Predicting Chemical Toxicity and Fate - Section 3 ppt

SECTION 3 QSARs for Human Health Endpoints © 2004 by CRC Press LLC C HAPTER 8 Prediction of Human Health Endpoints: Mutagenicity and Carcinogenicity Romualdo Benigni CONTENTS I. Introduction II. Data for QSAR Modeling A. Public Sources of Carcinogenicity and Mutagenicity Data III. QSAR Modeling of Mutagenicity and Carcinogenicity A. QSARs for Individual Chemical Classes B. An Example: QSARs for the Aromatic Amines C. QSAR Models for Noncongeneric Chemicals IV. The Assessment of the Prediction Ability A. The First NTP Comparative Exercise on the Prediction of Rodent Carcinogenicity B. The Second NTP Comparative Prediction Exercise on the Prediction of Rodent Carcinogenicity C. Lessons from the Comparative Exercises on the Prediction of Carcinogenicity . V. How Should a User Approach the Prediction of Mutagenicity and Carcinogenicity? A. The Human Expert Approach VI. Recommendations: A Summary References I. INTRODUCTION One of the most ambitious goals of structure-activity relationship/quantitative structure-activity relationship (SAR/QSAR) applications to toxicology is the modeling of the chemical carcinoge- nicity; this because of the severity of its consequences on the quality of life and because of the enormous investment in time, financial resources, and animal lives required to test chemicals adequately. Mutagenicity is another important toxicological endpoint: chemical mutagens provoke heritable — mostly deleterious — changes to the genetic material. From a mechanistic point of view, carcinogens can be classified as: (1) genotoxic and (2) epigenetic carcinogens (Woo and Lai, 2003). Genotoxic carcinogens, also known as deoxyribonucleic acid- (DNA) © 2004 by CRC Press LLC reactive carcinogens, interact directly with DNA either as parent chemicals or as reactive metabolites (Miller and Miller, 1977). Genotoxic, or mutagenic, carcinogens are thought to work by inducing mutations; the first step in several carcinogenic processes often consists of one or more mutations (the somatic mutation theory of cancer) (Arcos and Argus, 1995). The major classes of genotoxic carcin- ogens are: direct-acting carcinogens (such as epoxides, aziridines, nitrogen and sulfur mustards, F-halo- ethers, and lactones); aromatic amines and nitroaromatics; nitrosoamines and nitrosoamides; hydroazo and azoxy compounds; carbamates; organophosphates; aflatoxin-type furocoumarins; and homocyclic, heterocyclic, and polycyclic aromatic hydrocarbons (Ashby, 1995; Woo et al., 1995; 2002). Epigenetic carcinogens act through mechanisms that do not involve direct DNA damage. In reality, there is seldom an absolute demarcation, and a better definition would be that of carcinogens that are predominantly genotoxic and predominantly epigenetic (Woo and Lai, 2003). Epigenetic carcinogens include cytotoxic chemicals that induce compensatory regenerative hyperplasia, agents that act via receptors, agents that cause indirect DNA damage via reactive oxygen species, and agents that regulate gene expression. For an updated review on this explosively growing literature see Woo and Lai (2003). As opposed to genotoxic carcinogens, no unifying mechanistic theory exists for the action of epigenetic carcinogens, and each class has to be studied separately. II. DATA FOR QSAR MODELING A direct way to assess the mutagenic and carcinogenic potential of a chemical is to test it in an appropriate experimental system. At the same time, collation of these experimental data consti- tutes the necessary database to build SAR/QSAR models that can be used for predicting the activity of untested chemicals. A wide range of experimental systems have been generated to determine mutagenicity (Zeiger, 1987; 1994). In their classical form, the mutagenicity tests (e.g., those based on the various Salmonella typhimurium bacterial strains) provide both a yes/no (mutagenic/nonmutagenic) out- come, and a quantitative definition of potency of the mutagenic compounds (e.g., increase of mutants per dose). Since chemical mutagenicity can be studied in model experimental systems more quickly and easily than carcinogenicity, the study of the mutagenicity has contributed remarkably to the study of cancer, and several short-term mutagenicity tests are used as surrogates and predictors of carcinogenicity. The main tool to assess the carcinogenic potential of a chemical is the rodent bioassay. Because of its central role in the regulation of chemicals, the rodent bioassay has been under intense scrutiny. The overall evidence points to the validity of the bioassay as a basis for human risk assessment (Fung et al., 1995; Haseman et al., 2001; Huff, 1999; Huff, 2002; Tomatis et al., 1997). The bioassay provides three types of information: 1. Yes/no response (if a chemical has to be considered as being carcinogenic or not) in the various experimental groups. These individual group yes/no responses allow for the generation of the overall carcinogenicity score. 2. Potency of the carcinogenic compounds. For each tumor type induced, a potency index can be calculated (e.g., TD 50 , which is the dose required to halve the probability of the animals remaining tumorless). A measure often used is the geometrical mean of the TD 50 ’s, averaged over the whole range of tumors (Gold et al., 1991). 3. The profile of tumors (e.g., target organs) induced by the chemical. These three endpoints are quite different in terms of relevance for the human health, and in terms of suitability for modeling. The yes/no response is highly relevant and predictive for the human health. All human carcin- ogens are also animal carcinogens, and several human carcinogens were first discovered in animals (Huff, 1993; 1999). © 2004 by CRC Press LLC Carcinogenic potency is also very relevant. There is evidence of a strong correlation between the ranking of potency in rats and mice (Benigni and Giuliani, 1999). Moreover, a strong correlation between carcinogenic potency estimated from epidemiologic data and that estimated from animal carcinogenesis bioassays, hence between human and rodent carcinogenic potency, has been dem- onstrated (Allen et al., 1988; Goodman and Wilson, 1991). The similar ranking of carcinogenic potency in different species suggests that potency is an intrinsic property of a chemical carcinogen that is derived to a greater extent from its chemical reactivity. As opposed to the generality of carcinogenic potency, the tumor types induced appear to be highly variable from species to species. Not only do they depend on the species, but also may vary with the condition of use (e.g., age of the host, as well as dose and route of administration) of the carcinogen. Differences in the tumor profiles may result not only from differences in targeted reactions of the ultimate carcinogens, but also in the myriad of events that mediate and surround these reactions (Bucci, 1985). In this sense, the information on tumor profiles is of limited impor- tance for the extrapolation of the risk to humans. An important issue concerns the quality and reproducibility of the rodent carcinogenicity bioassay data. The bioassay, as performed by the standard protocols, is very costly and time consuming, and full replicate experiments are seldom performed. Based on analyses of a relatively small set of 38 replicate experiments, Gold et al. (1987) have estimated the overall reproducibility of the rat bioassay to be 85% and the mouse bioassay to be 80%; both values are quite satisfactory. Gottmann et al. (2001) analyzed a larger set of 121 chemicals for which replicate rodent bioassay results for the same chemicals, but tested under different protocols, were available. The estimated overall concordance in rodent carcinogenicity classification was only 57%. Since the results ana- lyzed by Gold et al. were all generated under the strict protocols adopted by the U.S. National Toxicology Program (NTP), and the results considered by Gottmann et al. were of more varied origin, the conclusion that can be made is that adherence to strict experimental protocols is an essential requirement. At the same time, attention to the origin of the data, and to the protocols used for their generation, is equally necessary for those who want to build QSAR models. An important issue for QSAR modeling is an appreciation of the manner in which data are reported. For example, the typical outcome of the Salmonella typhimurium mutagenicity assay consists of results in a number of tester strains. The results from each strain are a measure of potency (number of mutants) and a yes/no score (positive/negative; mutagenic/nonmutagenic). In addition, a summary score is defined (Salmonella positive if the chemical is positive in at least one of the tester strains, otherwise it is Salmonella negative). The data selected have important impli- cations for the QSAR modeling. If the goal of the study is to provide mechanistic insight into the activity under consideration, then the experimental data have to provide a clear measure of the chemical-induced biological activity. In this case, models will be built for each strain, and separately for the yes/no and potency responses. If the goal is to create a model for use in hazard assessment, then the summary scores can be used for a coarser grain analysis. A similar issue applies to the rodent carcinogenicity bioassay. The NTP protocol for this assay consists of four experimental groups (rat and mouse, male and female); each group produces both a yes/no and a potency measure. These outcomes can be summarized into an overall carcinogenicity score (Huff, 1999). A. Public Sources of Carcinogenicity and Mutagenicity Data The issues related to the availability of data from public sources, and how this influences the practical feasibility of QSAR modeling, are discussed in detail by Richard (2003). Richard also discussed the ongoing attempts to develop databases that combine toxicological and chemical structure information (Richard, 2003; Richard and Williams, 2002). Table 8.1 provides a listing and brief description of websites that are the most prominent public sources of chemical mutage- nicity and carcinogenicity data. Further information on sources of toxicological information is given in Chapter 2. © 2004 by CRC Press LLC III. QSAR MODELING OF MUTAGENICITY AND CARCINOGENICITY As for other biological activities, QSAR modeling is a powerful tool for understanding the determinants (substructures, chemical physical forces, etc.) of a chemical’s action (Hansch and Leo, 1995). It thus contributes remarkably to the comprehension of the mechanisms of chemical mutagenicity and carcinogenicity. Another important goal of the use of QSAR analyses is risk assessment: QSARs can be employed to estimate the activity of other chemicals not tested exper- imentally. Thousands of chemicals currently in commerce and in the environment have not under- gone carcinogenicity testing (e.g., the European INventory of Existing Commercial Substances (EINECS) compilation of 101,000 chemicals existing before toxicity testing was required for new chemicals imported or produced in the European Union [McCutcheon, 1994]). Moreover, the accelerating pace of chemical discovery and synthesis has heightened the need for efficient prior- itization and toxicity screening methods. The most informative QSAR analyses are those performed on individual classes of congeneric chemicals: chemicals that share a basically similar structure, act by the same mechanism of action, and share the same rate-limiting step in their mechanism. For the best modeling results, the chemicals in the set should induce the same well-defined biological effect. A well-defined biological effect is, for example, the induction of mutations in one specific Salmonella typhimurium strain, rather than a summary score of mutagenicity based on the entire profile of responses in the various strains. In this case, only one mechanism is (usually) acting, and this can be modeled quite efficiently; the resulting QSAR points to the chemical determinants and can be used to predict the effect of other chemicals possessing chemical features in the same range of the modeled set of chemicals. A. QSARs for Individual Chemical Classes QSARs have been generated for a number of individual chemical classes, (Benigni and Giuliani, 1996; Cronin and Dearden, 1995a; Debnath et al., 1994; Hansch, 1991; Passerini, 2003). The majority relate to in vitro mutagenicity, but a number of QSAR models for animal carcinogenicity exist as well. Among the carcinogens, the QSARs refer almost exclusively to genotoxic carcinogens. Overall, they provide a consistent picture of the genotoxic mechanisms of toxicity of the chemical mutagens and carcinogens. Table 8.1 Selection of the Main Public Databases of Mutagenicity and Carcinogenicity Data Website Program/Database ntp-server.niehs.nih.gov National Cancer Institute (NCI)/National Toxicology Program (NTP) toxnet.nlm.nih.gov/ National Library of Medicine (NLM)/TOXNET Environmental Protection Agency (EPA)/Gene-Tox NCI/Chemical Carcinogenesis Research Information System (CCRIS) EPA/Integrated Risk Information System (IRIS) National Institute for Occupational Safety and Health(NIOSH)/Registry of Toxic Effects of Chemical Substances (RTECS) potency.berkeley.edu/cpdb.html University of California – Berkeley/Carcinogenic Potency Database (CPDB) Project www.epa.gov/gap-db EPA/Genetic Activity Profiles (GAP) monographs.iarc.fr World Health Organization (WHO)/International Agency for Research on Cancer (IARC) Note: The Gene-Tox and GAP databases specifically focus on mutagenicity data; all the other databases contain both mutagenicity and carcinogenicity data. For a detailed discussion of the databases, see Richard (2003) and Richard and Williams (2002). © 2004 by CRC Press LLC The most important conclusion to be made from these studies is the great importance of hydrophobicity in the modulation of the potential for mutagenicity and carcinogenicity. Hansch and coworkers have showed that compounds that require S9 activation to become mutagenic in bacteria all have log K ow terms with coefficients near 1.0 (Debnath et al., 1994). Other QSARs show that where a direct chemical reaction with DNA appears to occur, without metabolic activation, no hydrophobic term enters into the equation (Hansch et al., 2001). In these cases, usually only the electronic (reactivity) properties are important. Notable examples of QSARs based on electronic terms and without a hydrophobic term relate to the mutagenicity to Salmonella of aniloacridines, cis-platinum analogs, lactones, and epoxides. All of these examples are for chemicals that do not require activation (Hansch et al., 2001). Table 8.2 provides a list of representative QSARs for individual classes of mutagens and carcinogens. The QSARs in the original references can be used in two ways. First, the equations can be used to estimate the activity of untested chemicals belonging to the same chemical class. It is intended that interpolation, and not extrapolation, should be performed; the untested chemicals should have parameters in the same range of the original set. Second, the inspection of the published QSARs may suggest parameters and methods for new QSAR analyses of sets of chemicals similar to those already considered in the literature. The next section presents in detail the results of QSAR analyses of the most studied chemical class: the aromatic amines. B. An Example: QSARs for the Aromatic Amines The aromatic amines are chemicals with a great environmental and industrial importance, so a large database of experimental results has been generated (Woo and Lai, 2001). The availability of such a large quantity of data has stimulated several investigators to develop QSARs for the aromatic amines. From a practical point of view, this gives the opportunity to estimate the mutage- nicity and carcinogenicity of untested amines. This is of great importance, since new amines are produced continuously by the chemical industry; the QSAR predictions can help to lead production toward safer aromatic amines. From a methodological point of view, the availability of several Table 8.2 Selected QSARs for Individual Classes of Mutagens and Carcinogens Chemical Class Reference Mutagenicity Aromatic amines Debnath et al. (1992a) Nitroaromatics Debnath et al. (1992b) Quinolines Debnath et al. (1992c); Smith (1997) Thiazoles Biagi et al. (1986) Carbazoles Andre et al. (1995) Triazenes Shusterman et al. (1989) Furanones Tuppurainen (1999) Halogenated methanes Benigni et al. (1993) Propylene oxides Hooberman et al. (1993) Styrene oxides Tamura et al. (1982) Nitrofurans Debnath et al. (1993) Carcinogenicity Aromatic amines Benigni et al. (2000); Franke et al. (2001) N-Nitroso compounds Dunn III and Wold (1981) Polycyclic aromatics Norden et al. (1978); Richard and Woo (1990); Zhang et al. (1992) Miscellaneous Loew et al. (1985) © 2004 by CRC Press LLC QSAR models that relate to different organisms permits an interesting discussion of the issues related to the modeling of carcinogenicity and mutagenicity data. In our first QSAR analysis of the carcinogenicity of the aromatic amines, we considered only the carcinogenic aromatic amines, and we investigated the structural factors that influence the gradation of carcinogenic potency in rodents (Benigni et al., 2000). The study focused on an homogeneous class of nonheterocyclic amines. The following are the QSAR models that emerged from the analysis of the bioassay data (BRM = carcinogenic potency in mice; BRR = carcinogenic potency in rats): (8.1) n = 37, r = 0.907, r2 = 0.823, s = 0.381, F = 16.3, p < 0.001 (8.2) n = 41, r = 0.933, r 2 = 0.871 s = 0.398, F = 47.4, p < 0.001 where BRM = log (MW/TD 50 ) mouse and BRR = log (MW/TD 50 ) rat . TD 50 is the daily dose required to halve the probability of an experimental animal to remain tumorless to the end of its standard life span (Gold et al., 1991). The chemical parameters in the equations are: log K ow , which is a measure of hydrophobicity; E HOMO , energy of the highest occupied molecular orbital; E LUMO , energy of the lowest unoccupied molecular orbital; 7 MR 2,6 , sum of molar refractivity of substituents in the ortho-positions of the aniline ring; MR 3 , molar refractivity of substituents in the meta-position of the aniline ring; Es(R), Charton’s substituent constant for substituents at the functional amino group; I(monoNH 2 ) = 1 for compounds with only one amino group; I(diNH 2 ) = 1 for compounds with more than one amino group; I(Bi) = 1 for biphenyls; I(I(BiBr) = 1 for biphenyls with a bridge between the phenyl rings; I(RNNO) = 1 for compounds with the group N(Me)NO; and I(F) = 1 for fluoroamines. N(Me)NO is a nitroso group, with a methyl substitution at the amino nitrogen. E HOMO and E LUMO were calculated by the SYBYL software (Tripos) after optimization with the Austin Model 1 (AM1) Hamiltonian; log K ow was calculated from the TSAR software (Oxford Molecular, now Accelrys). The key factor for carcinogenic potency is hydrophobicity (log K ow ). Both BRM and BRR increase with increasing hydrophobicity. In the case of BRM (mice) the influence of hydrophobicity is stronger for compounds with one amino group (characterized by the indicator variable I[monoNH 2 ]) in comparison with compounds with more than one amino group (characterized by the indicator variable I[diNH 2 ]). For BRM, electronic factors also play a role: potency increases with the increasing E HOMO and with the decreasing E LUMO . Such effects seem to be less important for BRR (rats); no electronic terms occur in Equation 8.2. Carcinogenic potency also depends on the type of the ring system. Aminobiphenyls (indicator variable I[Bi]) and, in the case of BRR, fluorenamines (indicator variable I[F]) are intrinsically more active than anilines or naphthylamines. The bridge between the rings in the biphenyls decreases potency (I[BiBr]). Steric factors are involved in the case of BRM, but cannot be detected in the case of BRR. BRM strongly decreases with the addition of bulky substituents adjacent to the functional amino group, on the nitrogen BRM K I monoNH K I diNH EE MR MR E R ow ow HOMO LUMO S !s   s   s  s  s  § s  s  s 088 027 029 020 138 076 128 054 106 034 110 080 020 016 075 075 22 26 3 . . log * . . log * ,   s  I diNH 2 11 16 6 68 BRR K I Bi I F I BiBr I RNNO ow !s  s  s  s   s   s  035 018 193 048 115 060 106 053 275 064 048 030 log © 2004 by CRC Press LLC itself and in position 3. The latter effects are not so important. In the case of BRR, R = (Me)NO strongly enhances potency (compounds with this substituent have no measured value for BRM). Equation 8.1 and Equation 8.2 were derived from the analysis of carcinogenic aromatic amines only, and are very powerful to help explain the gradation of their carcinogenic potency. However, when we applied the equations to the noncarcinogenic amines, we found that the equations did not predict the lack of carcinogenic effects well (the non-carcinogens were predicted as having some, albeit low, degree of activity). This means that the molecular determinants that rule the gradation of carcinogenic potency are not the same as those that determine the difference between carcinogens and noncarcinogens. In a subsequent report we studied the differences in molecular properties between the two classes of carcinogenic and noncarcinogenic aromatic amines specifically (Franke et al., 2001). Four equations were derived, one for each of the experimental groups (rat and mouse, male and female). The 2 classes were coded as: 1= inactive and 2 = active compounds. The following discriminant function achieves a highly significant separation of classes for female rat carcinogenicity: (8.3) w (mean,class1) = 1.05, N 1 = 30 w (mean,class2) = –1.21, N 2 = 26 where L(R) is the length of the substituent at the amino group, I(An) = 1 for anilines, and I(o-NH 2 ) = 1 if nonsubstituted amino group occurs in the ortho-position to the functional amino group. w (mean,class1) is the mean of the w values of the Class 1 chemicals, and w (mean,class2) is the mean of the w values of the Class 2 chemicals. Chemicals with calculated w values closer to 1.05 are reclassified (predicted) as inactives; chemicals with calculated w values closer to –1.21 are reclassified as actives. The correct reclassification rate of discriminant function (Equation 8.3) amounts to 91.1% (Class 1: 93.3%; Class 2: 88.5%) with a fairly stable cross validation (all compounds: 80.4%; Class 1: 76.7%; Class 2: 84.6%). Cross validation is a tool to assess the robustness of the model, and is performed by constructing a model on two thirds of the compounds, and checking the ability of the model to predict the activity of the remaining one third correctly. For male rat carcinogenicity a good separation of classes is achieved by the following discrim- inant function: (8.4) w (mean,class1) = 1.15, N 1 = 28 w (mean,class2) = –1.01, N 2 = 32 The correct reclassification rate amounts to 91.7% (Class 1: 92.9%; Class 2: 90.6%) with a good result for cross-validation (all compounds: 83.3%; Class 1: 82.1%; Class 2: 84.4%). The results obtained for male and female rats resemble each other. Of key importance for class separation are the electronic properties as expressed by E HOMO and E LUMO , the type of ring system, wLR E E MR MR IAn I o NH I BiBr I diNH K I diNH HOMO LUMO ow !            065 079 154 076 050 132 053 099 099 108 2 5 222 . . . . . log *- wLR E E MR IAn I o NH MR I diNH K I diNH I BiBr HOMO LUMO ow !            048 090 143 072 113 054 045 070 080 065 2 2 5 22 . . . . . log * . - © 2004 by CRC Press LLC and substitution in the ortho-position as well as at the amino nitrogen. The probability of a compound being assigned to the active class increases with increasing values of E LUMO , decreasing values of E HOMO , decreasing bulk of substituents in position 2 (ortho-position), decreasing length (or bulk) of substituents at the amino nitrogen, and increasing number of aromatic rings (anilines have a distinctively lower probability to be active than biphenyls, fluorenes, or naphthalenes). Another important feature promoting carcinogenicity is the occurrence of an amino group in ortho- position to the functional amino group. Of lesser importance are the variables I(diNH 2 ), I(BiBr), MR 5 , and the cross product log K ow *I(diNH 2 ). It appears that the key factors differentiating active and inactive compounds on the one hand and governing potency within the group of active compounds are different. The most pronounced differences are with respect to the importance of hydrophobicity and the directionality of electronic effects. For female mouse carcinogenicity, the following discriminant function reclassifies 85.7% of the compounds correctly (Class 1: 87.9%; Class 2: 83.3%) and has acceptable cross-validation (all compounds: 81.0%; Class 1: 84.8%; Class 2: 76.7%): (8.5) w (mean,class1) = –0.92, N 1 = 33 w (mean,class2) = 1.01, N 2 = 30 where I(NR) = 1 if the amino nitrogen is substituted. For male mouse carcinogenicity, the following discriminant function is obtained: (8.6) w (mean,class1) = –1.11, N 1 = 25 w (mean,class2) = 1.16, N 2 = 24 where B 5 is the maximal width of the substituent at the amino group. It should be noted that the difference in sign of the average w values for the 2 classes in Equations 8.3 to 8.6 is only formal, and does not have any relevance on mechanisms. The discriminant function in Equation 8.6 shows a good reclassification rate (all compounds: 89.8%; Class 1: 96.0%; Class 2: 83.3%) and stability in cross validation (all compounds: 83.7%; Class 1: 96.0%; Class 2: 70.8%). The results for mice were similar to those found for rats. Hydrophobicity is a key factor determining the gradation of the carcinogenic potency (Equations 8.1 and 8.2), but only of small importance for yes/no activity (Equations 8.3 to 8.6). The reverse is true for electronic properties (E HOMO , E LUMO ), which show a minor effect for the gradation of potency, but a pronounced effect for yes/no activity. Equations 8.4 to 8.6 also demonstrate the importance of steric (shape, size) factors for yes/no activity. For example, in all four equations the first term indicates that the probability of being noncarcinogenic increases with increasing length of the substituent (L[R]) or simply with the presence of a substituent (I[NR]) on the amino nitrogen. Hydrophobicity is a force involved in the absorption and transport of the drugs in the cells and organisms, as well as in the w I NR K I monoNH K I diNH I An I o NH MR I BiBr ow ow !             047 138 168 037 431 033 2 055 045 22 5 . . log * . log * . wLRBRE E IAn I o NH MR MR MR I diNH K I diNH I BiBr HOMO LUMO ow !              196 169 083 097 122 073 2 059 069 077 076 109 2 079 5 3 5 6 2 . . . . log * . - © 2004 by CRC Press LLC interaction between drugs and metabolizing enzymes. The electronic parameters are measures of chemical reactivity, and hence of the ability to undergo metabolic transformations. It should be noted that the results of the QSAR analyses agree with the notion that the aromatic amines require metabolic activation to become carcinogenic (Woo and Lai, 2001). For amines and amides, this typically involves an initial oxidation to N-hydroxylamine and N-hydroxylamide. In particular, E HOMO is a parameter for oxidation reactions. The successful QSARs obtained in modeling the rodent carcinogenicity of the aromatic amines contradict the view that carcinogenicity is difficult to model and predict. This argument is largely based on the recognition that chemical carcinogenesis is a multistage, multifactorial process that involves exogenous and endogenous factors that are often intertwined in an interrelated network. Moreover, the carcinogenesis process has three operational stages: initiation, promotion, and pro- gression. The ideal QSAR model of carcinogenicity should consider all the different stages and factors. Fortunately, this is not the case. As a generality, it should be remembered that no model is a complete representation of reality, but only a description of a sufficient number of elements that are relevant for the problem under consideration. In particular, QSAR modeling attempts to discover the rate-limiting factors of the (often complex) interaction between chemicals and biolog- ical systems. The same applies to the QSARs of physical and chemical reactions, where the concept of the rate-limiting step is even more familiar. Thus, complex and multi-step processes are often modeled, with a very good fit, by just one or a few parameters (Hansch and Leo, 1995). The example of the aromatic amines demonstrates that rodent carcinogenicity can be modeled success- fully, with 80 to 90% accuracy; the critical requirement is that sufficient data are available to build the QSAR model. As a matter of fact, the large industrial and environmental impact of the aromatic amines has been instrumental in the testing of a large number of these chemicals: a total of 200 aromatic amines were found in a database of about 800 chemicals bioassayed (unpublished results). In addition to the rodent bioassay, the aromatic amines have been studied in the shorter term test Salmonella typhimurium mutagenicity as well as in a variety of acute toxicity assays. A number of QSARs have been generated from such data. The work of Hansch in recent years has demon- strated that the comparison of the QSAR models obtained in different systems, by putting them in a wider perspective, can provide useful clues in the study of the mechanisms of action of individual chemical classes, and can give precious hints on how appropriate the specific models and parameters selected are (Hansch, 2001; Hansch et al., 2002). An exercise of the mechanistic comparison of QSARs has been performed on aromatic amines (Benigni and Passerini, 2002). The results are detailed below. Debnath et al. (1992a) collected a large database of chemicals with various different basic structures (e.g., aniline, biphenyl, anthracene, pyrene, quinoline, carbazole, etc.). The experimental data referred to Salmonella TA98 and TA100 strains, with S9 metabolic activation. The mutagenic potency was expressed as log (revertants/nmol). The AM1 molecular orbital energies are given in electron volts. The mutagenic potency in TA98 + S9 was modeled by: (8.7) n = 88, r = 0.898, s = 0.860 where I L is an indicator variable that assumes a value of 1 for compounds with 3 or more fused rings. The electronic terms E HOMO and E LUMO , though statistically significant, accounted for only 4% of the variance of the biological data, whereas log K ow alone accounted for almost 50%. The most hydrophilic amines (n = 11) could not be treated by Equation 8.7, and were modeled by a separate equation containing only log K ow , suggesting that these amines may act by a different mechanism. The mutagenic potency in the Salmonella strain TA100 + S9 was expressed by: log . . log . . . . TA K E E I ow HOMO LUMO L 98 1 08 0 26 1 28 0 64 0 73 0 41 146 056 720 54 !s  s  s  s  s  © 2004 by CRC Press LLC [...]... AS Number: 3- ( 4-tert-butylphenyl)propanal 1812 7-0 1-0 O Test Data: ( 3- ( 4-tert-butylphenyl)propanal) 1 Species: guinea pig Assay: maximization test Result: strong References: Title: Multivariate QSAR analysis of a skin sensitization database Author: Cronin MTD and Basketter DA Source: SAR and QSAR in Environmental Research, 1994, 2, 15 9-1 79 Example 2 CAS Number: 3- ( 4-isopropyl-phenyl )-2 -methyl-propionaldehyde... and is found on the standard estrogenic chemical, 17-beta-estradiol (see Combes, 2000) Other examples of molecules possessing this biophore include 4-hydroxytamoxifen, 2-chloro-4hydroxybiphenyl, 3, 4-dihydroxyfluorene, and 2, 2-( bis-4-hydroxyphenyl–1,1,1-trichloroethane) b Tubulin Inhibition Biophores of some tubulin inhibitors were identified by CASE on molecules such as colchicine, podophyllotoxin, and. .. 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E., and Tallec, A., Mutagenicity of nitro -and amino-substituted carbazoles in Salmonella typhimurium. 2. ortho-aminonitro derivatives of 9H-carbazole, Mutation Research, 34 5: 1 1-2 5. Anon., Predicting. toxicological infor- mation. Searches in chemical abstracts can provide a wealth of chemical and biochemical data on individual chemicals. Whilst large pharmaceutical and chemical companies have. rats): (8.1) n = 37 , r = 0.907, r2 = 0.8 23, s = 0 .38 1, F = 16 .3, p < 0.001 (8.2) n = 41, r = 0. 933 , r 2 = 0.871 s = 0 .39 8, F = 47.4, p < 0.001 where BRM = log (MW/TD 50 ) mouse and BRR = log