Joseph F. Contrera

Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.

MDL QSAR (formerly SciVision QSAR IS) software is one of the several software systems under evaluation by the Informatics and Computational Safety Analysis Staff (ICSAS) of the FDA Center for Drug Evaluation and Research for regulatory and scientific decision support applications. MDL QSAR software contains an integrated set of tools for similarity searching, compound clustering, and modeling molecular structure related parameters that includes 240 electrotopological E-state, connectivity, and other descriptors. These molecular descriptors can be statistically correlated with toxicological or biological endpoints. The goal of this research was to evaluate the feasibility of using MDL QSAR software to develop structure-activity relationship (SAR) models that can be used to predict the carcinogenic potential of pharmaceuticals and organic chemicals. A validation study of 108 compounds that include 86 pharmaceuticals and 22 chemicals that were not present in a control rodent carcinogenicity data set of 1275 compounds demonstrated that MDL QSAR models had excellent coverage (93%) and good sensitivity (72%) and specificity (72%) for rodent carcinogenicity. The software correctly predicted 72% of non-carcinogenic compounds and compounds with carcinogenic findings. E-state descriptors contributed to more than half of the SAR models used to predict carcinogenic activity. We believe that electrotopological E-state descriptors and QSAR IS (MDL QSAR) software are promising new in silico approaches for modeling and predicting rodent carcinogenicity and may have application for other toxicological endpoints.

Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents

ABSTRACT This report describes a coordinated use of four quantitative structure-activity relationship (QSAR) programs and an expert knowledge base system to predict the occurrence and the mode of action of chemical carcinogenesis in rodents. QSAR models were based upon a weight-of-evidence paradigm of carcinogenic activity that was linked to chemical structures (n = 1,572). Identical training data sets were configured for four QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Leadscope PDM), and QSAR models were constructed for the male rat, female rat, composite rat, male mouse, female mouse, composite mouse, and rodent composite endpoints. Model predictions were adjusted to favor high specificity (>80%). Performance was shown to be affected by the method used to score carcinogenicity study findings and the ratio of the number of active to inactive chemicals in the QSAR training data set. Results demonstrated that the four QSAR programs were complementary, each detecting different profiles of carcinogens. Accepting any positive prediction from two programs showed better overall performance than either of the single programs alone; specificity, sensitivity, and Chi-square values were 72.9%, 65.9%, and 223, respectively, compared to 84.5%, 45.8%, and 151. Accepting only consensus-positive predictions using any two programs had the best overall performance and higher confidence; specificity, sensitivity, and Chi-square values were 85.3%, 57.5%, and 287, respectively. Specific examples are provided to demonstrate that consensus-positive predictions of carcinogenicity by two QSAR programs identified both genotoxic and nongenotoxic carcinogens and that they detected 98.7% of the carcinogens linked in this study to Derek for Windows defined modes of action.

Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models

ABSTRACT Drug-induced phospholipidosis (PL) is a condition characterized by the accumulation of phospholipids and drug in lysosomes, and is found in a variety of tissue types. PL is frequently manifested in preclinical studies and may delay or prevent the development of pharmaceuticals. This report describes the construction of a database of PL findings in a variety of animal species and its use as a training data set for computational toxicology software. PL data and chemical structures were compiled from the published literature, existing pharmaceutical databases, and Food and Drug Administration (FDA) internal reports yielding a total of 583 compounds suitable for modeling. The database contained 190 (33%) positive drugs and 393 (77%) negative drugs, of which 39 were electron microscopy–confirmed negative compounds and 354 were classified as negatives due to the absence of positive reported data. Of the 190 positive findings, 76 were electron microscopy confirmed and 114 were considered positive based on other evidence. Quantitative structure-activity relationship (QSAR) models were constructed using two commercially available software programs, MC4PC and MDL-QSAR, and internal cross-validation (10 × 10%) experiments were performed to assess their predictive performance. Performance parameters for the MC4PC model were specificity 92%, sensitivity 50%, concordance 78%, positive predictivity 76%, and negative predictivity 78%. For MDL-QSAR, predictive performance was similar: specificity 80%, sensitivity 76%, concordance 79%, positive predictivity 65%, and negative predictivity 87%. By combining the output of the two QSAR programs, the overall predictive performance was vastly improved and sensitivity could be optimized to 81% without significant loss of specificity (79%). Many of the structural alerts and significant molecular descriptors obtained from the QSAR software were found to be associated with parts of active molecules known for their cationic amphiphilic drug (CAD) properties supporting the hypothesis that the endpoint of PL is statistically correlated with chemical structure. QSAR models can be useful tools for screening drug candidate molecules for potential PL.

Understanding Genetic Toxicity Through Data Mining: The Process of Building Knowledge by Integrating Multiple Genetic Toxicity Databases

ABSTRACT Genetic toxicity data from various sources were integrated into a rigorously designed database using the ToxML schema. The public database sources include the U.S. Food and Drug Administration (FDA) submission data from approved new drug applications, food contact notifications, generally recognized as safe food ingredients, and chemicals from the NTP and CCRIS databases. The data from public sources were then combined with data from private industry according to ToxML criteria. The resulting “integrated” database, enriched in pharmaceuticals, was used for data mining analysis. Structural features describing the database were used to differentiate the chemical spaces of drugs/candidates, food ingredients, and industrial chemicals. In general, structures for drugs/candidates and food ingredients are associated with lower frequencies of mutagenicity and clastogenicity, whereas industrial chemicals as a group contain a much higher proportion of positives. Structural features were selected to analyze endpoint outcomes of the genetic toxicity studies. Although most of the well-known genotoxic carcinogenic alerts were identified, some discrepancies from the classic Ashby-Tennant alerts were observed. Using these influential features as the independent variables, the results of four types of genotoxicity studies were correlated. High Pearson correlations were found between the results of Salmonella mutagenicity and mouse lymphoma assay testing as well as those from in vitro chromosome aberration studies. This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies.

An Evaluation of the Hemizygous Transgenic Tg.AC Mouse for Carcinogenicity Testing of Pharmaceuticals. I. Evidence for a Confounding Nonresponder Phenotype

We have completed 2 26-wk studies to evaluate the hemizygous transgenic Tg.AC mouse, which has been proposed as an alternative short term model for testing carcinogenicity. We attempted to evaluate the response to the known rodent carcinogens cyclophosphamide, phenolphthalein, and tamoxifen and to the noncarcinogen chlorpheniramine following topical application. In the first study, a weak response (2/17 animals) was observed to the positive control 12-O-tetradecanoylphorbol 13-acetate (TPA in ethanol, 1.25 μg), and no response was observed to cyclophosphamide, phenolphthalein, or chlorpheniramine, despite evidence for skin penetration. The second study compared 1.25 μg and 6.25 μg of TPA in ethanol and acetone solutions. Tamoxifen was also evaluated in both solvents and orally. No significant response was observed to tamoxifen by skin paint or oral routes. Over 60% of the high dose TPA-treated animals showed no (0 or 1) papilloma response, and 30% of the animals each developed more than 32 papillomas. The heterogenous response to high dose TPA may be related to variability in the responsiveness of hemizygous animals. In light of these findings, further Tg.AC studies should employ homozygous animals, and the underlying cause for heterogeneity in the tumorigenic response of Tg.AC mice should be identified and eliminated.

A Systemic Exposure-Based Alternative to the Maximum Tolerated Dose for Carcinogenicity Studies of Human Therapeutics

A systemic exposure-based alternative to the maximum tolerated dose (MTD) for high-dose selection in carcinogenicity studies for human therapeutics was accepted at the Second International Conference on Harmonization (ICH-2). The systemic exposure-based alternative to the MTD is suitable for nongenotoxic compounds with low rodent toxicity that are metabolized similarly in rodents and humans. This is the first product of an evaluation of current standards for rodent carcinogenicity studies of therapeutics. The relative systemic exposure is the ratio of the rat plasma area under the plasma concentration-time curve (AUC) at the MTD/human plasma AUC at the maximum recommended daily dose. An appropriate systemic exposure ratio for high-dose selection in carcinogenicity studies was empirically derived from the distribution of systemic exposure ratios attained by 35 compounds from 11 therapeutic categories in a Food and Drug Administration (FDA) database. Approximately one-third achieved a relative systemic exposure ratio <1 and two-thirds attained an exposure ratio of 10 or less, at the MTD. A systemic exposure ratio of at least 25 was accepted for high-dose selection in carcinogenicity studies at ICH-2. This ratio is high enough to detect all compounds with positive studies in the FDA database and would detect IARC 1 and 2A carcinogenic drugs. A ratio of 25 exceeds the systemic exposure ratio attained by 75% of drugs tested at the MTD in the FDA database and represents an adequate margin of safety which can be attained by a significant proportion of drugs.

In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State, Connectivity, and Molecular Property Descriptors

ABSTRACT Genetic toxicity testing is a critical parameter in the safety assessment of pharmaceuticals, food constituents, and environmental and industrial chemicals. Quantitative structure-activity relationship (QSAR) software offers a rapid, cost-effective means of prioritizing the genotoxic potential of chemicals. Our goal is to develop and validate a complete battery of complementary QSAR models for genetic toxicity. We previously reported the development of MDL-QSAR models for the prediction of mutations in Salmonella typhimurium and Escherichia coli (); this report describes the development of eight additional models for mutagenicity, clastogenicity, and DNA damage. The models were created using MDL-QSAR atom-type E-state, simple connectivity and molecular property descriptor categories, and nonparametric discriminant analysis. In 10% leave-group-out internal validation studies, the specificity of the models ranged from 63% for the mouse lymphoma (L5178Y-tk) model to 88% for chromosome aberrations in vivo. Sensitivity ranged from a high of 74% for the mouse lymphoma model to a low of 39% for the unscheduled DNA synthesis model. The receiver operator characteristic (ROC) was ≥2.00, a value indicative of good predictive performance. The predictive performance of MDL-QSAR models was also shown to compare favorably to the results of MultiCase MC4PC () genotoxicity models prepared with the same training data sets. MDL-QSAR software models exhibit good specificity, sensitivity, and coverage and they can provide rapid and cost-effective large-scale screening of compounds for genotoxic potential by the chemical and pharmaceutical industry and for regulatory decision support applications.

Response to Monro and Mehta proposal for use of single-dose toxicology studies to support single-dose studies of new drugs in humans.

Clinical Pharmacology & Therapeutics (1996) 59, 265–267; doi: