In silico' toxicology methods in drug safety assessment (original) (raw)

In silico methods to predict drug toxicity

Current Opinion in Pharmacology, 2013

This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding.These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology.

Computational toxicology

Human & Experimental Toxicology, 2015

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them.

The in chemico-in silico interface: Challenges for integrating experimental and computational chemistry to identify toxicity

Alternatives to laboratory animals: ATLA

A number of toxic effects are brought about by the covalent interaction between the toxicant and biological macromolecules. In chemico assays are available that attempt to identify reactive compounds. These approaches have been developed independently for pharmaceuticals and for other nonpharmaceutical compounds. The assays vary widely in terms of the macromolecule (typically a peptide) and the analytical technique utilised. For both sets of methods, there are great opportunities to capture in chemico information by using in silico methods to provide computational tools for screening purposes. In order to use these in chemico and in silico methods, integrated testing strategies are required for individual toxicity endpoints. The potential for the use of these approaches is described, and a number of recommendations to improve this extremely useful technique, in terms of implementing the Three Rs in toxicity testing, are presented.

The In Chemico–In Silico Interface: Challenges for Integrating Experimental and Computational Chemistry to Identify Toxicity

Atla-alternatives To Laboratory Animals, 2009

A number of toxic effects are brought about by the covalent interaction between the toxicant and biological macromolecules. In chemico assays are available that attempt to identify reactive compounds. These approaches have been developed independently for pharmaceuticals and for other nonpharmaceutical compounds. The assays vary widely in terms of the macromolecule (typically a peptide) and the analytical technique utilised. For both sets of methods, there are great opportunities to capture in chemico information by using in silico methods to provide computational tools for screening purposes. In order to use these in chemico and in silico methods, integrated testing strategies are required for individual toxicity endpoints. The potential for the use of these approaches is described, and a number of recommendations to improve this extremely useful technique, in terms of implementing the Three Rs in toxicity testing, are presented.

In silico prediction of drug toxicity

Journal of computer-aided molecular design

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of...

Computational toxicology in drug development

Drug Discovery Today, 2008

Computational tools for predicting toxicity have been envisaged for their potential to considerably impact the attrition rate of compounds in drug discovery and development. In silico techniques like knowledge-based expert systems (quantitative) structure activity relationship tools and modeling approaches may therefore help to significantly reduce drug development costs by succeeding in predicting adverse drug reactions in preclinical studies. It has been shown that commercial as well as proprietary systems can be successfully applied in the pharmaceutical industry. As the prediction has been exhaustively optimized for early safety-relevant endpoints like genotoxicity, future activities will now be directed to prevent the occurrence of undesired toxicity in patients by making these tools more relevant to human disease.

The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries

Drug Discovery Today, 2012

In silico toxicology prediction is an extremely challenging area because many toxicological effects are a result of changes in multiple physiological processes. In this article we discuss limitations and strengths of these in silico tools. Additionally, we look at different parameters that are necessary to make the best use of these tools, and also how to gain acceptance outside the modelling community and into the regulatory arena. As a solution, we propose an integrated workflow for combined use of data extraction, quantitative structure activity relationships and read-across methods. We also discuss how the recent advances in this field can enable transition to a new paradigm of the discovery process, as exemplified by the Toxicity Testing in the 21st Century initiative.