PB81-187239
State-of-the-Art Report on
Structure-Activity Methods Development
(U.S.) Environmental Research Lab.
Duluth, MN
Apr 81
U.S. DEPARTMENT OF COMMERCE
National Technical Information Service
NTIS
-------
United States EPA 600/3-81-029
Environmental Protection April 1981
Agency
Research and
eveiopment
STATE-OF-THE-ART REPORT ON STRUCTURE-ACTIVITY
METHODS DEVELOPMENT
OFFICE OF TOXIC SUBSTANCES
Prepared by
Environmental Research
Laboratory
Duluth MN 55804
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-6QQ/3-81-02q
Prnhlpm-flHpntpH Ropnrt
!> neriPIENT'S ACCESSION NO.
F881 187239
4. TITLE AND SUBTITLE
State-of-the-Art of Structure Activity
Methods Development
5. REPORT DATE
April 1981
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Oilman D. Veith
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
U. S. Environmental Protection Agency
Environmental Research Laboratory-Duluth
6201 Congdon Boulevard
Duluth, Minnesota 55804
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
U. S. Environmental Protection Agency
Environmental Research Laboratory-Duluth
6201 Congdon Boulevard
Duluth, Minnesota 55804
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
IS. SUPPLEMENTARY NOTES
16. ABSTRACT T;he overall objective of this project is to provide the Agency with the tech-
nical basis for estimating the toxicity and environmental behavior of organic chemicals
from molecular structure. The project is directed toward the evaluation of quantitative
structure-activity relationships (QSAR) for use by EPA Program Offices and toward the
development of new data and QSAR methods to extend the technique to meet Agency needs.
Specifically, the objective of the Structure-Activity Project is to develop meth-
ods to predict the toxicity, persisitence, and treatability of large numbers of untest-
ed chemicals using QSAR based on structural, chemical, and biological properties of
representative reference data bases. Development of QSAR is being tailored for use in
the (1) initial screening of chemicals under TSCA, (2) development of risk assessment
strategies, (3) prioritization of chemicals for Water Quality Criteria development, and
(4) the optimization of national monitoring programs for toxic chemicals.
This report summarizes the progress during the first six months of the project.
The report provides a literature review and perspective for applying structure-activity
methods to aquatic toxicity of industrial chemicals. Experimental work centered on
developing methods for estimating molecular descriptors such as log P and connectivity
indexes and on the development of a systematic structure-activity data base for aquatic
toxicity. A new program for entering structures into a computer and calculating con-
nectivity indexes is discussed. A general model for predicting 96 hour LC50 for
narcotic chemicals is presented.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lOENTIFIERS/OPEN ENDED TERMS C. COS ATI Field/Group
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport)
Unclassified
21.
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (Rซv. 4-77) PREVIOUS EDITION is OBSOLETE
-------
NOTICE
THIS DOCUMENT HAS BEEN REPRODUCED
FROM TH'E BEST COPY FURNISHED US BY
THE SPONSORING AGENCY. ALTHOUGH IT
IS RECOGNIZED THAT CERTAIN PORTIONS
ARE ILLEGIBLE, IT I'S BEING RELEASED
IN THE ''INTEREST OF MAKING AVAILA-BLE
AS MUCH INFORMATION AS POSSIBLE.
-------
EPA 600/3-81-029
April 1981
STRUCTURE-ACTIVITY RESEARCH AT THE ENVIRONMENTAL RESEARCH LABORATORY-DULUTH
State-of-the-Art Report on Structure-Activity Methods Development
October, 1980
Prepared by
Oilman D. Veith
Environmental Research Laboratory-Duluth
6201 Congdon'Boulevard
Duluth, Minnesota 55804
List of Contributors:
ERL-Duluth
Rosemarie Russo
David Weininger
Brad Greenwood
Gary Phipps
Gary Holcombe
Dave DeFoe
University of Wisconsin-
Superior
Daniel Call
Larry Brooke
Nasim Ahmad
Carolanne Curtis ';"
Anne Lima
Steven Lozano
Joseph Richter
Karen Studders
University of Minnesota-
Duluth
Vincent. Magnuson
Donald Harriss
Wayne Maanum
::-. Marc ia:-Fulton ;-
-------
Table .of Contents
Section
Title
Page
I. Project Objectives
II. Project Overview
III. Project Organization -
IV. Estimation of .Acute Toxicity of Organic Chemicals
to Fish
V. Mathematical Methods for QSAR
VI. Molecular Descriptor Generation
VII. QSAR Information System
VIII. Activity Reports
IX. Evaluation of Rapid-Screening Tests for QSAR
X. Literature Review on Narcosis
XI. References
1
2
6
8
26
36
54
57
63
88
101
-------
ERL-D Structure-Activity Research Project
I. PROJECT OBJECTIVES
The overall objective of this project is to provide the Agency with the
technical basis for estimating the toxicity and environmental behavior of
organic chemicals from molecular structure. The project is directed toward
the evaluation of quantitative structure-activity relationships (QSAR) for
use by EPA Program Offices and toward the development of new data and QSAR
methods to extend the technique to meet Agency needs.
Specifically, the objective of the Structure-Activity Project is to
develop methods to predict the toxicity, persistence, and treatability of
large numbers of untested chemicals using QSAR based on structural, chemical,
and biological properties- of representative reference data bases.
Development of QSAR is being tailored for use in the (1) initial screening of
chemicals under TSCA, (2) development of risk assessment strategies, (3)
prioritization of chemicals for Water Quality Criteria development, and (4)
the optimization of national monitoring programs for toxic chemicals.
-------
II. PROJECT OVERVIEW
The broad objectives of predicting tioxicity, persistence, and
treatibility of chemicals require considerable definition at the outset. The
use of QSAR carries with it the assumption that a well-defined chemical or
biological activity can be related to a combination of free-energy,
'electronic, and steric properties of the molecule. This assumption dictates
th'at the measured activity (hereafter called endpoints) be well-defined and
dependent on the chemical rather than the environment into which the chemical
is placed. While aquatic toxicity may be estimated directly by predicting
'the acute LC50 or chronic MATC from reference data bases, estimations of ',:.'.:
persistence or treatability are likely to be as dependent on the system '-.
studied as on the properties of the chemical. Therefore, QSAR in these areas.
'can only be used in predicting rate constants and equilibria which are needed
inputs to process models for persistence and treatability. QSAR can be
developed to estimate hydrolysis, adsorption, volatilization, and
biodegradability - but the full use of this approach will only be realized in
the development of transport and transformation process models such as the
EXAMS model development at ERL-Athens.
The overall QSAR development process is outlined in Figure 1. Most
'often, a data base is compiled for the endpoints of interest (acute toxicity,
volatility, etc.) and selected molecular descriptors which are thought to be
important controlling factors in the relative activities of the chemicals.
The data base is often classified into homologous or semi-homologous series
of chemicals to increase the likelihood of common modes of action or reaction
mechanisms. Where critical data; are lacking, a data generation program is
needed to provide the data. The compiled molecular descriptors and endpoints
are analyzed by a variety of mathematical techniques such as multiple
' ' ' 2 '
-------
QSAR PROCESS
Classify
Reference
Base
p* .-^..|
~* ^~
Select
Molecular
Descriptors
Conduct
Sv^tpmntir
Testina
Compile
Molecular
Descriptors
J
?
1ป- Dpuplnn
QSAR
: 8.
nฐ
o 3
Untested
Chemicals
Generate
Molecular
Descriptors
Estimate,^.
Toxicity an9
Fate End points
OUTPUT
PREDICTIONS
Figure 1. Flow-Chart of the QSAR Process
-------
regression and pattern recognition to derive a quantitative
structure-activity relationship for the selected class of chemicals and the
specified endpoint.
In making predictions beyond the tested chemicals, the chemical must be
classified to select the proper QSAR model. The molecular descriptors which
are the variables in the QSAR for the chemical must be generated, and the
endpoint is computed as a QSAR prediction. Obviously, only endpoints for
which there are QSAR can be predicted.
Figure 1 shows that QSAR must be developed along many research fronts if
it is;to be useful for large numbers of diverse chemicals. In the initial
phase of this project, it was immediately apparent that there were few data
of'use-in QSAR development. The aquatic toxicity literature contained little
systematically derived data due to past emphasis on testing relatively few
highly'hazardous chemicals extensively. Moreover, important molecular
descriptors were nearly non-existentfor chemicals outside the interests of'
the''drug and pesticide industry. It would-be oflittle value to develop QSAR
models which required parameters not available for individual chemicals.
Limitations with respect to the level and mechanism of funding for this
project required that the first year'objectives^be redefined as follows. It
was apparent that numerous testing laboratories could develop QSAR models
independent of this project if the necessary molecular descriptors were made
av'ailable to them. Therefore, approximately 25 percent of the resources are
assigned to the evaluation, calculation, and compilation of molecular
descriptors. An additional 25 percent of the resources are assigned to the
development of a user oriented, computeriz-ed data dissemination system called
the Information System for"Hazardous Organic^ in Water (ISHOW). The
-------
remaining 50 percent of the resources are assigned to the data generation
effort.
Because of the expertise of this laboratory in aquatic toxicity testing,
data generation is being focused on developing a systematic aquatic toxicity
data base for industrial chemicals. In anticipation that the data base will
have to contain approximately 20 chemicals of each chemical class, the use of
chronic tests is precluded by the cost and time requirements. Therefore, the
initial data generation consists primarily of the determination of the 96
hour LC50 of industrial chemicals for fish. Lesser efforts are being made on
expanding the model developed to predict the bioaccumulation potential of
chemicals in aquatic organisms, and comparing the relative sensitivity of
different aquatic species.
' .'tt.-'is-anticipated that testing will expand to include additional
toxicity endpoints after the usefulness of the QSAR technique is
demonstrated. Furthermore, coordination of this project with other ORD
laboratories which have the expertise in studies of persistence and
treatability of organic chemicals is underway. Joint efforts are being
discussed for the generation of data bases of key endpoints needed in the
modeling efforts in the area commonly called "fate".
-------
III. PROJECT ORGANIZATION .
The organization of ..the ERL-Duluth QSAR project is presented in general
terms in Figure 2. The project is centered with a core group of ERL-Duluth
staff which is supported by the ORD Decision Unit for Toxic - Ecological
Effects and Transport and Fate. The ERL-Duluth staff provides project
planning and coordination, tailoring' of QSAR methods to meet EPA needs, and a
nucleus for toxicity testing. A cooperative agreement between ERL-Duluth and
the University of Wisconsin-Superior and funded jointly by ORD and OTS
provides the project with data compilation, toxicity data generation,
toxicological interpretation of test results and the development of rapid
screening methods for the structure activity project. A cooperative
agreement with the University of Minnesota-Duluth funded by ORD and
EPA-Region V is1 focused on the'dissemination 'of QSAR predictive technology
j* ...
for large numbers of chemicals, and the development of QSAR models to be
tested. The progress of each of the teams listed in Figure 2 will be
presented in this report.
-------
Figure 2. Organization of QSAR Project
University of Wisconsin-
Superior
Cooperative Agreement
ERL-Duluth
Literature Review an<
Data Compilation
Lima
Studders
2 Technicians
(Screening Test Methods}
Richter
Curtis
{Toxicology!
Ahmad
Joilymore
JToxicity Test Teama[
Test Operations
Shubat
Poirer
Anderson
Call
Brooke
Analysis and Methods
Knuth
Hammermeister
Hoglund
[Project Coordination}
Veith
Modeling and Computer
Programs
University of Minnesota-
Dululth
Cooperative Agreement
[ISHOW Development)
Magnuson
Maanum
Fulton
LaLiberte
Weiniger
Greenwood
(Biostat 1st ics)
Lozano
[Toxic ity Test TeamS}
Test Operations
DeFoe
Analysis and Methods
Huot
JToxicity Test Teamfl
Test Operations
Phipps
Holcombe
Analysis and Mehtods
Fiandt
Felhaber
Walbridge
IQSAR Models!
Harriss
2 Associate
Scientists
-------
IV. ESTIMATION OF ACUTE TOXICITY OF ORGANIC CHEMICALS TO FISH
A. Definition of Boundaries
The literature concerning the application of QSAR to environmental
sciences is growing rapidly. As with the drug and pharmacology literature,
many of the QSAR equations are isolated bits and pieces of information which
fail to put any individual study into a larger perspective. This
fragmentation becomes especially frustrating when the literature is searched
for possible solutions to the problems facing EPA. In considering the
problem of estimating the behavior of 30,000-40,000 industrial chemicals in
the aquatic environment, it seems necessary to approach the literature with
the belief that such things as toxicity and persistence follow very 'simple
relationships among chemicals and that there are some boundary conditions to
the problem. Without:this belief; our future generations will be continuing
our testing in the "hit and miss" fashion of the past 30 years.
In 1968, Corwin Hansch (Hansch, et al. 1968) provided an illustrative
example which demonstrated that organic chemicals in water behaved in an
orderly fashion. Due to the relationship between factors controlling dis-
solution of organic chemicals in water and that of partitioning the chemical
between a water phase and a lipid phase, Hansch demonstrated quantitative
relationships between the water' solubility of chemicals and their
n-octanol/water partition coefficients. The 'relationships were simple:-
log 1 = a log P +i b
where s is the solubility in moles/1, P is the partition coefficient, and a
and b are constants. Using data for 156 chemicals of 9 chemical classes, the
equation
log 1 = 1.339 (+_ 0.07) log P - 0.978 (^ 0.15) r - 0.935
held over a range of about 100,000 in water solubility. When the data were
separated into individual classes, values of r were as great as 0.990.
.8
-------
Therefore, it is apparent that log P is a free-energy term which is a
manifestation of much of the structural information which affects the
behavior of chemicals in aqueous systems.
Implicit in these relationships is the likelihood that the log P will
also be important in the partitioning of chemicals from water into aquatic
organisms and onto organic-coated surfaces. Therefore, it is almost anti-
climactic to have published papers like that of Veith et al. (1978) showing
linear relationships between the bioconcentration factor in aquatic organisms
and log P, or that of Karickhoff et al. (1978) showing relationships between
the sediment/water partition coefficients and log P as follows:
Kp = 0.6 x P x (O.C.)
where K_ is the sediment/water partition coefficient and O.C. is the
fractional mass of organic carbon with sediment. Lopez-Avila and Kites
(1980) reported that the concentration of numerous organic chemicals in river
sediments downstream from discharges can be modeled with the empirically
derived equation
log CQ = bQ + b2 (distance/log P)
C
where CQ is the concentration in the discharge and bQ and b2 are
constants for the river system.
The work presented by Hansch is important to aquatic toxicity studies
because it helps to define the boundaries of the "testing space". For
example, Figure 3 presents a plot of the "testing space" for chemicals.over
the log P range from -2 to 8. This is 10 orders of magnitude of lipo-
phillicity and includes the vast majority of. industrial organic chemicals on
the TSCA inventory. The vertical axis in Figure 3 is the. logarithm of the
concentration of chemicals expressed as moles/liter which varies from 2 to -8,
9
-------
0
-2
-4
0
-2
-4 J.
O
o
-J
Figure 3. Aquatic Toxicity Testing Space Boundaries
-------
which is approximately Che range of concentrations of interest in testing for
most toxic effects in the aquatic system. The right-hand scale is the water
solubility of chemical expressed as the logarithm of the molar concentration
at saturation. The line represents the equation presented by Hansch
reanalyzed to include the 95 percent prediction limits of the relationship
between water solubility and log P. This "testing space" should enclose most
of the organic chemicals encountered in this study. Quite obviously, if the
estimated LC50 for a chemical of known log P lies above the water solubility
line, it should not be possible to measure the LC50 in aquatic systems.
B. Model for Physical Toxicity in Acute Tests
Little has been done in the area of predicting the toxicity of organic
chemicals to aquatic organisms from structure since the work of Meyer and
Overton in 1899-1901. ..The literature contains a 'few isolated examples
demonstrating toxicity-structure relationships; however, a perspective
suitable for shaping research to address the problems facing EPA, and OTS in
particular, is absent.
There is a thread running through the literature which may give the
necessary guidance in tackling the problem. Richet's Rule published in 1893
proposed that the toxicity of a chemical is roughly inversely proportional to
its water solubility. Implicit in this rule is the fact that comparable
modes of action are considered. Ferguson (1939) proposed that when a physio-
logical effect is reversible, when an equilibrium exists between the organism
and the external phase, and when the physiological effect is a function of
the external concentration, the toxic effect must be physical in character.
He suggested that all substances at some concentration will exert toxic
11
-------
action by physical mechanisms and even nitrogen gas at high enough pressure
can produce effects. The physical effect may be masked by irreversible
chemical effects; but in the absence of chemical effects, the primary process
is reversible and physical in nature.
The physical effect of Ferguson was expressed as
1
C = kSn
where S is the water solubility expressed in moles/liter. This physical
effect is called narcosis which is any reversible decrease in physiological
function induced by physical or chemical agents (Mullin, 1954). Ferguson
proposed that the chemical activity for chemicals at concentrations producing
narcosis is constant. Since the thetmodynamic activity of a 'chemical
producing narcosis (f^a^ is calculated by
.
;.; ..-.-,-. /. .-. . _. -; cs '_".::. ; :::; ' - . -.' ..-.-
'where Cnar ฃs tne concentration:producing'harcosis and Cg is the
"solubility of the chemical in water expressed as moles/liter, if the
activities of chemicals for the same physiological effect are constant for
series of chemicals, the' observed effects should" be a constant ratio with
respect to the water solubility as expressed by Richet's Rule.
Mullin (1954) presented data consistent with this idea and further
elaborated on the nature of physical and chemical toxicity. He defined
'narcosis as any reversible decrease in physiological function where the
effects can be divided into narcosis of cell division and narcosis of the
central nervous system (general anesthesia). Most chemicals can cause both
physical and chemical toxicity; however, if the chemical toxicity is small
under the exposure time studied, the predominant effect will be physical
U
-------
toxicity or narcosis as the exposure concentration approaches the water
solubility of the chemical. Thus, in a simplistic way, the prediction of
toxicity of chemicals to fish may parallel the zero based budgeting (ZBB)
method in that, unless specific functional groups that produce chemical
toxicity are present in a chemical of interest, the chemical is likely to
cause physical toxicity, or narcosis, at some fraction of its water
solubility. If chemical toxicity occurs, the physical toxicity is masked and
the observed toxic effects are likely to be at lower concentrations than the
concentration needed for narcosis.
The model immediately posed a problem for this project. If the
thermodynamic activity at equipotency for narcosis is constant within a
series, it would be impossible for higher members of the series (i.e. higher
log P) to. be non-lethal at water solubility. However, it is a common
occurrence in toxicity tests with fish to find chemicals non-lethal at
solubility, with hexachlorobenzene and tridecanbl being prime examples.
Glave and Hansch (1972) provide the answer to this apparent discrepancy.
They presented evidence that the linear relationship between narcosis and
lipophillicity as measured by log P (inversely proportional to water
solubility) was only linear for the more water soluble chemicals. The
relationship between toxicity and log P was actually parabolic in nature and
was termed the "parabolic effect". The biological effect is actually modeled
by:
logฃ = -a(log P)2 + b log P+ c
Thus, at small values of log P, the equation may appear linear. However,.as
the range of log P is extended, the relationship between biological activity
and log P reverses and'the t'dxicity actual.ly'decreases with increasing log
P.
13
-------
Since the log P and water solubility are inversely but linearly related, the
expected physical toxicity for chemicals with-a large log P- could be above
the expected water solubility and no effects would be measured.
The "parabolic effect" has been interpreted in many ways. The fact that
toxicity decreases with increasing log P at the high end of the log P scale
seems to indicate that the chemicals either diffuse slower through membranes
or that the fatty tissue act as a storage media and remove the chemical from
the blood and key receptors. Since the bioconcentration process is linear
with log P, chemicals of high log P may .produce large residues in fish at
water concentrations which are not toxic to the fish. This is classically
illustrated with PCBs where the hazards to consumers of fish due to the
accumulated residues far outweight the hazards to fish at environmental
concentrations.
The "parabolic effect" may be more accurately described as a bilinear
effect resulting from competing membrane transport phenomena. These models
have been described by Yalkowsky and Flynn (1973) and Kubinyi (1979). The
bilinear model is expressed as:
log 1 = a log P - b log(3P + 1) + c
This is more consistent with the understanding of membrane transport which
suggests that water-soluble-chemical transport may be controlled by the
membrane while lipid-soluble-chemical transport may be controlled by the
diffusion layer. j j
C. Experimental Procedures
The chemicals initially selected for -this study include a wide variety
of alcohols, ketones, aldehydes, ethers, phenols, and chlorinated aliphatic
and aromatic hydrocarbons. Figure 4 illustrates the procedure for selecting
14
-------
Figure 4. Flow-Chart for Selecting Test Chemicals
SELECTION OF
TEST CHEMICALS
AVAILABILITY OF
I PURE CHEMICAL.
HEALTH AND SAFETY
APPROVAL
PROCUREMENT REQUESI
(100 gm)
ANALYTICAL METHODS
DEVELOPMENT
PROCUREMENT REQUEST
TEST QUANTITIES
15
-------
chemicals for QSAR modeling. The QSAR modeling group selects appropriate'
classes of chemicals for consideration. The classes of chemicals are mapped
onto a subset of the TSCA- inventory to select chemicals which meet the QSAR
criteria and are in actual production in industry. The list is further
reduced to those which can be purchased as reasonable cost in a pure state.
The list is then screened by the ERL-D Health and Safety Officer to insure
worker safety. Small quantities of the chemicals are purchased to develop
suitable analytical methods and conduct range finding tests so that an
appropriate amount of chemical can be purchased. After the test is
conducted, evaluated and entered into the data base, the data are used to
determine if additional chemicals in the class should be tested to extend the
predictive capabilities of the QSAR models. The entire process in Figure 4
requires approximately 4 to 5 months. Therefore, there is not the rapid
feedback needed for a cost-effective testing program and this project is
optimizing the testing resources by testing several classes of chemicals in a
4 to 5 month "leap-frog" manner. This permits the testing teams to test
.initial chemicals of one class while the modeling team is selecting
additional chemicals of another class.
Exposure Systems. Tests were conducted, in proportional diluters (Mount
and.Brungs, 1967) each with a dilution factor of 0.6. Each diluter delivered
12 L every 20 min cycle to six flow splitting chambers which delivered 0.5 L
to duplicate test chambers. Tests chambers were glass aquaria measuring 20 x
35 x 25 cm with a 9 cm standpipe, providing a 6.3 L volume. Fluourescent
bulbs provided 28-34 ft candles of light at the water surface. A 16 hr
light, 8 hr dark photo period was used. . .
16
-------
Water Characteristics. The water source was Lake Superior water
maintained at 25ฐC in the test chambers. Hardness and alkalinity were either
measured in the control tanks during the test or from a separate line weekly.
pH was measured once during each test at three concentrations and in the
control. Dissolved oxygen was also measured at three concentrations and the
control at 0, 48, and 96 hours. Ten tests yielded a mean hardness of 56.3
mg/L CaCC>3, a mean alkalinity of 42.2 mg/L CaCO} and an arithmetic mean
pH of 7.5. Dissolved oxygen was maintained at 60% saturation or higher.
Other chemical characteristics of Lake Superior water are presented in
Table 1.
Toxicant Additions. Toxicants were usually introduced directly into the
diluter by a metering pump (FMI Corp.). Some tests required delivery of a
saturated solution of the toxicant in lake water. The saturator systems
consisted of either a 20 L glass tank or 20 L glass jar which contained
constant volume of water and excess of the toxicant. The water was stirred
continuously to maintain saturation in the incoming water as the saturated
solution was pumped to the diluter. Two or three tanks and/or jars,
connected with siphons were installed when toxicity was very close to
saturation.
Test Organisms. All tests were conducted with fathead minnows
(Pimephales promelas) from the Environmental Research Laboratory-Duluth
culture units. Fish were hatched and reared in Lake Superior water and fed
live brine shrimp at least twice daily. Typical fish tested were 30 days old
and weighed 0.12 g. Twenty-five fish were randomly assigned to each of .
twelve tanks in lots of five. Fish were not fed during the 96 hour tests.
Deaths were recorded at 1, 3, 6, 12, and 24 hr -and every 24 hrs thereafter
17
-------
Table 1. Chemical characteristics of dilution water
from Lake Superior (all values in ug/liter except
where noted otherwise)
pH (pH units)
Hardness
Alkalinity
Chloride
Sulfate
Total phosphate
Ammonia-nitrogen
Nitrite-nitrogen .
Nitrate-nitrogen
Silica
Specific conductance
(ymho, 25ฐ C)
Aluminum
Arsenic
Cadmium
Calcium . .
7.7
46,000
40,000
1,300
3,800
2
N.D.a
1
220 :
2,100
93
8
0.7
0.04 '
13,000
Chromium
Cobalt
Copper
Iron
Lead
Magnesium
Manganese
.Mercury
Nickel
Potassium
Silver
Selenium
Sodium
Zinc
<0.1
<0.4
1.0
28.0
<0.2
1,400
5.6
; <0.01
<0.5
600
<0.02
0.6
1,300
0.9
N.D.
not detected
18
-------
for the remainder of the test. Other observations such as changes in
equilibrium, schooling, and distribution of the fish in the tank were
recorded.
Data Analysis. Lethal effects of the toxicants were determined by
calculating LC50 values at several time intervals for each test. All
calculations were made on the ERL-D1s PDP-11 computer using the trimmed
Spearman-Karber Method (Hamilton et al., 1977).
Analytical Techniques for Toxicity Test Chemicals. Concentrations of
toxicity test chemicals are regularly determined in the exposure chambers
during each test. Water samples for the polar chemicals were collected and
analyzed either by direct aqueous injection on a gas-liquid chromatograph
(GLC) or by UV-spectroscopy. GLC analysis was performed on a Hewlett Packard
Model 5730A gas chromatograph with dual flame ionization detectors. The
instrument was equipped with a 122 cm x 2 mm I.D. column packed with the
porous polymer, Tenax-GC (Applied Science, Riviera Beach, Fla.). The flame
detector was operated at 300ฐC and inlet temperatures were either 200 or
250ฐC depending on the compound being analyzed. Nitrogen was used as the
carrier gas and hydrogen and air were used for flame operation. Nitrogen,
hydrogen, and air flow rates were 15, 25, and 240 ml-min"'-,
respectively.
Peak area calculations were performed by a Hewlett-Packard Laboratory
Automation Data System. Gas chromatographic parameters for each compound
analyzed are listed in Table 2.
Standards for alcohols, ketones and aldehydes were prepared in Lake
Superior water or distilled water just prior to each test. No differences
were noticed in standards prepared in either lake water or distilled water.
19
-------
Table 2. GLC Parameters and Analytical Quality Control Results for Test Compounds
Test Compound
Ethanol
2-Propanol
l-Butanol
1-Hexanol
1-Octanol
1 Decanol
1 -Methy 1 - 1 -propane 1
2-Chloroethanol
2,2,2.-Trlchloroethanol
2 ,3-0 1 bromopr opano 1
2-Mathy 1 -2 , 4-pentaned lo 1
Cyclohexanol
2-Phenoxyethanol
Acetone
2-Butanone
2-Octanone
2-Decanone
N> 4 -Methy l-2-pentanone
5-Methy 1 -2-hexanona
6-Methy l-hepten-2-one
2 , 4-Pentaned 1 one
Cyc 1 ohexanone
Cyclohexanone
Ethanol
Buta-nol
1 sotherma 1
GLC
Temp (C)
100
100
130
180
185
205
130
140
180
205
200
180
225
100
130
185
205
165
165
200
165
180
180
80
120
Retention'
Time
(Win)
0.95
2.11
2.80
1.13
3.52
3.9
1.1
1.7
1.6
2.7
1.0
1.7
2.1
1.95
1.6
2.1
2.9
1.1
2.0
1.54
1.8
2.0
2.0
1.6
2.3
Attenuation
5,120
1,600
640
40
3 ,:
2
160
8
16
8
12,800
40
10
3,200
640 .
8
2
160
40
16
8 '."
80
80
4
' 2
Working2
Range .
(ug/mT1)
2,000-25,000
1,800-30,000
300-10,000
15-400
30-32
0.6-10
60-1,900
12-300
48-480
5-130
2,500-25,000
70-1,200
25-900
1,500-25,000
600-5,000
5-80
0.4-12
220-4,400
120-2,000
18-200
7-390
80-1,300
80-1,300
10-200
10-120
Mean % Recovery
X + s.d.
n = 4-5
99.3 j* 1.2
-
-
103.6 +_ 9. 1
94.6 jf 4.9
104.6 +_6.2
95.8 _f 4.6
104.1 +_ 18.3
94.8 _+ 9.0
99.4 +_ 10.6
100.3 _+ 1.3
100.7 +_ 1.02
102.8 _+ 3;9
105.7 +_ 2.2
110.8 _t 4.1
121.4 i 4.4
89.7jH 9.6
104.4 +_ 3.0
122.4 _+ 2.8
112.2^5-2
108.5 _+ 13.8
101.6 +.2.3
100.3 _+ 3.9
104.4 +_ 1.7
99.6 + 3.3
% Agreement
of Dupl Icates
n = 4-5
98.1 +_ 1.4
97.6 +_ 2.2 ,
97.4 jf 2.2
95.6^3.6
97.8 _+ 2.2
97. 1 +. 3.3
94.8 _+ 3.8
95.0 ฑ_ 5.3
97.3 jt 2.9
94.2^4.3
98.9 _+ 0.9
98.7^0.4
94.8^ 4.4
99.0 +_ 0.3
98.4 jf 1.0
90.0 i 11.3
90.1 _+ 8.8
97.2 +_ 1.4
92.5 _+ 6.6
98.8 *_ 0.8
93.3 ฑ 4.5
98.6 +_ 1.1
98.0 _+ 1.0
99.3^ 0.8
96.9 + 1.9
2 working range Is dependent on toxlclty exposure levels and not on GLC capabilities.
-------
For water soluble compounds (>100 ppm) standards were prepared by.direct
addition of the compounds into water. Stock solutions of compounds with
lower water solubility ซ100 ppm) were made in acetone or metHanoi. Working
standards were made by diluting the stock solutions with water. The solvent
concentration in the aqueous working standards was kept below 10% (V/V).
Quality control consisted of a duplicate and a spiked water sample with every
set of 6 to 12 samples. Spikes were prepared in the same manner as the
standards. The spikes provided information on the accuracy of the standards,
the stability of standards with time, and the reproducibility of spike
preparations.
D. Experimental Results
During the first phase of this project, in excess of 80 chemicals have .
been tested. The absence of molecular descriptors such as log P in the
literature precludes a detailed analysis of all data at this time. However,
to test the hypothesis described in the previous sections, a subset of 37
alcohols, ketones, ethers and alkyl halides which appeared to cause death by
physical toxicity and for which log P estimates could be made were
considered. The 96-hr LC50 for these chemicals are presented in Table 3.
The data show that the LC50 (expressed as mg/l) ranged from 28,200 for
methano1 to 1.53 for hexachloroethane. The log P for the chemicals ranged
from approximately -0.85 for methanol to 5.6 -for tridecanol. The 96 hr LC50
for tridecanol is greater than the water..solubility of this chemical.
The data in Table 3 are plotted (converted to moles/liter) vs. the
respective log P values in. Figure 5 which is similar to the general
description of the aquatic "testing space" described in Figure 3. The water
solubility line is calculated from the- equations presented by :Hansch, '
excluding alkanes. With.in experimental error, the 96 hr LC50 decreases
21
-------
Table 3. Summary of Acute Tbxicity Tests
96 Hour Tests with Fathead Minnows
Chemical Name LC50 (mg/L)
N-Butyl ether .. 32.5
Phenyl ether . 4.0
Tetrachloroethylene 13.5
1,1,2,2,-Tetrachloroethane 20.3
Pentachloroethane 7.3
1,1.,2-Trichloroethane . 81.7
1,1,2-Trichloroethylene 44.1
1,2-Dichloropropane 139
1,2-Dichloroethane 118
Hexachloroethane 1.5
1,3-Dichloropropane 131
Methanol . 28,200
Ethanol 14,800
2-Propanol 9,640
1-Butanol 1,740
1-Hexanol 97.2
1-Octanol 13.4 '
2,2,2-Trichloroethanol 298
2,3-Dibromopropanol 71
2-Methyl-l-propanol 1,460
2,4-Pentanedione 96.0
2-Methyl-2,4-pentanediol 10,700
22
-------
Table 3. (Continued)
Chemical Name
LC50 (mg/L)
Cyclohexanol
2-Phenoxyethanol
1-Decanol
Tridecanol
Acetone
2-Butanone
2-Octanone
5-Methyl-2-Hexanone
4-Methyl-2-Pentanone
Cyclohexanone
2-Decanone
3,3-Dimethyl-2-butanone
5-Nonanone
Acetophenone
3-Pentanone
705
346
2.3
No toxicity at
solubility
8,140
3,200
37.0
158
509
630
5.7
86.0
31.0
161
1,530
23
-------
Linearly with increasing log P (i.e. toxicity increases linearly with log P)
at least until log P ฐ 3 is reached. In this region of the testing space for
these chemicals producing narcosis, the 96 hr LC50 appears to be a constant
proportion of the expected water solubility as suggested by the previous
discussion. Although, it is premature to propose a constant factor to
estimate toxicity of narcotics, the LC50 for the group of chemicals appears
to be approximately 0.017 of the water solubility.
The LC50 values for chemicals'which have log P values near 4 appear to
be much closer to the water solubility. I-t is proposed that this is the
region where the "parabolic effect" is evident. The data point plotted just
above the solubility line is that of tridecahol which is non-lethal to fish
in a saturated solution of the chemical. The LC50 for decanol is below water
solubility (log P 4.07) which suggests the "toxicity line" and the "water
so-lubilty line" cross between log P of 4 and 5.5. Chemicals with log P
values in this range are scheduled for testing next month to better define
the toxicity relationship in this range. At the same time, a computer
program incorporating the bilinear model is being developed specifically for
these data.
In preparing Figure 5, seven chemicals appeared to have anomalous
behaviour. Four of these were found to have incorrect log P values reported
including an apparent typographical error of log P-.= -1.70 for decanol.
Corrections of these errors -removed apparent discrepancies. The apparent
outlying point in Figure 5 with an LC50 of -3.0 is that of 2,4-pentanediol.
The log P value for this chemical was estimated using the substituent
constant approach. Since 2-methyl-2,4-pentanediol is not anomolous, it is
reasonable to expect these discrepancies can be evaluated when more reliable
log P values are available.
24
-------
0
-D
N3
Ol
-3
|moles/liter{ . -,
-4
' ' ' ,-5
-7
-8
-2
dฉ ฉ
?
j 'i'.l.'J ' 1 ' i
I 1 I
LOG P
Figure 5. Relationship oiTLog LC50 and Log P for Narcotics
8
-5
-6
-7
-8
jmoJes/8itef|
-------
Two chemicals which were clearly below the "physical toxicity" line were
2-chloroethanol and ir-amino-2-propanol, both of which were much more toxic
than would be predicted from this model. 'These chemicals (not plotted) both
produced symptoms different from narcosis including bursting of the eyes from
the fish. ' These chemicals are somewhat similar in that a single chemical
substituent is located on the carbon adjacent to the carbon with the hydroxyl
group. Tests are underway to evaluate the relationship between substitution
patterns, metabolism, and lethal effects. This may be a good example of how
physical toxicity is masked by an irreversible chemical toxicity which is
observed in concentrations well below concentrations needed to produce
narcosis.
26
-------
V. MATHEMATICAL METHODS FOR QSAR
The development of Quantitative Structure Activity Relationships (QSAR)
and pattern recognition techniques for drug design have grown rapidly since
the Hansch and Free-Wilson methods and discriminant analysis (Jurs et al.
1969) were introduced. After computer time and statistical expertise became
available, refinements and new methods were added to the organic chemist's
analytical techniques. Free-Wilson, linear free-energy and bilinear models
are separated from the other techniques only for convenience since all models
and analyses share many common features. In discussion of each approach,.the
development of the mathematical model rather than chemical application should
be stressed.
No matter how elegant the data treatment, the results will only be
useful if the chemical and biological thinking is sound. Statistical
significance will never replace intuition and logical thinking. Within a
homologous series, regression analysis is the simplest method to show
dependence of one variable on another. Usually the coefficients of
regression analysis can be understood in terms of the independent parameters
which allows the chemist to relate these parameters to biological
mechanisms(s) and to make new predictions from the regression equations. If
there is a choice of multiple regression methods, all possible subsets gives
the best results. If nonlinear regression is available, the bilinear model
best describes the apparent parabolic effect of biological activity on
hydrophilic parameters.
When pattern recognition is used to describe large, diverse data sets,
the first choice of methods should be factor and cluster analysis.' Martin.
and Panas (1979) propose useful criteria and steps for. using these methods in
series design. Discriminant analysis can be useful when seeking broad
27
-------
classification, but consideration should be given to the limitation and
interpretation of results. Finally, all statistical analyses should include
correlation tables of -descriptor variables and -development of equations
(Ogina et al., 1980; Wu et:al;, 1980).
Free-Wilson Model. The Free-Wilson :model assumes .that each substituent
makes an additive contribution to the biological response (BA) . The model is
expressed in equation 1,
where u is an overall average, and Gฑ: is the contribution of the
substituent X^ in position j. With the additive assumption, each
substituent effect at- each binding. .site. can;be' estimated by multiple
regression analysis. The biological and -mathematical assumptions and.
implications as well as.- modifications , of the Free-Wilson model are summarized
by"Kubinyi and Kehrhahn- (1976a,b) . They: have shown, that the FujitarBan model
has'; several advantages . over the .classical.! Free-Wilson and Cammarata, models.
In summary, the Fujita-Ban model require.s. fewer restrictions on the-:. data
matrix, gives estimates of the theoretical predictive activity value of the
parent compound, shows lack of linear -dependence and has a close mathematical
relationship to the Hansch linear- free energy model. Schaad and Hess (1977.)
reviewed the theory of linear equations and suggest ways to detect .and
circumvent linear dependence difficulties for all variations of the
Free-Wilson model. Two general- problems with the classical or modified
Free-Wilson model is the inability to generalize biological mechanisms for a
class of chemical compounds and' the difficulty of preparing the data matrix
before analysis.
Hansch Linear Free-Energy Model. The linear free-energy model has been
used and modified numerous times . The model assumes a linear and additive
28 : : '-
-------
contribution from the hydrophobia, electronic, and steric properties of a
drug or chemical compound on biological activity. Usually the log of the
inverse of the biological response (log ฃ) is correlated to various
physical-chemical parameters. The general form, equation 2,
log J = a^i + bjEj + ckSk + d
where the hydrophobic (H^), electronic (Ej) and steric (Sk) effects are
estimated by multiple linear regression coefficients a^, bj, and c^
respectively. The ease of calculation, availability of canned programs, and
potential for describing biological mechanisms led to numerous publications
of correlation studies.
Most problems that arise from using multiple linear regression are
related to the choice and number of variables that are used to describe the
biological response. The ratio of observations to variables should be
greater than five. Topliss and Edwards (1979) have developed control charts
for determining the probability of a chance correlation'based on the number
of observations and variables used in regression. These charts are based on
the total number of variables that were tried as well as taking into account
the collinearity of the descriptor variables. Unger and Hansch (1973) and
Otto et al. (1979) have stressed the point that the investigator should not
maximize the correlation coefficient at the cost of prediction by including
more variables than can be supported by the data. Draper and Smith (1966)
offer numerous statistical methods for evaluating the usefulness of
regression coefficients and equations. The interdependence between physical
parameters must also be kept to a minimum if correlations are going to
describe different biological mechanisms of action. Craig (1971) and Hansch
et al. (1973) have tested the interdependence of numerous substituent
parameters by correlation analysis.
29
-------
To' further improve the interpret at ion of correlation s-tudles, linger and
Hans'ch (1973) have presented criteria' for' selecting the' "best equation".
They have also listed the"minimal information that needs to be included in
all multiple linear regression analyses". Otto et al. (1979) have described a
method that validates the final regression model. By applying the
"leave-L-out" technique, they were 'ab'le to 'show' remarkable stability in 'their
regression equations by keeping'' the nuiab"er Jof variables to a minimum.
Recent papers by Iwam'ur a (1980) and Wu "et !al." '(1980) have included
useful tables with a comple'te" cross 'correlation of 'variables and the
development of equat'ions*." Heymans">et al. '(1980) 'fdundHa global stefic effect
in MR'(molar refraction)' but "coii'd have improved their interpretation 'by--
using 'an indicator variable' for steric ef feet's "atM* an independent value for
es't'imating'-'the hydropKob'ic 'ef'fe^ct'XB'^ag'i"^^ 'aft1, "198b!;'Wu'et al . , 1980) .
Botli 'Biagi et al. anS Wu' et*"atV 'fdu'nd'1 tn^a't^a^stericr'indicitor' variable 'was"
superior than MR since 'MR waV'higty Xorre''^^^' with log" P (Hejrmaris ''et al'. ,
1980 also reported this result) . Thev"in? t'.n^iiH "t'"i '' ' ' . ;.
authors to test for specif ic "steric1 "effects Jthat would 'otherwise be averaged'
out by MR. These analyses 'are Tsim]p"l>if ie"dr"verssi%ns>'of the mixed model
suggested by Kubinyi and" Kehrh''^^!!'^!^^^; "wh'ere "a Fujita-Ban arid Harisch model
are combined. Biagi et' al. (l^St))' 'als'o fou'nd' "that the regression technique
of all possible subsets "'was''superiorr"to'"forwar'd "stepwTse regression. One
final problem with variable select ion''is" the'^sp'read'^or'standard deviation of
the variable. Unless there is "an adeqiia'te'range' for each variable used in
the "regression model then 'there is little chance"of detecting the biological
response to 'that variable (Martin arid" Panas^ 1979).
Bilinear Model. The'linear refatioo'snip between biological activity and
the hydrophobic feature (log^P) of a compound will not continue indefinitely.
: 30 " .-,:.;
-------
Hansch and Fujita (1964) suggested a parabolic regression model to explain
the nonlinear biological response to the increasing hydrophobic effect of a
compound. To account for the strictly linear response for lower log P
values, Kubinyi (1977) has proposed a generalized bilinear model, adapted
from the McFarland (1970) probability model,
log ฃ = a log P - b log (BP + 1) + C
This generalized model generates curves with unsymmetrical, linear ascending
and decending sides and a parabolic portion around the optimal value for log
P. Through residual and statistical comparisons of 57 data sets, Kubinyi has
shown the bilinear model superior to the parabolic model proposed by Hansch
and Fujita. Kubinyi found situations when the parabolic model gives a better
approximation of the biological response (few data points or small range for
log P). This result can be attributed to the number of variables (4 versus
3) and calculation procedure (nonlinear regression).
Drug Design and Classification. The demonstrated relationship between
biological activity and physical-chemical properties of chemical compounds
has led to the search for methods that identify promising new leads. These
methods can be separated into two categories, pattern recognition and
non-mathematical searches.
(a) Pattern Recognition - Within the general field of pattern
recognition, three categories can be found; linear discriminant analysis,
cluster analysis and factor analysis. One of the first uses of discriminant
analysis was to classify mass spectra (Jurs et al., 1969). Methods used for
discriminant analysis include positive feedback functions (linear learning
machines), linear discriminant functions, K-nearest neighbor, and threshold
logical units. There are numerous examples that use discriminant analysis to
31
-------
classify biological activity into two or three categories (Chu et al., 1975;
Rasmussen et al., 1979; Henry and Block, 1979). All method of discriminant
analysis, except K-nearest neighbor, attempt to divide a multidimensional
data array by a hyperplane. K-nearest neighbor measures the distance between
two or more positions in hyperspace (Albano et al., 1978). To improve the
linear separation and reduce chance correlations, various data treatments
were devised that verify the usefulness of the linear discriminant function
(Kowalski and Bender, 1974; Weisel and Fasching, 1977). Before analysis data
should be autoscaled, i.e. normalized to a zero mean and variance of one.
After scaling, a subset can be used as a training set and another subset for
verification of the discriminant function. Another common way to verify the
discriminant analysis is by the .Jackknife (leaye-one-put) iterative procedure
(Weisel and Fasching, 1977).
" There are numerous factors that need to be-considered before'doing a
discriminant analysis. Frank et al. (1965) and Morrison (1969) have
summarized the difficulties in interpreting results from a "canned"
statistical program. These considerations include equality of numbers and
covariance matrices of observations between classes and the upward bias that
results from verifying results with the same measurements used for
classification. It is also true that the discriminant function may not be
the "best" equation, since many solutions- are possible for any data set.
Gray (1976) found that the "noise" feature will influence the final
discriminant function. Usually the ratio of observations to variables should
be greater than three to avoid unstable (data dependent) discriminant
functions.
A good example of how to properly use and discuss a discriminant
analyses is given by Ogino et al. (1980). They have included tables of
. , 32
-------
variable correlations and the development of the linear discriminant
function. These tables, which are similar to the tables used by Biagi et al.
(1980), and Wu et al. (1980), are absolutely necessary for comparison and
interpretation of the discriminant analysis.
Hansch and linger (1973) were the first to use cluster analysis as a tool
to find the most different analogue for a lead molecule. They used
extrathermal substituent parameters as their factors and chose one compound
from each cluster for synthesis. Cluster analysis uses the correlation
. matrix to find patterns of similar structure. There.are numerous methods
that group variables by the size and interdependence of their correlation
coefficients. The outcome of any cluster analysis is dependent on the choice
of information that goes into the analysis. Hopfinger et al. (1979) were not
able to explain toxicity except in terms of molecular weight. This
illustrates the need to choose factors that are orthogonal (not highly
correlated) and descriptive of the chemical factors influencing toxicity.
The indiscriminant inclusion of meaningless factors (unrelated to the
biological property) will lower the predictive capability of the analysis and
increase the possibility of getting a useless but successful classification.
The branch of statistical science called factor analysis has been used
by psychologists since 1904. It wasn't until Cammarata and Menon (1976)
suggested a classification scheme for handling large bodies of information
that factor analysis was used to describe toxicity data. Their flowchart
included factor analysis, discriminant anlaysis, cluster analysis, and
finally correlation analysis to follow up a new lead compound. Several
authors (Saxberg et al., 1978; Albano et al., 1978; Dunn and Wold, 1978) have
compared factor analysis with discriminant, analysis and. h-ave-'proposed a
hierachy of classification.ability with discriminant analysis'on the lowest
33
-------
levei and factor analysis on Che highest.- Martin and Panas (1979) have.
proposed another strategy for exploring new "leads" in drug design. Based on
a four part criteria, Martin and Fanas suggest that factor analysis and
cluster analysis will measure independence and uniqueness of each chemical
compound.
Just as in any pattern recognition method, selection of variables is
important. Martin and Panas (1979) state that the standard deviation of each
variable be greater than one. They also suggest that the variables be tested
for independence by factor analysis. Malinowski (1977a,b) has developed
several methods for evaluating the usefulness of each factor by examining the
error in the data matrix. Martin and Panas (1979) selected the number of
factors used for classification by examining eigenvalues.
(b) Non-Mathematical Searches - Two non-mathematical approaches to
optimize a lead chemical are the Topliss manual method (Topliss, 1977) and a
Fibonacci search (Santara and Augary, 1975). Both methods select and rank a
set of analogues by molecular properties usually found in a Hansch
correlation study. The most active analogues are selected for synthesis
based on the relationship between biological activity and physical
parameters. The. advantage of both methods is that neither requires a
computer for analysis. Martin and Panas (1979) evaluated the Topliss manual
method by factor and cluster analyses. They'found that the .strategy w.as
useful but not ideal for finding the least!number of compounds that covered
the substituent space.
Perspective. The literature is replete with computer programs with
intriguing names which are reported by their developers as QSAR methods. In
general, the programs consist of one or more of the techniques found in
systems such as BMDP which are available in many computing centers. In some
34
-------
cases, such "canned" programs are tailored wich respect to input/output
routines to aid the analyst. The specific program used is dependent on the
types of questions being asked.
This project will be evaluating many of the techniques discussed above
using systematic data sets to demonstrate the relative contribution of each
in addressing EPA needs.
35
-------
VI. MOLECULAR DESCRIPTOR GENERATION , .
Attempts to predict biological" or chemical'act'ivities from structural
characteristics have involved well over 30 different characteristics which
are loosely called molecular descriptors. Common'descriptors, or predictors,
of activity include log P., Hammet constants, Taft steric constants, molar
volume, molar refractivity, CIDS keys, molecular orbital parameters, and
connectivity indices. In many cases, each type of descriptor includes a
family of parameters. Some descriptors can be selected to model certain
activities because the descriptor is an important factor mechanistically in
the process being modeled. However, many times the descriptors are selected
on a statistical basis and their importance are rationalized as the model
develops.
-To keep this QSAR project directed toward the Agency's need for
"screening thousands of industrial chemicals, several constraints were placed
'on the selection of molecular descriptors. The first is to minimize the
number of molecular descriptors which are used as independent variables in
the QSAR models. The literature demonstrates clearly that there are few
descriptors readily available for large numbers of industrial chemicals, and
building models which require several descriptors further reduces the number
of chemicals for which estimates of activity could be made from the models.
the second is that molecular descriptors be selected where possible, which
can be calculated directly from the structure of the chemicals. This would
permit extensive computerized evaluations of the QSAR models on large numbers
of chemicals.
36
-------
A. Calculation of Log P
The discussion in previous sections of this report has demonstrated the
usefulness of log P in estimating toxicity and BCF. Log P has been selected
because it is a measure of the hydrophobic properties of the chemicals which
is directly related to transport and binding in membranes. Moreover, the log
P for thousands of chemicals can be calculated with adequate accuracy from
structure. A project was developed with Dr. Al Leo and Peter Jurs to
computerize the log P calculations. When a lack of funds prevented the
timely initiation of the project, the Office of Toxic Substances funded the
project. It is anticipated the first version of the program will be
available to EPA in November, 1980. This project stands ready to evaluate
the calculations on a subset of 15,000 industrial chemicals when the program
is received.
B. Calculation of Molecular Connectivity Indices
Molecular connectivity indices are a family of constants which result
from the application of graph theory to molecular topology. A review of the
calculation and examples from use of these indices in physical and medicinal
chemistry has been presented by Kier and Hall (1976) and is beyond the scope
of this report. Briefly, the connectivity indices are calculated by
subdividing each molecule into a series of subgraphs with the atoms as
"vertices" and the bonds between them as "edges". The subgraphs are
categorized as paths, clusters, path-clusters, and chains which are
self-explanatory. The subgraphs are further divided into connectivity orders
which are the number of edges. In general only the first four orders of
indices have to be used in QSAR due, in part, to the difficulty in
calculating the higher order indices for multicyc.lic'molecules. For
example,
37
-------
endrin has in excess of .1,000 sixth order subgraphs, each of which muse"be - .
analyzed individually and summed to give the individual sixth order, path,.
cluster, path-cluster, and chain indices.
Kier and Hall further divided the indices into simple and valence-
corrected indices. The simple indices are calculated as if all the atoms
were carbon atoms but all bonding was the same as in the original molecule.
The valence-corrected indices apply empirically derived correction factors
for each type of heteroatom present in the molecule. The valence correction
is intended to describe the variations in electron density around the
heteroatom which may contribute to the reactivity of the molecule. The
terminology employed is X to represent the simple indices and Xv to
'represent the valence-corrected indices. An additional superscript denotes
the order such as ^Xv being the fourth-order valence-corrected indices.
If no subscripts are present, it is implied that the index is the sum of all
the paths, clusters, etc. of that order. If individual indices are used, the
subscripts "p", "c", "pc", and "Ch" denote the path, cluster, path-cluster,
and chain indices, respectively. As an example, Xvpc represents a
sixth-order valence-corrected path-cluster index.
A program has been developed by this project to calculate indices
through the tenth-order indices for molecules including the multicyclic
^molecules such as mirex and endrin. The program calculates the
valence-corrected indices, the simple indices which is the molecule reduced
to a hydrocarbon of identical topology, and framework indices which is the
molecule reduced to a saturated hydrocarbon of the same topology. An example
of the data for molecular connectivity indices from this program is presented
in Figure 6. The molecule considered is 2,2-dichloro-l-(2,4,5-trichloro-
phenyl) ethanone which has the structure indicated in the upper left. The
38
-------
!- i-( >, ! .s-iv iciii.'ii.iii'HK'm. ji-'i
K'lu UN 24-SF:f-bO
ฃIIIIGKปHIIS:
a ง CI
0
0 1
PATii 14 14
CI.
CM
TllTAl. 14 14
tt Mi ':' ft CliKKLCrKI) OIllNiiCTlV ITY IMllTlKS:
0 1
MitPi.K <'UMnKCn VI TV 1 Mil 1 C 1 KS> :
m 2. i I'LiTM.:. W.'lj')^ b.20lf.
51ฐ"! ri'AMc.piH'ri i:n,,Ni:.rri iMTY ninirK.S:
' /'fill
234
4.<ป7f>y 4.122S 3.6B13
~i 3 4
4.4313 i.USjti I.6l7b
.
4.4313 3.f"ซS 3.3bOO
2 3 4
h.lVO-/ 4.Mln7 3.1147
-lIHDt.K---
2 3
4
2n 24 2u
b 0
0 0
20
20 30 4o
S
1.3073
0.27H3
o.oooo
i.87yo
3.^Sy7
5
l.lluO
O.OOuO
1.7040
J.07IS
S
0 . b 3 3 9
u. 00(10
3.7001
b
o.fiyon
o.oooo
2.84lu
3.4b?a
6
O.S09J
0.0000
0.020o
2.<>b5-*
t>
0.0000
O.OSSo
1 1 A*SII
b 6 7
1 u 24 1 H
1 0 i
u 1 4
3ป H7 133
73 112 1-57
7 a
0.^8B4 O.Obfly
O.U365 O.OOOi)
o.oC57 O.I38S
2.7798 7.5ttOb
3. l'ป04 2.HUBO
fKEDUCRu
7 U
0.233b 0.0703
0.046S O.OOOO
O.U721 0.1071
b y
10
920
010
u 13 iy
184 2CH 179
201 224 19U
0
0.01/4
0.0171
O.l3b7
2.2046
10
0.0000
0.0000
0.13B6
1.6133
2.37bO t.7biy
in imiHOCArtHuNJ
4
0.0120
0.0170
O.lOoS
2.b707 2.2784 I.b3b1
(i(Fi)MCKI/ TO SATUI'ATtn lllDhf
7 b
0.i>?'*S 0.2091
0.1341 0.0000
0.19t>7 0.313b
o.2f>63 6.2bft3
q
0 . 0 3 V 0
0.0556
0.35S/I
b.4fiyi
K U ') tt fi
10
o.oooo
0.0000
o. i i 3y
1.1b42
1 .2681
10
0.0000
o.oooo
0.40M
3 . 9 '/ 7 y
4 Ixdli
Figure 6. Connectivity Indices for 2,2-dichloro-l-(2,4,5-trichlorophenyl) ethanone
-------
table in Che upper right summarizes' the number of subgraphs in the molecule.
The molecule has 14 atoms as evidenced.by. the 14 zero-order subgraphs. .The 6
third-order clusters are located at the six carbon atoms which are substitut-
ed. The phenyl ring obviously accounts for the sixth-order chain. The lower
half of Figure 7 presents the individual and summed indices for this
morecule.
Dr. David Weininger and Brad Greenwood at ERL-D have a complete package
of programs called INDEX which allow structures of chemicals to be entered
either manually on a remote TTY or with a graphics terminal, to check the
molecule for proper valencies to calculate a complete set of up to tenth
order indices, file the files along with other data on chemicals for input to
programs such as BMDP or other statistical packages. Approximately 800 chem-
icals which are part- of one.of the QSAR'data;bases have-been-entered'into the
system.
The INDEX system is a group of programs, command files, and structured
data files which currently allow:
-interactive graphic molecular structure specification,
-verification of "foreign" files of molecular structures,
-hard- or CRT-graphic display of chemical structures on files,
-computation of connectivity indices of the Ocn to lO'*1 orders',
-storage of connectivity indices for up to 20,000 chemicals in
i
compressed form (the master file), ' , . ^
-a variety of options for retrieval from the master file,-
-generation of master file table of contents ordered by location or by
empirical formula,
-automatic detection of duplicate entries on master file,
-encoding an additional parameter along with the connectivity indices of
selected compounds for subsequent BMDP analysis,
, . 40 . . ..
-------
Figure 7. Flow-Chart of Program INDEX 5
FLOWCHART OF PROGRAM
Zero out all arrays.
T
Attempt to read in molecular structure
from .[IF-type input file.
Set up topologica! matricies.
Check validity.
Make valence corrections.
Calculate empirical formula.
Generate all exclusive
combinations of atoms.
Is
NO /suograph \ YES
connected'
Write results to output
file INDXOUT.DAT
Output results to line
printer 1 molecule/page
Evaluate the entire
subgraph formed by this
combination of atoms.
Break all exclusive
combinations of bonds,
creating disconnected or
less cyclic subgraph.
Evaluate the hidden
paths in the chain.
YES
Evaluate the suograph
which was hidden.
41
-------
-locating the nearest neighbor (and most distant) of the master file
compounds in connectivity factor space,
-and several other utility operations.
Additional capabilities expected to be implemented by 11/80 include automatic
master file backup and a routine for determining if a given molecular
structure falls inside or outside the hypersolid defined by a group of
molecules in factor space. A general description of the INDEX programs and
files follows.
Creating and Handling INDEX Input Files. The first step in the process
of molecular structure description is unambiguously specifying a molecular
structure. The format used in INDEX is an extended, linearized connectivity
matrix; files containing lists of molecular structures in this format can be
identified by the file'extension ".IIF".(Index Input, Format). Although not
yet tested, the translation of "foreign" topological matrixes to IIF format
should be straightforward.
Program MOSS (MOlecular Structure Specification) is intended to be the
primary means of initially specifying molecular structures. MOSS is an
interactive graphics program designed to be run on a Tektronics 4025 Graphics
Terminal. The program outputs the structures of graphically displayed
molecules in a verified IIF format file. Verification includes checking atom
types and valence validity and extended topological matrix symmetry. The
user may specify the entire structure or modify any structure stored on an
IIF file. It is up to the MOSS user to verify that the correct name is
associated with a molecular structure.
Creating and Filing Connectivity Indices. The program INDEX5 reads
molecular structures from an IIF file, computes molecular connectivity
indices, prints tabulated results, and writes the results in the file
42
-------
INDXOUT.DAT. The program uses a general algorithm for connectivity index
computation; the following limitations are imposed by available valence
corrections, computation time, and storage effectiveness considerations:
-allowable non-H atoms: C, N, 0, S, Br, Cl, I,
-maximum number of non-H atoms per molecule = 50,
-highest order connectivity index computed = 10, and
-highest allowable cyclic order of 11-atom-subgraph = 5.
The INDXOUT.DAT file contains the 100 character name, the coded
empirical formula, and the following information for each of. the 34 possible
subgraph types of orders 0-10: the number of unique subgraphs, the
valence-corrected CI, the simple CI, and the framework CI.
The formatted sequential nature of the INDXOUT.DAT file makes it
inefficient with respect to disk storage space. Therefore, the information
contained in INDXOUT.DAT is transferred to a compressed master file
(MINDEX.DAT) by the program LOADEM. A successful INDEX5 run is normally
followed by a LOADEM ruii. This two step process is used to ensure the
integrity of MINDEX.DAT in the event of a fatal error during INDEX5
execution.
Accessing Connectivity Indices on the Master File. All user access of
the master file must be done via program control because the file contents
are written in binary. The master file, MINDEX.DAT, is set up for "direct
access" which allows any record to be directly retrieved by specifying a
location.
Three programs are specifically designed to retrieve.master file
contents for specified chemicals. Programs GETEM and FILEM are essentially
identical programs except for the output file: GETEM prints the retrieved'
entries in their original tabular form, FILEM writes entries into the file,
43
-------
SHMIND.DAT. Boch programs require that the master file locations be
specified and provide a means of locating an entry-by empirical formula. Any
number of entries can be retrieved during a single GETEM or FILEM run.
A third retrieval program, PREPEM, is similar to FILEM but also has
these additional features: .. ,
-The input file can be the master file, the file INDXOUT.DAT, and/or any
other file in the INDXOUT.DAT format. ,
-An additional molecular descriptor can be specified (eg., "LOG P") for
each compound included in the output file.
-The molecular descriptor name and values are stored in the 1'ast 20
characters of the 100 character name.
-Any output file.name may be . specified. This file can be directly used
?f ' - ~ .. ~ ' .,-... _ '-
by any BMDP program as 'a data file. '. '-'.:' .-.V.'-,..-:';..''.' ' -
The program INTDIST is .used to find which.compounds in .the master file
.are'most and least distant from a given chemical in the 34-dimensional
Balance corrected connectivity space. The user can select the "given"
compound from the master file on any INDXOUT.DAT-type file. INTDIST will
list the nearest and farthest compounds :in the master file, automatically
. "** ' . ' ' ' . :.' :
discounting duplicates. :
Operation of the INDEX5 Program. INDEX5 is the fifth version of a
series of programs which compute molecular connectivity indices for organic
imoleculesi As discussed above, INDEX5 us'es a general algorithm for
.-.''.'"'. : , ' ' ' .' ' ซ
connectivity index computation. A flowchart of INDEX5 is presented in Figure
.8. Each molecular structure in an INDEX Input Format (IIF) file is
rtransformed initially into two topological matrices: the simple^,conhection\,
matrix which contains only 1's and O's (T), and an extended matrix which
44
-------
contains the topoLogical valence for each connection (PT). Valences are then
calculated based on both topological valence (number of connections to a
vertex) and elemental valence corrections.
The INDEX5 algorithm is vertex oriented. All exclusive combinations of
up to 11 atoms are generated and the connectivity index contributions are
evaluated for the subgraphs which include all atoms in-the combination only.
The decision to evaluate up to 10th order connectivity indices requires that
up to 11 atoms must be considered at a time. An efficient pseudo-recursive
(stack based) algorithm is used to generate the combinations. CONECT's high
efficiency is obtained by automatically suppressing generation of
disconnected combinations.
Each connected combination generated by CONECT is evaluated by the first
level routine EVAL. The entire subgraph (represented by all atoms and bonds
in the.combination) is evaluated. The term "evaluate" is used here to mean
"determining subgraph type and order, incrementing the ennumerative counters,
computing the subgraph index contributions, and adding these contributions to
the appropriate index accumulators". If the subgraph is cyclic, it will
contain a series of hidden subgraphs which must be evaluated. In the case of
a simple chain of N atoms (all topological valences = 2), EVAL evaluates the
N hidden paths of N-l order. In more complex cases, EVAL determines the
number of cycles in the subgraph and passes the hidden subgraph evaluation to
the routine CYCLIC.
The subroutine CYCLIC evaluates hidden subgraphs by using the following
theorem: "If an edge is removed from a graph which is K cyclic, the
resultant graph is either (K-l) cyclic or disconnected". Combinations of
bonds (edges) are broken such that not only are .the broken bond combinations
45
-------
exclusive, but so are the combinations of remaining bonds. Subroutine CYCLIC
is capable of breaking up to five edges of a subgraph simultaneously enabling
analysis of 5-cycle, 11-atom subgraphs. The above algorithm circumvents the
difficulties associated with multiple cyclic subgraphs; by breaking up to K
bonds of a K cyclic subgraph one is assured that all possible hidden
subgraphs will be uncovered (whether or not they are in themselves cyclic).
Responsibility for evaluation of the generated subgraphs is passed to EV-AL2.
Cyclic subgraphs generated by edge removal present a further difficulty.
It is not possible to directly determine from the topological valences
whether the subgraph is connected or whether it represents two connected
subgraphs which are disconnected from each other. A special routine, .ICHECK,
is designed to cope with this problem. ICHECK uses a "route-finding"
algorithm to ensure .that thefe~;is a route'from'every'vertex-to every other.
. Subroutine EVAL2 is similar to EVAL except that EVAL2 does not need to
determine cyclic order and does need to use ICHECK to' determine
connectivity. .
After all evaluations have been made for the combination of atoms
generated by CONECT, control is passed back to CONECT and the next
combination is generated. After all exclusive connected combinations of
atoms have been evaluated, CONECT passes control back to the main program for
output of results.
I . j
An output routine, OUTPUT, writes the. results to the file INDXOUT.DAT.
- ' ' ' ' ' i
This file is a formatted sequential file which contains: the molecule's name
and empirical formula, the data run, enumeration of each subgraph type and
order, and valence corrected, simple, and framework connectivity indices for
orders 0 to 10. OUTPUT also sends this information to the line printer in a
tabular form which requires one page per molecule.
46 .
-------
Glossary of-Programs and Files. All important programs, command files,
and data files used in the INDEX system are listed below with brief
explanations. Alphabetical order is used with the file extensions having
priority. In general, file types can be identified by their file
extensions:
.BIG - an IIF file contining "big" molecules only
.BKP - master file backup
.CMD - command file
.DAT - general data file
.FTN - Fortran code (used here for subroutines)
.IIF - INDEX input file
.MED - an IIF file containing "medium" molecules only
.SML - an IIF file containing "small" molecules only
.TOC - at table of contents file
.TSK - an executable task image of a program
Note that many programs are listed which will never be-run directly and some
files are listed which are transparent to the user. Almost all user
executions will use only .CMD and .TSK listings below. The program INDEX5
and its subroutines are not described in the glossary (see "Operation of the
INDEX5 Program", below).
xxxxxx.BIG - An IIF-type file written by the program SPLITTER which
contains only molecules with 23 atoms or more.
xxxxx.BKP - A backup file for the master file which was created on the
date "xxxxx" (e.g. OCT17.BKP). This file is in the
INDXODT.DAT format.
DFILE.CMD - Command for duplicate search of the master'file.
DSORT.CMD - Command for a PDS sort used i-n DFILE.CMD.
47
-------
PSTOC.CMD - Command to print table of contents of the' master file
ordered by location- or by molecular formula. The-master ..
file can also be output to the line printer in tabular form
with this command.
TOC.CMD - Command for a PDS sort used in PSTOC.CMD.
SHMIND.DAT - An INDXOUT-type file containing selected entries from the
master file written by the program FILEM.
DISCOM.FTN - This subroutine takes a given compound with its
connectivity indices from the program IND1ST, then searches
the master file computing the Euclidean distance for each
compound in a connectivity 34-space. The most and least
distant compounds are then output to the user's terminal.
MFORM.FTN , :!- This routine .outputskmole.cularv formulas to the line .- ;''
printer. . "' .
PUTOUT.FTN -, This routine takes an entry from the INDEX5 master file and
outputs it to the line printer in table form.
RITE47.FTN - This routine writes a sequential file with parameters in
the title field of each record retrieved from a user
selected sequential file in INDXOUT.DAT format.
RITEM.FTN - This routine retrieves an entry from the master file and
writes it to a sequential file on logical unit 3,.
RRITEM.FTN - This routine writes a sequential file with parameters in
the title field of each record retrieved from the INDEX5
master file. '
SEQDIST.FTN - See write up for DISCOM.FTN. The difference is that here
the initial compound is retrieved from a sequential file in
INDXOUT.DAT format rather than from the INDEX5-master file.
' 48
-------
xxxxxx.IIF - A file in Che INDEX input format. Contains coded molecular
structures. (e.g. PHENOLS.DAT)
MASTERxxx.IIF - An IIF file which is saved. Normally a number of .IIF
files are appended together to form the type of file (eg.
MASTERS.IIF).
xxxxxx.MED - See xxxxxx.BIG; .MED contains only molecules with 12-22
atoms per molecule.
xxxxxx.SML - See xxxxxx.BIG: .SML contains only molecules with 11 or
fewer atoms per molecule.
TOC.TOC - A transparent temporary file for input to a PDS sort
(TOC.CMD) used in the table of contents programs
(PTOC.CMD).
STOC.TOC - A sorted transparent temporary file output from TOC.CMD in
the table of contents programs.
BURP.TSK - This program initializes an available space list for the
master filing system of INDEX5.
CHECKERS.TSK - This program checks the validity of molecular structure
entries in an IIF file. The program is designed to be run
on a Tektronics 4010 terminal. Each molecule is drawn;
hard copies are optionally available. "Wheat" and "chaff"
filing optionally available..
CINAMES.TSK - This program prints the names of molecules on an
INDXOUT.DAT-type file.
DFILEM.FTN - This program is part of the duplicate entry search of the
INDEX5 master file. It writes the.entire master into a
temporary sequential file to be used in. a PDS SORT command
(DSORT.CMD). :
49
-------
DSFILE.FTN
FILEM.TSK
GETEM.TSK
HIBURP.TSK
IIFCOUNT.TSK
IIFNAMES.TSK
INDIST.TSK
INTMIND.TSK
LOADEM.TSK
This program uses the files created by DFILEM and DSORT to
Locate duplicate entries in the INDEX5 master file.
This program writes a sequential file in INDXOUT.DAT format
from sequentially or randomly retrieved compounds from the
master file.
This program retrieves entries in the INDEX5 master file by
location and outputs them to the line printer in table
form.
This program initializes the hash table for the INDEX5
master file.
This program counts the number of molecules in an IIF .file
and prints a frequency histogram of the number of atoms per
molecule. .. . . ' .
This program will print a list of the names of molecular
structures in an IIF file.
This program takes a user selected compound from the INDEX5
master file or a sequential file in INDXOUT.DAT format and
compares it to every non-garbage, non-duplicate entry in
the master file and computes the most and least distant
compounds with respect to the connectivity indices in
34-space.
This program initializes! the master file MINDEX.DAT for the
INDEX5 filing system.
This program uses the output file (INDXOUT.DAT) created by
INDEX5. After the data in the file has been verified, this
program is run to insert the entries into the master file.
50
ฉ
-------
MOSS.TSK - This is an interactive Molecular Structure Specification
program which outputs a verified IIF-type file. The user
can specify entire structures or modify any existing
structure already on an IIF file. Designed to be run on a
Teletronics 4025 terminal.
PREPEM.TSK - This program creates a user named output file with a
parameter name and value for each logical record in the
title field for the compound. Input can be taken from the
master file or any file in INDXOUT.DAT format.
PSTOC.TSK - This program prints out a table of contents for the INDEX5
master file sorted by location or molecular formula. It
can also output the entire master file to the line printer
in tabular -form.
PTOC.TSK - This program creates a temporary sequential file to be used
for input for TOG.CUD and PSTOC.TSK.
SPLITTER.TSK - This program splits an IIF file into three files (.SML,
.MED, and .BIG) based on the number of atoms per molecule.
WIPEM.TSK - This program deletes entries from the master file given the
entry location.
Despite the immediate usefulness of some connectivity indices and the
insignificant cost of calculating them for large numbers of chemicals, there
are two problems which must be solved before a complete set can be prepared.
The first is that the computer program operates from a connection table which
must be generated or purchased. Even if 10 structures/hour could be encoded
into the system, over 500 working days would be needed to input the TSCA in-
ventory in the connectivity program. An alternative would be to purchase the
51
-------
connection cables from existing files; however funds to accomplish this have
not been available to this project. The second limitation, is that adequate
valence correction factors have not been determined for all heteroatoms.
Consequently, valence-corrected indices cannot yet be calculated for
organophosphorus chemicals. This work can proceed if funding is available.
One of the potential uses of the connectivity indices in addition to
single preditions is the development of a "similarity index" for chemicals in
the _n dimensional structure space. The purpose of this project is to provide
the Agency with the capability to "indict" a chemical because of its
similarity to hazardous chemicals for which data are available. The
"similarity index" may evolve into a ranking system for testing priorities.
For example, each chemical structure can be represented by a single point in
_n dimensional (often'more than 30 dimensions) structure space. Chemicals
with similar properties form clusters in structure space. The distance of
any new chemical structure from a given cluster can be calculated to
'determine statistically whether the new structure is "inside" or "outside"
the cluster and, if outside, how it is distributed between clusters
representing contrasting environmental behaviors. This part of the QSAR
project is evolving as a "back-burner" project until funds can be redirected
from testing to develop it more rapidly.
For a more detailed account on possible 'uses of the indices, the reader
is referred to Kier and Hall (1976). As a summary, it has been shown that
the boiling point of classes of chemicals can be estimated from the following
equations:
52
-------
aLkanes b.p. = 55.691X. + 4.7084Xpc - 96.13 r = 0.9969
alcohols b.p. = ISl.SSix- 155. 23^ - 17.993X
+ 21.743Xvp - 2.743xc - 29.05 r = 0.99
alkyl halides b.p. = 36.36^ + 16.363Xp +
2.8693XVC - 31.037 R= 0.9982
The water solubility of alcohols can be estimated from
In S = 9.417 - 11.2661X + 8.643XXV r = 0.9945
Log P can be estimated from a similar series of equations such as for
aliphatic carboxylic acids
Log P = -0.859 + 1.6151XV - 0.550^ r = 0.9979
Similar equations have been developed for many biological activity endpoints
such as narcosis, conversion of cytochrome P-450 to P-420 in rat livers,
thymidine phosphorylase inhibition, microbial inhibition, vapor toxicities,
and potency of mutagens in the Ames test. '
53
-------
VII. QSAR INFORMATION SYSTEM . ..'.-.
An information storage and retrieval system was first begun as part of
the structure-activity studies. It was clear that any computer modeling
study would require the storage, retrieval and manipulation of large amounts
of physical, chemical, and biological data. Efforts to determine the
availability of a commercial or public information system capable of meeting
the needs of the project met with little success and work was begun on the
design of a local interactive information storage and retrieval system and a
corresponding data base.
The first data base contained only 248 compounds with the following data
elements: a local identification number, Chemical Abstracts registry number,
molecular weight, molecular formula, freezing point, boiling point, vapor
pressure, solubility in water', 'Wiswesser line notation (WLN), logarithm of
the partition coefficient (log P) , gas chromatographic retention data,
bioconcentration factors, acute aquatic toxicity data (LC50), and acid
dissocation constant data.
Initially the information storage and retrieval system was quite
primitive as searches could only be carried out for the following: one local
identification number, one registry number, a specific or ranged molecular
weight, a ranged, partial or complete molecular* formula, or a specific or
ranged log P value. The commands available sit that' time were:
SEARCH ' : COMMAND
DISPLAY END
CLEAR
In the next phase of system development an additional 2,000 compounds
were added to the data base along with new data elements such as maximum
allowable toxic concentration and 8th and 9th Chemical Abstracts Collective
54
-------
Index, systematic, common, and trade names. Many improvements were made in
searching capabilities, for example, an exact name search was added and all
properties could be "inventoried". Numerous new commands were added:
INTERSECT . SHOW
MERGE HELP
LIST STATUS
OUTPUT COMMENT
INFORM STOP
During this development the acronym ISHOW Conformation jjystem for jlazardous
ta-ganics in V[ater) was coined, a User's Manual prepared, and the system was
made available to the public.
Additional data were added to the data base and considerable effort was
expended in continued software modification to provide for rapid'searching
compatible with efficient storage. A major effort was the development of a
software manual along with complete documentation of the computer software.
ISHOW is currently undergoing another major change to test methods of
evaluating QSAR on large numbers of industrial chemicals. Using information
obtained from the Office of Toxic Substances, the ISHOW data base is being
expanded to contain an additional 12,000-13,000 compounds as a training set
of data. Through the support of EPA Region V an expanded ISHOW will contain
compounds manufactured or used in the Great Lakes watershed or EPA Region V.
Corresponding information on manufacturers or users of these compounds in the
Great Lakes is being added, along with new data elements which include
biochemical oxygen demand, chronic toxicity (aquatic), and the Kovats index
for gas chromatography data. Software to store and search these parameters"
is also being developed.
55
-------
The purposes of this part of the project are twofold. First, this
project is attempting to make data concerning the biological and chemical .
activities as well as key molecular descriptors available to many researchers
with the assumption that this field of study will progress more rapidly.
Second, it is attempting to develop and test predictive techniques as they
are developed on large numbers of chemicals such as those found in the TSCA
inventory. Such evaluations are judged to be essential if QSAR is to become
an acceptable screening technique in evaluating the environmental efficacy of
organic chemicals on a major scale.
56
-------
ACTIVITY REPORTS
A. Compilation of the Aquatic Toxicity Data Base
A review of the aquatic toxicity data in the literature indicated early
in the study that the amount of data as well as the quality of comparable
data precluded serous QSAR modeling attempts. This conclusion was a major
factor in the decision to generate a systematic set of toxicity data with
aquatic organisms. In the event that data in the literature can be used in
the QSAR models after the patterns begin to emerge from the testing program,
the QSAR project is compiling an aquatic toxicity data base.
The data are being compiled from manual and computerized literature
searches using the compiling form listed in Figure 8. The form was designed
with the cooperation of ASTM and OTS and is intended to meet the needs of
several computerized data bases. The data will be loaded into a computer
file at ERL-Duluth from which subsets of the data will be sent on magnetic
tapes to the ISHOW and HEEDA developers according to their respective needs.
To date, information on the toxicity of approxiately 700 chemicals has
been compiled. The data include tests on approximately 75 freshwater and
marine organisms. Acute tests make up approximately 675 of the tests and
chronic tests constitute about 75 of the tests. Fathead minnows, rainbow
trout, guppies, bluegills, daphnids, scuds, and stoneflies were most
frequently tested. In addition to the toxicity data, approximately 50 tests
have been reported for bioconcentration. At present ISHOW contains test
information on 350 chemicals and information on approximately 150 chemicals
is ready to be loaded into ISHOW. Tests have been performed on about 200
chemicals and/or mixtures for which Chemical Abstract Service registry
numbers have not yet been located.
57
-------
Figure 8. Aquatic Toxicity Data Compiling Form
CHEMICAL NAME
UimilAI AI1SIHACI HEGISIIU NIIMUEH (CAS ซ)
Ol
oo
IE SI UIIMICAI
Chemical Gratlo
III II ItlHCI
ItirtlllNCE
1(1 II HI NCI
IIIIIKINU
Source
INI) till
Effect
Cmlpoln
cr
:OMC.W
-------
B. Estimation of the Bioconcentration Factor
The measurement and estimation of the bioconcentration factor (BCF) in
fish have been reviewed by Veith et al. (1979, 1980). It has been shown that
the BCF can be estimated using the equations:
log BCF = 0.85 log P - 0.70
or log BCF = 0.76 log P - 0.23
depending on the data base selected. The accuracy of predictions made with
these QSAR models is limited by the inherent variances in the BCF test and
the variation of species tested in the data base. The equations were
intended for an estimation of the bioconcentration potential within an order
of magnitude for screening purposes. Chemicals for which metabolic routes
not reflected in the log P are possible may give BCF values lower than the
bioconcentration potential predicted by log P. For the majority of chemicals
tested the present predictive capability can estimate the BCF within a factor
of three.
Improvements in these predictive capabilities are being sought along
several fronts. The first is the continuing effort to compile BCF data into
a QSAR computer file which is interfaced with automatic plotting and
regression programs. This will permit frequent updating of the QSAR for BCF
at the request of the EPA program offices.
The second project is a re-analysis of BCF test data to improve the
calculated BCF values reported for individual chemicals using methods
recommended by OTS or ASTM. This effort began with a study of the BCF data
for trichlorobenzene, DDE, and hexachlorobenzene as part of an EPA
round-robin test validation program for OTS. A complete analysis of this
data will be reported elsewhere and summarized in subsequent progress reports
of this project.
59
-------
The third project is aimed at uncovering the variation of the BCF among
several species tested. . In an at tempt, to relate the BCF-of specific ... . .
chemicals between fathead minnows, bluegills, rainbow trout, etc., a
simplified bioenergetics model is being tested to determine the extent to
which the differences in observed BCF between species is dependent on
different metabolic activities of the species. Exposure equipment has been
constructed to measure the BCF and the oxygen consumption of fish
simultaneously. Since oxygen consumption may reflect the amount of water
pumped across the gills, it is hypothesized that this value may "normalize"
the BCF value for different species. This study will be discussed in more
detail when appropriate.
C. Estimation of Water Solubility
The solubility of many organic chemicals has. been estimted from the log
P by numerous investigators. Hansch et al. (1968) presented relationships
for 9 classes of organic chemicals showing that linear inverse relationships
exist over approximately six orders of magnitude of water solubility as shown
in Figure 9. If the data are separated into chemical classes, a series of
equations estimating solubility from log P are available with a correlation
coefficient greater than 0.96 for most classes and greater than 0.93 for all
classes.
Because of the importance of water solubility in defining the aquatic
toxicity testing regime as discussed previously and in the distribution of
chemicals in the environment, a computer data base of solubility data has
been established at ERL-D. Although there are insufficient funds at this
time to generate water solubility data in this project, an effort is being
made to update the data base from literature reviews. Subsequent reports
60
-------
Figure 9. Relationship between Log P and Water Solubility
(ฃ2)
O
-6
0
6
LOG P
-------
from this project will provide evaluations of the state-of-the-art in
estimating water solubility by QSAR.
62
-------
IX. EVALUATION OF RAPID SCREENING TESTS FOR QSAR
la Che event that the QSAR analysis of aquatic toxicity data leads to
the need to test more chemical classes than is feasible using the four day
acute test, this project began evaluating several more rapid tests for the
reliability in estimating relative toxicity of chemicals. The tests
initially selected were the residual oxygen bioassay, the Microtoxฎ bacterial
bioluminescence bioassay, and the fluorescence algal stress bioassay.
A. Residual Oxygen Bioassay
The residual oxygen bioassay is a 6-8 hour test performed in sealed
bottles. Briefly, about 3 gm of fish are placed into each of a series of BOD
bottles containing water saturated with oxygen and varying concentrations of
the test chemical. The fish die quickly at lethal concentrations of the
chemical or after longer periods at sub-lethal concentrations due to oxygen
depletion. The oxygen is measured in each bottle as the last fish dies and
the residual oxygen concentration is plotted against the concentration of the
chemical.
At high concentrations, the fish are killed quickly by the chemical
before the oxygen can be reduced. In control exposures, the residual oxygen
remaining for fathead minnows is approximately 0.5 mg/L. The concentration
of chemical at which the residual oxygen rises above that of the control
residual is the lethal threshold for the chemical. This study is an
evaluation of the relationship between the lethal threshold and the 96 hour
LC50 for fathead minnows.
Tests were conducted in 300-ml BOD bottles containing the desired
concentrations of test chemicals. Stock solutions were prepared by either of
two ways. For some solutions the calculated amount of chemical (solid or
63
-------
liquid) was weighed out, transferred to a 2-liter volumetric flask,' and
diluted to volume; aliquots of this stock solution were then transferred to
the BOD test bottles and dilution water was added to obtain the desired test
concentration. For other solutions the stock solution was prepared by taking
2 liters of solution from a saturator, analyzing this solution, and then
preparing the desired concentrations from aliquots of the stock solution as
before. The dilution water used in all tests was Lake Superior water warmed.
to room temperature (20-22ฐC).
Fathead minnows (Pimephales promelas) from the Environmental Research
Laboratory-Duluth stock were used as the test fish; these fish ranged from
30-day-old fry to adults. Fish were held in1the same water at the same
temperature as that used for test dilution water. During holding, the fish
.were fed either frozen brine-.shrimp (San Francisco Bay Brand, Inc.,;Newwark,
CA) or a commercial salmon ration (Glencoe Mills, Inc., Glencoe, MN) twice
daily. Fish were not fed for at least 16 hr prior to testing nor during
testing.
Tests were conducted by preparing the test solutions as described above,
usually using 9-11 test concentrations plus a control (dilution water only)
for each test. Fish were weighed to obtain a 3 to 3.5-g loading for each BOD
bottle, were rapidly placed in the test solution,,and the bottles were
immediately capped. When all the fish at a given concentration died, the
dissolved oxygen concentration and pH of the test solution were measured, and
the number of test fish was counted.
Dissolved oxygen concentration was determined using a Beckman 0260
oxygen analyzer calibrated by a Winkler titration (APHA et al., 1976) on
water from a holding tank; pH was determined with a Corning Model 12 meter.
64
-------
Averaging of pH was accomplished using a computer program which converts pH
values to their corresponding hydrogen ion concentrations, .averages these,
then reconverts the average to pH units.
Data Treatment. The dissolved oxygen concentration and toxicant
concentration test data for each test were analyzed by computer to determine
the lethal threshold concentration. The lethal threshold endpoint was
determined by means of the computer program HOCK(ey-stick) which fits the
data to a model for the bioassay and used "hockey stick" statistics to
estimate the endpoint confidence intervals. This program was developed to
offer maximum input/output flexibility.
Version I enables the user to input data directly into the HOCK program
and receive output from the line printer. This is for single time usage.
Version II uses a data file to store information for multiple use. Version
III gives examples of data modification and terminal output and can be used
with either a data file or terminal data input.
Figure 10(a) illustrates Version I where test data are entered directly
through the terminal for a test with 4-pentylphenol. The threshold is
computed to be 2.61 mg/1 with the 95% confidence interval being 2.37 to 2.88
mg/1. Finally, the data are plotted at the terminal to show graphically how
well the model fits the data (Figure 10(b)).
The chemicals tested in the evaluation o'f this screening method are
presented in Table 4 and the correlation between the LC50 and the lethal
threshold is presented in Figure 11. The correlation for this set of
chemicals produced an R^ = 0.92. Although the data indicate that a
relationship exists between these two endpoints for toxicity, it is the
current opinion that the use of the residual oxygen bioassay as a screening
test is o.f marginal value. The test requires all of the fish, culturing
65
-------
Figure 10. Input of Residual Oxygen Test Data .to Program HOCK
IAS PROGRAM DEVELOPMENT SYSTEM :
11:50:01 3-OCT-80
ENVIRONMENTAL RESEARCH LAB SYSTEM
PDS> LOGIN EPARCR RCR
User EPARCR UIC [350,40] TT01: Job-id 333 11:50:13 3-OCT-80
NOTE: NEU DEC-MOS MEMORY HAS BEEN INSTALLED (UED. 24-SEP-80).
LAST SYSTEM BACKUP: TUES. 30-SEP-80 (4 1/2 TAPES UERE USED.) GETTING BETTER!
PDS> RUN HOCK
11:50:26
PROGRAM HOCK(EY-STICK)
RESIDUAL DO BIOASSAY ANALYSIS
WHAT IS THE INPUT FILE? IF TERMINAL TYPE
TI:
TI:
ENTER PAIRUISE DATA ( CTOX3 & R-DO ). CTRL-Z TO QUIT
0.09,0.39 ..,--
0.96,0.24 . " .
2.9,0.96
4.8,4.91
6.8,6.99 '.'..'
8.7,8.48
10.6,7.84
14.5,8.22
24.,8.59 "
48.,8.48
97.,8.10
"I
ft
1
2
3
4
5
6
7
8
9
10
11
CTOX3
9.0000004E-02
0.9600000
2.900000
4.800000
6.800000
8.700000
10.60000
14.50000
24.00000
48.00000
97.00000
LOGtTOX]
-1.045758
-1.7728778E-02
0.4623981
0.6812413
0.8325090
0.9395193
1.025306
1.161368
1.380211
1.681241
1,986772
R-DO
0.3900000
0.2400000
0.9600000
4. j9 10000
6 ,)99'0000
3.480000
7.840000
3.220000
3.590000
8.480000
8.100000
IS THAT OK? (YES OR NO)
YES
NAME THE PLOT (120 CHARS. MAX.)
TEST 97 4-PENTYLPHENOL 6/9/80
TYPE 5 TO GET THE OUTPUT HERE, TYPE 6 TO SEND PRINT IT
6
66
-------
Figure 11. Residual Oxygen Test Results for 4-pentylphenol
<ป-PENTYl.PHENOli' 6/9/HO
II
t:
s
i
0
u
A
1,
m. v------------- -_________________ ____-.___-_______-_-__--__ --_...--_._.-....--__._..__.---_------.--
I . :
1 in<1CTt'Y ThMHSHfiLD = i.fallOy . PPM. 95% LIMITS ARt 2.38945 TO ^.S)7V3fa
I . ' . . .
1 i)HSKiHVATinris=iu)
y.U-1 MllDKLs********* '.''''.'
1 IH/tHljAP ztfUiaBaMlo
i HKI-SCM.N: DATA=!
I 0 0 tJ
in TuPf SimLP Rt.<", ION .' ซ*ป*ปซซซ
-------
Table 4
Compound
Phenol
2 ,4-Dichlorophenol
2 ,4 ,6-Trichlorophenol
Pent ach lor ophenol
2 ,4 ,6-Tribromophenol
4-Nitrophenol
2 ,4-Dinitrophenol
2,6-Dimethylphenbl ;.':!.''
2 ,4-Ditnethylphenol
2 ,4-Dinitro-o-crosol
3-Methoxyphenol
Tetrachloroethylene
1 ,1 ,2,2-Tetrachloroethane
2-Chlorophenol
1 ,1 ,2-Trichloroethylene
1 ,2-Dichloropropane
1 ,2-Dichloroethene
Lethal
Threshold
(mg/L)
20
4.2
1.3
0.40
12 .
11
-3.2
;--;-;>-.36--:-;^; .;
.-/. ;/-32 .:-
0.32
120
14
19
58
66
40 ;
. . 138 i .'
96-Hour
LC50
(mg/L)
28.8
8.23
9.17
0.23
, ' 6.64
60.5
'-''.'. 16.7
:,-^.--:v;;..;,:22.6:V;
16.8
2.04
76.3
13.5
20.3
12.4
44.1
. . 139
118
68
-------
Table 4. (Continued)
Compound
Methanol
Ethanol
2-Propanol
3,4-Xylenol
1-Butanol
sec-Butyl alcohol
1-Octanol
1-Hexanol - : .
2-Methyl-l-propanol
l-Amino-2-propanol
o-Phenylphenol
1-Naphthol
4-Butylphenol
2-(2-Ethoxyethoxy) ethanol
4-Pentylphenol
Cyclohexanol
4-Phenylazophenol .
4-Chloro-3-methylphenol
Lethal
Threshold
(mg/L)
17,600
12,700
5,490
30.8
2,090
2,650
13
128 .
2,020
147
5.8
8.9
3.1
17,700
2.6
755 '
3.4
8.2
96-Hour
LC50
(mg/L)
28,200
15,000
9,640
14
1,740
1,910
14.0
. 97.2'
1,460
327
6.0
4.24
5.13
26,363
1.09
705
1.09
7.58
69
-------
LI
-------
and handling facilities of Che 4 day acuce test and, since the test requires
an entire working day as a minimum, the time saved to produce an indirect
estimate of the acute toxicity is judged to be insufficient to recommend its
use as a screening test.
71
-------
B. Microtoxฎ Bacterial Bioluminesence Test
This rapid screening technique is the bacterial assay developed by
Beckman Instruments Inc., which measures the decrease in natural light
emission from the luminescent bacteria Photobacterium phosphoreum in response
to a toxicant. The decrease in light is expressed as a 5-min EC50 (the
concentration effecting a 50% reduction in light output).
Bacterial ECSOs for 76 compounds were measured to evaluate the
relationship between the EC50 and the 96-hr LC50 values for one species of
fish, the fathead minnow (Pimephales promelas).
To eliminate variability in the fathead minnow LC50 values due to
changes in water quality, chemicals were selected for bacterial testing only
if.-the fathead minnow LC50s.,were derived from tests conducted at the
-. Environmental Research Laboratpry-Duluth. Chemical, selection was further .
restricted to.tests conducted in flow-through systems using Lake Superior
water, and when exposure concentrations were measured.
Preparation of Test Solutions. All solutions for bacterial bioassay
were prepared in distilled, deionized water. If the compound was readily
.spl.uble in water, concentrations were not measured and bacterial EC50 values
were calculated from nominal concentrations. When testing less soluble
compounds, a saturator consisting of a volumetric flask and a magnetic
stir-rer was used. Chemical concentrations fo'r less soluble compounds were
measured by gas chromatography.
Bacterial EC50 Determination. All bacterial bioassays were conducted
using the Microtox Toxicity Analyser" (Beckman Instruments, Inc., 6200 El
Camino Real, Carlsbad, CA). Testing was done following the method described
72
-------
by Beckman (1979) who supplied the reagents and lyophilized bacteria.
Inhibition of luminescence (M) was calculated for each concentration:
X= (initial light output x blank ratio/final light output) - 1
where the Blank ratio = final light output of the blank/initial light
output of the blank.
A graph of log^g ^vs. logio concentrations was plotted and the
concentration causing 50% inhibition of luminescence was determined from that
graph.
For most compounds it was possible to prepare a saturated stock
solution that was at least double the EC50 concentration. When this was not
possible, the saturated solution was tested and the reduction in light due to
the toxicant was calculated as follows:
mean chart reading for the Cl Blank ' ~1
mean chart reading for 100% Toxicant solution
Statistical Methods. The regression correlation for logjo 96-hour
fathead minnow LC50 and logjQ 5-minute bacterial EC50 was calculated for
selected ethanes, alcohols, and ketones to determine the effects of chemical
substitution on toxicity to fish and bacteria.
In addition, log^Q EC50 vs. number of carbons and log^Q fish
LC50 vs. number of carbons was plotted for a homologous series of
unsubstituted ketones and alcohols, and simil'ar plots of EC50 and LC50 vs.
number of chlorines were drawn for a homologous series of ethanes.
Prediction intervals were calculated at the 95% level.
Evaluation of the Bacterial Bioassay. The correlations between EC50 and
LC50 are summarized in Table 5. The correlation comparing 96-hour fathead
minnow LCSOs to 5-minute bacterial EC50s for unsubstituted alcohols
(R^=.96), unsubstituted ketones (R^=.81) and chlorinated ethanes
73
-------
were evident. Toxicity of chemicals to both fish and bacteria
increased with addition of chlorine groups to ethanes. Addition of carbon
groups also increased the toxicity of alcohols and ketones to bacteria and
fish. A comparison of the reproducibility of acute fish and bacteria assay
results (Table 6) shows no statistically significant differences in the
standard deviation in the percent difference between replicates in the two
tests.
The bacterial bioassay is a rapid and relatively inexpensive test. The
entire test, including temperature equilibration for all reagents takes about
30 minutes to complete. The bacterial bioassay exhibits precision comparable
to the precision of the acute bioassay 'with fish. Bulich (1979) found a
coefficient of variance of 18.6 for 81 tests on sodium lauryl sulfate using a
Microtoxฎ Toxicity Analyzer. ;'..'."....''.'. ;,. /; . i : :''' -'/ .
From data obtained in this evaluation (Table 7), bacteria EC50 data
could be used to screen certain types of chemicals for potential fish
toxicity. This could be done using the prediction limits indicated in the
graphs (Figure 12) and may be demonstrated using a hypothetical example in
which a tier testing scheme included an acute toxicity trigger at an LC50 of
10 mg/L. If it were decided that extensive fish tests would not be required
for chemicals with a LC50 greater than 10 mg/1, the data obtained in the
evaluation could be partitioned accordingly. 'The point where the lower
prediction limit intersects LC50 = 10 defines the EC50 (501 mg/1)
corresponding to that fish toxicity value. The lower prediction limit is
chosen to ensure that 95 percent of the compounds having an EC50 greater than
the value will have an LC50 greater than 10 mg/1. Based on these data for
organic chemicals, chemicals with an EC50 greater than 501 mg/1 would have an
LC50 greater than 10 mg/1 and additional fish tests would not be necessary.
74
-------
Table 5. Correlations for Classes of Chemicals of
LC50 (96-Hr Fathead Minnow) vs. Log 5-Min EC50 (Bacteria)
Chemical Class
All Organic Chemicals 68 0.71
Unsubstituted Alcohols 9 0.96
Chlorinated Ethanes 4 0.98
Unsubstituted Ketones 10 0.81
75
-------
Table 6. Summary of 5-minute EC50 and 96-Hr LC50 Results of Replicate
Testing of Selected Chemicals in Bacteria and in Fathead Minnows
Chemical Compound
Acetone
2-Butanone
2-(2-Ethoxyethoxy)
Et Hanoi
Hexanol
2-Methyl-2 ,4-Pentanediol
2-Octanone
Phenol
5-Min Bacteria
EC50 (mg/1)
22,000
21,000
5,750
4,350
1,290
1,000
42.7
40:4
3,300
3,200
; 2,710 -...
20.5
15.0
39.8
40.7
Percent Difference in
Replicated ECSOs
5.0
28.0
25.3
3.12
7.8
30.98
2.23
Chemical Compound
1-Amino-Propanol
Butanol
Cyclohexanone
96^Hr Fathead
Minnow LC50
(mg/1)
327
287
16.2
13.4 '
732 ' ;
527
Percent Difference in
Replicated LCSOs
15.5
19.0
32.56
76
-------
Table 7. 96-Hr Fathead Minnow LC50s and 5-Min Bacterial EC50s.
Chemical Name
1-Decanol
*l-0ctanol
l-Amino-2-Propanol
l-Amino-2-Propanol
2-Pehnoxyethanol
1-Hexanol
1-Hexanol
Cyclohexanol
2 , 3-Dibromopropanol
2-Methyl-l-Propanol
2,2,2-Trichloroethanol
1-Butanpl
2-Methyl-2,4-Pentanediol
2-Methyl-2 ,4-Pentanediol
2-Methyl-2,4-Pentanediol
2-Chloroethanol
2-Propanol
Ethanol
Methanol
96-Hr
Fathead Minnow
LC50 (mg/1)
2.3
13.4
280
327
346
97.2
97.2
705
71
1,460
. . ;298 -
1,740
10,700
10,700
10,700
37
9,640
14,200
28,200
5-Min
Bacterial EC50
(mg/1)
1.16
6.3
27.2
27.2
32.7
40.2
40.4
115
320
1,670
1,800
2,300. .
. - -'
2,710
3,200
3,300
13,400
35,000
44,000
125,000
77
-------
Table 7.- (Continued)
Chemical Name
*2-Decanone
*2-Decanone
*2-Octanone
6-Methyl-5-Hepten-2-one
Cyclohexanone
Cyclohexanone
*2-Octanone
4,4-Dimethyl-Amino-3-Methyl-2-Butanone
4-Methyl-2-Pentanone
2-Butanone Oxime
5-Methyl-2-Hexanone
2,4-Pentanedione
5-Methyl-2-Hexanone
2-Butanone
2-Butanone
Acetone
Acetone
*p-tert-Butylphenol
Pent ach lor ophenol
4-Ch loro-3-Methy Iphenol
Phenylazophenol
96-Hr
Fathead Minnow
LC50 (mg/1)
5.7
5.7
37
85.8
527
732
37
8.4
509
' - 844 ;';'
158
96
158
3,200
3,200
8,140
8,140
1 1
5.15
0.220
7.59
1.09
5-Min
Bacterial EC50
(mg/1)
6.1
9.7
15
17.5
18.5
18.5
20
42.1
80
950
980
1,050
1,448
4,350
5,750
21,000
22,000
0.21
0.08
0.58
0.96
78
-------
Table 7. (Continued)
Chemical Name
4-Chloro-3-Methylphenol
*0-Phenylphenol
2 ,4 ,6-Tribromophenol
2 ,4-Dichlorophenol
2,4-Dimethylphenol
*l-Napthol
4,6-Dinitro-o-Cresol
2 ,4,6-Trichlorophenol
*2-Allylphenol
*p-Nitrophenol
2,4-Dinitrophenol
o-Chlorophenol
*4-Amino-2-Nitrophenol
Phenol
Phenol
*HexachloroeChane
*Penc ach lor oe chane
*Tetrachloroe thane
*Trich lor oe thane
*Kel thane
*Permethrin
96-Hr
Fathead Minnow
LC50 (mg/1)
7.59
6.0
6.64
8.23
16.81
4.24
2.04
9.17
'' 15.9
60.5
16.7
12.4
34.3
28.8
28.8
1.53
7.3
20.3
81.7
0.51
0.017
5-Min
Bacterial EC50
(mg/1)
1.86
2.05
2.7
3.63
4.4
5.66
6.6
7.20
10
13.0
15.8
22.1
35.9
39.9
40.4
0.14
0.75
8.6
105
0.45
0.56
79
-------
-Table 7. (Continued)
Chemical Name
96-Hr
Fathead Minnow
LC50 (mg/1)
..;.:'. 5-M in
Bacterial EC50
(mg/1)
*Diazinon
Dimethyl Formamide
*N-Butyl Ether
.2-(2-Ethoxyethoxy)-Ethanol
2-(2-Ethoxyethoxy)-Ethanol
Triethylene Glycol
*Butanal
*Ethanal
6.65
10,600
32.5
26,400
26,400
61,000
16.2
31.2
9.8
20,000
63.0
1,000
1,290
33,000
16.4
342
80
-------
Figure 13. Relationship Between 96-hr LC50 and Microtoxฎ EC50
I 7
6-fl
2C
a
8-
<
k,
K
X
i
(O
CO
Q
2.5
0.0
-2.5
-6.0
-2
0
6
LOQ10 6-MINUTE BACTERIA EC50
-------
If this screening technique had been applied Co the compounds in this
evaluation, fish tests would not have been performed on 35 of the 76
compounds. Because a bacterial bioassay costs considerably less than the
estimated $2,000 for a fish test, the savings would be substantial.
Compounds which fall between the upper and lower prediction limits for
LC50 = 10 would require additional testing with fish. Another decision
scheme might use the screening test to circumvent intermediate testing on
compounds likely to be highly toxic and "flag" them for long term testing.
Those compounds could be determined from the regression, plot using the same
process described above and substituting the upper prediction limit. As
testing continues on several species at the ERL-Duluth, it will be possible
to make similar comparisons on other homologous series of chemicals. At
present it appears that bacteria can be used to determine the relative
toxicity to fish of selected groups of organic chemicals.
82
-------
C. Algal Fluorescence Test
The "Algal Fluorescence Test" being developed is a. sensitive and rapid
test procedure based on the fluorescence of chlorophyll &_ from algae.
Normally, chlorophyll _a in algae will re-emit a small percentage of absorbed
light as red fluorescence, the remainder being used in photosynthesis. If
the overall health of the algal cell deteriorates the percentage of
fluorescence increases. The increase in fluorescence has been determined to
be inversely proportional to the photosynthetic capacity of the cell. By
poisoning the cell with DCMU, a very potent herbicide, it is possible to
totally block photosynthesis and achieve maximum fluorescence. DCMU causes a
larger percent fluorescence increase in healthy cells, whereas in cells
stressed by a toxicant this increase will be reduced. Using these principles
we are refining our techniques for assaying toxicity using algae.
Cultivation. The green algae Selenastrum capricornutum and the blue
green algae Anabaena flos-aquae' were* obtained from EPA-Corvallis, Oregon and
were cultivated in AAP medium (U.S. EPA, 1978). Algal cultures were main-
tained on magnetic stir plates at 25ฐC. All cultures and test systems
received continous irradiance of 350 ft. c. provided by 40 W "cool white"
fluorescence bulbs. Exponentially growing cultures were used in all tests.
Test Procedures. The test system consisted of 24-5 ml test tubes in a
plexiglass rack. Algae were either innoculat-ed into lake water containing
various concentrations of toxicant or the toxicant was added directly to an
algae culture in AAP medium. Final test volume was 4 ml. Volatile compounds
was tested in stoppered tubes. The test system was kept under continuous
light until actual fluorometer readings were taken, usually 2 hours.
83
-------
Fluorescence Measurements. All fluorescence measurements were done, on a
Baird-Atomic, Model SFR'1'00 Spectrofluorimeter at an excitation of 430 nm and
an emission of 680 nm. The test system was kept under continous illumination
until actual measurements were taken so as not to alter the photosynthetic
state of the culture. After the incubation period the culture tube was read
directly in the fluorometer and an initial reading was recorded after the
signal stabilized (F^) . Similarly a second reading was recorded after the
addition of enough DCMU (3-(3,4-dichlorophenyl)-l,1-dimethyl-urea), a
herbicide which blocks the electron transport pathway in photosynthesis, to
obtain a 5 MM solution (Fp). These readings were used to calculate, a
percent fluorescence increase:
(Fp - FM) x 100 = % fluorescence increase.
% .'.:.... ... ; :.-,... ..: . .... ......
These values were- compared statistically to the control to detnnine. effect
concentrations of a variety of organic compounds.
Evaluation of the Algal Fluorescence Test. Typical results with several
organic toxicants are presented in Table 8. Although data are insufficient
to be statistically analyzed with data from other algal tests, the results
indicate the relative sensitivity of algae to selected compounds. A dose-
response curve is presented in Figure 14. The method appears to be
sensitive, reliable, and fast (2 hours) to a broad range of toxicants. With
the chemicals tested thus far, effect concentrations are generally in the
range of the fathead acute values being generated. The initial results with
natural surface waters being tested for the "Ambient Metals Study - EPA"
indicate that the method can also detect metal toxicity.
The method will continue to be refined with respect to test techniques
for analyzing the data. A systematic testing of those chemicals being used
. 84
-------
Table 8. Effect concentrations using-the algal fluorescence test
Compound
Ethanol
Methanol
Butanol
Hexanol
Hexanal
Hexanal
Acetone
DMF
Tetrachlorophenol
1 ,2 ,4-Trichlorobenzene
1 , 3-Dichlorobenzene
1 ,1 ,2,2-Tetrachloroethane
Algae
Selenastrum
Selenastrum
Selenastrum
Selenastrum
Selenastrum
Anabaena
Selenastrum
Selenastrum
Selenastrum
Selenastrum
Selenastrum
Selenastrum
Effect3
Concentration
50 ppm*b
>32,000 ppm
300 ppm**
>320 ppm
.' .80 , ppm*
160 ppm*
4,000 ppm*
6,250 ppm**b
15.4 ppm**
' . 8 ppm*
60 ppm**
51.1 ppm**
Test
Duration
2 hr
2 hr
2 hr
2 hr
2 hr
2 hr
2 hr
2 hr .
2 hr
24 hr
3 hr
1.5 hr
a Lowest concentration tested showed significant difference from control
b Lowest concentration tested
* P = 95
**p = 99
85
-------
Figure 14. Dose-Response of Algal Fluorescen
ce to Trichlorobenzene
J26 t-
I
UJ
CO
I
o
co
I
cf
100
76 |-
60 H
26 1-
1,2,4 TRICHLOROBENZENE
CL
-------
in che QSAR program will be performed. Plans have been made Co use a green
algae and a blue green algae Co determine comparacive sensicivicies Co
Cesc compounds.
The literature and data generaCed chus far suggest that Che reduce ion or
enhancement of chlorophyll a_ fluorescence before Che addition of DCMU may not
only indicate toxicity, buC also Che sice of action of Che parCicular
inhibicor. This variable fluorescence response has Che potential Co
delineaCe between various modes of action of structurally different
compounds. For example, DCMU Cype compounds block non-cyclic phoCosynChecic
eleccron transport and increase FJJ, whereas benzoquinone, a well known Hill
oxidanc, lowers FJJ. Along wich our continued effort Co improve Che method
we will also be considering Chese "behavioral" responses of chlorophyll _a
fluorescence'in an attempt to describe mode specific'chemical accivicy'. ' A
literature review on Che poCency and modes of action of chemicals in
inhibiting Che Hill reacCion in photosynthesis will be Che subjecC of
subsequent reporCs.
87
-------
X. LITERATURE REVIEW ON NARCOSIS :
In an effort to summarize the major modes of action of common industrial
chemicals, a series of literature reviews are planned. These reviews are an
integral part of QSAR because they help to determine truely anomalous
behavior within a model from changes in mode of action which suggest an
additional model is needed. The first review is concerned with narcosis for
the reasons stated previously in this report.
Narcosis is a reversible state of arrested activity of various
protoplasmic structures under the influence of certain chemicals. Kaufman
(1977) defines "general anesthesia as a pharmacologically induced reversible
disruption resulting- in a coordinated sequence of changes in neural
activity". The terms narcosis and anesthesia are used interchangeably in
medical literature. Narcosis of'the central, nervous system is. referred to as
general anesthesia. The chemicals or drugs producing arrested activity of
any cellular organization are referred to as narcotics, biological
depressants, hypnotics, and general anesthetics (Albert 1965). A variety of
chemical- agents of different chemical structures and properties produce
narcosis in animals. These agents may'be gases OHesser et al., 1978)
aliphatic or aromatic hydrocarbons, chlorinated hydrocarbons, alcohols,
ethers, ketones, aldehydes, weak acids, weak bases, and some aliphatic
nitrocompounds (Albert, 1965; Gero, 1965; Roth, 1980).
Most of the biological cells are susceptible to the actions of general
anesthetics (Roth, 1980). However, it appears that all anesthetics or
narcotics produce a general depression of the central nervous system (CNS) of
the organisms. Vandam (1965) pointed out that whatever may be the mechanism
of narcosis, the clinical signs of general anesthesia are related to the
88
-------
amount of anesthetic made available to the CNS. Anesthetics may affect not
only the CNS but a variety of cell types and several synthetic model
preparations (Halsey et al., 1974; Miller and Millet, 1975; Roth, 1979;
Seeman, 1972). In a recent study, Schwab and Schwab (1979) have shown that
protozoan (Allogromia laticollaris Arnold) cells after treatment with 1 mM of
the volatile anesthetic, halothane, showed disappearance of cytoplasmic
microtubles. These workers also demonstrated the appearance of tubulin
polymers which were helices of paracrystalline structures.
During the last few years new information has accumulated concerning the
pharmacological effects, of narcotic agents. With the development of novel
monitoring devices and in vitro techniques, it has been demonstrated that
anesthetics can exert profound and direct effects on cardiac muscles and
alter regional blood flows in a number of organs (Price and Ohnishi, 1980;
Longnecker and Harris, 1980). Many narcotic agents are capable of altering
tissue and plasma concentrations of cyclic nucleotides such as cAMP or GMP
and of neurohumoral substances such as catecholamines, dopamine, serotonin,
and renin (Altura, 1980; Rahwan, 1975; Zaleska, 1979). In addition these
anesthetics have the ability to perturb lipid-bilayer membranes and alter ion
transport (Roth, 1980; Lee, 1976; Schettini and Furniss, 1979).
A mechanism of action of some reactive anesthetics has been sought in an
inhibition of enzymic processes. For example, a number of anesthetics
inhibit mitochondria! oxygen consumption. The mode of action-in mitochondria
appears to be chiefly inhibition of electron transport with slight uncoupling
of.oxidative phosphorylation (Altura, 1980).
In spite of intensive pharmacodynamics, physicochemical, biochemical and
clinical research,- the .exact .changes in. the CNS -remain unknown. It is quite
puzzling how some chemically inert agents and others which are rather ..
89
-------
reactive chemicals may induce reversible changes in the functions of the CNS
in such a way that loss of consciousness and insensitivity to pain occurs
(Kuschinsky and Lullmann, 1973; Adriani and Naraghi, 1977).
There are several hypotheses which have been proposed to explain the
mode of action of narcosis. It is generally accepted that the majority of
general anesthetics affect most biological cells in a non-specific fashion.
However, due to their diverse chemical structures, a similar or unitary
mechanism of action appears to be inconceivable. Recent studies suggest that
all anesthetics may not act via the same mechanism (Stockard and Bickford,
1975; Trudell, 1977).
One of the oldest and most widely quoted theories of anesthetic action
is the Meyer-Overton "lipoid hypothesis of narcosis". Meyer (1899) and
Overton (1901) independently proposed that anesthetic.action correlates with
a.high oil/water partition coefficient. K. H. Meyer (1937) stated that
"narcosis (anesthesia) commences when any chemical substance has attained a
certain molar concentration in the lipids of the cells." It is true that
most anesthetics, especially volatile anesthetics, have high oil/water
.partition coefficients, but this does little to provide an explanation of how
they function as depressants (Roth, 1980; Gero, 1965; Koblin and Eger, 1979).
This theory may only explain why such anesthetics accumulate in the lipid
rich CNS (Roth and Seaman, 1971). In addition, there are some outstanding
exceptions to the Meyer-Overton hypothesis' of anesthesia, such as alcohols
which are nonspecifically acting anesthetics despite their low oil/water
distribution coefficient (Albert 1965). Furthermore, oils and certain other
substances with very high partition coefficients, do not produce narcosis.
Traube (1904) proposed a different physicochemical hypothesis of
narcosis. This theory describes a lowering of surface tension between water
90
-------
and a second phase such as oil or air. The alcohols in homologous series fit
this generalization, but even familiar hydrocarbons like chloroform and
ethylchloride do not lower interfacial tension at the surface boundary of
water/air or water/oil. Furthermore, many detergents which are known to
lower surface tension are not depressants.
Ferguson (1939, 1951) provided a physicochemical explanation based on
the thertnodynamic or chemical potential of the depressants in the internal
phase and the biophase (site of biological action). He pointed out that a
stable level of narcosis is achieved when an equilibrium of drugs is reached
between the two phases. Ferguson's principle provides a satisfactory
correlation between chemical potential and the activity of nonspecifically
acting drugs, but does not explain the true mode of action of depressants.
Depression of physiological function.generally appears to be-the typical
action of compounds which do not enter into specific reactions with the
organisms (Albert, 1965).
Pauling (1961) postulated that hydrophobia depressants cause the
formation of hydrate microcrystals in the brain. Such hydrates are known to
be formed at low temperatures around nonpolar molecules and nonpolar groups
within molecules, and are referred to as "clathrates". These clathrates are
ordinarily unstable at body temperature, but it is also known that clathrates
formed around several kinds of molecules or groups (mixed clathrate hydrates)
are more stable (Gero, 1965). Pauling further assumed that clathrate
hydrates are formed in brain cells in the presence of volatile anesthetics
which then combine with the alkyl groups of the proteins, and phospholipids.
These water clathrates are stable under physiological conditions and1they
cause water molecules in their vicinity to change to the denser structure of
ice. The ice microcrystals formed in this way tie up water, increasing the
91
-------
impedance of Che neural network of conductors and hindering- Che movement of.
ions.in the brain. Unlike other theories, Pauling's hypothesis centers
around Che aqueous phase and not on the lipid material of the CNS, and also
seeks to connect depressant action with a process of reversible
disorganizacion.
More recenC Cheories of narcosis seek the mechanism of depressanc action
in interactions of anesthetics with cell membranes (McElroy, 1947; Miller-and
Pang, 1976; Rosenberg et al., 1975; Ashcroft et al., 1977ab; Haydon et al.,
1977; Jain et al., 1978). It is well accepted that conduccion of impulses in
Che neurons and muscle fibers depends on their cable structure and occurence
of specific changes in Che ion permeability of the surface membranes (Keynes,
1972). Biological membranes are composed of a phospholipid bilayer and
cholesterol, arranged wich polar (hydrophill'ic) headgroups facing outward and
Che lipophillic or hydrophobic moeity inward-. The protein of Che membrane on
eicher side of Che phospholipid bilayer forms a trilaminar sandwich (Keynes,
1972) . Membrane structures have pores or channels through which sodium and
potassium can move across the membrane during the transmission of nerve
impulses (Ashcroft et al., 1977). Most investigators have envisioned
membrane lipids as the targec of aneschecic actions. However, some believe
chac general anesthetics inaccivate proteins, which are essential for CNS
function (Eyring et al., 1973; Kaufman, 1977)'.
Recent Theories of Anesthesia Si
(a) Phase Transition - Three recent theories propose that anesthetics
disrupt the lipid bilayer of the membrane. Lee (1976) postulated an "annular
transition model", also called a "phase transition hypothesis". This
hypothesis proposed that the sodium channel of an excitable membrane is
surrounded by an annulus of lipid which is in the "crystalline" or gel state
92
-------
(solid phase), which helps Co maintain an open sodium channel. Addition of
an anesthetic triggers a change in the lipid to a fluid or liquid crystalline
state and this alteration allows the closure of the sodium channel, resulting
in anesthesia. An increase in temperature can also alter the gel phase to a
liquid phase, and the temperature at which this transition occurs is called
the phase-transition temperature (Roth, 1979). The "phase transition
hypothesis" is supported by the studies of Hill (1978) on a phospholipid
membrane model, and of Karaaya et al. (1979) who showed that high pressure
antagonizes both anesthesia and the anesthetic-induced decrease in phase
transition temperature of dipalmitoyl phosphatidylcholine bilayers. However,
a recent study by Pringle and Miller (1978) which utilized structural isomers
of tetradecanol, indicated no direct relationship between lipid phase
transition and the mechanism of anesthesia. . Gases,-barbiturates, steroids
and alcohol anesthetics have all been shown to fluidize
phosphatidylcholine-cholestrol lipid bilayers and some biological membranes.
Miller and Pang (1976) using different lipid composition and anesthetics
demonstrated that the fluidizing efficacy of an anesthetic varied with
different anesthetics and also with the lipid composition of the membrane.
(b) Lateral Phase Separation - This hypothesis by Trudell (1977)
proposes that lipids under normal conditions exist as gel and liquid phases.
The region where a gel phase coexists with a.liquid phase is defined as a
lateral phase separation (Trudell, 1977). Conversion of one phase to another
phase allows the membranes to expand or contract with less energy than would
be necessary if the membrane was only in gel or fluid form. Also the regions
(fluid or gel) may be associated with functional proteins. Trudell-proposed
that anesthetics may reduce the gel phase in'membranes and"thus decrease the
lateral phase separation. This decrease in the lateral phase separation
93
-------
would prevent the functional protein from 'altering its conformation necessary
for trans location'of ions across the post synaptic membrane or for the
release of a neurotransmiter, thus resulting in depression or narcosis
(Trudell, 1977; Trudell et al., 1975).
(c) Fluidized Lipid Hypothesis - This hypothesis postulates that
anesthetics can disorder the motion of the lipid membrane components (Gage
and Hamill, 1976), and that due to increases in the lateral and rotational
motion of the lipid component, the stability of the channel proteins is
reduced. This facilitates the closure of the ionic channels in the membrane
(Gage and Hamill, 1976; Miller and Pang, 1976; Miller, 1977; Roth, 1980).
Theories discussed above as "phase transition" (Lee 1976), "lateral
phase separations" (Trudell 1977) and the "fluidized lipid" hypothesis all
propose that. the. phenomenon of narcosis is dependent on fluidization of the
lipid membrane. The spin label study of Pringle and Miller (1978) showed
that unsaturated long chain alcohols and cis or trans-tetradecanol could
fluidize egg lecithin cholesterol membranes to similar levels. However,
contrary to that, cis-tetradecanol lowered the transition temperature of
dipalmitoylphosphatidylcholine while trans-tetradecanol elevated this
transition temperature. These results are consistent with a lipid fluidity ...
model of anesthetic action, but provide little support for a "lateral phase
separation" theory of anesthesia. The author's suggest direct interaction of
alcohols with the membrane protein (Pringle; and Miller, 1978). Studies
conducted by Jain et al. (1975) on the transition profile of dipalmetoyl
lecithin liposomes exposed to a variety of drugs support the theory of
fluidity disorder of the lipid bilayer.
(d) Critical Volume Hypothesis - Mullins (.1954) proposed a critical
volume hypothesis. According to"this hypothesis, narcosis is produced by
.94
-------
expansion of the critical hydrophobic site due to the adsorption of
anesthetic molecules. Mull ins (1954) suggested that anesthetic molecules
should have a complementary shape or size to "fit" into the free spaces of
the membrane's chemical lattice. This critical volume of occupation will
thus result in decreased sodium permeability and depression of membrane
excitability. ...-
Several studies lend support to Mullin's critical volume expansion
hypothesis. For example, application of hydrostatic pressure (pressure
reversal of anesthesia) removed, at least partially, the effects of general
anesthetics in certain amphibians and mice (Lever et al., 1971; Miller et
al., 1973). The phenomenon of pressure reversal of anesthesia was first
observed in luminous bacteria (Johnson et al., 1942), which led Lever et al.
(1971) to formulate a unifying concept of critical volume expansion as a
general mechanism of narcosis. They postulated that anesthetics expand the
size of a critical hydrophobic site, while the hydrostatic pressure contracts
this site and causes reversal of anesthesia. Lever et al. (1971) calculated
that general anesthetics at critical concentrations in the lipids would
produce an expansion of lipids of the order of 0.4%. Further studies on
quantitative measurements of erythrocyte membranes (Roth and Seeman, 1971,
1972ab), and of lipoprotein films (Clements and Wilson, 1962) support the
estimate of 0.5% expansion of biomembranes in the presence of general
anesthetics. High pressure of approximately 100 atmospheres indeed
antagonizes the effect of general anesthesia, but the degree of the high
pressure reversal varies" with different anesthetics, and the antagonism of
high pressure may not be proportional or linear with the' pressure (Halsey et
al., 1978). .... . .
95
-------
A multisite expansion hypothesis was proposed by Ha-Lsey et al. (1978.).
They stated that a general' anesthetic'inay influence the expansion of more
than one molecular site with different physical properties, and the physical
properties of molecular sites may be influenced by anesthetics and pressure.
In addition, the molecular sites have a finite size and limited occupancy,
and the pressure does not necessarily influence the same site as anesthetics.
Finally the molecular sites for narcosis may not be disturbed by decreases in
temperature in a manner analogous to increases in pressure. The multisite
expansion hypothesis based .on intact animal studies is more relevant,
particularly with complete membranes in which the molecular sites are
energetically linked together (Halsey et al., 1978).
(e) Degenerate Protein Perturbation Hypothesis - Previous studies
discussed in this review all support a common structural.'perturbation, in the'
excitable membrane which is caused by anesthetics of diverse chemical
structures. This unitary hypothesis of narcosis was derived from experi-
mental evidence that anesthetic potency correlates approximately with
oil/water partition coefficients of narcotic agents (Seeiaan, 1972; Miller et
al., 1977). However, direct experimental evidence for any unitary mechanism
has not been presented to describe the interactions of anesthetics with
target protein structures (Richards et al., 1978). Studies conducted by"
Richards et al. (1978) utilizing n-alkanols do not provide support to a
unitary mechanism of narcosis. These worker's have suggested that anesthesia
is produced by direct interactions between the anesthetic molecules and
target membrane protein. They assume that smaller anesthetic molecules
(halothane or cyclopropene) distribute in one set of hydrophobic sites of
appropriate dimensions within the protein, while larger molecules like
barbituates, steroids, and aromatic amines may bind to distinct sets of sites
96
-------
on the target protein (Richards et al., 1978). Eyring et al. (1973) had also
proposed that anesthetics may interact with the hydrophobic region of a
protein and change its conformation to make it less active. The protein
pertubation theory of anesthesia does not provide a distinct mechanism of
narcosis (Koblin and Eger, 1979), rather indirect evidence is derived from
thermodynamic principles (Eyring et al. , 1973) and the lack of correlation
between lipid membrane fluidization with n-alkanols.
(f) Cyclic AMP Metabolism - Cohn and Cohn (1980) proposed that anes-
thetics interact with the activity of a specific enzyme protein hydrophobic
site which binds with the adenine moeity of cAMP. According to these authors
cAMP in vivo regulates the duration of narcosis without altering the cAMP
concentrations in the brain. These workers have shown that pentobarbital
causes dose dependent inhibition of brain adenylate deaminase which converts
AMP to inosine (Cohn and Cohn, 1980). It has been shown that dibutyryl
cyclic AMP shortens the duration of narcosis induced by a wide variety of
narcotics and hypnotics, and protects against the lethal doses of amobarbital
(Cohn et al., 1980). The effects of anesthetics on transmembrane regulatory
systems may require further studies. The role of hormone and
neurotransmitter-dependent adenylate cyclase is known in transmembrane
regulatory systems and production of cAMP (Rimon et al., 1978).
Our knowledge of the mode of action of anesthetic chemicals awaits
further understanding of the structure of phospholipid bilayers, axonal
transport, conformation of nerve proteins, synaptic transmission in the
central nervous system..and the interactions of anesthetics with endogenous
components of the neurons. Current theories of narcosis emphasize lipid
membrane disorders in. the CNS without- specif ic.ity.-(Roth,.. 1980).
97
-------
In mammalian systems, four, definite stages of-anesthesia can be
distinguished after the administration of anesthetics (Vandam, 1965). Stage
I anesthesia is characterized by motor excitation and inability to coordinate
motor activity (ataxia). Stage II anesthesia is characterized by delirium
when the animals show hallucinatory and cataleptic behaviour. Stage III
anesthesia, also called surgical anesthesia, is further progression of stage
II with increasing immobility and loss of reflexes. During this stage the
subject becomes unresponsive to painful stimuli. Further increases in
anesthetic dosage may cause a depression of the total central nervous system
activity (Stage IV), and irreversible damage to the brian which results in
medullary paralysis and death. These different stages of anesthesia
classified by Guedel (1951) as unidirectional schema were revised on the
basis of neufophysiological studies (Winters, 1976).
Fishes treated with anesthetic agents have been shown to undergo similar
stages of anesthesia (McFarland and Klontz, 1969; Klontz and Smith,, 1968;
Bonath, 1977). The phenomenon of narcosis in fishes has not been extensively
studied. Hunn and Allen, (1974) reviewed the recent studies on MS222 and
quinaldine, used widely as fish anesthetics. Such compounds are readily
absorbed through the gills of fish, as demonstrated with rainbow trout (Salmo
gairdneri), channel catfish (ictalurus punctatus), marine dog fish (Squalus
..acanthias), brook trout (Salvelinus fontinalis), bluegills (Lepomis
macrochirus) and largemouth bass (Micropterus salmoides). High lipid
solubility and rapid rates of diffusion across the gill membrane appear to be
important properties for fish anesthetic agents (Hunn and Allen, 1974). For
example, MS222 has been shown to be readily taken up through the gills of
freshwater fishes according to its concentration gradient. From the gills it
is distributed to many body tissues, especially the cerebrospinal fluid (CSF)
98
-------
in che brain (Hunn, 1970; Hargens et al., 1974; Stenger and Maren, 1974;
Daxboeck and Holeton, 1980).
Fish have been shown to possess superficial and internal (or central)
oxygen receptors which initiate active cardiac and ventilatory responses
under environmental hypoxia. Anesthetics may interact with these receptors
(Daxboeck and Holeton, 1978; Smith and Jones, 1978; Bamford 1974a,b; Marvin
and Burton, 1973) . Mechanisms by which such chemical agents disrupt various
physiological functions in fishes largely remains unknown and deserves
further studies.
Studies conducted in our laboratory with the fathead minnow (Pimephales
promelas) to determine 24, 48, and 96 hour median lethal concentrations
(LC50) of a group of alcohols, aldehydes, ketones and ethers indicate that
these chemicals^cause fish immobility, hyperexcitation .and loss of equili-.
brium before the onset of toxic symptoms. These symptoms are apparently
dependent on the chemical dosage and the exposure time. It is probable that
these chemicals produce lethal effects due to medullary paralysis, as shown
in mammals after dosing with higher concentrations of the narcotics. The
typical signs of the stage I anesthesia (indicated by motor excitation and
ataxia) in fish may be determined _in vivo without killing the test fish and
it may provide useful insight.
Previous studies on long chain alcohols have shown that they induce
narcosis and anesthesia in different animal species. Jain and Wray (1978)
have shown that the lipid bilayer/water partition coefficient for n-alkanols
are at least 3 fold smaller than their partition coefficient in the bulk
solvents. They imply that alcohols may induce changes in the lipid
biomembranes at much lower concentrations than can.be predicted from their
octanol/H20 partition coefficients (Jain and Wray,' 1978). Similar studies
- 99.
-------
on the partition coefficients of a variety of chemicals with actual :...
biological tissues (eig'. with-.-fish- g-ill/^O or-'blood/^O) may-provide'-
useful informtion on the movement and uptake of these chemicals, and may have
some degree of correlation with their toxicity. McCreery and Hunt, (1978)
studied 62 compounds, including a broad range of alcohols for their abilities
to intoxicate rats in vivo. These workers suggested that the shape of a
molecule had little effect on,its ability to intoxicate, but the compound's
amphiphillicity was important for its ability to partition into neuronal
membranes (McCreery and Hunt, 1978). Several studies have indicated that
membrane/water partition coefficients are. more meaningful than partition
coefficients with artificial solvents (Roth and-Seeman, 1972a,b; Jain and
Wray, 1978; Mullins, 1954; Seaman, 1972). Biologically active compounds must
reach a definite target site in'order-.to-elicit-a particular response, and
such compounds cross multiple hydrophyllic and 'hy.dr.ophobic barriers before
reaching the site of action (Kubinyi, .1978).
100
-------
REFERENCES
Adrian!, J. and M. Naraghi. 1977. The pharmacoLogic principles of regional
pain relief. Ann. Rev. Pharmacol. 17: 223-242.
Albano, C., W. Dunn III, U. Edlund, E. Johansson, B. Norden, M. Sojostrom and
S. Worl. 1978. Four levels of pattern recognition. Anal. Chim. Acta
103: 429-443.
Albert, A. 1965. Selective Toxicity. 3rd edition, John Wiley & Sons, Inc.,
New York, pp. 3-380.
Altura, B. M. 1980. Cardiovascular actions of general anesthetics. Fed.
Proceed. 39: 1574.
Ashcroft, R. G., H. G. L. Coster, and J. R. Smith. 1977a. Local anesthetic
benzyl alcohol increases membrane thicknes's-. Nature 269: 819-820;
Ashcroft, R. G., H. G. L. Coster, and J. R. Smith. 1977b. .The.molecular
organization of bimolecular lipid membranes. Biochem. Biophys. Acta
469: 13-22. .
Bamford, 0. S. 1974a. Oxygen reception in the rainbow trout (Salmo
gairdneri. Comp. Biochem. Physiol. 48A: 69-76.
Bamford, 0. S. 1974b. Respiratory neurons in rainbow trout (Salmo
gairdneri). Comp. Biochem. Physiol. 48A: 77-83.
Biagi, G. L., A. M. Barbaro, M. C. Guerra, M. Babbini, M. Gaiardi, and M.
Bartoletti. 1980. ^ values and structure-activity of
benzodiazepines. J. Med. Chem. 23: 193-201.
Bonath, K. 1977. Information on' experimental animals _+4 Narcosis of
repitiles, amphibians and fish, Part 3. pp. 99-149. Published by
Verlag-Paul. Parey, Berlin, Germany. _. ' ....- .
101
-------
Bulich, A. A..M. W. Greene, and D. L. Isenberg. 1979. The reliablity of one
bacterial luminescence- assay-for the determination of toxic ity of pure
compounds and complex effluents. ASTM, Proceedings of Chicago Meeting,
1979, (unpublished).
Cammarata, A. 1972. Interrelationship of the regression models used for
structure-activity analyses. J. Med. Chem. 15: 573-577.
Cammarata, A. and G. K. Menn. 1976. Pattern recognition classification of
therapeutic agents according to pharmacophores. J. Med. Chem. 19:
739-747.
Chu, K. C., R. J. Feldmann, M. B. Shapiro, G. F. Hazard, Jr., and R. I.
Geran. 1975. Pattern recognition and structure-activity relationship
studies. Computer-assisted prediction of antitumor activity in
strkucturally diverse drugs in an experimental-mouse brain tumor system.:
J. Med. Chem. 18(6): 539-545.
Clements, J. A. and K. M. Wilson. 1962. The affinity of narcotic agents for
interfacial films. Proceed. Natl. Acad. Sci. USA 48: 1008-1014.
Cohn, M., M. L. Cohn, S. K. Wolfson, and F. M. Steichen. 1980. Dibutyryl
cyclic AMP - an effective antidote against lethal doses of amobarbital
in rhesus monkey. Fed. Proceed. 39: 406.
Cohn, M. L. and M. Cohn. 1980. A new molecular theory of anesthesia. Fed.
Proceed. 39: 406.
Craig, P. N. 1971. Interdependence between physical parameters and
selection of substituent groups for correlation studies. J. Med.
Chem.14: 680-684.
Daxboeck, C. and G. F. Holeton. .1978. Oxygen receptors in the rainbow trout
(Salmo gairdneri). Can .J. Zool. 56: 1254-1259.
102
-------
Daxboeck, C. and G. F. Holeton. 1980. The effect of MS222 on the hypoxic
response of rainbow trout (Salmo gairdneri).
Draper, N. R. and H. Smith. 1966. Applied Regression Analysis. Wiley and
Sons, New York, NY.
Dunn, N. J., Ill, and S. Wold. 1978. A structure-carcinogenicity study of
4-nitroquinoline 1-oxide using the SIMCA method of pattern recognition.
J. Med. Chem. 21: 1001-1007.
Eyring, H., J. W. Woodbury, and J. S. D'Arrigo. 1973. A molecular mechanism
of general anesthesia. Anesthesiology 38: 415-424.
Ferguson, J. 1939. The use of chemial potentials as indices of toxicity.
Proceed. Roy Soc. London 127B: 387-404.
Ferguson, J. 1951. Mechanisme de la narcose. IN: Colloques Internat'ionaux
; du Centre National de la Recherche Scieritifique, Paris, p. 25.
Frank, R. E., W. E. Massy and D. G. Morrison. .1965. Bias in multiple
discriminant analysis. J. Market. Res. 2: 250-257.
Fujita, T. and T. Ban. 1971. Structure-activity of phenethylamines as
substrates of biosynthetic enzymes of sympathetic transmitters. J. Med.
Chem. 14: 148-152.
Gage, P. W. and P. 0. Hamill. 1976. Effects of several inhalation
anesthetics on the kinetics of postsynaptic conductance changes in mouse
diaphram. Br. J. Pharmacol. 57: 263-272.
Gero, A. 1965. Intimate study of drug-action III: Possible mechanism of
drug action IN: Drill's Pharmacology in Medicine (ed. J. R. DiPalma),
Blackiston Div., McGraw Hill Book Co.., New York, 3rd edition, pp.
47-69. .
Glave, W. R. and C. Hansch. 1972. Relationship between lipophilic character
and anesthetic activity. J. Pharm. Sci. 61(4): 589-591.
'103
-------
Gray, N. A. B. 1976. Constraints on "learning machine" classification
methods. Anal. Chem. 48: 2265-2268.
Guedel, A. E. 1951. Inhalation anesthesia, 2nd ed. pp. 10-52. McMillan Co.,
New York.
Halsey, M. J., R. A. Millar, and J. A. Button. 1974. Molecular mechanisms
in general anesthesia. Published by Churchill-Livingstone, Edinburgh,
England.
Halsey, M. J., B. W. Smith, and:C. J Green. 1978. Pressure reversal of
general anesthesia - a multisite expansion hypothesis. Br. J. Anaesth.
50: 1091-1097.
Hansch, C. and T. Fujita. 1964. p-^-^analysis A method for the correlation
of biological activity and chemical structure. J. Amer. Chem. Soc. 68:
' 16=16-.-' v '.'..'- >.';:->; :':;':;:;:.'-;/-'"-^';'''^; ;-;--'--;' =V-".V--. v ''.''-'::
Hansch, C., J. E. Quinlan, and G. L. Lawrence. 1968. The linear free-energy
relationship between partition coefficients and the aqueous.solubility
of organic liquids. J. Org. Chem. 33(1): 347-350.
Hansch, C., A. Leo, S. H. Unger, K. H. Kim, D. Nikaitani, and E. J. Lien.
1973. "Aromatic" sub.stitue.nt constants for structure-activity
correlations. J. Med. Chem. 16(11): 1207-
Hansch, C, S. H. Unger, and A. B. Forsythe. 1973. Strategy in drug design.
.cluster analysis as an aid in the selection of- substituents. J. Med.
Chem. 16: 1217-1222. ' >
Hargens, A. R., R. W. Millard, and K. Johanse.n. 1974. High capillary
permiability in fishes. Comp. Biochem. Physiol. 48A: 675-680.
Haydon, D. A., B. M. Hendry, S. R. Levinson, and.J. Requena. 1977.
Anesthesia by n-alkanes.- A comparative study, of nerve impulse blockage
,104 .
-------
and the properties of black lipid bilayer membranes. Biochem. Biophys.
Acta 470: 17-34.
Henry, D. R. and J. H. Block. 1979. Classification of drugs by discriminant
analysis using fragment molecular connectivity values. J. Med. Chrm.
22: 465-472.
Hesser, C. M., L. Fagraeus, and J. Adolfson. 1978. Roles of nitrogen,
oxygen and carbon dioxide in compressed air narcosis. Undersea Biomed.
Res. 5: 391-400.
.Heymans, F., L. Le Therizien, J.-J. God.froid. .1.979.. .Quantitative
structure-activity relationships for N-[(N1,N"-distributed-amino)acetyl]
jarylamines for local anesthetic activity and acute toxicity. J. Med.
Chera. 23: 184-193.
Hill,.M. W. 1978. Interaction of lipid vesicles with-anesthetics. Ann..
N.Y. Acad., Sci. 308: 101-110.
Hopfinger, A. J., R. Potenzone, Jr., R. Pearlstein, 0. Kikuchi, M. Shapiro,
G.-W. A. Milne, and B. R. Heller. 1979. Structure activity analyses
int he classification of toxic chemicals. Proc. Sym. on Safe Handling
of Chemial carcinogens. Ann Arbor Press, Ann Arbor, MI Vol 2, p
385-407, 1979.
Hudson, D. R., G. E. Bass and W. P. Purcell. 1970. Quantitative
structure-activity modesl, some conditions for application and
statistical interpretation. J. Med. Chrm. 13: 1184-1189.
Hunn, J. B. 1970. Dynamics of MS222 in the blood and brain of freshwater
fishes during anesthesia. U.S. Bur. Sport Fish Wildlife 42: 1-8.
Hunn, J. B. and J. L. Allen. 1974. Movement of drugs across the gills of
"fishes. Ann. Rve. Pharmacol. 14: 47-55.
105
-------
Iwamura, H. 1980. Structure-taste relationship of perillartine and nitro-
and cyanoanline derivatives. J. Med. Chem. 23: 308-312.
Jain, M. K., J. Gleeson, A. Opreti, and G. C. Upreti. 1978. Intrinsic
perturbing ability of alkanols in lipid bilayers. Biochem. Biophys.
Acta 507: 1-8.
Jain, M. K. and L. V. Wray, Jr. 1978. Partition coefficients of alkanols in
lipid bilayer/water. Biochem. Pharmacol. 27: 1294-1296.
Jain, M. K., N. Y. Wu. and L. V. Wray. 1975. Drug-induced phase change in
bilayer as possible mode of action of membrane expanding drugs. Nature
255: 494-495. .
Johnson, F. H., D. E. S. Brown, and D. A. Marsland. 1942. Pressure reversal
of the actions of certain narcotics. J. Cell COmp. Physiol. 20:
; 269-276. ''".""'": -:' ; ' ' ' '" :'<"^ ' '' "]"'"''' ' ". '
Jurs, P. C., B. R. Kowalski, and T. L. Isenbour. 1969. Computerized
learning machines applied to chemical problems. Anal. CHem. 41: 21-27.
Kamaya, H., I. Ueda, and P. S. Moore. 1979. Antagonism between high
pressure and anesthetics in the thermal phase-transition of dipalmitoyl
phosphatidylcholine bilayer. Biochim. Biophys. Acta 550: 131-137.
Karickhoff, S. W., D. S. Brown and T. A. Scott. 1979. Sorption of
hydrophobic pollutants on natural sediments. Water Res. 13: 241-248.
Kaufman, R. D. 1977. Biophysical mechanisms' of anesthetic action:
Historical perspective and review of; c'urrent theories. Anesthesioiogy
46: 49-62.
Keynes, R. D. 1972. Excitable membranes. Nature 239: 29-32.
Kier, L. B., and L. H. Hall. 1976. Molecular Connectivity in Chemistry and
Drug Research. Academic Press, Inc., New York, N.Y. 257 pp.
106
-------
Klontz, G. W. and L. S. Smith. 1968. Methods of using fish as biological
research subjects in method of animal experimentation (ed. Cay, W. I.)
Vol. 3, pp. 323-379. Academic Press, New York.
Koblin, D. D. and E. I. Eger. 1979. Current concepts theories of narcosis.
N. Engl. J. Med. 301(22): 1222-1224.
Kowalski, B. R. and C. F. Bender. 1974. The application of pattern
recognition to screening prospective anticancer drugs. Adenocarcinoma
755 biological activity test. J. Amer. CHem. Soc. 93: 916-918.
Kubinyi, H. 1979. Lipophilicity and biological activity. Drug Res. 29(11),
Nr. 8: 1067-1080.
Kubinyi, H. 1978. Non-linear dependence of biological activity on
hydrophobic characteri: the bilinear model. II Farmaco, Ed. Sc. 34:
248-276. " ' ' ' ' : ' '/''- '..'''. . ': -: ;"';'-/ ".:'-":--'
Kubinyi, H. 1977. Quantitative structure-activity relationships. 7. Tje
bilinear model, a new model for nonlinear dependence of biological
activity on hydrophobic character. J. Med. Chem. 20: 625-629.
Kubinyi, H. and O.-H. Kehrhahn. 1976a. Quantitative structure-activity
relationships. 1. The modified Free-Wilson approach. J. Med. Chem. 19:
578-586.
Kubinyi, H. and O.-H. Kehrhahn. 1976b. Quantitative structure-activity
relationships. 3. A comparison of different Free-Wilson models. J. Med.
Chem. 19(8): 1042-1049.
Kuschinsky, G. and Lullmann, H. 1973. Textbook of Pharmacology, Chapter 4.
Acad. Press Inc., New York, N. Y. 1-407.
Lee, A. G. 1976. Model for action of local anaesthetics... .Nature 262:
. 545-548. ' ' ..
107
-------
Lever, M. J., K. W. Miller, W. D..M. Paton, and E. B. Smith. 1971. Pressure
''""reversal of anesthesia. Nature 231: 368-371.
Llinas, R. R. and E. J. Henser. 1977. Depolarization-release coupling
systems in neurons. Neurosci. Res. Program Bull. 15: 556-687.
Longnecker, D. E. and P. D. Harris. 1980. Microcirculatory actions of
general anesthetics. Fed. Proceed. 39: 1580-1583.
Malinowski, E. R. 1977a. Determination of the number of factors and the
experimental error in a data matrix. Anal. Chem. 49(4): 612-617.
Malinowski, E. R. 1977b. Theory of error in factor analysis. Anal. chem.
49(4): 606-612.
Martin, Y. C. and H. N. Farias. 1979. Mathematical considerations in series
design. J. Med. Chem. 22: 784-791.
Marvin, D; E. and D.< T. 'Burton. ,1973. Cardiac and'respiratory .responses-of .
rainbow trout, blue gills and brown bullhead catfish during rapid'
hypoxia and recovery under normaxic conditions. Comp. Biochem. Physio.
46A: 755-766.
McCreery, M. J. and W. A. Hunt. 1978. Physico-chemical correlates of
alcohol intoxication. Neuropharmacol. 17: 451-461.
McElroy, W. D. 1947. Mechanism of inhibition of cellular activity by
narcotics. Quart. Rev.. Biol. 22: 25-58.
McFarland, J. W. 1970. On the parabolic relationship between drug potency
and hydrop'hobicity. J. Med. CHem. 13:' 1192-1196.
McFarland, W. N. and G. W. Klontz. 1969. Anesthesia in fishes. Fed.
Proceed. 28: 1535-1540.
108
-------
Meyer, K. H. 1899. Zur Theorie der Alkoholnarkose. Arch. Exper. Path. U.
Pharmakol. 42: 109-118.
Meyer, K. H. 1937. Contributions to the theory of narcosis. Trans. Faraday
Soc. 33: 1062-1068.
Miller, K. W., W. D. M. Paton, and R. A. Smith. 1973. The pressure reversal
of general anesthesia and the critical volume hypothesis. Mol.
Pharmacol. 9: 131-143.
Miller, J. C. and K. W. Miller. 1975. Approaches to the mechanisms of
action of general anesthetics. Blaschko, H. F. K. ed., Physiological
and pharmacological biochemistry. Biochem, Series One, Vol. 12, .
Publisher Butterworth, london, pp. 33-76.
Miller, K. W. 1977. Towards the molecular bases of anesthetic action.
. Anesthesiology 46: 2-4. : ': ' . , ; . . : '.-'
Miller, K. W. and K. Y. Y. Pang. 1976. General anesthetics can selectively
perturb lipid bilayer membranes. Nature 263: 253-255.
Morrison, D. G. 1969. On the interpretation of discriminant analysis. J.
Market. Res. 6: 156-163.
Mullins, L. J. 1954. Some physical mechanisms in narcosis. Chem. Rev. 54:
289-323.
Ogino, A., S. Matsumura, and T. Fujita. 1980. Structure-activity study of
antiulcerous and antiinflammatory drugs "by discriminant analysis. J.
Med. Chrm. 23: 437-444.
Operating Instructions Microtox Toxicity Analyzer Model 2055, Interim Manual
Number 110679, Beckman Instruments, Inc., Microbics.Operations, .
Carlsbad, CA, 1979. . . '-.".. .. .'-- ' '
109
-------
Otto, P., M. Steel, J; Ladiki, R. Muller. 1979. From retrospective to
predictive structure-activity correlations. J. Theor. Biol. 78:..
197-210.
Overton, E. 1901. Studied uber de narcose Zugleich ein Beitrag zur
allgemeinen pharmakologie Jena: Verlag von Gustav Fischer: 1901.
Pauling, L. 1961. Molecular theory of general anesthesia. Science 134:
15-21.
Price, H. L. and S. T. Ohnishi. 1980. Effects of anesthetics on heart.
Fed. Proceed. 39: 1575-1579.
Pringle,,M. J. and K. W. Miller. 1978. Structural isoraers of tetradecenol
discriminate between lipid fluidity and phase transition theories of
anesthesia. Biochem. Biophys. Res. Commun. 85: 1192-1198.
Rahwan, R. G. :1975. Toxic effects of e;thanpl: possible role' of
acetaldehyde, tetrahydroisoquinolines and tetrahydro-B-carbolines.
Toxicol. Appl. Pharmacol. 34: 3-27.
Rasmussen, G. T., T. L. Isenhour, S. R. Lowry, and G. L. Ritter. 1978.
Principal component anlaysis of the infrared spectra of mixtures. Anal.
Chim. Acta 103: 213-221.
Richards, C. D., K. Martin, S. Gregory, C. A. Keightley, .T. R. Hesketh, G. A.
Smith, G. B. Warren and J. C. Metcalfe. 1978. Degenerate perturbations
of protein structure as the mechanism of anesthetic actions. 'Nature
276: 775-779. ' ,:
Rimon, G., E. Hanski, S. Braun, and A. Levitzki. 1978. Mode of coupling
between hormone receptors and adenyLate eyelase elucidated by modulation
of membrane fluidity. Nature 276: 394-396.
110.
-------
Rosenberg, Ph. H., E. Hansjorg, and S. Anton. 1975. Biphasic effects of
halothane on phospholipid and synaptic plasma membranes: a spin label
study. Mol. Pharmacol. 11: 879-888.
Roth, S. H. 1979. Physical mechanism of anesthesia. Ann. Rev. Pharmacol.
Toxcol. 19: 159-178.
Roth, S. H. 1980. Membrane and cellular actions of anesthetic agents. Fed.
Proceed. 39: 1595-1599.
Roth, S. H. and P. Seeraan. 1971. All lipid soluble anesthetics protect red
cells. Nature New Biol. 231: 284-285.
Roth, S. H. and P. Seeman. 1972a. Anesthetics expand erythrocyte membranes
without causing loss of potassium. Biochem. Biophys. Acta 255:
190-198.
Roth, S. and P. Seeman. 1972b. The membrane concentrations of' neutral and ;
positive anesthetics (alcohols, chlorpromzaine, morphine) fit the
Meyer-Overton rule of anesthesia: negative narcosis do not. Biochem.
Biophys. Acta 255: 207-219.
Santara, N. J. and K. Augary. 1975. Non-computer approach to
structure-activity study. An expanded fibonacci search applied to
structurally diverse types of compounds. J. Med. Chem. 18: 959-963.
Saxberg, B. E.H., D. E. Duewer, J. L. Booker and B. R. Kowalski. 1978.
Pattern recognition and blind assay techniques applied to forensic
separation of whiskies. Anal. Chim. Acta 103: 201-212.
Schaad, L. J. and B. A. Hess, Jr. 1977. Theory of linear equations as
applied to quantitative structure-activity correlations. J.-Med. Chem.
20: 619-625. .. '. ' -
111
-------
Schettini, A. and W. W. Furniss. 1979. Brain water and electrolyte
distribution during the inhal-ation: of halothane. Br. J. Anaesth. 51:
1117-1124.
Schwab, S. H. and D. Schwab. 1979. The transformation of cytoplasmic
microtubules into helices and paracrystals by halothane in the
foraminifer Allogromi l-'at icollar'is'. . Arnold Z. Mikrbsk Anat. Forsch
93(4): 751-762.
Seeman, P. 1972. Membrane actions of anesthetics and tranquillizers.
Pharmacol. Rev. 24: 583-655.
Smith, F. M. and D. R. Jones'. 1978. Localization of receptors cau'sing
hypoxic bradycardia in trout (-Sai'mo gairdneri). Can. J. Zool. 56:
1260-1265.
Steriger, V. G. 'arid' T. Hv Mar Vri.-f 1974. The; pharmacology;'of .;MS222" (ethyl:- ;
m-aminobenzoate) in Squalus acanthias. Com'p. Gen. Pharmacol. 5: 23-35.
Stockard J. and R. Bickford'. 1975. The neurophysiology of anesthesia.
Gorden E., ed. A basis and practice of neuroanesthesia. Amsterdam:
Excepta-Med. pp. 3-4,6.
Topliss, J. G. 1977. A manual method for applying the Hansch approach to
drug design. J. Medy Chem!. 20('4>:. 463-469'.
Toplis.s, J.. G. and R. P. Edward's. 1-979'v Chance factors in studies of
quantitative structure-activity relationships. J. . Med...Chem. 22: ,,
1238-1244.
Traube, J. 1904. Theorie-der Osmose- und Narkbse. Arch. Geo. Phys:iol. 105:
541-558.
Trudell, J. R. 1977.- A unitary theory of anestriesda bas'ed on lateral, phase
separations in the. nerve membranes-. Ariesthesiblogy 46: 5-10'.
112'
-------
Trudell, J. R., D. G. Payan, and J. H. Chin. 1975. The antagonistic effect
of an inhalation anesthetic and high pressure on the phase diagram of
mixed dipalmitoyl-dimyristoylphosphatidylcholine bilayers,. Proceed.
Natl. Acad. Sci. USA 72: 201-210.
Unger, S. H. and C. Hansch. 1973. On model building in structure-activity
relationships. A reexamination of adrenergic blocking activitr of
B-Halo-B-arylalkylamineS; J. Med. Chem. 16(7): 745-749.
U.S. EPA. 1978. Experimental design, application and data interpretation
protocol. U.S. EPA, ORD-Corvallis, OR. EPA-600/9/78-018. 126 pp.
Vandam, L. 0. 1965. Uptake and transport of anesthetics and stages of
1 anesthesia IN: Drill's Pharmacology in Medicine (ed. J. R. DePalma),
Blakiston Div. McGraw-Hill Book Co., New York, 3rd edition, pp. 85-99.
Veith, G. D., N. M. Austin, and R. T.- Morris. .1979. A rapid method for. ;
estimating log P for organic chemicals. Water Res.' 13: 43-47.
\ , - - ' . ' , ~ - ' ' '' - .
Veith, G. D., D. L. DeFoe, and B.'V: Bergstedt. 1979. Measuring and
estimating the bioconcentration factor of chemicals in fish. J. Fish.
Res. Board Can. 36: 1040-1048.
Warren, G. B., M. D. Housley, J. C. Metcalfe, and N. J. M. Birdsall. 1975.
Cholesterol is excluded from the phospholipid annulus surrounding an
-*' ^
active calcium transport protein. Nature 255: 684-687.
Weisel, C. P. and J. L. Fasching. 1977. Dec-eptive "correct" separation by
the linear learning machine. Anal. Chem. 49: 2114-2116.
Winters, W. D. 1976. Effects of drugs on the electrical activity of the
brain: anesthetics. Ann. Rev. Pharmacol. 413-426.
113
-------
Wu, R. S., M. K. Wolpert-DeFilippes, and F. R. Quinn. 1980. Quantitative
structure-activity correlations of rifamycins as inhibitors of viral
RNA-directed DNA'polymerase and mammalian a and 8 DNA polymerases. J.
Med. Chem. 23: 256-261.
Yalkowsky, S. H. and G. L. Flynn. 1973.. Transport of alkyl homologs across
synthetic and .biological membranes: A new model for chain
length-activity relationships. J. Pharm. Sci. 62(2): 210-217.
Zaleska, M. 1979. Effect.of pentobarbital anaesthesia on serotonin
.".- ' -'^ > ..'. '-.
metabolism in hypoxic rat brain. Pol. J. Pharraaco.l. Pharm. 31(3):
179-186.
. 114
-------
------- |