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

Predictive computational toxicology to support drug safety assessment

Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

Predicting in vivo safety characteristics using physiochemical properties and in vitro assays

There is increasing pressure on the pharmaceutical industry to deliver safer and more effective medicines while constraining research and development costs. In order to meet these demands, the industry is looking for basic design principles in terms of physicochemical properties as well as the use of higher throughput in vitro assays to select and evaluate new molecular entities for further development. Recent advances in understanding the relationships between a chemical’s properties and its propensity for adverse events, as well as the development of new in vitro screening technologies, have enhanced our ability to potentially select molecules more likely to succeed in becoming drugs. However, these approaches are still limited by the availability of data and our lack of understanding of the mechanisms by which compounds can cause toxicity.

Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements?

INTRODUCTION

Structure-activity modelling for predicting toxicology as a discipline is now 50 years old, and great strides have been taken in developing methods for the physicochemical analysis of molecules and their toxicity evaluation, both essential stages in modelling. Computational toxicology also has huge potential for speeding up the screening and prioritisation of chemicals for further testing and for reducing the numbers of expensive and time-consuming conventional tests. Yet, the realisation of this potential has been largely stifled by many problems inherent in developing and validating new structure-activity models of toxicity.

AREAS COVERED

Problems of computational toxicology discussed include i) the use of inappropriate molecular descriptors and tools that are not transparent; ii) the undetected existence of chemicals that cause large changes in toxicity with only small differences in molecular structure (causing 'activity cliffs' in the structure-activity landscape); iii) spurious correlations between structure and activity; iv) lack of quality control of toxicity data; v) difficulties in determining predictivity for novel chemicals; and vi) an over-reliance on complex mathematics and statistics.

EXPERT OPINION

Greater emphasis needs to be placed on i) the selection of training and test sets of chemicals to enable both internal and external validation of models to be undertaken for more accurate assessment of model predictivity and ii) the use of recently developed techniques for characterising structure-activity landscapes.

Genetic toxicology in the 21st century: reflections and future directions

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24-28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

Overall impact of the regulatory requirements for genotoxic impurities on the drug development process

In the last decade a considerable effort has been made both by the regulators and the pharmaceutical industry to assess genotoxic impurities (GTI) in pharmaceutical products. Though the control of impurities in drug substances and products is a well established and consolidated procedure, its extension to GTI has given rise to a number of problems, both in terms of setting the limits and detecting these impurities in pharmaceutical products. Several papers have dealt with this issue, discussing available regulations, providing strategies to evaluate the genotoxic potential of chemical substances, and trying to address the analytical challenge of detecting GTI at trace levels. In this review we would like to discuss the available regulations, the toxicological background for establishing limits, as well as the analytical approaches used for GTI assessment. The final aim is that of providing a complete overview of the topic with updated available information, to address the overall GTI issue during the development of new drug substances.

Computational Toxicology Approaches at the US Food and Drug Administration

For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products — however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the “black box” paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.

An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity

Information from the 1999 through 2008 Physicians' Desk Reference (PDR) was used to evaluate the genotoxicity of marketed drugs. Where available, data regarding the rodent carcinogenicity results were included (PDR and Gold potency database). In addition, computational predictivity of genotoxicity (DEREK, MC4PC) is included and expanded upon from two previous reviews. The present paper contains genotoxicity data on 545 marketed drugs. Excluded from analysis were most cytotoxic anti-cancer and antiviral drugs, nucleosides (all with known mechanistic genotoxicity), steroids with class-specific genotoxicity and biologicals or peptide-based drugs. Per assay type, the percentage of positive drugs was: Bacterial mutagenesis assay: 38/525 (7.1%), in vitro chromosome aberrations: 88/380 (26.1%); mouse lymphoma assays (MLA): 32/163 (19.1%), in vivo cytogenetics: 49/438 (11.1%). The relationship among positive genetic toxicity findings, rodent carcinogenicity, and in silico prediction is discussed. Finally, supporting evidence is presented for the idea that the presence of an N-dialkyl group or piperidine aryl ketone may somehow be associated with genotoxicity, perhaps through DNA intercalation and consequent DNA topoisomerase II inhibition.