Quantitative knowledge-based analysis in compound safety assessment

Abstract

Introduction: Despite rapid progress in OMICs and computational technologies in compound safety assessment, drug failure rate due to toxicity is still unacceptably high. One reason for this is an inadequate interpretation of high-throughput preclinical data. Another reason is the poor mechanistic understanding of drug side effects as currently just a few compound targets are linked to specific adverse reactions.

Areas covered: Current performance issues with statistical analysis of OMICs data or gene/protein/compound lists are discussed, illustrating potential advantages of knowledge-based approaches in prediction of human toxicity. The authors show several examples of quantitative functional analysis, including cross-tissue toxicity predictions and integrated analysis of different types of OMICs data. They also describe novel approaches linking compound targets and associated pathways to side effects. The reader will gain an update on the recent developments in knowledge-based analysis in toxicogenomics and computational methods correlating protein targets with adverse reactions.

Expert opinion: Quantitative pathway analysis is a useful approach for deriving multi-variant predictive biomarkers for drug safety. However, more comprehensive studies are needed for direct comparison of performance between pathway- and gene-centric methods.

Keywords::

• gene signature
• knowledge base
• OMICs data
• pathway analysis
• side effect
• target prediction
• toxicity
• toxicogenomics

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