Use of structure-activity relationships for probing biochemical mechanisms: glutathione transferase zeta conjugation of haloacids.
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Acetates↗
Biomedical subjects
A M Richard
Publications and source records attributed to A M Richard.
An evaluation of the mutagenicity, metabolism, and DNA adduct formation of 5-nitrobenzo[b]naphtho[2,1-d]thiophene.
Thioarenes, sulfur-containing polycyclic aromatic compounds, are environmental contaminants suspected of posing human health risks. In this study, 5-nitrobenzo[b]naphtho[2,1-d]thiophene (5-nitro-BNT), a nitrated-thioarene, was examined for its mutagenicity, metabolism and subsequent formation of DNA adducts. 5-Nitro-BNT was weakly mutagenic in Salmonella typhimurium strains TA98 and TA100 without Aroclor-1254-induced rat liver S9 (S9), and its activity was increased in the presence of S9. Anaerobic metabolism of 5-nitro-BNT by S9 or xanthine oxidase (XO) produced one major metabolite, identified as 5-amino-BNT by NMR, MS, and UV spectroscopy and by comparison with an authentic standard. Aerobic S9 metabolism of 5-nitro-BNT produced a major metabolite, identified as trans-9,10-dihydroxy-9,10-dihydro-5-nitro-BNT (5-nitro-BNT-9,10-diol). Also present was a minor amount of 5-amino-BNT and trans-9,10-dihydroxy-9,10-dihydro-5-amino-BNT (5-amino-BNT-9,10-diol). DNA adduct analyses were performed using the (32)P-postlabeling assay and reversed-phase HPLC. Three major XO-derived calf thymus DNA adducts were detected. On the basis of their chromatographic mobilities, two adducts were identified as reaction products of 5-nitro-BNT with 2'-deoxyguanosine and one adduct with 2'-deoxyadenosine. Incorporation of allopurinol (a specific XO inhibitor) in the incubation mixture resulted in loss of all three adducts, confirming enzymatic mediation by XO. Aerobic S9 activation of 5-nitro-BNT with calf thymus DNA produced three adducts. On the basis of their chromatographic mobilities, two were identified as reaction products of 5-nitro-BNT with 2'-deoxyguanosine and one with 2'-deoxyadenosine. Incorporation of 1-aminobenzotriazole (a P450 inhibitor) in the incubation mixture resulted in a loss of these adducts, confirming enzymatic mediation by P450. Aerobic S9-catalyzed metabolism of 5-nitro-BNT-9,10-diol produced the same DNA adducts as observed with 5-nitro-BNT. Aerobic S9-catalyzed metabolism of 5-amino-BNT-9,10-diol produced the same deoxyadenosine-derived DNA adducts as observed with 5-nitro-BNT and 5-nitro-BNT-9,10-diol. These results provide additional information that both ring oxidation and nitroreduction are involved in the metabolism, DNA adduct formation and mutagenicity of 5-nitro-BNT.
Animals↗
Glucagon-like peptide 1 stimulates lipolysis in clonal pancreatic beta-cells (HIT).
Glucagon-like peptide 1 (GLP-1) is the most potent physiological incretin for insulin secretion from the pancreatic beta-cell, but its mechanism of action has not been established. It interacts with specific cell-surface receptors, generates cAMP, and thereby activates protein kinase A (PKA). Many changes in pancreatic beta-cell function have been attributed to PKA activation, but the contribution of each one to the secretory response is unknown. We show here for the first time that GLP-1 rapidly released free fatty acids (FFAs) from cellular stores, thereby lowering intracellular pH (pHi) and stimulating FFA oxidation in clonal beta-cells (HIT). Similar changes were observed with forskolin, suggesting that stimulation of lipolysis was a function of PKA activation in beta-cells. Triacsin C, which inhibits the conversion of FFAs to long-chain acyl CoA (LC-CoA), enhanced basal FFA efflux as well as GLP-1-induced acidification and efflux of FFAs from the cell. Increasing the concentration of the lipase inhibitor orlistat progressively and largely diminished the increment in secretion caused by forskolin. However, glucose-stimulated secretion was less inhibited by orlistat and only at the highest concentration tested. Because the acute addition of FFAs also increases glucose-stimulated insulin secretion, these data suggest that the incretin function of GLP-1 may involve a major role for lipolysis in cAMP-mediated potentiation of secretion.
Calcium↗
Mutation spectra in Salmonella of analogues of MX: implications of chemical structure for mutational mechanisms.
We determined the mutation spectra in Salmonella of four chlorinated butenoic acid analogues (BA-1 through BA-4) of the drinking water mutagen 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX) and compared the results with those generated previously by us for MX and a related compound, MCF. We then considered relationships between the properties of mutagenic potency and mutational specificity for these six chlorinated butenoic acid analogues. In TA98, the three most potent mutagens, BA-3, BA-4, MX, and the organic extract, all induced large percentages of complex frameshifts (33-67%), which distinguish these agents from any other class of compound studied previously. In TA100, which has only GC sites for mutation recovery, >71% of the mutations induced by all of the agents were GC-->TA transversions. The availability of both GC and TA sites for mutation in TA104 resulted in greater distinctions in mutational specificity than in TA100. MX targeted GC sites almost exclusively (98%); the structurally similar BA-4 and BA-2 produced mutations at similar frequencies at both GC and AT sites; and the structurally similar BA-3 and BA-1 induced most mutations at AT sites (69%). Thus, large variations in structural properties influencing relative mutagenic potency appeared to be distinct from the more localized similar structural features influencing mutagenic specificity in TA104. Among a set of physicochemical properties examined for the six butenoic acids, a significant correlation was found between pK(a) and mutagenic potency in TA100, even when the unionized fraction of the activity dose was considered. In addition, a correlation in CLOGP for BA-1 to BA-4 suggested a role for bioavailability in determining mutagenic potency. These results illustrate the potential value of structural analyses for exploring the relationship between chemical structure and mutational mechanisms. To our knowledge, this is the first study in which such analyses have been applied to structural analogues for which both mutagenic potency and mutation spectra date were available.
Base Sequence↗
Mutation spectra of the drinking water mutagen 3-chloro-4-methyl-5-hydroxy-2(5H)-furanone (MCF) in Salmonella TA100 and TA104: comparison to MX.
The chlorinated drinking water mutagen 3-chloro-4-methyl-5-hydroxy-2(5H)-furanone (MCF) occurs at concentrations similar to or greater than that of the related furanone 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX). MCF and MX differ structurally only by replacement of a 3-methyl in MCF with a 3-dichloromethyl in MX; yet, MCF is significantly less mutagenic than MX and produces different adducts when reacted with nucleosides or DNA. To explore further the effects that these structural differences might have on the biological activity of MCF and MX, we determined the mutation spectra of MCF in Salmonella strains TA100 and TA104 and of MX in strain TA104; the spectrum of MX in TA100 had been determined previously. In TA100, which presents only GC targets for mutagenesis, MCF induced primarily (75%) GC --> TA transversions, with most of the remaining revertants (20%) being GC --> AT transitions. This spectrum was not significantly different from that of MX in TA100 (P = 0.07). In TA104, which presents both GC and AT targets, MCF induced a lower percentage (57%) of GC --> TA transversions, with most of the remaining revertants (33%) being AT --> TA transversions. In contrast, MX induced almost only (98%) GC --> TA transversions in TA104, with the remaining revertants (2%) being AT --> TA transversions. Thus, almost all (98%) of the MX mutations were targeted at GC sites in TA104, whereas only 63% of the MCF mutations were so targeted. These results are consistent with the published findings that MX: (1) forms an adduct on guanosine when reacted with guanosine, (2) induces apurinic sites in DNA, and (3) forms a minor adduct on adenosine when reacted with adenosine or DNA. The results are also consistent with evidence that MCF forms adenosine adducts when reacted with adenosine. Our results show that the replacement of the 4-methyl in MCF with a 4-dichloromethyl to form MX not only increases dramatically the mutagenic potency but also shifts significantly the mutagenic specificity from almost equal targeting of GC and AT sites by MCF to almost exclusive targeting of GC sites by MX. Environ. Mol. Mutagen. 35:106-113, 2000 Published 2000 Wiley-Liss, Inc.
Alleles↗
Theoretical study of the SNV reaction of trichloroethylene (TCE) and CH3S- as a model for glutathione conjugation of TCE.
Trichloroethylene (TCE), a major environmental pollutant, is activated to mutagenic and nephrotoxic intermediates through a glutathione (GSH) conjugation pathway. Three product isomers of GSH-TCE conjugation, having potentially different toxicities, are theoretically possible: cis- or trans-S-(1, 2-dichlorovinyl)glutathione (cis- or trans-1,2-DCVG, respectively) or 2,2-DCVG. This study involved application of ab initio molecular orbital theory to computing potential energy profiles (PEPs) and predicting product outcome of the reaction of CH3S- with TCE as a model for GSH-TCE conjugation in biological systems. A goal of this study was to determine the extent to which a body of chemical knowledge pertaining to nucleophilic vinylic substitution (SNV) reactions, of which the GSH-TCE conjugation is a representative example, is relevant to this biological conjugation problem. PEPs were computed for all studied species at the HF/6-31+G level of theory; electron correlation effects were estimated at the MP2/6-31+G and MP4/6-31+G levels, and the influence of solvation was estimated using the PS-GVB solvation model. Multiple proposed reaction pathways were considered, including conjugation at the C1 or C2 site on TCE, by in-plane (sigma) or out-of-plane (pi) approach of the nucleophile. Some aspects of the MP2 and HF PEPs were found to differ significantly. However, on the basis of comparison of activation barriers, calculations at all levels of theory predict preference for C2 conjugation over C1 conjugation and formation of the trans-1,2-DCVM product over the cis-1,2-DCVM product. These predictions are consistent with GSH-TCE conjugation results from in vivo experiments. In contrast, relative product energies appear to be a poor indicator of the product outcome for this system. Hence, theoretical consideration of the reaction chemistry in the vicinity of the site of nucleophilic addition appears to be necessary and sufficient to predict the outcome of the enzyme-mediated GSH-TCE conjugation.
Environmental Pollutants↗
Commercial toxicology prediction systems: a regulatory perspective.
The use of commercial toxicity prediction systems in a regulatory setting must consider both the limitations and capabilities of the methods, as well as the ultimate use of the predictions, e.g. for testing prioritization, screening, or supporting regulatory decisions. Current systems are better suited to hazard identification (i.e. positive identification of activity-conferring features) than to ruling out hazard. Two recent examples (an EPA testing prioritization exercise for water disinfection byproducts and a regulatory action on 2,4,6-tribromophenol) illustrate issues involved in regulatory applications of SAR and commercial prediction systems. The challenge for the future will be to improve technologies for prediction within the constraints of available data, make optimal use of new test data, and better integrate elements of quantitative modeling (QSAR), empirical association, and biological and chemical mechanisms towards the goal of toxicity prediction.
Disinfection↗
Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?
There is a great deal of current interest in the use of commercial, automated programs for the prediction of mutagenicity and carcinogenicity based on chemical structure. However, the goal of accurate and reliable toxicity prediction for any chemical, based solely on structural information remains elusive. The toxicity prediction challenge is global in its objective, but limited in its solution, to within local domains of chemicals acting according to similar mechanisms of action in the biological system; to predict, we must be able to generalize based on chemical structure, but the biology fundamentally limits our ability to do so. Available commercial systems for mutagenicity and/or carcinogenicity prediction differ in their specifics, yet most fall in two major categories: (1) automated approaches that rely on the use of statistics for extracting correlations between structure and activity; and (2) knowledge-based expert systems that rely on a set of programmed rules distilled from available knowledge and human expert judgement. These two categories of approaches differ in the ways that they represent, process, and generalize chemical-biological activity information. An application of four commercial systems (TOPKAT, CASE/MULTI-CASE, DEREK, and OncoLogic) to mutagenicity and carcinogenicity prediction for a particular class of chemicals-the haloacetic acids (HAs)-is presented to highlight these differences. Some discussion is devoted to the issue of gauging the relative performance of commercial prediction systems, as well as to the role of prospective prediction exercises in this effort. And finally, an alternative approach that stops short of delivering a prediction to a user, involving structure-searching and data base exploration, is briefly considered.
Animals↗
Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity.
Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more "personalized" configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.
Automation↗
Theoretical evaluation of two plausible routes for bioactivation of S-(1,1-difluoro-2,2-dihaloethyl)-L-cysteine conjugates: thiirane vs thionoacyl fluoride pathway.
The selective nephrotoxicity of halogenated alkenes has been attributed to a glutathione (GSH) S-conjugate pathway involving enzymatic hydrolysis to the cysteine S-conjugate and beta-lyase bioactivation to thiolates, which are presumed to give rise to the ultimate mutagenic or cytotoxic reactive species. Studies have shown that the brominated S-(2,2-dihalo-1,1-difluoroethyl)-L-cysteine conjugates are mutagenic in the Ames test, whereas the nonbrominated analogues are nonmutagenic. While careful experimentation has contributed much to current understanding, the ultimate reactive species responsible for the differing mutagenic effects remain unknown. Computational methods were applied to the investigation of two proposed metabolic pathways leading from the thiolate to either a thiirane or thionoacyl fluoride intermediate, both electrophilic species presumed capable of binding to proteins or DNA. Studied were six F-, Cl-, and Br-substituted 2,2-dihalo-1,1-difluoroethane-1-thiolates (2,2-dihalo-DFETs). Pathway preference was determined for each thiolate by comparison of reaction energy profiles and activation energies. At all but the lowest level of ab initio theory, a thionoacyl fluoride pathway was predicted for 2,2-difluoro-DFET, while a thiirane pathway was energetically preferred for the brominated 2,2-dihalo-DFETs. These results offer a clear mechanism-based rationale for distinguishing 2,2-difluoro-DFET from the brominated 2,2-dihalo-DFETs, while the results are less clear for the 2,2-dichloro and 2-chloro-2-fluoro-DFETs, which at the highest level of ab initio treatment had a relatively small energy preference (2.4 kcal/mol) for the thiirane pathway. The predicted clear preference for a thiirane pathway for the brominated 2,2-dihalo-DFETs is not consistent with a recently proposed pathway involving alpha-thiolactone formation through a thionoacyl fluoride intermediate [Finkelstein, M. B., et al. (1995) J. Am. Chem. Soc. 117, 9590-9591], but is supported by results of a recent study providing experimental evidence for thiirane formation from the brominated 2,2-dihalo-DFETs [Finkelstein, M. B., et al. (1996) Chem. Res. Toxicol. 9, 227-231].
Biotransformation↗
Glutathione S-transferase-mediated induction of GC-->AT transitions by halomethanes in Salmonella.
Halomethanes are among the most common mutagenic and carcinogenic disinfection by-products present in the volatile/semivolatile fraction of chlorinated drinking water. Recent studies have demonstrated that the mutagenicity of dichloromethane (CH2Cl2) and bromodichloromethane (BrCHCl2) can be mediated by a theta-class glutathione S-transferase (GSTT1-1). These studies used strain RSJ100 of Salmonella, which is a derivative of the base-substitution strain TA1535 (hisG46, rfa, delta uvrB), into which has been cloned the GSTT1-1 gene from rat. In the present report, we have extended these studies by demonstrating that the mutagenicity of two additional brominated trihalomethanes, bromoform (CHBr3) and chlorodibromomethane (CICHBr2), are also mediated by GSTT1-1 in RSJ100. Using a Tedlar bag vaporization technique, the mutagenic potencies (revertants/ppm) for these two compounds as well as the compounds tested previously rank as follows: CHBr3 approximately CICHBr2 > BrCHCl2 approximately CH2Cl2. To explore the mutational mechanism, we determined the mutation spectra of all four halomethanes at the hisG46 allele by performing colony probe hybridizations of approximately 100 revertants induced by each compound. The majority (96-100%) of the mutations were GC-->AT transitions, and 87-100% of these were at the second position of the CCC/GGG target. In contrast, only 15% of mutants induced by CH2Cl2 were GC-->AT transitions in the absence of the GSTT1-1 gene in strain TA100 (a homologue of TA1535 containing the plasmid pKM101). The ability of GSTT1-1 to mediate the mutagenicity of these di- and trihalomethanes and the induction of almost exclusively GC-->AT transitions by these compounds suggest that these halomethanes are activated by similar pathways in RSJ100, possibly through similar reactive intermediates. The implications of these findings are discussed in relation to previous experimental work on the GST-mediated bioactivation of dihalomethanes, which includes the possible formation of GSH intermediates and/or GSH-DNA adducts.
DNA↗
QSARS of mutagens and carcinogens: two case studies illustrating problems in the construction of models for noncongeneric chemicals.
There is a strong motivation to develop QSAR models for toxicity prediction for use in screening, for setting testing priorities, and for reducing reliance on animal testing. Decisions must be made daily by toxicologists in governments and industry to direct limited testing to the most urgent public health problems, and to direct the types of chemical synthesis and product development efforts undertaken. This need has motivated attempts to construct general QSAR models (e.g., for rodent carcinogenicity), not tailored to congeneric series of chemicals. These various attempts have provided interesting and important scientific evidence; however, they have also shared a limited overall performance. The goal of this paper is to illustrate, by two unrelated actual examples of QSARs for mutagens and carcinogens, some fundamental problems relative to the application of general QSAR approaches to noncongeneric chemicals. Both examples consider data sets that are noncongeneric in a chemical structure and mechanism of action sense: in the first case, a mean mutagenic potency defined as an average over multiple genetic toxicity endpoints, and, in the second case, the NTP two-sexes, two species rodent carcinogenicity bioassay results for 280 carcinogens and noncarcinogens. The problems encountered with the QSAR analyses of these two cases indicate that a successful approach to the problem of QSAR modeling of noncongeneric data will need to consider the multidimensional nature of the problem in both a chemical and a biological sense. Since different chemical classes represent largely independent action mechanisms, some means for extracting local QSARs for constituent classes will be necessary. Alternatively, a general QSAR derived for a noncongeneric data set will need to be scrutinized and decomposed along chemical class lines in order to establish boundaries for application and confidence levels for prediction.
Animals↗
Quantitative structure-activity relationships for the developmental toxicity of haloacetic acids in mammalian whole embryo culture.
Developmental toxicity in mouse whole embryo culture assay has been reported for acetic acid (AA) and a series of ten haloacetic acids, including mono-, di-, tri-fluoro (MFA, DFA, TFA), chloro (MCA, DCA, TCA), bromo (MBA, DBA, TBA), and monoiodo (MIA) acetic acids. Benchmark concentrations (BCm), calculated as the lower 95% confidence limit of molar acid concentration producing a 5% increase in embryos with neural tube defects, provided potency estimates for development of quantitative structure-activity relationships (QSARs). The best overall regression was obtained for the ten halo-acids (excluding AA) and related log (1/BCm) to the energy of the lowest unoccupied molecular orbital (Elumo) and acid dissociation constant (pKa) with a correlation coefficient of r = 0.97, and a sample size-adjusted r2 = 0.92. This QSAR suggested a common basis for the mechanism of HA activity, which would imply additivity for mixtures of these acids. Examination of QSARs for subsets of the total data set (e.g., monohaloacids) highlighted parameter relationships embedded in the total QSAR, helping to unravel the separate contributions of Elumo and pKa to the overall potency. The relevance of these parameters is discussed in terms of postulated mechanisms of developmental toxicity involving changes in intercellular pH and redox metabolism. The whole embryo assay results pertain to direct embryo exposure and toxicity without the confounding influence of maternal factors. The resulting QSAR model offers possible insight into the mechanism of embryo toxicity that will hopefully contribute to understanding of the more complex, in vivo teratogenicity problem.
Acetates↗
Role of computational chemistry in support of hazard identification (ID): mechanism-based SARs.
A mechanism-based structure-activity relationship (SAR) study examines the structural basis for a chemical/biological activity by targeting a single or a few stages in a postulated mechanism of action. Computational chemistry approaches provide a valuable complement to experiment for probing such associations, but require a highly focused viewpoint that neglects much of the full biological and chemical interaction problem. Research questions are formulated in terms of fundamental structure and reactivity properties and are designed to test key assumptions of a postulated mechanism of activity. The results of such studies can aid in the generation of new hypotheses, suggest new experiments, and provide scientific rationale for extrapolation in hazard identification (ID). Toxicologists and computational chemists bring very different, yet complementary viewpoints, approaches, and expertise to bear on the hazard ID problem. However, improved communication and interaction between these two groups is needed to most productively address hazard ID issues.
Algorithms↗
Synthesis and characterization of adducts of alachlor and 2-chloro-N-(2,6-diethylphenyl)acetamide with 2'-deoxyguanosine, thymidine, and their 3'-monophosphates.
Adducts of the preemergence herbicide 2-chloro-N-(methoxymethyl)-N-(2,6-diethylphenyl)-acetamide (alachlor) and 2-chloro-N-(2,6-diethylphenyl)acetamide (CDEPA) with 2'-deoxyguanosine, thymidine, 2'-deoxyguanosine 3'-monophosphate, and thymidine 3'-monophosphate have been synthesized and characterized. Under mildly basic conditions alachlor and CDEPA form N-1 adducts with 2'-deoxyguanosine and N-3 adducts with thymidine as a result of chlorine displacement. In addition, alachlor formed an N-7 adduct with 2'-deoxyguanosine, 7-[[(N-(methoxymethyl)-N-(2,6-diethylphenyl)carbamoyl]methyl]guani ne. N-1 adducts of alachlor and CDEPA with 2'-deoxyguanosine 3'-monophosphate and N-3 adducts with thymidine 3'-monophosphate are also described. In addition to spectroscopic data, structural proof included the dephosphorylation of each nucleotide adduct to its corresponding nucleoside adduct by nuclease P1. Alachlor and alachlor adducts but not CDEPA and CDEPA adducts exhibited rotational isomerism as evidenced by proton and 13C NMR studies. These rotamers were attributed to hindered rotation about the shortened N-carbonyl bond. Computational methods employing molecular mechanics and quantum mechanics were used to characterize the structures and energies of these rotamers to account for the patterns of duplicate NMR resonances observed.
Acetamides↗
International Commission for Protection Against Environmental Mutagens and Carcinogens. Application of SAR methods to non-congeneric data bases associated with carcinogenicity and mutagenicity: issues and approaches.
In both industry and government, structure-activity relationships (SAR) are capable of playing an important decision-support role in estimating the potential mutagenicity or carcinogenicity of chemicals for which bioassay test results are unavailable. Traditional SAR modeling approaches, however, are usually restricted to the consideration of structurally similar chemical congeners. The highly structurally diverse nature of current carcinogenicity and mutagenicity data bases has motivated development of more general SAR approaches, potentially applicable to the treatment of diverse, non-congeneric mutagenicity and carcinogenicity data bases. Three specific approaches are considered in some detail--Ashby's structural alerts model, classified as a "rule-based" SAR approach, and the computerized CASE fragment-based method and TOPKAT linear discriminant equation method, both classified as "correlative" SAR approaches. Relative strengths and limitations, and a number of common features and important distinctions between these 3 methods are discussed. Rule-base methods are highly flexible and able to incorporate many different types of relevant information, yet are biased towards current knowledge, viewpoints, and mechanistic assumptions, that may or may not hold true. Correlative SAR methods are less biased and offer the promise of "discovering" potentially new SAR associations that could lend fresh insight into the basis for a structure-activity association. However, problems associated with their application to non-congeneric data bases relate to: modeling multiple or overlapping mechanisms of action with a single relationship; defining the range of applicability of models in complex multi-dimensional structure-activity space; assigning confidence levels to predictions in the absence of knowledge concerning mechanisms of activity; and determining the potential mechanistic significance of diverse model parameters. It is argued that many of these concerns can be partially alleviated by careful application of statistical procedures, scrutiny of model results, and establishment of reasoned limits to the range of model applicability. The most significant confidence-building measure, however, will be a rationalization of the correlative SAR model and model parameters in terms of principles of chemical reactivity and postulated molecular mechanism(s) for the biological activity. Hence, it is recommended that models and model descriptors be designed to facilitate mechanistic interpretation and hypothesis generation. Finally, problems in comparing the relative predictive capabilities of different SAR approaches are discussed, and strategies for SAR investigation involving integration of existing techniques are suggested.
Animals↗
A CASE-SAR analysis of polycyclic aromatic hydrocarbon carcinogenicity.
A CASE SAR analysis was performed on a selected database of PAHs to investigate the possible use of the CASE method as an aid for preliminary assessment of carcinogenic potential of untested environmental PAHs. A data set, denoted LEARN, consisting of 78 PAHs and their experimental carcinogenicities was used to 'train' the CASE method and derive the CASE fragments. 8 activating fragments and 4 inactivating fragments were identified. These fragments predicted the activities of 94% of the LEARN set correctly. The biological significance of several of these fragments are rationalized in light of the current theories of PAH carcinogenesis. Using these fragments, the potential activities of a database of 106, mostly untested PAHs, denoted TEST, were predicted. These were compared to 'expert judgement' predictions based on mechanistic considerations in order to evaluate the extent of concordance between these two methods and their respective strengths and weaknesses. Initial poor agreement (64%) was attributed to limitations of the LEARN database involving inadequate representation of 2- and 3-ring PAH subclasses. When these subclasses were excluded from the TEST database, the concordance improved to 90%. The CASE fragments were also used to predict the activities of a database of 24 PAHs, denoted VALIDATE (not included in the LEARN set) for which carcinogenicity data were available. The total prediction accuracy of 75% (89% of the actives correctly identified), despite the structural diversity of the VALIDATE set, provided independent evidence of the utility of the present CASE results. A close examination of the CASE incorrect predictions was conducted to delineate inadequancies of these CASE results in order to provide cautionary guidance for future application of the method. Finally, the present results were compared to the results of a previous CASE analysis based on a more limited PAH data set, and were found to be of greater general utility. It is concluded that the CASE fragments derived in the current study should provide a useful tool for assisting and complementing 'expert judgement' in the preliminary screening of PAHs for carcinogenic activity.
Carcinogenicity Tests↗
Strategies for the use of computational SAR methods in assessing genotoxicity.
The relationship between computational SAR studies and relevant data gathering and generation activities is complex. First, the chemical class to be studied is selected on the basis of information requirements for hazard identification and assessment. Membership in the class is determined by consideration of chemical structure and reactivity. Compilation of the existing bioassay data for this chemical class follows immediately from the specification of the class. Bioassay data, qualitative knowledge of general chemical reactivities in this class, and knowledge concerning potential interactions with biomolecular targets all contribute to the derivation of possible mechanisms for biological activity. Computational studies based on modeling the proposed mechanism of action and/or the existing data base can provide a quantitative basis for the differentiation between chemicals. There is the opportunity for continuing feedback between the quantitative computational studies and the development of a relevant bioassay data base for this chemical class. The qualitative and quantitative information on the potential biological responses obtained will provide a rational basis for extrapolation from the extant data base to the chemicals of interest, and to biological responses significant to the assessment for which complete data are unavailable. Knowledge concerning possible mechanisms of action and preexisting data determine the type of computational study that will be most useful.
Animals↗