you have a way to scale  up

 9     from  in vitro  assays,  demonstrate it, and then  as  we

10     did with  the interaction patterns before, you have a

11     way to  sort of generalize  for untested chemicals  or

12     untested  mixtures.

13                  Another revelation we had, as I  mentioned

14     briefly,  in looking  at the  data and  the   published

15     studies was  too many definitions of interaction, too

16     many definitions of additivity.  So being a regulatory

17     agency, we have the power to make  our own definitions.

18     So we decided,  first  of all,  to simplify.

19                  That was  to  have  dose  addition as our

20     default, as our no-interaction, so we don't talk  about

21     additive  interactions.    We  just   talk about   dose

22     addition as no interaction. Anything higher  than that

23     is synergism.  Anything lower than that is antagonism.

24

25                  That made it extremely  simple,  and  in a

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 1

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 3

 4

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 7

 8

 9

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19

20

21

22

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25
  regulatory sense it's very useful that way.  We don't

  need to know the other things.  We just need to know,

  if  we're making  a mistake,  which  direction  are  we

  erring in -- on,  toward?

               I  think I'll  move on.   Another thing  we

  found in looking at the data is that synergism wasn't

  reported that often. We thought that, since academics

  do most of this kind of research, you need to publish.

  You need to  publish positive results.

               So you would  want to have a selection  of

  chemicals  that  would  show  you  some  interaction.

  Surprisingly,  it  did not  happen.

               When  we  looked  at  trying   to  estimate

  magnitude  for  the  handful  of  studies  that  were

  actually  useful  for doing  this,  we  had high  dose

  interactions of tenfold to twentyfold or more.  We had

  low dose interactions that  were  two, three,  five,  in

  that range.

               So  our default  so  far   for our  mixtures

  guidance  is  a magnitude  of  five.   It's a  fivefold

  decrease in  effective  dose,  if you're  talking about
  synergism.
              This is the hazard index.  It's the basis
  of  dose  addition.
Superfund  has  this  in  their
  guidance.   The  bottom expression is  what's used for

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  1      the  dioxins  where  you replace  the  mixture by  its

  2      surrogate or index  chemical,  the equivalent dose in

  3      that  chemical.    It's  the  same  formula,  but  you

  4      translate to each other.

  5                  We had one improvement,  and this is where

  6      I'll give you some  more  lessons learned.   In '92 we

  7      came up with an approach to replace  the  conservatism

  8      in  using   the   reference   dose,    the   reference

  9      concentration  as  a  scaling  factor for  relative

10      potency.

11                  The problem with  those  is that  they  are

12      bounding estimates.  They  are supposed to be the --

13      representing the critical effect.  If you  are trying

14      to do some common mode of  action that is  some  other

15      effect,  then you are going to be over-regulating or

16      overestimating  the   risk,  because  you're   using

17      acceptable  doses or dose scaling that's way,  way  too

18      low.

19                  So we  could just  do the same  kind  of

20      process but do it for the effective concern.  If  we're

21      looking  at  neurotoxicity,  then make  sure that all  of

22      these  pseudo  reference doses are now based only upon

23      neurotoxicity data.   Then  you don't have  the  over-

24      conservatism.

25                 Well,  so we  wrote this  up,  and we  got

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 l     slammed hard by our regions and our program  offices,

 2     because EPA has no procedure in place for developing

 3     these  target organ toxicity doses.  So why advertise

 4     this  as a  great new method  if  we can't do  it?   So

 5     that's my  lesson  learned  number  three or  four,  I

 6     guess,  by this point.

 7                  Good ideas need  to  be published, but  if

 8     you put them in guidance, then someone expects you to

 9     actually do it.

10                  Now once again we're back in the case  of

11     not having enough information.   Here you see again a

12     lot of places, not as many as we  were concerned about,

13     but you see that in many cases we don't have enough to

14     do a TTD so we  can  resort back to using the reference

15     dose.   Okay,  we have five or six cases here  where  we

16     are essentially going  back to defaults.

17                  So a good  approach.   Just  doesn't have

18     enough information to  do it.

19                  This was raised several times. So what  do

20     you  do  if  you  do have  interactions?   How do you

21     account for them?  We talked to a lot of people who  do

22     interaction studies.  We looked at a lot of literature

23     on   pharmacokinetic  modeling   and  mechanisms   of

24     interaction.

25                  It   seemed  that  we  had  some   general

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 l     principles that seemed to come out.  One was something

 2     that,  if you didn't  have your  chemicals  at  equally

 3     toxic  doses  where  one  starts to  dominate, then  the

 4     interaction often starts to die off.  So we would want

 5     to have  some  kind of  way to reflect that,  and that's

 6     what this function does down here.  As  the chemicals

 7     get more dissimilar,  you have a curve that goes like

 8     this.    One   chemical becomes  more  dominant.    The

 9     interaction goes  down.

10                 These  are   just   general  qualitative

11     characteristics.   What  we  have here,  basically,   is

12     this  part  over  here  is  the  hazard   index,  dose

13     addition, and we  just took  every hazard  quotient  and

14     modified it  by   a  function  that  represents   the

15     interactions  to that  chemical.

16                 So this is all based upon pair-wise data.

17     That's weight of  evidence considerations,  or  the  Bs

18     right  there.    So    this is  a  way we  can  kind   of

19     incorporate  the  data we  do have  on  interactions,

20     quantify it,   end  up  with  a   change   in  the risk

21     estimate.

22                 What  we need is more information  so that

23     all of these  functions in here can  be replaced  by real

24     dose interaction/magnitude relationships and not just

25     sort of  plausible relations.

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 1                  Okay.    This  just  kind of  explains  the

 2     different  pieces.

 3                  So here's  my  summary  of  our lessons.

 4     First  of all,  we  have to  be flexible in our methods.

 5     We can't require testing or new data for  every mixture

 6     we  encounter.   There are too many  mixtures and  too

 7     many varieties of exposure situations, changes  in  the

 8     proportions,  in total dose.

 9                  You  saw  the influence  of changes   in

10     sequence,  changes in repeated exposures  as  opposed to

11     a single  shot.  All  those things  have to be somehow

12     reflected.   When you have a mixture  and  you're  trying

13     to   do  something    to   build   up   from  component

14     information, the component information is not the same

15     for each chemical.

16                  So that's what I mean by flexibility.  You

17     have to have a way  to use the different varieties of

18     information.

19                  We don't have a lot of  experience yet with

20     mixtures.   Superfund is  our only  main office  that's

21     done this  for  any length  of time.  So approaches such

22     as the acceptable daily  intake  that ran FDA and  EPA

23     for a  long time and  build up a history,  we don't have

24     that  with  mixtures.   So  we  don't have  a lot   of

25     procedures  set in concrete yet  that  we can  rely on as

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 1     passing the public sector.
 2                 This  is some examples of the problems  of

 3     multiple data types and qualities.  This is one of  the

 4     concerns,  I  know, with  OPP when they  put out their

 5     guidance,  was  what  do  you  do  when  you  have   one

 6     chemical  that's  just dominating  the  whole mixture

 7     assessment because it has  a huge uncertainty factor.
 8                 I haven't heard whether that got resolved,

 9     but there's some  nice suggestions on how to fix that.
10     So I'm looking  forward to  the next version to  see  if

11     it's included in  there.
12                 Incorporating   interactions:   There   are

13     lots of pairs.  A mixture of ten chemicals  has  -- what

14     is it,  1,000 pairs or so? We  can't information on  all

15     those pairs.  We  know that.  So you're going to have

16     to have these data gaps  and have some way to fill  in

17     those holes with  something.

18                 Then  this is typical of the  government,  is

19     that you will have defaults so you can make  a decision

20     in spite of lack of data. When you have defaults, you
21     have to combine it with  real  information.   How  do you

22     do that in a nice,  scientific fashion without  having

23     the defaults just dominate  everything?
24                 If they dominate,  then there's no point in

25     gathering the better information, because it no  longer
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 1     plays  a  role.   We found this with NOELs, doses  based
 2     upon NOELs.
 3                  If you  have  other chemicals  that  have
 4     really good ED10  or ED01,  it doesn't  matter a  whole
 5     lot, because the  uncertainty surrounding the NOELs is
 6     so much  higher that  it just  swamps the extra accuracy
 7     that you get  from the other chemicals that have  ED10.
 8                  We are  all  hoping we  can  move toward
 9     pharmacokinetic modeling, but  we  need to have  human
10     data.    There  is  already a  lot of  questions  being
11     raised about overuse of  PBPK models based only on the
12     rat.   So we need to have some better validation,  I
13     think, there.  As I said  before,  interaction magnitude

14     -- we're really in the dark there.
15                  Okay.   We do have  a  lot  of good ideas.
16     It's really exciting for me to work with  the  Pesticide
17     Office and watch them go through their haranguing to
18     put together guidance after we spent so many  years and

19     still  hadn't had any new guidance come out.
20                 A  lot of new ideas  came out.  There  are a
21     lot of ideas just today.  A nice advantage,  I guess,
22     of being one of the  last  speakers  is you  see many new
23     concepts being put  forth, but  to  have them  in  their
24     practical method,  a  guidance for an agency to use,  we
25     need more, and we have to have ways to know what  to do
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 1     when those pieces  of  information are not present.

 2                 The bottom line -- It's still there.  This

 3     is the one we had  hoped for 15 years ago.  So any  of

 4     you that have any  insight on how to do these studies

 5     at the really low environmental levels, start showing

 6     us  where these  thresholds  occur,  where  below that

 7     level we don't have to  worry about interactions.   It

 8     would really make  our job much,  much easier.

 9                 Thank  you.

10                  (Applause.)

11                 DR.  NORRIS:   Are  there  questions  now?

12     Thank you.

13                 Our next speaker is Dr.  Herman Koeter, and

14     I'll make another announcement after that. Thank you.

15     What I'd like to suggest  is that, in lieu  of taking a

16     break and then coming back,  if our speakers wouldn't

17     mind, if we could talk to you for a  few minutes during

18     the  break over  cookies  and soda  in  the back  room

19     there.  We can skip the panel discussion and just have

20     a more  intimate conversation in  the  back  over our

21     break, and then we will  excuse ourselves from there.

22     Thank you very much.   Now we'll be set  up here in a

23     moment for Dr. Koeter.

24                 DR. KOETER:   Thank you very much for the

25     invitation to speak to you here today,  and I consider

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 1     that a privilege to have an opportunity to  explain a

 2     little  bit  more  about  the work  of  OECD  and,   in

 3     particular, with respect to  the work that we do on  the

 4     classification of mixtures and the development of test

 5     guidelines.

 6                  I apologize for  the  sort of prehistorical

 7     way of presenting my information.  The slides  that I

 8     use are  --It only needs a bit of focusing.  Here we

 9     go.

10                  Well, first of all,  the OECD, as some of

11     you  do know  and many  of us  probably don't,  is  an

12     organization   for   economical   cooperation    and

13     development,  and  the E  for economical is  quite  an

14     important  one.   What that means actually that  we  try

15     to improve the  economies of  our member countries,  and

16     that's the main goal of that organization.

17                  Now you may wonder what chemicals have to

18     do with  economy in  that  respect.   By the way,  these

19     are the  current  members of  OECD, and  I  say current

20     because, well,  there is sort of a continuous  debate on

21     the adherence.  A lot of  countries do OECD.  The ones

22     that you see  up here, some of them have been members

23     since  the  early  sixties when OECD  was established.

24     Others like Mexico,  Korea are members that joined  the

25     organization  only recently.   That  means  in the last

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 1     couple  or  four or five years.

 2                  As I  said,  it's an economic organization,

 3     and chemicals  do  play  an important  role in such a way

 4     that  we feel  that  the management of  chemicals  in

 5     member  countries, if we could harmonize  that  in one

 6     way  or  another,  would save  a  lot  of money,  and  we

 7     published  a  document on that that shows that slowly,

 8     because of the work done by one division on chemical

 9     safety  and the harmonization of  methodologies,  that

10     that  would save a numerous amount  of  dollars.    As a

11     matter  of  fact, it's around approximately  $56 million

12     in  OECD member  countries  on  a yearly  basis  just

13     because we share information between  countries.  We

14     share registration procedures, and we have harmonized

15     testing methodology.

16                  As you  all  know,  the risk  assessment

17     paradigm consists of the hazard  identification, and

18     from there on further down to risk assessment.   In our

19     work on harmonization of classification, I would  like

20     to start to  show you what role mixtures  play in our

21     work and to give you a  little  bit  of an impression how

22     much work  it is to  harmonize  certain things.

23                  We identify these steps here,  the hazard

24     identification steps, which we would like to harmonize

25     between countries and among countries, and then after

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 1     that  and based on  that hazard  identification,  the

 2     classification of  chemicals and mixtures,  for  that

 3     matter   and,   when   that   is   done,   the   hazard

 4     communication.

 5                  Well,  the latter part is a part where we

 6     share the work with other international organizations

 7     such  as  WHO  and   ILO.     You  can  imagine   that

 8     communicating a certain hazard and  the  detail of a

 9     hazard   to   people  that  are  illiterate,  live  in

10     countries where cultures are completely different from

11     western  countries  or from OECD member  countries in

12     that respect is quite a challenge in itself.

13                  Well,  in order to organize that work, we

14     started  to  establish a comity,  a task force, we  call

15     that.    Unfortunately,  in   OECD  we  have  rules  and

16     regulations what you will call a group,  whether that

17     will be  a working group or a task force or whatever.

18     But that is very clearly defined, and a task force is

19     a relatively higher  level.  That means  there's most

20     definitely  a policy  component  to that as well,  not

21     only technical.

22                  Here you  see the task force that we  set up

23     for the classification and labeling work.   It consists

24     not only of member country representatives.  They  are

25     all  nominated  by  their governments,  but  also  the

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 1     European  Commission  representative.    That   is   in

 2     addition to members  from the European member states.

 3                 We have  chairs from other groups that  do

 4     related work.   You  see the task  force  of  NCs.   That

 5     stands for national  coordinators  of the TGP, which  is

 6     the test guidelines  program, and  I will talk a little

 7     bit about that in a  minute.

 8                 We have  the chair of  our working group  on

 9     pesticides .  That is a working group that works  on the

10     harmonization  of pesticides  work.   Then we have a

11     whole  lot  of  acronyms that  I will not  explain, but

12     they all deal  with international  organizations  that,

13     in one way or another,  are  involved in classification.

14     You  can  imagine,  classification  is not only for

15     consumer use and for  pesticides.   It's  also used  in

16     transportation   quite   a   lot    and   in   marine

17     transportation,  the  big tankers, it makes a really big

18     difference  whether  their  contents is classified  as

19     hazardous or nonhazardous, and  within that hazardous

20     category also  further details.

21                 So IMO, International  Marine Organization,

22     transport  of  dangerous goods,   U.N.  organizations.

23     BIAC and TUAC  stands  for  the official  organizations

24     that represent industry.   That  is BIAC,  Business and

25     Industry Advisory Committee  to  the OECD,  and TUAC  is

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 1     worker unions, international worker unions.  They all

 2     play  a role  in  that work,  and  they  all  disagree

 3     usually.

 4                 We recognize a couple  of  steps to  sort of

 5     facilitate and organize the work, because we  deal  with

 6     classification  systems  that  are  around,   that   are

 7     substantially different  between  countries,  and  we

 8     first thought, well,  as a  first step we need to  know

 9     what exactly do these classification systems  look  like

10     in  the various  member  countries.    Where  do  they

11     differ, and why do  they differ,  and  do they all  work

12     in  the  same  way   if  you would  compare them,   run

13     chemicals through one system into another, do you end

14     up  with different  classification  and so on  and  so

15     forth.  That is step one,  and we call that a detailed

16     review document.

17                 These   documents  can  be  substantial,

18     usually  100  pages   or  more  where  it  has  a  clear

19     comparison between  all the systems.

20                 As a  next step,  we would then propose a

21     harmonized system which is based on existing systems.

22     We had no intention to develop new systems.  Although

23     some of  the experts would really  like  to  take  that

24     opportunity  and  say,  well,  we've  learned  from  the

25     past, let's develop things again, we try not  to do so.

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 l                  The reason  for  that is  that  we want  to

 2     avoid  that  after  you have  developed  a  harmonized

 3     system that  everybody has  to  classify everything that

 4     had  been  classified before.   So  we would  like  to

 5     minimize  the  changes in  classification  that  would

 6     occur  once you  have adopted  the  harmonized system,

 7     which  is  very difficult in itself.

 8                  Step three  is then the  discussion that

 9     would  fall out at  the various levels,  the technical

10     level, policy levels, and so on.

11                  Step four would  be  the endorsement,  and

12     that is an official endorsements by the governments of

13     the  OECD member  countries.    Since we  have  only  29

14     member countries, together producing  still 85 percent

15     of all the chemicals in the  world, we  want to  reach

16     out further.

17                  That's  why we have  sought  acceptance  in

18     the U.N.  system and the decision making system  there

19     in  the U.N.  which  we  will  do  together  with  other

20     international organizations and especially those that

21     are also  involved in the hazard communication.  That

22     means  in  that center part which you see  there,  IOM

23     stands for the International  Organization of --  well,

24     I  forgot  it  myself  for  the  moment.    It's   an

25     international group of organizations  that, like U.N.

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 1     organizations  and OECD and  the  Commission would  all

 2     work  together  and to bring that together to U.N.,  to

 3     ECOSOC where it will be adopted in the near future.

 4                  Well,  where are  we  today?   That  is  the

 5     sort  of  framework   in which   we  work.    We  have

 6     identified  these  endpoints  that you see  up  there,

 7     because  classification systems,  most of them but  not

 8     all,  include  all  these  endpoints  in  one way   or

 9     another.

10                  You see,  the ones that  are listed at  the

11     upper part are all endpoints that are considered,  and

12     most  of them are real,  true endpoints.  The last one,

13     chemical  mixtures, is  a  different  entity.   We have

14     added that, because currently do there exist specific

15     classification systems for mixtures  that are different

16     tihan  for  chemical  substances.

17                  There  are also a couple of endpoints that

18     are not  covered  in  any  existing system,  which  are

19     neurotoxicity, unfortunately immunotoxicity, and then

20     a  couple  of smaller  hazards.   We  call  them smaller

21     hazards,  because  they are not really interesting  for

22     all   the  member   countries,   like   water  activated

23     toxicity  such as in contact with water would vaporize

24     and,  by  that,   cause  a  hazard,  aspiration hazards,

25     hazards of defatting  agents,  and so on and so forth.

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 1                  In order to deal with all  these different
 2     subjects,  we need  different groups of  experts.   We
 3     cannot  just put  all  the  experts  together and  say,
 4     well, let's talk about that, because we need different
 5     experts  for  these different groups.
 6                  So we have established groups  of experts
 7     on acute toxicity, mutagenicity,  repro-tox, all  these
 8     different  things.    These  groups  came  together  to
 9     discuss their various endpoints and to  reach consensus
10     or to fight  about that.
11                  In that last  slide  you saw one group  on
12     chemical mixtures,  and I will not go into the details
13     of the work  that we did on substances,  because  it's
14     not so much  of interest for this audience  today.   So
15     I will focus a bit more on  how we dealt with mixtures.
16                  One of the things that surprised me today,
17     being the  last speaker --  we've heard many speakers,
18     and  nobody  really  started  to  discuss what   is  a
19     mixture.   Well, Debbie did it,  to some extent.
20                  Well, there are a lot of people  that would
21     say, yes,  that is a mixture.  A mixture is now being
22     considered,  at  least as  a working definition in OECD,
23     as a  mixture  of  two or more chemicals that  do not
24     react.  Especially the latter addition  is an important
25     one, because you  can put chemicals together and  they
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 l     react.   Well,  that can become a mixture after  the

 2     reaction process is done,  then fine.   Then you have a

 3     mixture  that will no longer react,  and that  is what

 4     you  work with.

 5                  Indeed  we  have  spent meetings  talking

 6     about  where does it stop.   Is this  table a mixture?

 7     Is the dress a mixture?   These are  all part  of that

 8     definition.   We had to stop that discussion and say,

 9     well,  we  all  sort of  understand  what  a chemical

10     mixture  is,  and let's don't really bother  about that.

11     So we  left it  aside,  but  we consider  it  all  in  the

12     sort of  the  general  way that  we  in our  various

13     legislatures and regulations consider a mixture.

14                  The   expert    group,    all    in    the

15     classification  for  mixtures,  we have extended

16     expanded our group beyond OECD where you see countries

17     here like Brazil, because we felt that classification

18     of mixtures  is especially  important  in  developing

19     countries where a lot  of  the chemical -- not  so much

20     the  production of the  chemicals,  but the preparation

21     of mixtures  is  being done.  Products are being made,

22     and  people  are exposed to large  extent.   We want  to

23     have specific input  from  those countries as well.

24                  You see, the  group is more limited here.

25     We wanted  to have a sort  of  regional representation

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 1     from the southeast like Australia, Canada and Mexico,

 2     then also  to sort of represent developing countries;

 3     two people from EU member states.  There's a  couple of

 4     countries that alternate on that, and then we have the

 5     U.S.   I think I  forgot to mention the U.S.   on  that.

 6     The U.S. is on it  definitely,  the European Commission,

 7     Brazil, BIAC,  TUAC,  and a couple of other people.

 8                  The   branching  of  the  tree  continues,

 9     because below that  group there are  again  subgroups

10     that  deal with   the  various   endpoints  that  are  of

11     importance for mixtures.

12                  Of course, in principle,  all  the  endpoints

13     that  I listed before for substances  are  also  being

14     considered  for mixtures, but it was  not considered

15     necessary to have separate groups of experts for each

16     of  the endpoints,  because we  have  dealt  now  with

17     substances,  and  I didn't mention that  before.   But

18     that is the  past  station.   We have reached  agreement

19     on that.

20                  So using  the harmonized  classification

21     systems for  a  particular  endpoint like sensitization

22     and dermal and eye irritation and  corrosion, people

23     felt sufficiently confident as a bigger group to work

24     with that and  try to harmonize that.

25                  The ones that are  listed here are the ones

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 1     that  are considered  difficult.   Acute  toxicity  has

 2     been  and still is  extremely difficult,  how to deal

 3     with  that.

 4                  C&M stands for  cancer  and tnutagenicity,

 5     and  reproductive toxicity  are also  very difficult.

 6     Environmental hazards are still in its infancy anyway,

 7     the   classification  for  chemical   hazards  to   the

 8     environment.  Then we have a separate  group that tries

 9     to put  this  altogether into a proposal.

10                  Again,  I will not focus  too much on  all

11     these endpoints,  because  I  can  spend hours  just

12     talking about  that.

13                  One thing that struck  me is that within

14     the discussions or part of the discussions, one of  the

15     biggest  differences  in  all  the  discussions  about

16     mixtures is  that  additivity of effects is considered

17     a very  important  aspect in Europe.

18                  In  U.S.  it's not considered an important

19     aspect.   I  just learned this  afternoon,  at  least  in

20     EPA,  in  your   guidelines  that  are  coming   out   for

21     mixtures,  additivity  is  considered.    But as   you

22     understand,  in this international  forum we  deal,   of

23     course,  with EPA, but we also deal  with FDA.

24                  We   deal   with   your  Department    of

25     Transportation.   We  deal  with OSHA.  We deal with

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 1     CPSC,  and all  the other agencies, they don't  want to

 2     be   bothered   about  additivity.     They  have   a

 3     classification system for mixtures which  is based on

 4     the  individual hazards, and  they just apply  sort  of

 5     cutoffs  for each of those separate hazards and don't


 6     add  them up.

 7                  In  Europe  they  have  very   complicated


 8     systems  for additivity,  and they do not -- Well,  they

 9     do consider also synergism, but they do that in a sort


10     of  a,  let's say,  expert judgment type  of approach,

11     which  is more  or less on a case by  case  rather  than

12     using  formalized  or  other stuff, which  they  do for

13     additivity.

14                 This is just  to  show you  the number  of

15     meetings that we have only on  that particular group of

16     mixtures in order  to  reach consensus.    The  seventh


17     meeting  is  in  parentheses,  because we hope that  next

18     month  at  the  sixth  meeting  we will  reach  final


19     consensus.

20                 You see also -- This is  just  to give you

21     an  impression  how much  work  is  involved  here,  that


22     this is only the list of  meetings of the expert  group.

23     All the  subgroups below  that are not listed, and  then

24     in addition we  have more than 50 chemicals.

25                 We've   heard  them  already   today,  and

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 1     teleconferences is one of the nightmares  that  I  sort

 2     of  have,  because I have teleconferences almost every

 3     day,  if  not  every  day,  on  all kinds  of different

 4     subjects.

 5                  So  where are  we  with mixtures  today?

 6     Well,  first of  all,  we have the  data review document.

 7     This  is available on the Web,  and one of the  slides

 8     that  will follow this gives you the address.   If  you

 9     forget about   it,   it's  just   www.oecd.org.ehs   for

10     Environmental  Hazards Safety, but it comes up  in  one

11     of  the slides.

12                 Here we compare the classification systems

13     that  are  existing  today, and  in  addition to the North

14     American  from  Canada and  U.S.  and the  European  EU,

15     Sweden being a  member of the European Union still  has

16     a classification system that's slightly different from

17     the Commission.

18                 As  you may know, Sweden is  one  of  the more

19     recent members  of  the European Union, and  still is in

20     a period  of transition,  and now tries to get some of

21     their specific aspects into  this  harmonized system,

22     which they could  not manage  at the time they were

23     becoming  a member in Europe.   But we also see that

24     countries like  Slovenia, Korea  -- there are countries

25     in  the world  where you would  not  have expected that

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 1     they have  detailed systems  for a  chemical mixtures

 2     classification, and they have.

 3                 They  have all been considered.  We have

 4     compared consumer products, transport, pesticides, new

 5     chemicals,  hazardous wastes,  and  the  title  of the

 6     document you see  there  below.

 7                 The step  two proposal  that  we have now,

 8     the first one,  and the second one  is coming up  in May,

 9     has these chapters:   General consideration, building

10     block approach  --  and I wish I  had more time  to talk

11     about the building blocks to what really constitutes

12     harmonization,    because   harmonization    is   not

13     standardization   --   definitions,   and   then  these

14     endpoints are all  part  of that.

15                 Here  at  the bottom you see  target organ

16     toxicity,  and  that   is  the whole  on  neurotoxicity

17     today.    It  was  considered  by  member  countries

18     officially  that   there  was no   need  to  classify

19     substances specifically for neurotoxicity,  no  need to

20     classify mixtures  specifically  for neurotoxicity.

21                 What was  agreed  is that we would consider

22     target organ as the home for  that,  and if we know that

23     the  target  organ  is  the brain  or central  nervous

24     system, we could mention that, but not have a special

25     system for it.

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 1                  Within  mixtures  we  had  this  sort  of

 2     ranking.  We classify --We  have a system to  classify

 3     whether the data are  available  for the mixture  as  a

 4     whole.    You would  say,   well,   that's  the   ideal

 5     situation,  which is not always true.

 6                  There are countries, especially in Europe,

 7     that feel that with respect to cancer and mutagenicity

 8     and even reproductive toxicity, data on the mixture as

 9     a whole is  not good data,  because you have dilutions

10     of the  substance that can cause that carcinogenicity

11     which will  not show up in your testing when  you  test

12     the mixture.

13                  So we have a system where you would  deal

14     with when you have the data of the mixture as  a whole.

15     Then  we  apply  bridging  principles  where  we   have

16     additivity,  synergism and  that sort  of thing  included

17     in rules and regulations.

18                  Then we  classify based on where we have no

19     data on the mixture as a  whole,  but we have data on

20     all the components.   You  can  imagine that we say --

21     easily  say  all the components that, we have  quite an

22     extensive discussion, what is a component?  How far do

23     you go?  Do you go to the .0001 percent contaminant in

24     your mixture or do you have a cutoff?   We did set a

25     cutoff  of one percent now.

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 1                 Then we have classification based on when

 2     data are available for  some of your ingredients, but

 3     not all.  But this is just  to show the complexity.

 4                 Finally,  because  I  understand  you're

 5     interested  in neurotoxicity, and  that's  what I said

 6     where we have neurotoxicity data,  we have separately

 7     identified  two systems, one  for effects of the single

 8     exposure and one for effects after repeated exposure.

 9                 These  have gone back and forward many

10     times,  because  they said,  well,  they should be all

11     included  into  one system,  both single and repeated

12     exposure.  Then  we didn't manage to reach a harmonized

13     system.  So it was decided to separate them.  Then we

14     had them back again.

15                 So we went  back and forth quite a while,

16     and we  ended up having two different  systems which

17     have two classes today, and the first class is based

18     on human  data,  basically,  and the  second  on animal

19     data.  That's one distinction.

20                 Also,  the  separation  is based on  the

21     severity of the  facts.   The severity of  effects,  of

22     course, can be related  to the testing where the test

23     methods for dose specific  endpoints  are  considered

24     important.

25                 The  criteria  -- We  have expert judgment

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 1     which  is the leading  thing.  You  can easily, well,

 2     that makes  it pretty vague.   Expert judgment in  one

 3     country  is very different  from expert  judgment  in

 4     another  country.   But  in  order to  harmonize even

 5     expert judgment, which is hard, we provide guidance of

 6     effects that are considered to support classification,

 7     effects   that   are   not   considered   to   support

 8     classification,  and we have added also guidance values

 9     for  cutoff of  effects,  which is currently  used  in

10     Europe mostly.

11                 Just one that  shows  a little  bit where we

12     have references to,  neurotoxicity or  at  least it  is

13     made clear  that neurotoxicity is definitely included

14     there,  like  under criteria  and like  under  effects

15     supporting  classification, we have statements there,

16     significant functional change  in central peripheral

17     nervous  system,   including  central  nervous  system

18     depression  and  special senses.

19                 These  sort   of  syntheses   are  added

20     scattered around in the document, and not  only for  the

21     endpoint  of neurotoxicity but also for  many of  the

22     other endpoints.

23                 A  few minutes  on  the  test guidelines

24     development in  OECD.   That is sort of the foundation

25     of the work on  classification, because you need test

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 1     data.  So you need  test  guidelines to do that.
 2                 These are based on what  we call the  MAD
 3     decision or the Mad Decision, which tells you that if
 4     data are generated according to OECD guidelines  and in
 5     compliance with OECD GOP, practices shall be accepted
 6     in all member countries,  which is today the case.
 7                 That  means that,  despite  the  fact that
 8     certain  member  countries,   including  the  U.S.   and
 9     Japan,  for that  matter,  still  have  their domestic
10     guidelines and even have different guidelines between
11     agencies or within an agency,  when a test is conducted
12     according  to  an  OECD guideline  and  then  that OECD
13     guideline  covers  the data  required  --  which,   of
14     course, that's a prerequisite -- this  will be accepted
15     by all member countries.
16                 Just to give you a clue about how big that
17     program  is,  we have  currently --  Well,  in  1981  we
18     published our first  set of 51 guidelines.  Since then,
19     the  11  addenda have  been published with 90  new  and
20     updated  guidelines  altogether,   and  we  have  now a
21     second  edition of  the work  which was published  in
22     1.993, and it's now today  available as  hard copy or  CD-
23     Rom, and also online  in  our OECD  bookshop.
24                 This  is  the  site where,  if you go there,
25     you'll  find  all  the  information  on  testing   and
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 1     assessments  and  also  on  the  classification.   The

 2     publication are, unfortunately, a priced publication,

 3     but  recently I've managed with the  OECD management

 4     that  they  can  also  be  bought  now  separately as

 5     guidelines,  individually  as guidelines  rather  than

 6     buying  the whole  set.

 7                 We distinguish various sort of groups  that

 8     involve physical  chemical  properties, biotic systems

 9     facts,  degradation,   health  effects,  and  special

10     activities.   You see  a number of projects that are

11     listed  that we have  today  in our portfolio.  We  have

12     one project on acute toxicity, for instance, which is

13     a  very large  one.     We   have  smaller   ones.    So

14     altogether you have a bit of an idea how many projects

15     we have.

16                 Yo  see  that  some projects  are  led by

17     member  countries.  They take the leads. They organize

18     the  work.   They  have  their experts  providing first

19     proposals   for   draft  guidelines,    the  guidance

20     documents.  They could be member countries,  could  even

21     be the  industry.

22                 Who do we involve in the work here?  We

23     involve not  only the  member countries,  but  we  have

24     partner organizations,  the European Chemicals Bureau,

25     ICH, which has been responsible for the harmonization

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 1     of pharmaceutical guidelines; IOMC, mentioned earlier,

 2     the  combination of international  organizations;  IZO

 3     standards;  industry organizations;  and then we have

 4     input  from academia,  government and  industry.    All

 5     these  things  come together  finally  in a proposal at

 6     the  Secretariat where we try to deal with that.

 7                 I  will now use only two more minutes, if

 8     you  don't mind.  I will skip the procedural part,  how

 9     we  develop.   I will  just show  you one  example of

10     neurotoxicity,  just to see how much time it sometimes

11     takes  to indeed develop a guideline.

12                 It started in '87, our Guideline 424.   For

13     those  who  know, that  guideline started in  '87 as a

14     combined proposal  from the U.S.  and the Netherlands

15     for  a neurotoxicity test.  After formatting, language

16     changes,  and  so  on  and  so forth,  in  '88  we   can

17     circulate that for review.

18                 Well,  you see, we had a follow-up meeting

19     here in Washington, an expert meeting in OECD where we

20     discussed the  guideline,  including the comments that

21     were received.  After  review by international experts,

22     and  just  besides,  national  experts means we  have a

23     database of about 6,000 experts  that  are all copied on

24     guidelines, and that means that we do receive a whole

25     lot  of responses and comments, and in the Secretariat

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 1     we try to deal  with that,  put it together and revise

 2     guidelines  accordingly.

 3                  In  ' 92 we had another consultation meeting

 4     where we  -- neurotoxicity,  the  relationship between

 5     that  and  the guidelines that  we have  for  systemic

 6     toxicity was discussed,  and  in  1994 we were finally

 7     able to circulate a new  proposal.

 8                  Another meeting was needed in Ottawa to

 9     further discuss  comments,  and  some of you  in this

10     audience have been  present at these  meetings.  In  '95

11     we revised  it again.  In '96 we were finally  ready to

12     bring it to the policy level, and that means we were

13     not  really  done  there,   because  after the  experts

14     finally  agreed  that  this   is  technically  a  good

15     guidance or a good  guideline,  then policy people  are

16     considering it.

17                  They  look at economical,  social impacts

18     and welfare sort  of things  pressuring countries,  and

19     finally it  was  adopted in 1997.  So  that took exactly

20     ten years.

21                  It's  one of  those examples  --  I think I

22     stop with  my overhead.   It's one of those examples

23     that I use when I  say, well,  if it takes more  than  ten

24     years, then this  is sort  of a continuous process,

25     because after about seven,  eight years of discussion

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 1     among  experts,  you  see  a new generation of  experts

 2     coming  in,  and that  is  very serious,-  because a  new

 3     generation has different views, disagreements --  they

 4     are elderly  people,  and  they come up with new ideas.

 5                  You   start   over  all   again   in   the

 6     discussion.   So we  feel  that we  have  experience  at

 7     this.   If it takes  more  than ten years,  you  better

 8     stop  it  and  start  all  over  again,  rather  than

 9     continue.

10                  So we were happy that we could  finalize

11     the neurotox in ten years.  I hope that developmental

12     neurotox which we started just a few  years ago in  '96,

13     so to  speak, and I have another  series of slides  on

14     that but because of time constraints, I will  not  show

15     them  to you  --  that we  will be  able to  manage  to

16     finalize that guideline within a year's  time from  now.

17                  I think I'll stop here, Debbie. Thank you

18     very much.

19                  (Applause.)

20                  QUESTION:  This may put  you on the spot a

21     little  bit.   In October the Netherlands  is having a

22     meeting to work on mixtures guidance or some mixtures

23     approaches,  I guess,  for  their country.

24                  I would  love to ask them a question about

25     how that fits into what you are doing.  How can I ask

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 1     that question?

 2                 DR.  KOETER:    Well,  you  can  ask  that

 3     question  probably to  address  it  to me,  although  I

 4     could   not  give   you   that   answer   right  away.

 5     Unfortunately,  member countries do  not always share

 6     information  about  their  national  events  with   the

 7     Secretariat.

 8                 I  think  --  I'm not  sure  whether  that

 9     meeting   that   you   refer  to  really  deals   with

10     classification  of  chemical mixtures in the same  way

11     that I was talking about that.   If that is  the case,

12     then, well, at least the Netherlands  should inform the

13     Secretariat.  So if you wish, I  can  ask them and  send

14     that information to you.   I do not  have  it here  at

15     hand.

16                 MR.  BLUOIN:   Thank you very much,   Dr.

17     Koeter.

18                 In  closing,  I  represent  the  Technical

19     Training Committee from OPPT.   I want to thank  all of

20     you for coming to this, those of you  that have made it

21     through the day.   The crowd has been diminished.

22                 Special   thanks  to   our  speakers   who

23     presented  us  with a worldwide  view of this  fairly

24     complex thing.

25                 Special  thanks goes to Debbie  Norris,  who

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                        COURT REPORTERS AND TRANSCRIBERS
                         1323 RHODE ISLAND AVENUE, N.W.
     (202) 234-4433               WASHINGTON. D.C. 20005               (202) 234-4433

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had this  bright idea about a year and a half ago.

             (Applause.)

             Thank you very much,  Deb.

             Last but  not  least,   thanks  to  all the

people that helped put it all together, everybody from

people  that did the nametags,  the  people  that are

sitting out  there,  to Bev Sjoblad for hand holding us

with the money and stuff like that.  So thank you all.

             There's cookies  and stuff out  there.    I

guess we have decided to not have  the panel.  So if we

have some discussion,  we  can have it  out there over

cookies.

             (Whereupon, the foregoing matter went off

the record at  4:07 p.m.)
     (202) 234-4433
                            NEAL R. GROSS
                        COURT REPORTERS AND TRANSCRIBERS
                          1323 RHODE ISLAND AVENUE, N.W.
                            WASHINGTON, D.C. 20005
                                                  (202) 234-4433

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                        CERTIFICATE
     This is to certify that the  foregoing transcript  in the




matter of:          Symposium
Before:
Date:
Place:
U.S. Environmental Protection Agency
April 27, 2000
Arlington, VA
represents  the  full   and   complete  proceedings  of  the




aforementioned   matter,  as   reported   and   reduced   to




typewriting.

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