PREDICTIVE TOXICOLOGY - CHAPTER 9 ppsx

9 Applications of Substructure-Based SAR in Toxicology HERBERT S. ROSENKRANZ Department of Biomedical Sciences, Florida Atlantic University, Boca Raton, Florida, U.S.A. BHAVANI P. THAMPATTY Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A. 1. INTRODUCTION The increased acceptance of SAR techniques in the regulatory arena to predict health and ecological hazards (1–6) has resulted in the development and marketing of a number of SAR programs (7). The approaches are of optimal usefulness when they are employed as adjuncts to the appropriate The authors have no commercial interest in any of the technologies described in this review. 309 © 2005 by Taylor & Francis Group, LLC human expertise. In addition to predicting specific toxicologi- cal endpoints, these methodologies, in the hands of an expert, can also be used to gain insight into the mechanistic basis of the action of toxicants and thereby allow a more refined health or ecological risk assessment (8,9). This review deals with aspects of SAR methodologies that are based upon substructural analyses that are driven primarily by statistical constraints [e.g., MULTICASE (10– 12)] as opposed to satisfying predetermined rules [e.g., DEREK, (13–15) ONCOLOGIC (16,17); ‘‘Structural Alerts’’ (18)]. It must, however, be made clear that human expertise is very much involved in most aspects of these knowledge- based substructural methods (8,9). Thus, the inclusion of experimental data into the ‘‘learning set’’ that forms the basis of any SAR model must adhere to previously agreed upon protocols and data handling procedures (Fig. 1). Moreover, prior to SAR modeling, the context in which the resulting model will be used has to be defined as it will affect the manner in which the biological=toxicological activities are encoded and the derived SAR model interpreted. Thus, it is commonly recognized (7,19) that the induction of cancers in rodents is one of the most challenging phenom- ena to model by SAR techniques. Yet, bearing in mind the Figure 1 Outline of the SAR approach indicating the interactions with the human expert. 310 Rosenkranz and Thampatty © 2005 by Taylor & Francis Group, LLC complexity of the phenomenon and the regulatory context in which SAR predictions were to be used, Matthews and Con- trera (20) of the U.S. Food and Drug Administration—by encoding the spectrum of activities, i.e., carcinogenicity in male and=or female rats and=or mice and devising rules on how the predictions were to be used—were able to develop a highly predictive MULTICASE SAR model of rodent carcino- genicity. It needs to be stressed that the success in developing the model was primarily the result of the human insight brought by the investigators (20). 2. THE ROLE OF HUMAN EXPERTISE Substructure-based SAR approaches can handle databases in which activities are expressed categorically, i.e., active, mar- ginally active, inactive, or in a continuous scale. However, it is not always a matter of simply inserting data into the model. Thus, the database for the induction of unscheduled DNA synthesis is indeed categorical (21) and allows the derivation of a coherent SAR model (22). On the other hand, the Salmo- nella mutagenicity database generated under the aegis of the U.S. National Toxicology Program (23) requires insight into how to express activities with respect to SAR modeling. Essentially, in that data set, each chemical is reported with respect to its ability to induce mutations in five Salmonella typhimurium tester strains in the presence or in the absence of several postmitochondrial activation mixtures (S9) derived from rats, mice, and hamsters induced or uninduced with the polychlorinated biphenyl mixture Aroclor 1254 (24). Each of the tester strains has a different specificity with respect to its response to mutagens. Moreover, the exogenous S9 mix- tures may contain different levels of cytochrome P450 activat- ing and deactivating enzymes which may act on the test chemical and=or its metabolites. If the purpose for deriving a SAR model is to understand the basis of the mutagenicity of a class of chemicals, then the Salmonella strain that is the most responsive to that chemical class should be used [e.g., the mutagenicity of nitrated polycyclic aromatic Applications of Substructure-Based SAR in Toxicology 311 © 2005 by Taylor & Francis Group, LLC hydrocarbons should be studied in Salmonella typhimurium TA98 in the absence of S9 (25–27)]. Similarly, if the aim is to understand the differences in mutagenicity in tester strains that respond to base substitution mutations vs. those that respond to frameshift mutations as a result of covalent binding to a DNA base, then one might model separately and then com- pare, for example, the responses of aromatic amines in Salmo- nella strains TA98 and TA100 in the presence of S9 (28). In such instances, for SAR modeling, the human expert would select the specific mutagenic potency (e.g., revertants=nmole= plate) reported for each chemical for the specific strain with or without S9. Moreover, based upon personal knowledge of the system and the specific class of chemicals, the expert would then have to select a cut-off value between mutagens and mar- ginal mutagens, and between marginal mutagens and non- mutagens. The expert would then be able to derive an equation relating mutagenic potency to an SAR unit scale compatible with the SAR program being used (see below). If, on the other hand, the purpose of deriving a SAR model is to identify potential ‘‘genotoxic’’ (i.e., mutagenic) car- cinogens, which is the class of agents associated with risk to humans (29–33), then one might consider deriving a dozen or more separate SAR models (e.g., TA 100-S9, TA100 þS9, TA 98, TA 1537, etc.) and then devise an algorithm to combine the results of the different models into a single prediction [see Refs. (34) and (35)]. This, however, is a tedious and time-con- suming process. Moreover, ‘‘genotoxic’’ carcinogenicity has not been associated with either a response in a specific tester strain or with the mutagenic potency in that strain. Rather, the association is a qualitative one between carcinogenicity and mutagenicity in any of the strains and carcinogenicity in rodents (36). Accordingly, consideration can then be given to the paradigm that a response in any one of the tester strains in the absence or the presence of a single S9 prepara- tion will be sufficient to identify a carcinogenic hazard. More- over, since different tester strains may respond differently qualitatively as well as quantitatively to individual chemi- cals, the indications of potencies that are used cannot be con- tinuous. In fact, they must be categorical and the expert may 312 Rosenkranz and Thampatty © 2005 by Taylor & Francis Group, LLC designate specific criteria for defining a mutagen, e.g., twice the spontaneous frequency of mutations and a linear dose– response (37,38). Depending upon an understanding of the mechanis- tic=biological basis of activity, there have been variations on the potency metrics. Thus, the Carcinogen Potency Data Base (CPDB) (39) reports results as TD 50 values, i.e., the daily dose that in a lifetime study will permit 50% of the treated animals to remain tumor-free. The TD 50 value is reported as mg=kg=day (39–41). However, given the widespread range in molecular weights of the chemicals in a data set (e.g., dimethylnitrosamine and benzo(a)pyrene, molecular weights 74 and 252 Da, respectively), for SAR studies that measure needs to be transformed into mmol=kg=day in order to yield a meaningful SAR model and the associated generation of ‘‘modulators’’ (see below) that affect the potency of the SAR projection. The human expert has to make a further decision: the definition of a ‘‘marginal carcinogen’’ and a ‘‘non-carcinogen.’’ Should only chemicals inducing no cancers even at the maxi- mum tolerated dose (42–44) be considered non-carcinogens or should there be a cut-off dose, above which even if tumors are induced, they would not be considered biologically or toxicolo- gically significant given the high dose needed? This would reflect Paracelsus’ dictum ‘‘that it is the dose that makes the toxin’’ (45). For the purpose of SAR modeling of CPDB, we chose cut- off values of 8 and 28 mmol=kg=day between carcinogens and marginal carcinogens, and between marginal carcinogens and non-carcinogens, respectively. Based upon the characteristics of the MULTICASE SAR methodology wherein SAR units 19 indicate non-carcinogenicity; 20–29 marginal carcino- genicity; and 30 carcinogenicity, this led to the relationship SAR activity ¼ð18:328 log 1=TD 50 Þþ46:55 ð1Þ On the other hand, the rodent carcinogenicity database generated under the auspices of the NTP has been classified Applications of Substructure-Based SAR in Toxicology 313 © 2005 by Taylor & Francis Group, LLC according to its spectrum of activities (29). The reason for that classification is derived from the realization that agents that are carcinogenic at multiple sites of both genders of rats and mice are generally found to be ‘‘genotoxic’’ (i.e., possess muta- genicity and=or structural alerts for DNA reactivity) (29,30). These characteristics are associated with a greater carcino- genic risk to humans than chemicals that are restricted to inducing cancers in a single tissue of a single gender of a single species (29,33). That spectrum of carcinogenicity can be captured by hav- ing the scale of carcinogenic activities (i.e., SAR units) reflect it, i.e., 10 SAR units for non-carcinogens; 20 for ‘‘equivocal’’ carcinogens; 30 for chemicals carcinogenic at only a single site in a single sex of a single species; 40 for chemicals carcino- genic at a single site in both sexes of one species; 50 for chemicals carcinogenic to a single species but at two or more sites; and 60 for chemicals carcinogenic to mice and rats at one or more sites (46). Because the spectrum of activities as well as the poten- cies reflect different aspects of the carcinogenic phenomenon, algorithms were developed to combine the results of the different SAR models of rodent carcinogenicity into a single prediction model (34,35). Although the approach used heretofore is a Bayesian one (47), there is no reason to suppose that other approaches (neural networks, genetic algorithm, rule learners) are not equally effective (e.g., see Refs. 48,49). Obviously, this integrative approach is not restricted only to SAR models of rodent carcinogenicity. They could include projections obtained with other SAR models related to mechanisms of carcinogenicity, i.e., SAR projections of carci- nogenicity combined with the prediction of the in vivo induc- tion of micronuclei (50) and of inhibition of gap junctional intercellular communication (51). Finally, the same approach can be explored to combine SAR projections with the experi- mental results of surrogate tests for carcinogenicity (e.g., induction of chromosomal aberration and of mutations at the tk þ= locus of mouse lymphoma cells). Finally, combining the results from different SAR approaches, e.g., knowledge-based 314 Rosenkranz and Thampatty © 2005 by Taylor & Francis Group, LLC (e.g., MULTICASE) with rule-based [e.g., DEREK (13–15) or ONCOLOGIC] (16,17) is a promising avenue that is worthy of further investigation. The point of the above examples is that human familiar- ity with an expertise in the biological phenomenon under investigation is essential for the maximal utilization of SAR techniques. Another instance in which human expertise was essential for the development of a coherent SAR model involves allergic contact dermatitis (ACD) in humans. In that endeavor, initial human insight was needed at several crucial steps: 1. The recognition that in spite of common practice and assumption, human and guinea pig ACD data were not equivalent and could not be pooled to develop a coherent SAR model (52). 2. That the inclusion of ‘‘case reports’’ among experi- mentally determined human ACD data degraded the performance of the SAR model unless the number of independent ‘‘case reports’’ was greater than 7 (53). 3. That an ACD response calibration based upon the challenge dose, the extent of the response, and the proportion of responders among challenged humans had to be developed to provide a potency scale (54). When these pre-SAR processing experimental data hand- ling procedures were resolved, a coherent and highly predic- tive SAR model of human ACD was developed (54). But again, it required the participation and collaboration of experimental immunologists and SAR experts. The same considerations entered in developing other models, e.g., human developmental toxicity which depended upon: (1) the acceptance of the results of an expert consensus panel, and (2) the rejection of results of borderline signifi- cance (55). Of course, it was also the reason for the success of the development of the aforementioned highly predictive SAR model of rodent carcinogenicity by Matthews and Contrera (20). Applications of Substructure-Based SAR in Toxicology 315 © 2005 by Taylor & Francis Group, LLC 3. MODEL VALIDATION: CHARACTERIZATION AND INTERPRETATION Irrespective of the SAR paradigm employed, knowledge and understanding of the performance of the resulting SAR model is crucial to its deployment. This is especially so as no SAR model is perfectly predictive. Yet, understanding a model’s limitations is needed if it is to be used and interpreted. The most widely accepted measure of a model’s performance is the concordance between experimentally determined results and SAR-derived predictions of chemicals external to the model. This parameter, in turn, is a function of a model’s sensitivity (correctly predicted actives=total actives) and specificity (correctly predicted inactives=total inactives). The most direct and preferable approach to determine these parameters is to randomly remove from the learning set a number of chemicals to be used as the ‘‘tester set.’’ The remaining chemicals can be used to develop the SAR model. The resulting models’ predictivity parameters and their sta- tistical significance can then be determined by challenge with this external ‘‘tester set.’’ However, most frequently that approach cannot be taken with respect to SAR models describing toxicological phenom- ena. This derives from the fact that the performance of a SAR model depends upon its size (i.e., the number of chemi- cals in the database) (10,56–58). For most databases of toxico- logical phenomena, there is a paucity of experimental results for chemicals. Accordingly, the predictive performance of the model will be negatively affected by removal of chemicals to be used as the external ‘‘tester set.’’ Because of this considera- tion, cross-validation and ‘‘leave-out one’’ approaches have been used (59). Thus, it has been demonstrated that the itera- tive random removal of chemicals (e.g., 5% of the total) and using the remaining ones (i.e., 95%) as the learning set and repeating the process (e.g., 20 times for a 5% removal), and determining the cumulative predictivity parameters are an acceptable approach (59). In most substructure-based SAR approaches, the signifi- cant structural determinant (e.g., biophores and toxicophores) 316 Rosenkranz and Thampatty © 2005 by Taylor & Francis Group, LLC identified will be a substructure enriched among active chemi- cals. Accordingly, the presence of the toxicophore is associated with a probability of activity and a baseline potency (Table 1; Fig. 2). While biophores=toxicophores are the significant as well as the principal determinants of biological and toxicological activity, toxicologists as well as health risk assessors are well aware that not all chemicals in a certain chemical class are toxicants even though the majority may be. Thus, only 83.3% of nitroarenes tested are Salmonella mutagens and only 74.4% of chloroarenes tested are reported to be rodent carcinogens (60). This situation is reflected in the fact that only 74% of the chemicals containing the toxicophore NH 2 – c–cH¼ (Fig. 2) are rodent carcinogens. The question then arises whether SAR approaches can be used to explain this dichotomy as well as to provide a basis for the difference in projected potencies. In MULTICASE SAR, this discrimination is provided by modulators (10–12). Thus each biophore= toxicophore is associated with a probability of activity and a basal potency. For the illustration in Fig. 2, the presence of the toxicophore is associated with a 75% probability of carcinogenicity and a potency of 50.3 SAR units. Based upon Eq. (1), 50.3 SAR units correspond to a TD 50 value of 0.62 mmol=kg=day. In MULTICASE, each biophore= toxicophore may be associated with a group of modulators (Table 2) which determine whether the potential for activity is realized and, if so, to what extent. Modulators are primarily structural elements that can either increase (Fig. 3), decrease (Fig. 4), or abolish (Fig. 5) the potential potency associated with a toxicophore. Additionally, the potential of a toxico- phore can be negated by the presence in the molecule of deac- tivating moieties that are derived from inactive molecules in the data set. The latter are not associated with chemicals that are at the origin of the toxicophore (e.g., Fig. 6). In addition to being substructural elements, modulators may also be physical chemical or quantum chemical in nat- ure. Thus, the rat-specific carcinogenic toxicophore associated with the activity of the chloroaniline derivative shown in Fig. 7, which defines a non-genotoxic rat carcinogenic species, Applications of Substructure-Based SAR in Toxicology 317 © 2005 by Taylor & Francis Group, LLC Table 1 Some of the Major Toxicophores Associated with Rodent Carcinogenicity: Non-congeneric Data Base Toxicophore 1–2–3–4–5–6–7–8–9–10 Number of Fragments Inactives Marginals Actives Number NH 2 –c¼cH– 65 15 3 47 1 NH–C¼N– 9 1 0 8 2 [Cl–]h–4.0A–i [Cl–] 21 2 0 19 3 CH 2 –N–CH 2 –2970224 O–CH¼ 7007 5 N–C¼ 5005 6 O–C¼ 14 1 0 13 7 O ^ –CH 2 –6 0068 Br–CH 2 –5 0059 cH¼cH–c¼cH–cH¼h3–Cli 14 3 0 11 10 PO–O 11 1 0 10 11 CH 3 –N–c¼cH–h2–CH 3 i 6105 12 cH¼c–cH¼cH–c <¼h2–NHi 611413 Cl–CH 2 –26412114 c. 00 –CO–c.¼ 700715 NO 2 –C¼CH– 14 0 0 14 16 cH¼c–cH¼cH–c¼h2–CH 3 i 500517 CH 3 –C¼cH–cH¼cH– 7 0 1 6 18 Toxicophore no. 1 is shown in Figs. 1–6, 18, and 19, no. 17 in Fig. 18. ‘‘c’’ and ‘‘C’’ refer to aromatic and acyclic atoms, respectively; c. indicates a carbon atom shared by two rings; O ^ indicates an epoxide; c 00 indicates a carbon atom connected by a double bond to another atom. h3–Cliindicates a chlorine atom substituted on the thrid non-hydro- gen atom from the left. 4.0 A! indicates a 2-D 4 Angstrom distance descriptor. In toxicophore no. 18, the second carbon from the left is shown as unsubstituted. This means that it can be substituted with any atom except hydrogen. On the other hand, for this toxicophore, the last carbon on the right is shown with an attached hydrogen. This means it cannot be substituted by any other atom but hydrogen. Finally, in toxicophore no. 10, the third non-hydrogen atom from the left is shown as unsubstituted. It can only be substituted by a chlorine atom. 318 Rosenkranz and Thampatty © 2005 by Taylor & Francis Group, LLC [...]... Group, LLC Applications of Substructure-Based SAR in Toxicology 325 Figure 7 Predicted carcinogenicity in rats of 3-( l,l,l,-trichloro-) propyl-p-chloroaniline The prediction is based on the toxicophore shown in bold The potency is modulated by ( 9 [water solubility]) The potency of 63.1 units corresponds to a TD50 value of 0.12 mmol=kg=day The analogous 3 propyl-p-chloraniline has a water solubility... exchanges (see Fig 10) The four structures are clearly different from 18-crown-6 © 2005 by Taylor & Francis Group, LLC Applications of Substructure-Based SAR in Toxicology 3 29 Figure 12 The prediction of the potential of 18-crown ether-6 to induce mutations at the tkþ=À locus of mouse lymphoma cells The structure of 18-crown ether-6 as well as of the seven molecules that gave rise to the toxicophore... model, especially when a cross-validation approach is used © 2005 by Taylor & Francis Group, LLC 326 Rosenkranz and Thampatty Figure 8 The identification of a moiety in 2,4-difluoro-N-methylaniline that is present once in the data set However, the molecule containing it (tetrafluoro-m-phenylenediamine) is a carcinogen with a TD50 value of 0.50 mmol=kg=day Accordingly, this N-methylaniline derivative must... using these substructure-based approaches and applying them to congeneric data sets and possibly improve the predictive performance and refine the structural information to better elucidate mechanisms This naturally © 2005 by Taylor & Francis Group, LLC 336 Rosenkranz and Thampatty requires a preliminary chemical class classification Thus, should a chemical such as 4-amino-3-chloro-2-methylphenol be classified... not necessarily have been identified where a toxicophore-restricted learning subset was selected On the other hand, when the non-congeneric data set was used, the resulting model predicted p-aminobenzoic acid (pABA) to be a carcinogen (Fig 19) In all probability, this Figure 19 The projected ‘‘carcinogenicity’’ of p-aminobenzoic acid based on the non-congeneric SAR model This physiological chemical is... chemicals possessing great potency, e.g., tetrafluoro-m-phenylenediamine with a TD50 value of 0.50 mmol=kg=day (Fig 8) One of the characteristics that differentiates SAR methods used in drug discovery from those used in toxicology © 2005 by Taylor & Francis Group, LLC 322 Rosenkranz and Thampatty Figure 4 The projected marginal potency of 2,6-dichloro-p-phenylenediamine The carcinogenic potency inherent... 328 Rosenkranz and Thampatty Figure 10 The prediction of the inability of 18-Crown ether-6 to induce sister chromatid exchanges in vitro The structure of 18crown ether-6 is shown in Fig 11 On the other hand, the determination of differences in environment reported in Fig 12 may not be justified as the test chemical, 18-Crown-6 ether, can be biotransformed to an acyclic structure that bears similarities... learning set is © 2005 by Taylor & Francis Group, LLC Applications of Substructure-Based SAR in Toxicology 337 Figure 18 The prediction of the carcinogenicity of 4-toluidine In addition to toxicophore A, this molecule contains toxicophore B which is derived from five non-arylamine carcinogens Based upon toxicophore B, the potency is 49. 1 SAR units or a TD50 value of 0.73 mmol=kg=day; i.e., the potency based...Applications of Substructure-Based SAR in Toxicology 3 19 Figure 2 Prediction of the carcinogenicity in rodents of m-cresidine The presence of toxicophore A is associated with a 75% probability of carcinogenicity and a basal potency of 50.3 SAR units which corresponds to a TD50 value of 0.62 mmol=kg=day [see Eq (1)] is modulated by 9 (water solubility of the chemical) In effect,... ‘‘human expert’’ would agree with the SAR model-generated prediction which is accompanied by a warning regarding the ‘‘environment.’’ Obviously, in the above © 2005 by Taylor & Francis Group, LLC Applications of Substructure-Based SAR in Toxicology 331 Figure 14 An example of a prediction subsequently overruled The SAR model predicts that curcumin induces a2m-globulin associated nephropathy in male rats . determining the predictive performance of the model, especially when a cross-validation approach is used. Figure 7 Predicted carcinogenicity in rats of 3-( l,l,l,-trichloro-) propyl-p-chloroaniline mutagenic) car- cinogens, which is the class of agents associated with risk to humans ( 29 33), then one might consider deriving a dozen or more separate SAR models (e.g., TA 100-S9, TA100 þS9, TA 98 , TA. 8 The identification of a moiety in 2,4-difluoro-N-methyla- niline that is present once in the data set. However, the molecule containing it (tetrafluoro-m-phenylenediamine) is a carcinogen with aTD 50 value