Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?

An evaluation of carcinogenicity predictors from short-term and sub chronic repeat-dose studies of agrochemicals in rats: Opportunities to refine and reduce animal use

The aim of this study was to examine whether short term, repeat dose, rat studies provide sufficient information about potential carcinogenicity to enable predictions about the carcinogenic potential of agrochemicals to be made earlier in compound development. This study aimed to identify any correlations between toxicity findings obtained for short term rat studies (28 day and 90 day) and neoplastic findings obtained from 24 month rat carcinogenicity studies for agrochemical compounds (18 compounds) tested in Han Wistar and Sprague Dawley rats. The macroscopic pathology, microscopic pathology, hematology, biochemistry, organ weights, estrogen receptor activation and genotoxicity results were examined. Seven out of 18 non genotoxic compounds developed tumors in treated rats in the carcinogenicity study and of these, two compounds showed no preneoplastic findings in the affected tissues (false positives). Of the remaining five true positives, correlations were noted between corneal opacity and keratitis (90 day study) as early indicators of squamous cell carcinoma and papilloma of the cornea of the eye (compound 1, a hydroxyphenylpyruvate dioxygenase inhibitor) and inflammation of the stomach and kidney (90 day study) and gastric squamous cell papilloma and squamous cell carcinoma and renal tubular adenoma and carcinoma, respectively (compound 12, a fungicide with multisite activity). Minor decreases in uterine weight and increases in estradiol hydroxylation activity at 28 days were associated with endometrial adenocarcinoma (compound 18, a mitochondrial complex II electron transport inhibitor). Early liver weight increases and hepatocellular centrilobular hypertrophy (28 day study) were associated with thyroid follicular adenomas (compound 11, a succinate dehydrogenase inhibitor) in female animals only. Hepatic centrilobular hypertrophy (28 day studies) correlated with thyroid adenomas in males in carcinogenicity studies (compound 2, a hydroxyphenylpyruvate dioxygenase inhibitor). In contrast, treatment related, nasopharynx tumors (compound 3, an elongase inhibitor) and uterine adenocarcinoma (compound 9, a succinate dehydrogenase inhibitor) could not be correlated with findings from the short term studies examined. Eleven compounds displayed preneoplastic findings with no tumors (false negatives) and there were no compounds with no preneoplastic findings and no tumors (true negatives). This work indicates the value of examining historical, short term studies for specific, nonneoplastic findings which correlate with tumors in carcinogenicity studies, which may obviate the need for further animal carcinogenicity studies.

Prediction of Rodent Carcinogenesis: An Evaluation of Prechronic Liver Lesions as Forecasters of Liver Tumors in NTP Carcinogenicity Studies

The National Toxicology Program (NTP) developed the chronic 2-year bioassay as a mechanism for predicting the carcinogenic potential of chemicals in humans. The cost and duration of these studies has limited their use to small numbers of selected chemicals. Many different short-term methods aimed at increasing predictive accuracy and the number of chemicals evaluated have been developed in attempts to successfully correlate their results with evidence of carcinogenicity (or lack of carcinogenicity). Using NTP studies, the effectiveness of correlating prechronic liver lesions with liver cancer encompassing multiple studies using mice (83 compounds) and rats (87 compounds) was assessed. These lesions include hepatocellular necrosis, hepatocellular hypertrophy, hepatocellular cytomegaly, bile duct hyperplasia, and hepatocellular degeneration, along with increased liver weight. Our results indicate that pooling 3 of these prechronic data points (hepatocellular necrosis, hepatocellular hypertrophy, and hepatocellular cytomegaly) can be very predictive of carcinogenicity in the 2-year study ( p < 0 .05). The inclusion of increased liver weight as an endpoint in the pool of data points increases the number of rodent liver carcinogens that are successfully predicted ( p < 0 .05), but also results in the prediction of increased numbers of noncarcinogenic chemicals as carcinogens. The use of multiple prechronic study endpoints provides supplementary information that enhances the predictivity of identifying chemicals with carcinogenic potential.

Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships

The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the TGx models' predictive accuracy (0.77, 0.77, and 0.82 for the 1-day, 3-day, and 5-day models, respectively) was much higher for an independent validation set than that of a QSAR model (0.55). Permutation tests confirmed the statistical significance of the model's prediction performance. The study concluded that a short-term 5-day TGx animal model holds the potential to predict nongenotoxic hepatocarcinogenicity.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

Umbuzeiro G, DeMarini DM. The Salmonella mutagenicity assay: the stethoscope of genetic toxicology for the 21st century

OBJECTIVES

According to the 2007 National Research Council report Toxicology for the Twenty-First Century, modern methods (e.g., "omics," in vitro assays, high-throughput testing, computational methods) will lead to the emergence of a new approach to toxicology. The Salmonella mammalian microsome mutagenicity assay has been central to the field of genetic toxicology since the 1970s. Here we document the paradigm shifts engendered by the assay, the validation and applications of the assay, and how the assay is a model for future in vitro toxicology assays.

DATA SOURCES

We searched PubMed, Scopus, and Web of Knowledge using key words relevant to the Salmonella assay and additional genotoxicity assays.

DATA EXTRACTION

We merged the citations, removing duplicates, and categorized the papers by year and topic.

DATA SYNTHESIS

The Salmonella assay led to two paradigm shifts: that some carcinogens were mutagens and that some environmental samples (e.g., air, water, soil, food, combustion emissions) were mutagenic. Although there are > 10,000 publications on the Salmonella assay, covering tens of thousands of agents, data on even more agents probably exist in unpublished form, largely as proprietary studies by industry. The Salmonella assay is a model for the development of 21st century in vitro toxicology assays in terms of the establishment of standard procedures, ability to test various agents, transferability across laboratories, validation and testing, and structure-activity analysis.

CONCLUSIONS

Similar to a stethoscope as a first-line, inexpensive tool in medicine, the Salmonella assay can serve a similar, indispensable role in the foreseeable future of 21st century toxicology.

New public QSAR model for carcinogenicity

BACKGROUND

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.

RESULTS

Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.

CONCLUSION

Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

New public QSAR model for carcinogenicity

BACKGROUND

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.

RESULTS

Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.

CONCLUSION

Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fill the gaps on the toxicological properties of chemicals that affect human health. Carcinogenicity is one of the endpoints under consideration. The information obtained from (quantitative) structure-activity relationship ((Q)SAR) models is accepted as an alternative solution to avoid expensive and time-consuming animal tests. The reported results were obtained within the framework of the European project 'Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR)'. In this article, we demonstrate intermediate results for counter propagation artificial neural network (CP ANN) models for the prediction category of the carcinogenic potency using two-dimensional (2D) descriptors from different software programs. A total of 805 non-congeneric chemicals were extracted from the Carcinogenic Potency Database (CPDBAS). The resulting models had prediction accuracies for internal (training) and external (test) sets as high as 91-93% and 68-70%, respectively. The sensitivity and specificity of the test set were 69-73 and 63-72% correspondingly. High specificity is critical in models for regulatory use that are aimed at ensuring public safety. Thus, the errors that give rise to false negatives are much more relevant. We discuss how we can increase the number of correctly predicted carcinogens using the correlation between the threshold and the values of the sensitivity and specificity.

High-throughput screening for analysis of in vitro toxicity

The influence of combinatorial chemistry and high-throughput screening (HTS) technologies in the pharmaceutical industry during the last 10 years has been enormous. However, the attrition rate of drugs in the clinic due to toxicity during this period still remained 40-50%. The need for reduced toxicity failure led to the development of early toxicity screening assays. This chapter describes the state of the art for assays in the area of genotoxicity, cytotoxicity, carcinogenicity, induction of specific enzymes from phase I and II metabolism, competition assays for enzymes of phase I and II metabolism, embryotoxicity as well as endocrine disruption and reprotoxicity. With respect to genotoxicity, the full Ames, Ames II, Vitotox, GreenScreen GC, RadarScreen, and non-genotoxic carcinogenicity assays are discussed. For cytotoxicity, cellular proliferation, calcein uptake, oxygen consumption, mitochondrial activity, radical formation, glutathione depletion as well as apoptosis are described. For high-content screening (HCS), the possibilities for analysis of cytotoxicity, micronuclei, centrosome formation and phospholipidosis are examined. For embryotoxicity, endocrine disruption and reprotoxicity alternative assays are reviewed for fast track analysis by means of nuclear receptors and membrane receptors. Moreover, solutions for analyzing enzyme induction by activation of nuclear receptors, like AhR, CAR, PXR, PPAR, FXR, LXR, TR and RAR are given.

Prediction of chemical carcinogenicity by machine learning approaches

In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.

Predictivity of QSAR

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.

Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies

The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.

Novel virtual screening protocol based on the combined use of molecular modeling and electron-ion interaction potential techniques to design HIV-1 integrase inhibitors

HIV-1 integrase (IN) is an essential enzyme for viral replication and represents an intriguing target for the development of new drugs. Although a large number of compounds have been reported to inhibit IN in biochemical assays, no drug active against this enzyme has been approved by the FDA so far. In this study, we report, for the first time, the use of the electron-ion interaction potential (EIIP) technique in combination with molecular modeling approaches for the identification of new IN inhibitors. An innovative virtual screening approach, based on the determination of both short- and long-range interactions between interacting molecules, was employed with the aim of identifying molecules able to inhibit the binding of IN to viral DNA. Moreover, results from a database screening on the commercial Asinex Gold Collection led to the selection of several compounds. One of them showed a significant inhibitory potency toward IN in the overall integration assay. Biological investigations also showed, in agreement with modeling studies, that these compounds prevent recognition of DNA by IN in a fluorescence fluctuation assay, probably by interacting with the DNA binding domain of IN.

The application of structure-based assessment to support safety and chemistry diligence to manage genotoxic impurities in active pharmaceutical ingredients during drug development

Starting materials and intermediates used to synthesize pharmaceuticals are reactive in nature and may be present as impurities in the active pharmaceutical ingredient (API) used for preclinical safety studies and clinical trials. Furthermore, starting materials and intermediates may be known or suspected mutagens and/or carcinogens. Therefore, during drug development due diligence need be applied from two perspectives (1) to understand potential mutagenic and carcinogenic risks associated with compounds used for synthesis and (2) to understand the capability of synthetic processes to control genotoxic impurities in the API. Recently, a task force comprised of experts from pharmaceutical industry proposed guidance, with recommendations for classification, testing, qualification and assessing risk of genotoxic impurities. In our experience the proposed structure-based classification, has differentiated 75% of starting materials and intermediates as mutagenic and non-mutagenic with high concordance (92%) when compared with Ames results. Structure-based assessment has been used to identify genotoxic hazards, and prompted evaluation of fate of genotoxic impurities in API. These two assessments (safety and chemistry) culminate in identification of genotoxic impurities known or suspected to exceed acceptable levels in API, thereby triggering actions needed to assure appropriate control and measurement methods are in place. Hypothetical case studies are presented demonstrating this multi-disciplinary approach.

Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction

The performance of the rat chronic lowest observed adverse effect level (LOAEL, the lowest exposure level at which there are biologically significant increases in the severity of adverse effects) model in Toxicity Prediction by Komputer Assisted Technology (TOPKAT), a commercial quantitative structure-activity relationship software package, was tested on a database of chemicals that are of interest to the U.S. EPA's Office of Pesticide Programs. The testing was repeated on a database of chemicals from three U.S. EPA sources that report peer-reviewed LOAELs. The results of this study were also contrasted with the results of the testing performed during TOPKAT's model-building process.

Improving the decision-making process in structural modification of drug candidates: reducing toxicity

The rule of three, relating to activity-exposure-toxicity, presents the single most difficult challenge in the design and advancement of drug candidates to the development stage. Absorption, distribution, metabolism and excretion (ADME) studies are widely used in drug discovery to optimize this balance of properties necessary to convert lead compounds into drugs that are both safe and effective for human patients. Idiosyncratic drug reactions (IDRs; referred to as type B reactions, which are mainly caused by reactive metabolites) are one type of adverse drug reaction that is important to human health and safety. This review highlights the strategies for the decision-making process involving substructures that, when found in drugs, can form reactive metabolites and are involved in toxicities in humans; the tools used to reduce IDRs are also discussed. Several examples are included to show how toxicity studies have influenced and guided drug design. Investigations of reactive intermediate formation in subcellular fractions with the use of radiolabeled reagents are also discussed.

Improving the decision-making process in the structural modification of drug candidates: enhancing metabolic stability

The activity-exposure-toxicity relationship, which can be described as "the rule of three", presents the single most difficult challenge in the design of drug candidates and their subsequent advancement to the development stage. ADME studies are widely used in drug discovery to optimize the balance of properties necessary to convert lead candidates into drugs that are safe and effective for humans. Metabolite characterization has become one of the key drivers of the drug discovery process, helping to optimize ADME properties and increase the success rate for drugs. Various strategies can influence drug design in the decision-making process in the structural modification of drug candidates to reduce metabolic instability.

Testing paradigm for prediction of development-limiting barriers and human drug toxicity

The financial investment grows exponentially as a new chemical entity advances through each stage of discovery and development. The opportunity exists for the modern toxicologist to significantly impact expenditures by the early prediction of potential toxicity/side effect barriers to development by aggressive evaluation of development-limiting liabilities early in drug discovery. Improved efficiency in pharmaceutical research and development lies both in leveraging "best in class" technology and integration with pharmacologic activities during hit-to-lead and early lead optimization stages. To meet this challenge, a discovery assay by stage (DABS) paradigm should be adopted. The DABS clearly delineates to discovery project teams the timing and type of assay required for advancement of compounds to each subsequent level of discovery and development. An integrative core pathology function unifying Drug Safety Evaluation, Molecular Technologies and Clinical Research groups that effectively spans all phases of drug discovery and development is encouraged to drive the DABS. The ultimate goal of such improved efficiency being the accurate prediction of toxicity and side effects that would occur in development before commitment of the large prerequisite resource. Good justification of this approach is that every reduction of development attrition by 10% results in an estimated increase in net present value by $100 million.

Searching for an enhanced predictive tool for mutagenicity

The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.

Strategies for dealing with metabolite elucidation in drug discovery and development

Structural information on metabolites can be a considerable asset for enhancing and streamlining the process of developing new drug candidates. Modern approaches that generate and use metabolite structural information can accelerate the drug discovery and development process by eliminating potentially harmful candidates earlier in the process and improving the safety of new drugs. This review examines the relative merits of current and potential strategies for dealing with metabolite characterization.

A multiple in silico program approach for the prediction of mutagenicity from chemical structure

We have conducted an evaluation of three of the most widely used commercial toxicity prediction programs, Toxicity Prediction by Komputer Assisted Technology (TOPKAT), Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows (DfW) and CASETOX. The three programs were evaluated for their ability to predict Ames test mutagenicity using 520 proprietary drug candidate (Test set 1) and 94 commercial (Test set 2) compounds. The study demonstrates that these three commercially available programs are useful, with limitations in their ability to predict mutagenicity over a wide range of chemical space, i.e. global predictivity. Individually, each of the programs performed at an acceptable level for overall accuracy, i.e. the ability to predict the correct outcome. However, analysis of the predictions indicates that the overall accuracy figure is heavily weighted by the ability of the programs to correctly predict non-mutagens, whereas none of the programs individually performed well in the prediction of novel mutagenic structures, i.e. Ames positive compounds. The performance of these programs' in predicting Ames positive mutagens appeared to be independent of the chemical utility of the compound, i.e. industrial, agricultural or pharmaceutical. The combination of program predictions provided some improvement in overall accuracy, sensitivity and specificity.

Computer systems for the prediction of toxicity: an update

In order to survive in the current economic climate, the pharmaceutical, agrochemical and personal product companies are required to produce large numbers of new, effective products whilst significantly reducing development time and costs. With the advent of combinatorial chemistry and high-throughput screening (HTS), the numbers of new candidate structures coming out of the discovery cycle has increased significantly. This has created a demand for faster screening of the toxicological properties of these candidates. Not surprisingly, computer methods for toxicity prediction offer an attractive solution to this problem because of their ability to screen large numbers of structures even before synthesis has occurred. In this paper the major, commercially available computer software systems for toxicity prediction are discussed together with their main strengths and limitations.

Unexpected tumour findings in lifetime rodent bioassay studies--what to do?

Currently, the majority of substances tested in lifetime bioassays in rodents are not mutagenic and, therefore, at the most weakly carcinogenic, generally by epigenetic mechanisms. It thus appears obvious that only marginal increases of tumour incidences can be expected in lifetime bioassays and that, therefore, every aspect of a potential carcinogenic effect must be thoroughly evaluated. This paper describes a series of key factors, which should be looked at in order to exclude that the lifetime bioassay in question is flawed for design, technical or qualification reasons. It also provides some hints whether there is indeed a real effect and not just a variation of the spontaneous tumour incidences. Tumour findings must be seen in the context of the animal model, the pharmcokinetics and pharmcodynamics of the test substance, as well as any other observation in the present or other studies with the test substance, including non-tumour findings and--in particular--potential precursor lesions and effects on feed intake and survival. The possibility that the observed carcinogenic effects may be species-specific and not relevant for man is discussed. It is also important to check what findings are reported with similar substances or substances with the same pharmacological effect. Data from additional investigations on material of the same study and/or mechanistic studies are often needed to support the final risk assessment.

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

Distributed structure-searchable toxicity (DSSTox) public database network: a proposal

The ability to assess the potential genotoxicity, carcinogenicity, or other toxicity of pharmaceutical or industrial chemicals based on chemical structure information is a highly coveted and shared goal of varied academic, commercial, and government regulatory groups. These diverse interests often employ different approaches and have different criteria and use for toxicity assessments, but they share a need for unrestricted access to existing public toxicity data linked with chemical structure information. Currently, there exists no central repository of toxicity information, commercial or public, that adequately meets the data requirements for flexible analogue searching, Structure-Activity Relationship (SAR) model development, or building of chemical relational databases (CRD). The distributed structure-searchable toxicity (DSSTox) public database network is being proposed as a community-supported, web-based effort to address these shared needs of the SAR and toxicology communities. The DSSTox project has the following major elements: (1) to adopt and encourage the use of a common standard file format (structure data file (SDF)) for public toxicity databases that includes chemical structure, text and property information, and that can easily be imported into available CRD applications; (2) to implement a distributed source approach, managed by a DSSTox Central Website, that will enable decentralized, free public access to structure-toxicity data files, and that will effectively link knowledgeable toxicity data sources with potential users of these data from other disciplines (such as chemistry, modeling, and computer science); and (3) to engage public/commercial/academic/industry groups in contributing to and expanding this community-wide, public data sharing and distribution effort. The DSSTox project's overall aims are to effect the closer association of chemical structure information with existing toxicity data, and to promote and facilitate structure-based exploration of these data within a common chemistry-based framework that spans toxicological disciplines.

Predicting toxicity: a mechanism of action model of chemical mutagenicity

The increasing importance of theoretical studies for predicting toxicology has aroused the interest of many computational chemists. A new approach has been developed, based on studying at the molecular level two potential mechanisms of action that are related to compound mutagenicity. This approach is the first example that considers both the toxicant and the biological target molecules involved in the interaction. Using some calculated descriptors and a simulation of the interaction chemical, compounds can be classified. More important, the approach helps in understanding and explaining both the correct and the incorrect results, and gives a deeper understanding of the toxic mechanisms. The model has been applied to many compounds and the results are compared with experimental results reported for the corresponding Salmonella tests.

Quantitative structure-pharmacokinetic relationship modelling

This article presents the current methods in quantitative structure-pharmacokinetic relationship (QSPkR) modelling along with examples using chemicals of toxicological significance. The common method involves: (i) collecting pharmacokinetic data or determining pharmacokinetic parameters (e.g. elimination half-life, volume of distribution) by fitting to experimental data; and (ii) associating them with the structural features of chemicals using a Free-Wilson model. Such QSPkRs have been developed for a few series of chemicals but their usefulness is limited to the exposure scenario and conditions under which the experimental data were originally collected. The alternative approach involves the development of quantitative structure-property relationship (QSPR) models for parameters, blood:air partition coefficient, tissue:blood partition coefficient, maximal velocity for metabolism and Michaelis affinity constant, of physiologically-based pharmacokinetic (PBPK) models which are useful for conducting species, route, dose and scenario extrapolations of the tissue dose of chemicals. Mechanistic QSPRs are available for predicting tissue:blood and blood:air partition coefficients from molecular structure information of chemicals, whereas such approaches are not currently available for hepatic metabolism parameters. However, at the present time, the pharmacokinetics of inhaled volatile organic chemicals can be simulated adequately by considering the physiological limits of the hepatic extraction ratio (0-1) and molecular structure-based estimates of partition coefficients in the PBPK model. This current state-of-the-art of structure-based modelling of pharmacokinetics will advance with the development of QSPRs for other chemical-specific parameters of PBPK models. Integrated QSPR-PBPK modelling should facilitate the identification of chemicals of a family that possess desired properties of bioaccumulation and blood concentration profile in both test animals and humans.

Evaluation of designed ligands by a multiple screening method: application to glycogen phosphorylase inhibitors constructed with a variety of approaches

Glycogen phosphorylase (GP) is an important enzyme that regulates blood glucose level and a key therapeutic target for the treatment of type II diabetes. In this study, a number of potential GP inhibitors are designed with a variety of computational approaches. They include the applications of MCSS, LUDI and CoMFA to identify additional fragments that can be attached to existing lead molecules; the use of 2D and 3D similarity-based QSAR models (HQSAR and SMGNN) and of the LUDI program to identify novel molecules that may bind to the glucose binding site. The designed ligands are evaluated by a multiple screening method, which is a combination of commercial and in-house ligand-receptor binding affinity prediction programs used in a previous study (So and Karplus, J. Comp.-Aid. Mol. Des., 13 (1999), 243-258). Each method is used at an appropriate point in the screening, as determined by both the accuracy of the calculations and the computational cost. A comparison of the strengths and weaknesses of the ligand design approaches is made.

Predicting human safety: screening and computational approaches

Current preclinical safety evaluation programs use a combination of computational methods, mechanistic in vitro screening and - primarily - in vivo experimentation to predict human toxicity. The rapid transition of pharmaceutical R&D into electronic R&D (e-R&D) makes it imperative that predictive safety testing also develops into an information-rich, knowledge-based process in the near future. Accordingly, enhanced databases and computational tools are expected to change the way the pharmaceutical industry assesses drug toxicity during discovery and early development. Expert use of prediction tools should lead to lower failure rates in drug development and decrease the cost and time involved in successful drug approval.

Knowledge discovery and data mining in toxicology

Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

Quantitative structure-property relationships in pharmaceutical research - Part 2

Part one of this two-part review described the advantages and limitations of quantitative structure-property relationships (QSPR), and offered an overview of the components involved in the development of correlations1. Part two provides a discussion of a few notable examples of relationships with organoleptic, physicochemical and pharmaceutical properties.

Commercial toxicology prediction systems: a regulatory perspective

The use of commercial toxicity prediction systems in a regulatory setting must consider both the limitations and capabilities of the methods, as well as the ultimate use of the predictions, e.g. for testing prioritization, screening, or supporting regulatory decisions. Current systems are better suited to hazard identification (i.e. positive identification of activity-conferring features) than to ruling out hazard. Two recent examples (an EPA testing prioritization exercise for water disinfection byproducts and a regulatory action on 2,4,6-tribromophenol) illustrate issues involved in regulatory applications of SAR and commercial prediction systems. The challenge for the future will be to improve technologies for prediction within the constraints of available data, make optimal use of new test data, and better integrate elements of quantitative modeling (QSAR), empirical association, and biological and chemical mechanisms towards the goal of toxicity prediction.