Vol. 1, No. 4 2004
Drug Discovery Today: Technologies Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead profiling
Computational prediction of drug toxicity: the case of mutagenicity and carcinogenicity Romualdo Benigni Laboratory of Comparative Toxicology, Environment and Health Department, Istituto Superiore di Sanita, Viale Regina Elena 299, 00161 Rome, Italy
The removal of pharmaceuticals from the market is a dramatic demonstration of the urgency of implementing better approaches for the early detection of drug toxicity. This paper summarizes the attempts to predict mutagenicity and carcinogenicity from the chemical
structure.
The
limitations
of
the
available
commercial systems are pointed out and the urgency of integrating higher levels of chemical knowledge is stressed. Introduction The removal of a pharmaceutical drug from the market because of unexpected adverse reactions is one of the most dramatic events that might take place during the long process ranging from design to marketing. Several drugs have been withdrawn or their use subjected to serious restrictions because of various toxicity problems (e.g. valvular heart disease, liver failure, ischaemic colitis, torsade de points) not recognized during pre-clinical and clinical experimentation [1,2]. The dimension of the problem is impressive: the toxic effects from marketed drugs, even when used appropriately, are estimated to rank among the top 10 causes of death in the United States [2]. The assessment of the toxicity of pharmaceutical drugs involves specific issues in addition to general issues common to all chemicals that come into contact with humans. A main point is that drugs are intrinsically reactive and for this reason E-mail address: [email protected]. 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.09.003
Section Editors: Han van de Waterbeemd, Christopher Kohl – Pfizer Global Research & Development, Sandwich Laboratories, Pharmacokinetics, Dynamics and Metabolism, Sandwich, Kent, UK CT13 9NJ Drug toxicity has been an area long neglected in the early stages of the drug discovery process. This has resulted in an increasing contribution of toxicity to the high attrition rate of candidate drugs. In the current economic climate, pharmaceutical companies have realized that early and reliable prediction of toxic properties is essential to reduce development cost and time. Romualdo Benigni is a recognized expert in the field. He reviews the software systems available for the computational prediction of toxicity from chemical structure; an approach which, if found reliable, could be employed on the chemist’s drawing board before compounds are even synthetised.
no pharmaceutical is completely safe. A demonstration of the specific dimension of drug toxicity includes the so-called idiosyncratic drug reactions (or type B reactions). These are largely unpredictable events, not accounted for by the pharmacological profile of the drugs, and in the majority of the cases are due to the formation of reactive intermediates by cytochrome P450 enzymes. These metabolites are usually generated at very low quantities and strongly depend on either the genetic disposition of the patient or transient external conditions (e.g. co-medication). Once generated, these metabolites will react with the macromolecules and will cause cell necrosis [3]. The randomness of such effects, together with their low-frequency, contributes to make the drug toxicity assessment particularly difficult, and explains why adverse effects are often recognized only after the number of treated patients is already large. www.drugdiscoverytoday.com
457
Drug Discovery Today: Technologies | Lead profiling
Glossary COMPACT: computer-optimized molecular parametric analysis of chemical toxicity. DEREK: deductive estimation of risk from existing knowledge; FALS: fuzzy adaptative least squares. IPCS: international program on chemical safety. IUCLID: international uniform chemical information database. MULTICASE: multiple computer automated structure evaluation. RASH: rapid screening of hazard. TOPKAT: toxicity prediction by komputer assisted technology.
At the same time, it should be acknowledged that the evaluation system of the drugs is in continuous evolution (e.g. see the debate on methods for the early detection of QT prolongation [1]), and new toxicity biomarkers are actively sought with the tools of the –omics technologies. However, as for the goal of drug discovery, the great expectations generated by the new –omics technologies have remained unfulfilled [4], and solutions have to be looked for in multiple directions. One approach is to exploit the wealth of chemical knowledge to build structure–activity-based predictive models for toxicity. This review will focus on structure–activity relationships (SAR) of chemical mutagens and carcinogens; mutagenicity and carcinogenicity are among the toxicological endpoints that pose the highest health concern, and the evolution of related predictive approaches is instructive.
Predictive models for mutagenicity and carcinogenicity Whereas the mutagenic potential of chemicals can be assessed with relatively simple tests, the carcinogenicity bioassay in rodents is long and costly, and requires the sacrifice of many animals. Thus, chemical carcinogenicity has been the target of numerous attempts to create alternative predictive models, from short-term biological assays (e.g. mutagenicity tests) to theoretical models. Among the latter, SAR studies have earned special prominence. The use of SAR concepts applies also to the mutagenic properties. The carcinogens can be classified into: (i) genotoxic carcinogens, which damage DNA (mutation is one of the first steps in the development of cancer as a result of these chemicals); and (ii) epigenetic carcinogens, which do not bind covalently to DNA, do not directly cause DNA damage, and are usually negative in the mutagenicity assays [5]. In contrast to the epigenetic carcinogens, which act through many different mechanisms, the genotoxic carcinogens are unified by the fact that they are electrophiles per se or that they can be metabolically activated to electrophilic reactive intermediates. Several chemical functional groups and substructures (structural alerts, SA) for genotoxic carcinogens are known, including carbonium ions (alkyl-, aryl- and benzylic-), nitre458
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
nium ions, epoxides and oxonium ions, aldehydes, polarized double bonds (a,b-unsaturated carbonyls or carboxylates), peroxides, free radicals and acylating intermediates [6,7]. By contrast, the recognition of SAs for the non-genotoxic carcinogens is far behind, because no unifying theory provides scientific support. The final toxicological effect of a SA depends heavily on the general structure and properties of the whole molecule. In spite of the strong statistical value of SAs as indicators of the propensity for a given chemical to be mutagenic and/or carcinogenic, a more efficient assessment of toxicity requires the more powerful tools of the quantitative SAR (QSAR) methods [8]. These QSAR applications contribute to the identification of the molecular determinants of activity, and provide mathematical models to predict the activity of untested chemicals, provided that information on similar (congeneric) chemicals, acting with the same mechanisms, is available. The range of congeneric classes of chemical mutagens and carcinogens investigated through QSAR is now large [8]; however, the database of experimental results is not populated extensively enough with representative chemicals to provide a representative basis for QSAR modelling of each chemical class of practical interest. In addition, the drugs in use change with the time, because new compounds are continuously produced and marketed. Thus, the urgency of responding to the goal of toxicity evaluation has generated several ‘‘general’’ (Q)SAR models aimed at predicting the mutagenicity and/or carcinogenicity of any new chemical, in spite of the intrinsic difficulty of modelling many mechanisms of action at the same time. These general models have ranged from human expert judgement, to methods that require prior hypothesis and human intervention in choosing relevant parameters, to automated rule-based approaches derived from human expert knowledge, to automated statistical, machine learning and pattern recognition approaches that independently derive algorithms for prediction from existing data. Many are driven by the recognition of SAs within the molecules (reviewed in Refs. [9,10]). Here, the most popular commercial systems are presented (Table 1) and their predictivity expanded upon. OncoLogic is an expert system tailored using the knowledge of human experts at the US Environmental Protection Agency (EPA). It consists of a large array of rules that link structural features with carcinogenicity probabilities. The rules are grouped by, and work for separate chemical classes. Another rule-based expert system is DEREK (see Glossary), which contains rules for multiple toxicity endpoints (including carcinogenicity, mutagenicity, skin sensitisation, irritancy, teratogenicity and neurotoxicity). MULTICASE is a pattern recognition approach that identifies the SAs automatically and in an unbiased manner. MULTICASE
[9,10] [26,27] [23–25]
[28,29]
Poorly user friendly; performance not tested in large validation studies Poorly user friendly; limited number of chemical classes available; modifications not allowed Training set to be purchased separately
A crucial point is the assessment of the predictive ability of the systems. Although several statistical methods can assess fitting and robustness, a more stringent criterion is external validation. Predictions are performed by applying the model to compounds for which experimental results do not exist or are unknown to the investigators at the time of the generation of the model. Several external validation exercises are now available in the literature. Particularly important were three prospective comparative exercises held under the aegis of the US National Toxicology Program (NTP). The exercises invited the modelling community to submit predictions on Salmonella typhimurium mutagenicity (one study) [11], and rodent carcinogenicity (two studies [12,13] of chemicals that were in the process of being assayed by the NTP. The experimental results were not known at the time of the prediction, thus contributing to the unbiased character of the assessment. The predictivity for S. typhimurium mutagenesis of two computer-based systems (TOPKAT and CASE), one physicochemical screening test (Ke), and one human expert (noncomputer-based) systems were compared using 100 chemicals [11]. The overall accuracy of the predictions are depicted in Table 2. CASE is an older version of MULTICASE, and was
[21,22]
Table 2. The rate of chemicals correctly predicted in the NTP comparative exercise on the prediction of S. typhimurium mutagenicity
References
Cons
Modifications not allowed; output file not provided
Limited number of rules for carcinogenicity
Nice output; modifications Co-operative effort from allowed both in the chemical academia and industry; rules derived from both private and public sources; database and rule base user friendly; high number of rules for mutagenicity; modifications allowed User friendly; cross-validated; structural coverage (applicability domain) checked; modifications allowed
Based on high-quality human expert knowledge; wide number of rules for each chemical class
considers in each molecule any molecular fragment 2–10 interconnected atoms long, and selects fragments associated with the biological effect; it does not depend on pre-defined lists of chemical substructures. TOPKAT is software consisting of different modules for the prediction of multiple toxic endpoints. The models for this system are based on predefined lists of chemical descriptors, including SAs and continuous-valued descriptors. Another software that uses a predefined list of descriptors is HAZARDEXPERT. Its predictions are derived from a combination of SAs, and factors that take into account bioaccumulation and bioavailability of the molecule as a function of molecular weight, lipophilicity, and pKa. HAZARDEXPERT predicts multiple toxic endpoints as well.
Assessing the predictive ability
User friendly; training set searchable for most similar compounds: cross-validated; applicability domain checked automatically Pros
Automatic exploration of Models based on a list of significant substructures substructures and continuous-valued descriptors http://www.multicase.com/ http://www.accelrys.com/ Names of specific technologies with associated companies and company websites
Rule-based expert system Models based on a list of Rule-based expert system http://www.epa.gov/opptintr/ http://www.chem.leeds.ac.uk/luk/derek/ substructures and continuous-valued descriptors cahp/actlocal/can.html http://www.compudrug.com/inside.php
Technology 5 Technology 4
DEREK
Technology 3
OncoLogic
Technology 2
MULTICASE TOPKAT
Technology 1
Drug Discovery Today: Technologies | Lead profiling
Name of specific type of technology
Table 1. Comparison summary table
HAZARDEXPERT
Vol. 1, No. 4 2004
Predictive system
Concordance
Structural alerts
0.72
Structural alerts+
0.76
TOPKAT
0.74
CASE/n
0.76
CASE/e
0.71
Ke
0.60
Keb
0.61 www.drugdiscoverytoday.com
459
Drug Discovery Today: Technologies | Lead profiling
Vol. 1, No. 4 2004
Table 3. The rate of chemicals correctly predicted in the first NTP comparative exercise on the prediction of rodent carcinogenicity Predictive system
Concordance
Tennant/Ashby
0.75
RASH
0.68
Weisburger
0.65
Ke
0.65
DEREK
0.59
TOPKAT
0.57
Benigni
0.57
S. typhimurium
0.57
DEREKh
0.56
Lijinsky
0.55
COMPACT
0.53
MULTICASE
0.49
applied in two versions (trained on two different databases). The physiochemical measurement of Ke (the electron rate attachment constant) describes the potential electrophilicity of a chemical (applied in two experimental settings). The assessment provided by the human experts relied upon the recognition of SAs (two versions with slightly different criteria). The human expert approach and the two computer-based systems produced equivalent results (71–76% concordance with S. typhimurium results), whereas the physicochemical system (Ke) produced a lower concordance (60–61%). For the first NTP prediction exercise on rodent carcinogenicity, 44 compounds of different chemical classes were selected [12]. The predictions were based on different approaches (Table 3). In addition to TOPKAT, MULTICASE and DEREK, there is the (Q)SAR approach COMPACT, which assesses the ability of chemicals either to act as substrate for the cytochrome P450 I enzymes, or to be able to interact with the Aryl hydrocarbon (Ah) receptor. Benigni presented predictions based on SAs and estimated Ke [12]. Human experts were: (i) Tennant and Ashby (predictions based on SAs, plus toxicity and mutagenicity data); (ii) RASH (a modification of the Tennant approach through alternative means for ranking toxic potencies); (iii) Weisburger (inspection of chemical structure); and (iv) Lijinsky (inspection of chemical structure). Most of the prediction systems were concordant in the identification of the powerful carcinogens, whereas several non-carcinogens were predicted to be positive by different systems: SAs were actually present in the noncarcinogens erroneously predicted. For approaches that relied solely on chemical structure, the overall accuracy was 50–65%, whereas Tennant and Ashby attained 75% accuracy [12]. 460
www.drugdiscoverytoday.com
The second NTP prediction exercise on rodent carcinogenicity included 30 chemicals. All systems, with related references, are detailed in [13]. Several participants were human experts. OncoLogic does not have rules for all chemical classes, therefore, it was used in conjunction with human expert judgement. Huff and colleagues considered SAs, plus toxicity data. Benigni and colleagues relied on chemical analogy. The human experts Tennant and Spalding, and Ashby mainly used biological evidence, in addition to the SAs. Purdy used a combination of human expert rules and structure–activity information, as did the COMPACT (supplemented with HAZARDEXPERT) approach, and the expert Bootman. FALS was an application of QSAR to non-congeneric sets. Two computerised models used organ-specific toxicity information (R1, R2). Other participants were MULTICASE, PROGOL (belonging to the same family of MULTICASE) and DEREK. Two experimental systems included a transformation assay in Syrian hamster embryo [SHE] cells and S. typhimurium mutagenicity assays. As in the first comparative exercise, several non-carcinogens were predicted as carcinogens, they either had SAs or a clear analogy with known carcinogens. Thus, the prediction approaches were often unable to make gradations between potential and actual carcinogenicity. Table 4 shows that the human expert-based predictions performed best overall. In addition, the SHE assay, an experimental system specifically designed to incorporate key elements of the transformation process
Table 4. The rate of chemicals correctly predicted in the second NTP comparative exercise on the prediction of rodent carcinogenicity Predictive system
Concordance
OncoLogic
0.65
SHE transformation
0.65
R1
0.64
Huff et al.
0.62
R2
0.61
Benigni et al.
0.61
Tennant et al.
0.60
Ashby
0.57
Bootman
0.53
FALS
0.50
RASH
0.45
COMPACT
0.43
DEREK
0.43
S. typhimurium
0.33
Purdy
0.32
Progol
0.29
MULTICASE
0.25
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Figure 1. A ROC graph displaying the performance of the main commercial systems in the prediction of the rodent carcinogenicity in different external validation exercises. In all studies shown, the predictive ability was assessed on sets of chemicals different from those used originally to develop the models. The codes NTP1 and NTP2 refer to the first and second NTP comparative exercises. The codes of the other validation studies are: ph, 142 pharmaceutical drugs [17]. Two different implementations of MULTICASE and TOPKAT were used (codes 1 and 2); p, 117 chemicals bioassayed by NTP [18]; b, 30 chemicals bioassayed by Bayer Toxicology [19]; i, 61 compounds from IUCLID database [19]; h, 29 compounds from IPCS database [20].
that a cell can undergo when becoming malignant, was among the best performing methods. The highest overall accuracy in this second exercise was in the range 65–70% [13]. In addition to the NTP prediction exercises on rodent carcinogenicity, other studies reported applications of the predictive systems to external data sets. An overall view of the main commercial systems is in Fig. 1, which displays the performance of MULTICASE, DEREK and TOPKAT in the NTP and other studies. Fig. 1 is a receiver operating characteristics (ROC) graph, organized in such a way that the ideal performance is on the top, left corner, and the diagonal line represents random results. The prediction performance was highly dependent on the set of chemicals used for the exercise: each system spanned a large portion of the ROC graph. This high-variability generates uncertainty on the confidence of a prediction, and points to the need of a thorough definition of the applicability domain of each model.
Conclusions Where are we now (see Outstanding issues)? Overall, the NTP exercises showed that, in all cases, the best performance was attained by approaches that relied largely on human expert judgement. For rodent carcinogenicity, a reasonable upper limit for accuracy of the available technologies is around 65% [13,14]. The weak side of the prediction approaches was their inability to
make gradations between potential (presence of SAs) and actual carcinogenicity. This evidence finds a partial explanation in the selection strategy for bioassays by NTP. Because of practical limitations, the selection process for chemicals tested in the rodent bioassay has always been biased toward chemicals suspected of potential carcinogenicity, with the consequence that the chemicals considered in the exercises were not random samples from the chemical universe, but were particularly ‘‘difficult’’ test sets, including many non-carcinogens with SAs. This evidence might mitigate the limited performance of the predictions, and suggests that the general level of our knowledge is more satisfactory than that expressed by the accuracy figures. One study specifically looked at human expert predictions of the carcinogenicity of pharmaceutical drugs [14]. Up to 84% of the chemicals that were carcinogenic in the four rodent groups were correctly predicted, against 30% correct prediction for the chemicals carcinogenic only in one rodent group. Because the knowledge of the SAs for genotoxic carcinogens is more advanced than that for non-genotoxic carcinogens, this might indicate that the chemicals with a wider carcinogenic activity spectrum (hence probably more harmful) are more probable to act via genotoxic mechanisms than those acting through epigenetic mechanisms. If this is correct, our ability to recognize more easily the potentially most harmful carcinogens is another positive aspect to be taken into account. www.drugdiscoverytoday.com
461
Drug Discovery Today: Technologies | Lead profiling
Vol. 1, No. 4 2004
Links
Outstanding issues
Distributed Structure-Searchable Toxicity (DSSTox) Database Network: http://www.epa.gov/nheerl/dsstox/ US Food and Drug Administration: http://www.fda.gov/default.htm TOXNET, a cluster of databases on toxicology, hazardous chemicals, and related areas: http://toxnet.nlm.nih.gov/ Carcinogenic Potency Database (CPDB): http:// potency.berkeley.edu/cpdb.html US National Toxicology Program: http://ntp-server.niehs.nih.gov/
Whereas structure–activity concepts are effective means to reduce the rate of toxic drugs in large numbers of candidates, the predictions for individual drugs cannot be taken at face value. In direct comparisons, human expert judgement has always outperformed the commercial, automatic prediction systems. Unfortunately, human expert judgement requires a large amount of individual, subjective skill. Currently available software does not sufficiently integrate and exploit the information (e.g. chemical theory) and data, which are more freely accessed by the human experts. The commercial systems, more than providing black-box predictions, should develop their ability to support the expert decisions with a wide range of information (e.g. intelligent interrogation of toxicity databases, calculation of chemical similarity in respect to known toxic drugs).
However, the above optimistic notes cannot cancel the large extent of uncertainty linked to the predictive models: the predictions for the individual chemicals cannot be taken at face value and cannot replace the experiments, when necessary. How are optimistic and pessimistic views reconciled? We should reject the naive view of QSAR as an automatic device for generating predictions. Here applies the same reflection made by Franke regarding the search for new drugs: ‘‘As the drug discovery process is of a very complex nature, effective drug design requires an entire spectrum of techniques in which QSAR methods still play an important role. . .. The real power of drug design methods is to extract and synthesize information from data to obtain hypotheses that can be put to experimental test. No dramatic overnight discoveries of wonder drug will result, but an increase in the chance of success due to indications of promising directions is a realistic expectation. . ..’’ [15]. A brilliant confirmation comes from the NTP priority setting for the rodent experimentation: the proportion of carcinogens among the ‘‘suspected’’ chemicals (i.e. with SAs or mutagenicity evidence) was almost 10 times higher than that relative to the chemicals selected only on production or exposure considerations [16]. Thus, at the level of large numbers of chemicals the careful use of (Q)SAR provides a very effective support. Within this context, there is much space for further research and improvements.
Related articles Loew, G.H. et al. (1985). Computer-assisted mechanistic structure– activity: application to diverse classes of chemical carcinogens. Environ. Health Perspect. 61, 69–96 Hansch, C. (1991). Structure–activity relationships of chemical mutagens and carcinogens. Sci. Total Environ. 109/110, 17–29 Cronin, M.T.D. and Dearden, J.C. (1995). QSAR in toxicology. 4. Prediction of non-lethal mammalian toxicological end points, and expert systems for toxicity prediction. Quant. Struct. -Act. Relat. 14, 518–523 Greene, N. (2002). Computer systems for the prediction of toxicity: an update. Adv. Drug Deliv. Rev. 54, 417–431 Nelson, S.D. (2001). Structure–toxicity relationships – how useful are they in predicting toxicities of new drugs? In Biological Reactive Intermediates VI (Dansette, P.M. et al., eds), pp. 33–43, Kluwer Academic/ Plenum Publishers
462
www.drugdiscoverytoday.com
References 1 Roden, D.M. (2004) Drug therapy: drug-induced prolongation of the QT interval. N. Engl. J. Med. 350, 1013–1022 2 Friedman, M.A. et al. (1999) The safety of newly approved medicines: do recent market removals mean there is a problem? J. Am. Med. Assoc. 281, 1728–1734 3 Petersen, K.U. (2002) From toxic precursors to safe drugs. Mechanisms and relevance of idiosyncratic drug reactions. Arzneim. Forsch. (Drug Res.) 52, 423–429 4 Kubinyi, H. (2003) Drug research: myths, hype and reality. Nat. Rev. Drug Discov. 2, 665–668 5 Woo, Y.T. (2003) Mechanisms of action of chemical carcinogens, and their role in structure–activity relationships (SAR) analysis and risk assessment. In Quantitative Structure–Activity Relationship (QSAR) Models of Mutagens and Carcinogens (Benigni, R., ed), pp. 41–80, CRC Press 6 Ashby, J. (1995) Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity. Environ. Mutagen. 7, 919–921 7 Woo, Y.T. et al. (2002) Use of mechanism-based structure–activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products. Environ. Health Perspect. 110, 75–87 8 Benigni, R., ed. (2003) Quantitative Structure–Activity Relationship (QSAR) Models of Mutagens and Carcinogens, CRC Press 9 Benigni, R. and Richard, A.M. (1998) Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity. Methods 14, 264–276 10 Dearden, J.C. et al. (1997) The development and validation of expert systems for predicting toxicity. Altern. Lab. Anim. 25, 223–252 11 Zeiger, E. et al. (1996) Prediction of Salmonella mutagenicity. Mutagenesis 11, 474–484 12 Benigni, R. (1997) The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results. Mutat. Res. 387, 35–45 13 Benigni, R. and Zito, R. (2004) The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results. Mutat. Res. Rev. 566, 49–63 14 Benigni, R. and Zito, R. (2003) Designing safer drugs: (Q)SAR-based identification of mutagens and carcinogens. Curr. Top. Med. Chem. 3, 1289–1300 15 Franke, R. and Gruska, A. (2003) General introduction to QSAR. In Quantitative Structure–Activity Relationhsip (QSAR) Models of Mutagens and Carcinogens (Benigni, R., ed), pp. 1–40, CRC Press 16 Fung, V.A. et al. (1995) The carcinogenesis biossay in perspective: application in identifying human cancer hazards. Environ. Health Perspect. 103, 680–683 17 Pearl, G.M. et al. (2001) Integration of computational analysis as a sentinel tool in toxicologic assessments. Curr. Top. Med. Chem. 1, 247–255 18 Prival, M.J. (2001) Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals. Environ. Mol. Mutagen. 37, 55–69
Vol. 1, No. 4 2004
19 ECETOC (2003) (Q)SARs: evaluation of the commercially available software for human health and environmental endpoints with respect to chemical management applications. Technical Report No. 89. ECETOC. 20 Hulzebos, E.M. and Posthumus, R. (2003) (Q)SARs: gatekeepers against risk on chemicals? SAR QSAR Environ. Res. 14, 285–316 21 Enslein, K. (1993) The future of toxicity prediction with QSAR. In vitro Toxicol. 6, 163–169 22 Enslein, K. et al. (1994) Use of SAR in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program. Mutat. Res. 305, 47–61 23 Klopman, G. (1984) Artificial intelligence approach to structure–activity studies. Computer automated structure evaluation of biological activity of organic molecules. J. Am. Chem. Soc. 106, 7315–7321 24 Klopman, G. (1992) Multicase 1. A hierarchical computer automated structure evaluation program. Quant. Struct. -Act. Relat. 11, 176– 184
Drug Discovery Today: Technologies | Lead profiling
25 Rosenkranz, H.S. (2003) SAR in the assessment of carcinogenesis: the MultiCASE approach. In Quantitative Structure–Activity Relationship (QSAR) Models of Chemical Mutagens and Carcinogens (Benigni, R., ed), pp. 175–206, CRC Press 26 Woo, Y.T. et al. (1995) Development of structure–activity relationship rules for predicting carcinogenic potential of chemicals. Toxicol. Lett. 79, 219–228 27 Woo, Y.T. et al. (1998) An integrative approach of combining mechanistically complementary short-term predictive tests as a basis for assessing the carcinogenic potential of chemicals. J. Environ. Sci. Health Part C Environ. Carcinog. Ecotoxicol. Rev. C16, 101–122 28 Sanderson, D.M. and Earnshaw, C.G. (1991) Computer prediction of possible toxic action from chemical structure: the DEREK system. Hum. Exp. Toxicol. 10, 261–271 29 Ridings, J.E. et al. (1996) Computer prediction of possible toxic action from chemical structure – an update on the DEREK system. Toxicology 106, 267–279
www.drugdiscoverytoday.com
463
Drug Discovery Today: Technologies Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead profiling
Computational prediction of drug toxicity: the case of mutagenicity and carcinogenicity Romualdo Benigni Laboratory of Comparative Toxicology, Environment and Health Department, Istituto Superiore di Sanita, Viale Regina Elena 299, 00161 Rome, Italy
The removal of pharmaceuticals from the market is a dramatic demonstration of the urgency of implementing better approaches for the early detection of drug toxicity. This paper summarizes the attempts to predict mutagenicity and carcinogenicity from the chemical
structure.
The
limitations
of
the
available
commercial systems are pointed out and the urgency of integrating higher levels of chemical knowledge is stressed. Introduction The removal of a pharmaceutical drug from the market because of unexpected adverse reactions is one of the most dramatic events that might take place during the long process ranging from design to marketing. Several drugs have been withdrawn or their use subjected to serious restrictions because of various toxicity problems (e.g. valvular heart disease, liver failure, ischaemic colitis, torsade de points) not recognized during pre-clinical and clinical experimentation [1,2]. The dimension of the problem is impressive: the toxic effects from marketed drugs, even when used appropriately, are estimated to rank among the top 10 causes of death in the United States [2]. The assessment of the toxicity of pharmaceutical drugs involves specific issues in addition to general issues common to all chemicals that come into contact with humans. A main point is that drugs are intrinsically reactive and for this reason E-mail address: [email protected]. 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.09.003
Section Editors: Han van de Waterbeemd, Christopher Kohl – Pfizer Global Research & Development, Sandwich Laboratories, Pharmacokinetics, Dynamics and Metabolism, Sandwich, Kent, UK CT13 9NJ Drug toxicity has been an area long neglected in the early stages of the drug discovery process. This has resulted in an increasing contribution of toxicity to the high attrition rate of candidate drugs. In the current economic climate, pharmaceutical companies have realized that early and reliable prediction of toxic properties is essential to reduce development cost and time. Romualdo Benigni is a recognized expert in the field. He reviews the software systems available for the computational prediction of toxicity from chemical structure; an approach which, if found reliable, could be employed on the chemist’s drawing board before compounds are even synthetised.
no pharmaceutical is completely safe. A demonstration of the specific dimension of drug toxicity includes the so-called idiosyncratic drug reactions (or type B reactions). These are largely unpredictable events, not accounted for by the pharmacological profile of the drugs, and in the majority of the cases are due to the formation of reactive intermediates by cytochrome P450 enzymes. These metabolites are usually generated at very low quantities and strongly depend on either the genetic disposition of the patient or transient external conditions (e.g. co-medication). Once generated, these metabolites will react with the macromolecules and will cause cell necrosis [3]. The randomness of such effects, together with their low-frequency, contributes to make the drug toxicity assessment particularly difficult, and explains why adverse effects are often recognized only after the number of treated patients is already large. www.drugdiscoverytoday.com
457
Drug Discovery Today: Technologies | Lead profiling
Glossary COMPACT: computer-optimized molecular parametric analysis of chemical toxicity. DEREK: deductive estimation of risk from existing knowledge; FALS: fuzzy adaptative least squares. IPCS: international program on chemical safety. IUCLID: international uniform chemical information database. MULTICASE: multiple computer automated structure evaluation. RASH: rapid screening of hazard. TOPKAT: toxicity prediction by komputer assisted technology.
At the same time, it should be acknowledged that the evaluation system of the drugs is in continuous evolution (e.g. see the debate on methods for the early detection of QT prolongation [1]), and new toxicity biomarkers are actively sought with the tools of the –omics technologies. However, as for the goal of drug discovery, the great expectations generated by the new –omics technologies have remained unfulfilled [4], and solutions have to be looked for in multiple directions. One approach is to exploit the wealth of chemical knowledge to build structure–activity-based predictive models for toxicity. This review will focus on structure–activity relationships (SAR) of chemical mutagens and carcinogens; mutagenicity and carcinogenicity are among the toxicological endpoints that pose the highest health concern, and the evolution of related predictive approaches is instructive.
Predictive models for mutagenicity and carcinogenicity Whereas the mutagenic potential of chemicals can be assessed with relatively simple tests, the carcinogenicity bioassay in rodents is long and costly, and requires the sacrifice of many animals. Thus, chemical carcinogenicity has been the target of numerous attempts to create alternative predictive models, from short-term biological assays (e.g. mutagenicity tests) to theoretical models. Among the latter, SAR studies have earned special prominence. The use of SAR concepts applies also to the mutagenic properties. The carcinogens can be classified into: (i) genotoxic carcinogens, which damage DNA (mutation is one of the first steps in the development of cancer as a result of these chemicals); and (ii) epigenetic carcinogens, which do not bind covalently to DNA, do not directly cause DNA damage, and are usually negative in the mutagenicity assays [5]. In contrast to the epigenetic carcinogens, which act through many different mechanisms, the genotoxic carcinogens are unified by the fact that they are electrophiles per se or that they can be metabolically activated to electrophilic reactive intermediates. Several chemical functional groups and substructures (structural alerts, SA) for genotoxic carcinogens are known, including carbonium ions (alkyl-, aryl- and benzylic-), nitre458
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
nium ions, epoxides and oxonium ions, aldehydes, polarized double bonds (a,b-unsaturated carbonyls or carboxylates), peroxides, free radicals and acylating intermediates [6,7]. By contrast, the recognition of SAs for the non-genotoxic carcinogens is far behind, because no unifying theory provides scientific support. The final toxicological effect of a SA depends heavily on the general structure and properties of the whole molecule. In spite of the strong statistical value of SAs as indicators of the propensity for a given chemical to be mutagenic and/or carcinogenic, a more efficient assessment of toxicity requires the more powerful tools of the quantitative SAR (QSAR) methods [8]. These QSAR applications contribute to the identification of the molecular determinants of activity, and provide mathematical models to predict the activity of untested chemicals, provided that information on similar (congeneric) chemicals, acting with the same mechanisms, is available. The range of congeneric classes of chemical mutagens and carcinogens investigated through QSAR is now large [8]; however, the database of experimental results is not populated extensively enough with representative chemicals to provide a representative basis for QSAR modelling of each chemical class of practical interest. In addition, the drugs in use change with the time, because new compounds are continuously produced and marketed. Thus, the urgency of responding to the goal of toxicity evaluation has generated several ‘‘general’’ (Q)SAR models aimed at predicting the mutagenicity and/or carcinogenicity of any new chemical, in spite of the intrinsic difficulty of modelling many mechanisms of action at the same time. These general models have ranged from human expert judgement, to methods that require prior hypothesis and human intervention in choosing relevant parameters, to automated rule-based approaches derived from human expert knowledge, to automated statistical, machine learning and pattern recognition approaches that independently derive algorithms for prediction from existing data. Many are driven by the recognition of SAs within the molecules (reviewed in Refs. [9,10]). Here, the most popular commercial systems are presented (Table 1) and their predictivity expanded upon. OncoLogic is an expert system tailored using the knowledge of human experts at the US Environmental Protection Agency (EPA). It consists of a large array of rules that link structural features with carcinogenicity probabilities. The rules are grouped by, and work for separate chemical classes. Another rule-based expert system is DEREK (see Glossary), which contains rules for multiple toxicity endpoints (including carcinogenicity, mutagenicity, skin sensitisation, irritancy, teratogenicity and neurotoxicity). MULTICASE is a pattern recognition approach that identifies the SAs automatically and in an unbiased manner. MULTICASE
[9,10] [26,27] [23–25]
[28,29]
Poorly user friendly; performance not tested in large validation studies Poorly user friendly; limited number of chemical classes available; modifications not allowed Training set to be purchased separately
A crucial point is the assessment of the predictive ability of the systems. Although several statistical methods can assess fitting and robustness, a more stringent criterion is external validation. Predictions are performed by applying the model to compounds for which experimental results do not exist or are unknown to the investigators at the time of the generation of the model. Several external validation exercises are now available in the literature. Particularly important were three prospective comparative exercises held under the aegis of the US National Toxicology Program (NTP). The exercises invited the modelling community to submit predictions on Salmonella typhimurium mutagenicity (one study) [11], and rodent carcinogenicity (two studies [12,13] of chemicals that were in the process of being assayed by the NTP. The experimental results were not known at the time of the prediction, thus contributing to the unbiased character of the assessment. The predictivity for S. typhimurium mutagenesis of two computer-based systems (TOPKAT and CASE), one physicochemical screening test (Ke), and one human expert (noncomputer-based) systems were compared using 100 chemicals [11]. The overall accuracy of the predictions are depicted in Table 2. CASE is an older version of MULTICASE, and was
[21,22]
Table 2. The rate of chemicals correctly predicted in the NTP comparative exercise on the prediction of S. typhimurium mutagenicity
References
Cons
Modifications not allowed; output file not provided
Limited number of rules for carcinogenicity
Nice output; modifications Co-operative effort from allowed both in the chemical academia and industry; rules derived from both private and public sources; database and rule base user friendly; high number of rules for mutagenicity; modifications allowed User friendly; cross-validated; structural coverage (applicability domain) checked; modifications allowed
Based on high-quality human expert knowledge; wide number of rules for each chemical class
considers in each molecule any molecular fragment 2–10 interconnected atoms long, and selects fragments associated with the biological effect; it does not depend on pre-defined lists of chemical substructures. TOPKAT is software consisting of different modules for the prediction of multiple toxic endpoints. The models for this system are based on predefined lists of chemical descriptors, including SAs and continuous-valued descriptors. Another software that uses a predefined list of descriptors is HAZARDEXPERT. Its predictions are derived from a combination of SAs, and factors that take into account bioaccumulation and bioavailability of the molecule as a function of molecular weight, lipophilicity, and pKa. HAZARDEXPERT predicts multiple toxic endpoints as well.
Assessing the predictive ability
User friendly; training set searchable for most similar compounds: cross-validated; applicability domain checked automatically Pros
Automatic exploration of Models based on a list of significant substructures substructures and continuous-valued descriptors http://www.multicase.com/ http://www.accelrys.com/ Names of specific technologies with associated companies and company websites
Rule-based expert system Models based on a list of Rule-based expert system http://www.epa.gov/opptintr/ http://www.chem.leeds.ac.uk/luk/derek/ substructures and continuous-valued descriptors cahp/actlocal/can.html http://www.compudrug.com/inside.php
Technology 5 Technology 4
DEREK
Technology 3
OncoLogic
Technology 2
MULTICASE TOPKAT
Technology 1
Drug Discovery Today: Technologies | Lead profiling
Name of specific type of technology
Table 1. Comparison summary table
HAZARDEXPERT
Vol. 1, No. 4 2004
Predictive system
Concordance
Structural alerts
0.72
Structural alerts+
0.76
TOPKAT
0.74
CASE/n
0.76
CASE/e
0.71
Ke
0.60
Keb
0.61 www.drugdiscoverytoday.com
459
Drug Discovery Today: Technologies | Lead profiling
Vol. 1, No. 4 2004
Table 3. The rate of chemicals correctly predicted in the first NTP comparative exercise on the prediction of rodent carcinogenicity Predictive system
Concordance
Tennant/Ashby
0.75
RASH
0.68
Weisburger
0.65
Ke
0.65
DEREK
0.59
TOPKAT
0.57
Benigni
0.57
S. typhimurium
0.57
DEREKh
0.56
Lijinsky
0.55
COMPACT
0.53
MULTICASE
0.49
applied in two versions (trained on two different databases). The physiochemical measurement of Ke (the electron rate attachment constant) describes the potential electrophilicity of a chemical (applied in two experimental settings). The assessment provided by the human experts relied upon the recognition of SAs (two versions with slightly different criteria). The human expert approach and the two computer-based systems produced equivalent results (71–76% concordance with S. typhimurium results), whereas the physicochemical system (Ke) produced a lower concordance (60–61%). For the first NTP prediction exercise on rodent carcinogenicity, 44 compounds of different chemical classes were selected [12]. The predictions were based on different approaches (Table 3). In addition to TOPKAT, MULTICASE and DEREK, there is the (Q)SAR approach COMPACT, which assesses the ability of chemicals either to act as substrate for the cytochrome P450 I enzymes, or to be able to interact with the Aryl hydrocarbon (Ah) receptor. Benigni presented predictions based on SAs and estimated Ke [12]. Human experts were: (i) Tennant and Ashby (predictions based on SAs, plus toxicity and mutagenicity data); (ii) RASH (a modification of the Tennant approach through alternative means for ranking toxic potencies); (iii) Weisburger (inspection of chemical structure); and (iv) Lijinsky (inspection of chemical structure). Most of the prediction systems were concordant in the identification of the powerful carcinogens, whereas several non-carcinogens were predicted to be positive by different systems: SAs were actually present in the noncarcinogens erroneously predicted. For approaches that relied solely on chemical structure, the overall accuracy was 50–65%, whereas Tennant and Ashby attained 75% accuracy [12]. 460
www.drugdiscoverytoday.com
The second NTP prediction exercise on rodent carcinogenicity included 30 chemicals. All systems, with related references, are detailed in [13]. Several participants were human experts. OncoLogic does not have rules for all chemical classes, therefore, it was used in conjunction with human expert judgement. Huff and colleagues considered SAs, plus toxicity data. Benigni and colleagues relied on chemical analogy. The human experts Tennant and Spalding, and Ashby mainly used biological evidence, in addition to the SAs. Purdy used a combination of human expert rules and structure–activity information, as did the COMPACT (supplemented with HAZARDEXPERT) approach, and the expert Bootman. FALS was an application of QSAR to non-congeneric sets. Two computerised models used organ-specific toxicity information (R1, R2). Other participants were MULTICASE, PROGOL (belonging to the same family of MULTICASE) and DEREK. Two experimental systems included a transformation assay in Syrian hamster embryo [SHE] cells and S. typhimurium mutagenicity assays. As in the first comparative exercise, several non-carcinogens were predicted as carcinogens, they either had SAs or a clear analogy with known carcinogens. Thus, the prediction approaches were often unable to make gradations between potential and actual carcinogenicity. Table 4 shows that the human expert-based predictions performed best overall. In addition, the SHE assay, an experimental system specifically designed to incorporate key elements of the transformation process
Table 4. The rate of chemicals correctly predicted in the second NTP comparative exercise on the prediction of rodent carcinogenicity Predictive system
Concordance
OncoLogic
0.65
SHE transformation
0.65
R1
0.64
Huff et al.
0.62
R2
0.61
Benigni et al.
0.61
Tennant et al.
0.60
Ashby
0.57
Bootman
0.53
FALS
0.50
RASH
0.45
COMPACT
0.43
DEREK
0.43
S. typhimurium
0.33
Purdy
0.32
Progol
0.29
MULTICASE
0.25
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Figure 1. A ROC graph displaying the performance of the main commercial systems in the prediction of the rodent carcinogenicity in different external validation exercises. In all studies shown, the predictive ability was assessed on sets of chemicals different from those used originally to develop the models. The codes NTP1 and NTP2 refer to the first and second NTP comparative exercises. The codes of the other validation studies are: ph, 142 pharmaceutical drugs [17]. Two different implementations of MULTICASE and TOPKAT were used (codes 1 and 2); p, 117 chemicals bioassayed by NTP [18]; b, 30 chemicals bioassayed by Bayer Toxicology [19]; i, 61 compounds from IUCLID database [19]; h, 29 compounds from IPCS database [20].
that a cell can undergo when becoming malignant, was among the best performing methods. The highest overall accuracy in this second exercise was in the range 65–70% [13]. In addition to the NTP prediction exercises on rodent carcinogenicity, other studies reported applications of the predictive systems to external data sets. An overall view of the main commercial systems is in Fig. 1, which displays the performance of MULTICASE, DEREK and TOPKAT in the NTP and other studies. Fig. 1 is a receiver operating characteristics (ROC) graph, organized in such a way that the ideal performance is on the top, left corner, and the diagonal line represents random results. The prediction performance was highly dependent on the set of chemicals used for the exercise: each system spanned a large portion of the ROC graph. This high-variability generates uncertainty on the confidence of a prediction, and points to the need of a thorough definition of the applicability domain of each model.
Conclusions Where are we now (see Outstanding issues)? Overall, the NTP exercises showed that, in all cases, the best performance was attained by approaches that relied largely on human expert judgement. For rodent carcinogenicity, a reasonable upper limit for accuracy of the available technologies is around 65% [13,14]. The weak side of the prediction approaches was their inability to
make gradations between potential (presence of SAs) and actual carcinogenicity. This evidence finds a partial explanation in the selection strategy for bioassays by NTP. Because of practical limitations, the selection process for chemicals tested in the rodent bioassay has always been biased toward chemicals suspected of potential carcinogenicity, with the consequence that the chemicals considered in the exercises were not random samples from the chemical universe, but were particularly ‘‘difficult’’ test sets, including many non-carcinogens with SAs. This evidence might mitigate the limited performance of the predictions, and suggests that the general level of our knowledge is more satisfactory than that expressed by the accuracy figures. One study specifically looked at human expert predictions of the carcinogenicity of pharmaceutical drugs [14]. Up to 84% of the chemicals that were carcinogenic in the four rodent groups were correctly predicted, against 30% correct prediction for the chemicals carcinogenic only in one rodent group. Because the knowledge of the SAs for genotoxic carcinogens is more advanced than that for non-genotoxic carcinogens, this might indicate that the chemicals with a wider carcinogenic activity spectrum (hence probably more harmful) are more probable to act via genotoxic mechanisms than those acting through epigenetic mechanisms. If this is correct, our ability to recognize more easily the potentially most harmful carcinogens is another positive aspect to be taken into account. www.drugdiscoverytoday.com
461
Drug Discovery Today: Technologies | Lead profiling
Vol. 1, No. 4 2004
Links
Outstanding issues
Distributed Structure-Searchable Toxicity (DSSTox) Database Network: http://www.epa.gov/nheerl/dsstox/ US Food and Drug Administration: http://www.fda.gov/default.htm TOXNET, a cluster of databases on toxicology, hazardous chemicals, and related areas: http://toxnet.nlm.nih.gov/ Carcinogenic Potency Database (CPDB): http:// potency.berkeley.edu/cpdb.html US National Toxicology Program: http://ntp-server.niehs.nih.gov/
Whereas structure–activity concepts are effective means to reduce the rate of toxic drugs in large numbers of candidates, the predictions for individual drugs cannot be taken at face value. In direct comparisons, human expert judgement has always outperformed the commercial, automatic prediction systems. Unfortunately, human expert judgement requires a large amount of individual, subjective skill. Currently available software does not sufficiently integrate and exploit the information (e.g. chemical theory) and data, which are more freely accessed by the human experts. The commercial systems, more than providing black-box predictions, should develop their ability to support the expert decisions with a wide range of information (e.g. intelligent interrogation of toxicity databases, calculation of chemical similarity in respect to known toxic drugs).
However, the above optimistic notes cannot cancel the large extent of uncertainty linked to the predictive models: the predictions for the individual chemicals cannot be taken at face value and cannot replace the experiments, when necessary. How are optimistic and pessimistic views reconciled? We should reject the naive view of QSAR as an automatic device for generating predictions. Here applies the same reflection made by Franke regarding the search for new drugs: ‘‘As the drug discovery process is of a very complex nature, effective drug design requires an entire spectrum of techniques in which QSAR methods still play an important role. . .. The real power of drug design methods is to extract and synthesize information from data to obtain hypotheses that can be put to experimental test. No dramatic overnight discoveries of wonder drug will result, but an increase in the chance of success due to indications of promising directions is a realistic expectation. . ..’’ [15]. A brilliant confirmation comes from the NTP priority setting for the rodent experimentation: the proportion of carcinogens among the ‘‘suspected’’ chemicals (i.e. with SAs or mutagenicity evidence) was almost 10 times higher than that relative to the chemicals selected only on production or exposure considerations [16]. Thus, at the level of large numbers of chemicals the careful use of (Q)SAR provides a very effective support. Within this context, there is much space for further research and improvements.
Related articles Loew, G.H. et al. (1985). Computer-assisted mechanistic structure– activity: application to diverse classes of chemical carcinogens. Environ. Health Perspect. 61, 69–96 Hansch, C. (1991). Structure–activity relationships of chemical mutagens and carcinogens. Sci. Total Environ. 109/110, 17–29 Cronin, M.T.D. and Dearden, J.C. (1995). QSAR in toxicology. 4. Prediction of non-lethal mammalian toxicological end points, and expert systems for toxicity prediction. Quant. Struct. -Act. Relat. 14, 518–523 Greene, N. (2002). Computer systems for the prediction of toxicity: an update. Adv. Drug Deliv. Rev. 54, 417–431 Nelson, S.D. (2001). Structure–toxicity relationships – how useful are they in predicting toxicities of new drugs? In Biological Reactive Intermediates VI (Dansette, P.M. et al., eds), pp. 33–43, Kluwer Academic/ Plenum Publishers
462
www.drugdiscoverytoday.com
References 1 Roden, D.M. (2004) Drug therapy: drug-induced prolongation of the QT interval. N. Engl. J. Med. 350, 1013–1022 2 Friedman, M.A. et al. (1999) The safety of newly approved medicines: do recent market removals mean there is a problem? J. Am. Med. Assoc. 281, 1728–1734 3 Petersen, K.U. (2002) From toxic precursors to safe drugs. Mechanisms and relevance of idiosyncratic drug reactions. Arzneim. Forsch. (Drug Res.) 52, 423–429 4 Kubinyi, H. (2003) Drug research: myths, hype and reality. Nat. Rev. Drug Discov. 2, 665–668 5 Woo, Y.T. (2003) Mechanisms of action of chemical carcinogens, and their role in structure–activity relationships (SAR) analysis and risk assessment. In Quantitative Structure–Activity Relationship (QSAR) Models of Mutagens and Carcinogens (Benigni, R., ed), pp. 41–80, CRC Press 6 Ashby, J. (1995) Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity. Environ. Mutagen. 7, 919–921 7 Woo, Y.T. et al. (2002) Use of mechanism-based structure–activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products. Environ. Health Perspect. 110, 75–87 8 Benigni, R., ed. (2003) Quantitative Structure–Activity Relationship (QSAR) Models of Mutagens and Carcinogens, CRC Press 9 Benigni, R. and Richard, A.M. (1998) Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity. Methods 14, 264–276 10 Dearden, J.C. et al. (1997) The development and validation of expert systems for predicting toxicity. Altern. Lab. Anim. 25, 223–252 11 Zeiger, E. et al. (1996) Prediction of Salmonella mutagenicity. Mutagenesis 11, 474–484 12 Benigni, R. (1997) The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results. Mutat. Res. 387, 35–45 13 Benigni, R. and Zito, R. (2004) The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results. Mutat. Res. Rev. 566, 49–63 14 Benigni, R. and Zito, R. (2003) Designing safer drugs: (Q)SAR-based identification of mutagens and carcinogens. Curr. Top. Med. Chem. 3, 1289–1300 15 Franke, R. and Gruska, A. (2003) General introduction to QSAR. In Quantitative Structure–Activity Relationhsip (QSAR) Models of Mutagens and Carcinogens (Benigni, R., ed), pp. 1–40, CRC Press 16 Fung, V.A. et al. (1995) The carcinogenesis biossay in perspective: application in identifying human cancer hazards. Environ. Health Perspect. 103, 680–683 17 Pearl, G.M. et al. (2001) Integration of computational analysis as a sentinel tool in toxicologic assessments. Curr. Top. Med. Chem. 1, 247–255 18 Prival, M.J. (2001) Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals. Environ. Mol. Mutagen. 37, 55–69
Vol. 1, No. 4 2004
19 ECETOC (2003) (Q)SARs: evaluation of the commercially available software for human health and environmental endpoints with respect to chemical management applications. Technical Report No. 89. ECETOC. 20 Hulzebos, E.M. and Posthumus, R. (2003) (Q)SARs: gatekeepers against risk on chemicals? SAR QSAR Environ. Res. 14, 285–316 21 Enslein, K. (1993) The future of toxicity prediction with QSAR. In vitro Toxicol. 6, 163–169 22 Enslein, K. et al. (1994) Use of SAR in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program. Mutat. Res. 305, 47–61 23 Klopman, G. (1984) Artificial intelligence approach to structure–activity studies. Computer automated structure evaluation of biological activity of organic molecules. J. Am. Chem. Soc. 106, 7315–7321 24 Klopman, G. (1992) Multicase 1. A hierarchical computer automated structure evaluation program. Quant. Struct. -Act. Relat. 11, 176– 184
Drug Discovery Today: Technologies | Lead profiling
25 Rosenkranz, H.S. (2003) SAR in the assessment of carcinogenesis: the MultiCASE approach. In Quantitative Structure–Activity Relationship (QSAR) Models of Chemical Mutagens and Carcinogens (Benigni, R., ed), pp. 175–206, CRC Press 26 Woo, Y.T. et al. (1995) Development of structure–activity relationship rules for predicting carcinogenic potential of chemicals. Toxicol. Lett. 79, 219–228 27 Woo, Y.T. et al. (1998) An integrative approach of combining mechanistically complementary short-term predictive tests as a basis for assessing the carcinogenic potential of chemicals. J. Environ. Sci. Health Part C Environ. Carcinog. Ecotoxicol. Rev. C16, 101–122 28 Sanderson, D.M. and Earnshaw, C.G. (1991) Computer prediction of possible toxic action from chemical structure: the DEREK system. Hum. Exp. Toxicol. 10, 261–271 29 Ridings, J.E. et al. (1996) Computer prediction of possible toxic action from chemical structure – an update on the DEREK system. Toxicology 106, 267–279
www.drugdiscoverytoday.com
463
