Use of Computers in Toxicology and Chemical Design - ACS

Chapter 7

Use of Computers in Toxicology and Chemical Design G. W. A. Milne, S. Wang, and V. Fung

Downloaded by GEORGETOWN UNIV on August 27, 2015 | http://pubs.acs.org Publication Date: May 5, 1996 | doi: 10.1021/bk-1996-0640.ch007

Laboratory of Medicinal Chemistry, National Cancer Institute, National Institutes of Health, Building 37, Room 5B29, Bethesda, MD 20892

Experimental determination of the acute or chronic toxicity of chemicals has traditionally been carried out by treating animals with different doses of the compound and determining the effects. This procedure requires that a sample of the material be available. The testing is expensive, particularly if chronic toxicity is to be examined and as the testing is done in animals, the extrapolation of the results to humans is often of dubious reliability. For these reasons, efforts have been expended in recent years to develop computer programs which can predict toxicityfromchemical composition. The accuracy of such predictions is improving steadily and currently they offer an alternative to traditional toxicological methods. Further, they can be used to predict the toxicities of chemicals which do not exist and thus can be used as a guide to ecologically sensitive chemical development.

The toxicity of a chemical compound is an important property which can place significant restrictions upon the acceptability of the compound for use in the normal environment. Because ofthis, the measurement oftoxicity is a well established task and, depending upon the specific type of toxicological information that is sought, there are standard procedures for making these measurements. Until relatively recently, these methods invariably involved exposure of animals to different doses of the compound and recording ofthe resulting symptoms, up to and including death. Testing of this sort has been done for many years to determine both acute and chronic toxicities. The measurement of chronic toxicity, primarily carcinogenicity, is particularly problematical, for various reasons. Such testing must continue for as long as two years and the expense of maintaining test animals in a controlled environment throughout such a long test can be prohibitive. The different endpoints associated with cancer are often difficult to observe: death is the only unequivocal endpoint, but even there, cause may be obscure. Finally, numerous cases are now known of chemicals which, while carcinogenic to rats or mice are not to humans and this casts doubt upon the relevance of the animal data. This chapter not subject to U.S. copyright Published 1996 American Chemical Society

In Designing Safer Chemicals; DeVito, S., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1996.


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Use of Computers in Toxicology and Chemical Design

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Beginning in the 1970s, the value of mutagenicity, measured in vitro, as a markerfor possible carcinogenicity was examined (7). After much work on this subject, the current consensus is that mutagenicity is indeed a possible marker for carcinogenicity. Mutagenesis testing is relatively inexpensive but the imperfect relationship between mutagenicity and carcinogenicity in animals has allowed animal testing for chronic toxicity to survive, its expense notwithstanding. An alternative to either of these approaches is the estimation,fromthe chemical structure, of the toxicity of the compound. Development of this technique began in the 1980s and considerable improvements have been made in the methods during the last ten years. Because they are almost without cost and require no physical sample of the chemical, these approaches have presented themselves as serious alternatives to either in vivo or in vitro testing. The mechanics of estimation methods and their role, present and future, in toxicology are the subject of this chapter. Cost of Toxicity Testing in Animals The cost of conducting toxicity testing in animals is considerable and increases steadily. Obviously, acute testing is much less expensive than chronic testing but even here, the cost of toxicity measurement conducted to the standards used in the U.S. is in the thousands of dollars, rangingfrom$3,500 for a primary eye irritation study to $10,000 for a dermal study or an acute oral study which provides a toxicity limit and an L D A ninety day sub-chronic study ranges from $375,000 (oral dosage) to $950,000 (inhalation) and a chronic, two year study of the sort used in carcinogenicity testing costs about $1,125,000 for an oral dosage and $3,900,000 for an inhalation dosage. These latter costs are so huge that they are in and of themselves a powerful incentive to the development of alternative methods. 50

Methods of Toxicity Estimation Methods for the calculation of the values of biological effects, such as toxicity, from chemical structure all pay great allegiance to statistics. Some are outright products of statistical analysis of a large dataset; others are so-called "expert systems" in which the dependence upon statistics is much less explicit. There are many expert systems for toxicity prediction and many more continue to be developed; so far, there is littie consensus within the toxicology community regarding the predictive quality of these systems. The methods described here represent only a sampling and begin with those most heavily dependent upon statistical analysis progressing to the expert systems. 1. TOPKAT Beginning in the late 1970s, Enslein and co-workers (2-14) conducted a major effort to relate the biological properties of molecules to their structure. Their methodology, which is summarized in Figure 1, was based upon a statistical analysis of a large dataset using stepwise regression techniques to establish a relationship between various structural features and the toxicity data. A 'learning database" was establishedfroma subset of the large database and each chemical structure in this subset was entered as



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its linear SMILES code (75) in which structures are represented by a string of characters. Thus C1CC1C(=0)0 denotes cyclopropane carboxylic acid, while clccccclC(=0)0 represents benzoic acid. Each chemical structure in the learning database is analyzed by a program which generatesforthat structure a set of descriptors which encode substructural features and other properties such as charge, shape and connectivity - all properties which can potentially exert an influence upon toxicity. Stepwise regression and discriminant analysis are next used to search out mathematical relationships between the structural descriptors and the toxicity. The result is an equation which relates the two and which can be used with other structures, not in the learning database, to predict their toxicity. In an early use of this method (2), 549 compounds were taken from the NIOSH Toxic Substances List (16). Substructural descriptors as well as descriptors encoding logP (n-octanol/water partition coefficient) and molecular weight were developed and these parameters were related to the oral L D of the compound in rats. The resulting equation had the form: 50

(1000*MW)/LD = (0.111*FG51R) + (0.0624*FG120) +... + (0.000225*MW) + 0.426 30

in which the parameters FGnnn (FG = functional group) assume the value 0 or 1 depending upon whether the specific substructural feature (FG51R represents a carbonate ester attached to a ring, FG120 an olefinic bond, and so on) is present in the structure. In the early attempt to correlate structure with acute toxicity, a correlation coefficient (R ) of 0.493 was obtained. This method was thus used as the basis of a program for prediction of approximate acute toxicity from chemical structure. Subsequent work by the Enslein group led to refinement of the method and exploration of its utility in ranking in terms of acute toxicity of chemicals regulated under the Toxic Substances Control Act. The method used by TOPKAT may in principle be used to attempt to correlate any biological activity with chemical structure. It is reasonable to expect some sort of correlation and, provided the appropriate structural features are examined, a useful regression equation should be obtained. The method was studied for its utility in the prediction of chronic toxicity, expressed as carcinogenicity (3-5, 7), mutagenicity (8), teratogenicity (9), skin irritation (10,11) and biodegradability (14). In the case of carcinogenicity, a retrospective examination of the carcinogenic potential of chemicals classified by the International Agency for Research in Cancer (IARC) was carried out (2). A total of343 "definite" carcinogens or non-carcinogens were takenfromVolumes 1-17 of the IARC Monographs (17) and used as a learning database. The resulting regression equation was validated with datafromIARC Volumes 18-24 and for the 38 compounds in those volumes, a correct classification was obtained for 28 (74%). There were 12 steroids in this group and 7 of these were mis-classified. Removal of the 12 steroidsfromthe dataset gave a correct classification rate of 87%. Results of this sort have established TOPKAT as one of several methods which could be used as an alternative to animal testing for chronic toxicity. This issue is explored further in a later section. 2


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2. ADAPT Hie group headed by Jurs at Pennsylvania State University has, over a period of several years, developed statistically based methodology for the correlation of chemical structure with molecular properties, including toxicity. Their approach uses newer statistical tools such as pattern recognition and neural networks and thus differs from that of Enslein, who relies upon the traditional statistical analysis methods. hi the ADAPT software developed by Jurs (18), all the chemical structures in the database are entered in the form of connection tables. The program generates structural descriptors using these 2-dimensional structures, physicochemical parameters, elemental composition, and molecular shape. Then, using pattern recognition techniques, classifiers based upon these descriptors are developed which can discriminate between carcinogens and non-carcinogens. Finally, the number of descriptors used is reduced as far as possible without degrading the classification accuracy. This approach was applied to a dataset of209 chemicals (19) containing 130 carcinogens and 79 non-carcinogens. It was not possible to classify all 209 compounds accurately; the best results, which used about 30 or fewer descriptors, had a classification accuracy of between 90% and 95%. The best set of descriptors had a predictive accuracy of about 85%, producing false positives (non-carcinogen predicted to be carcinogenic) more often than false negatives. Further development of the ADAPT method has been directed towards quantitative estimation oftoxicity, particularly acute toxicity (20) and has been quite successful. This method, a variant ofthe Enslein technique is a competing method of toxicity estimation. 3. SIMCA A variation on the approach embodied in ADAPT is found in SIMCA (21). This program treats the toxicity prediction question as one of classification of compounds as toxic or non-toxic and uses cluster analysis (22), another pattern recognition technique, to achieve this classification. Application of this technique allows classification ofunknowns as to type and degree of toxicity, but the method apparently has not been used by other workers in the field. SIMCA use partial least squares (PLS) analysis, a powerful statistical technique which has more recently seen successful application to Comparative Molecular Field Analysis (CoMFA), a form of 3D QSAR. 3. CASE CASE was developed by Klopman at Case Western Reserve University during the 1980s (23-28) and is a statistical analysis method closely related to those already described. This program, shown schematically in Figure 2 is based upon statistical analysis of a database and as such as similar to TOPKAT. It requires a learning set of compounds whose structure and biological activities are known. Structures can be entered in a variety of formats and are then decomposed into molecular fragments containing between 2 and 5 linearly connected non-hydrogen atoms. These fragments, together with other structure-based parameters are evaluated vis-a-vis the activity associated with them, using primarily discriminant analysis. After removal of outliers


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Toxicity Prediction by Komputer-assisted Technology (TOPKAT)

Generation of Parameters 1


Assembly of Databases 2

Generate Parameters

3

Simple Statistics

4

Structure-Activity model development scheme. After assembly of the database and generation of parameters, statistical techniques are used to find a correlation between the toxicity and some or all of the parameters defined in step 3. Step 8 is used to determine the predictive power of the model.

Stepwise Regression 5 Drop outliers and levers 6 Regress all subsets

7

Validation

8

Figure 1. Estimation of Toxicities by Stepwise Regression.

1 Structure 1 The CASE program. The Database is composed of structures and their activities and the statistical evaluation finds the most discriminatory of the descriptors.

1 Activity 1

| ~~ | DATABASE]

| 1 f Fragments T

1 |

physical Propertied

I

I STATISTICAL EVALUATION I I Discriminant Analysis |

| Best Descriptors

Classification

QSAR]

New Compounds PREDICTIONS Mode of Action

Figure 2. The CASE Program.


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Use of Computers in Toxicobgy and Chemical Design

and levers, the descriptors which relate best to activity or lack of activity are retained. For continuous properties, such as L D values, linear regression is used to provide a quantitative relationship between the presence of the fragments and other molecular properties and the toxicity value. When chronic toxicity is considered, CASE can be used to determine the structural basis of the carcinogenicity of chemical compounds (28). Analysis of the structures of


50

A

B

C

189 chemicals tested in the NCI/NTP Bioassay revealed 23 molecularfragmentsthat could be linked to carcinogenicity, or the lack thereof in the parent compounds. These fragments include aromatic amino, nitro and methoxyl, and various aliphatic fragments, such as ether oxygen. Armed with this knowledge base, CASE is a practical means of predicting carcinogenic potential. CASE analysis of 4-cMoro-/?-phenylenediamine shows it to contain three crucial substructuralfragments,A, B and C. The presence of A in a compound associated with a 79% likelihood that the compound will be a carcinogen. The figures for B and C are 79% and 66%, respectively. The probability that any compound containing all threefragmentwill be carcinogenic is calculated to be 96.8%. This suggests that in this structure at least, the two aromatic amino groups reinforce one another and the chlorine has no effect upon the carcinogenicity of the compound. A valuable enhancement to CASE, and its successor MULTICASE, is the program META, also developed by Klopman's group. This program provides the potential for CASE to predict the toxicity not only of a chemical, but also of its expected metabolites. META (29, 30) is a knowledge-based expert system, similar to those discussed below, which is designed to simulate the biotransformations of chemicals. It does this with reference to dictionaries of known biotransformations. 4. Structural Alerts In 1988, Ashby and Tennant conducted a detailed analysis (37) of the stracture-activity relationships in the voluminous datafromthe NCI/NTP Bioassay, in which over 300 chemicals had been tested in animals for carcinogenicity. The central conclusion of the analysis was that mutagenicity to Salmonella and presence in the structure of an electrophilic she were both criteria that are consistent with carcinogenicity. The correlation of either of these properties with carcinogenicity is high. They both appear however, to be necessary, but insufficient criteria. Bioavailability for example, is clearly important, but is neglected in this study. This painstaking analysis is in effect, a qualitative statistical analysis and lies


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somewhere between the statistical analyses discussed above and the expert systems described below. The analytical aspect of the work is augmented by the expertise of the authors and the outcome is a set of what have become known as "structural alerts" items such as specific substructures or mutagenesis data which are found to correlate well with carcinogenicity. The structural alerts that emergefromthe analysis by Ashby and Tennant are observation of mutagenic activity in the Salmonella assay, and presence in the structure of any of several critical substructures, such as aromatic amine, epoxide, nitrosamine, and so on To these critical factors, other more mechanical items are often added and the usual list of structural alerts is: 1. The structure or its metabolites are DNA-reactrve (electrophilic). 2. The compound is mutagenic to Salmonella. 3. The compound can be administered at doses which do not lead to acute toxicity. 4. Organ damage is observed at sub-chronic doses. 5. Ancillary factors such as solubility, evidence of carcinogenicity are present. The use of structural alerts involves a simple examination of each of these categories for the chemical in question. If any of the 5 alerts is triggered, the estimate of potential carcinogenicity may be made. If more than one item is checked, the potential increases. This method for determining carcinogenic potential is disarmingly simple: in place of factors derivedfroma statistical analysis of the database, one use structural alerts which evolvefromthe experience of experts. This is in feet a rudimentary "expert system" which has much in common with the expert systems that will be described below. 5. COMPACT It has been proposed that specificity towards one of the P450 family of enzymes is a surrogate for toxicity, particularly chronic toxicity and this is the basis of a toxicity prediction system called COMPACT (32). This system, shown in Figure 3, relies on prior data but focuses upon the dimensions and electronic structure of molecules which it regards as predictors of toxicity. Two key parameters are the molecular shape, expressed as length/width or and electronic activation energy AE, defined as: AE = E - E w

A o w o

where liono denotes the lowest unoccupied, and homo the highest occupied molecular orbital. When a shape parameter such as area/depth2 is plotted against AE, the scatterplot that that results (Figure 4) shows carcinogens and non-carcinogens to be fairly well distinguishedfromone another. 6. DEREK DEREK is an expert system (33) which can assimilate a chemical structure, decompose it into substructuralfragmentsand assess the toxic potential of eachfragment,and hence of the complete molecule with reference to a set of rules to which it has access.



MILNE ET AL.


Draw 2D, compute 3D structure Minimize structural energy Determine molecular dimensions Calculate electronic structure Cluster analysis for P450 specificity Predict toxicity of compound

Figure 3. COMPACT Flowchart.



A E = Eiumo - Ehomo.

2

^

AE (eV)

Figure 4. COMPACT and Cytochrome P450 Substrates.

Shape is calculated as area/depth and energy as

For carcinogens activated by P450, shape of and electron energy levels in the substrate o. should correlate with toxicity. gj



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A strength of the system is its facility in dealing with structures. One can enter a structure in graphical form, or retrieve a structure from a database and submit it to DEREK. Generation offragmentsis automatic as is consultation with the rulebase. The result is a listing of the possible risks associated with the entered structure. For each risk the apposite rule is eked so the perceived strocture-activity association can be seen. The rulebase is open-ended; many of the rules represent a distillation of much toxicological experience, others may be more arbitrary. As an example, the FDA structural alerts for carcinogenicity have been built into a rulebase. Examination by the program of/7-hydroxyacetanilide (1) led to its categorization as an irritant because it is a phenol with no counter base (rule 41) and as a carcinogen because it is an aromatic amido compound and as such triggers FDA structural alert #1 (rule 107). Similarly, obenzyl-p-chlorophenol (2) is classified as an irritant by rule 41 but it is ruled to be noncarcinogenic because it triggers none of the alerts. When DEREK encounters a OH

CI 1

2

3

structure which is not adequately covered by its rules, it demurs. Thus 3,4dihydrocoumarin (3) is dismissed with "no comment". 7. HAZARDEXPERT This system, which is produced by the CompuDrug Corporation of Budapest and is shown in Figure 5, uses physical properties and molecularfragmentsto predict the toxicity of a compound. It is thus similar to TOPKAT except that it derives all the parameters it uses, including logP and pKfromthe entered molecular structure. In the same way that CASE and DEREK attempt to account for toxic metabolites, HAZARDEXPERT uses a related program METABOLEXPERT to examine the possibility that a compound could give rise to toxic metabolites. This appears to be a sophisticated estimation program but performance data are not readily available. 8. ONCOLOGIC During the last several years, the Environmental Protection Agency (EPA) has supported the development of a toxicity estimation system called OncoLogic. This is afranklyexpert system which relies only indirectly upon statistics. The OncoLogic code was written by computer programmers in close consultation with experts in different areas of toxicology and is ambitious in that it attempts to deal with all manner of materials, including organic and inorganic chemicals, metals and metalloids, and complex materials such as fibers and polymers. Each of these requires a quite distinct approach and the program goes some way towards accommodation of these eclectic


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requirements. Hie code is in theformof a large tree structure. At each level of the tree, the material in question is categorised into a more precise area. Thus at the top level, as shown in Figure 6, chemicals compounds are classified asfibers,polymers, organic compounds or metals and metalloids. Next, as shown in Figure 7, organics for example, are further subdivided. Compounds in one of the new subgroups, aromatic hydrocarbons, are then examined in terms of a number of expert rules. The result of this examination will be a carcinogenicity rating which is based upon the most current knowledge available and appropriate to that case.


The National Cancer Institute/National Toxicology Program Bioassay The National Toxicology Program (NTP), operated jointly by the National Cancer Institute and the National Institute for Environmental Health Sciences has, as one of its major activities, the task of testing chemicals for carcinogenicity towards mammals, primarily mice and rats. This testing is slow; a complete test requires over two years and the NTP has completed work on between 300 and 400 chemicals. In 1990, a list was compiled (3(5) by the NTP of 44 chemicals for which carcinogenicity testing was to be completed by 1994. The publication of the list was accompanied by the suggestion that interested scientists might use it for prospective estimation of the carcinogenicity of the chemicals and, in this way, obtain an objective measure of the efficacy of the various programs. Several groups responded to this challenge and the computer programs described in this chapter that were tested in this way were TOPKAT, COMPACT and CASE. The list was also examined in terms of stnictural alerts and a comparison of these four, and other methods was thus possible. To date testing has been completed on 37 of the compounds; 11 were established by testing as non-carcinogens and 3 were classified as "unknown" (Table I) and of the remaining 30 (Table II) 22 were determined to possess carcinogenicity while 8 were found to have "equivocal" carcinogenicity. The performance of the different prediction systems is given in the Tables and summarized in Figure 8. This summary ignores all "unknown" or "equivocal" results and thus covers 31 compounds. As can be seen from Figure 8, COMPACT and the structural alerts performed equally well with 58% correct predictions. Both CASE and TOPKAT performed less well, although a mitigating factor for TOPKAT is that it declined to make a prediction for one third of all the compounds, on the grounds that it had too little relevant data. TOPKAT made 21 predictions and of these 12 (57%) were correct. The mixing of "equivocal" bioassay results with firm data reflects the uncertainties in chronic toxicological testing referred to earlier, but in spite of this, it seems fair to conclude from this experiment that all the methods have 50-60% accuracy with COMPACT and structural alerts out-performing TOPKAT and CASE. This accuracy is not adequate, but as research on these methods proceeds, it will presumably improve. Summary We are at an interesting stage in the development of toxicity estimation capabilities. There is available a large amount of experimental data, including acute toxicity data for


MILNE ET AL.


In HAZARDEXPERT, parameters are calculated from the entered structures and toxicities are estimated with regard to species, route and dose.

Enter molecular structure Calculate parameters, logP, MW, pK Identify toxic fragments Define species, route, dose


Predict toxicity Predict bioavailability Predict bioaccumulation

Figure 5. The HAZARDEXPERT System.

Chemical Compound

_i_

Physical State

Biological Properties

MUTAGENICITY

Molecular Weight?

>1QQQ

Use of Computers in Toxicology and Chemical Design - ACS

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