Computational science in drug metabolism and toxicology

Abstract

Computational scientific tools involving construction and testing of models, screening and data mining for drug and chemical induced toxicities and metabolism have significantly grown in experimental use to help guide product development and assist by enhancing certain areas of regulatory decision making. This themed issue of the journal entitled Computational Science in Drug Metabolism & Toxicology contains state-of-the-art review articles and perspectives covering a diversity of in silico approaches. Computational science tools have a strong potential for expediting our further understanding of drug metabolism and toxicity and are continually being developed and validated. The reader will gain an understanding of the current state of in silico tools and modeling approaches aimed at reducing these liabilities. In addition, how these tools are tested and developed for use in drug safety to support drug development efforts and a review of how they are used to predict genotoxic liabilities are covered in this issue. Computational science tools when properly validated and used judiciously can lend themselves as enablers to support drug safety assessment in investigative and applied settings.

Keywords::

• alternative methods
• carcinogenicity
• computational drug metabolism
• computational science
• computational tools
• computational toxicology
• cytochrome P450
• drug metabolism
• drug safety
• drug screening
• genetic toxicity
• in silico toxicology
• predictive toxicology
• QSAR
• structure–activity relationships

Acknowledgement

This research report is not an official US Food and Drug Administration guidance or policy statement. No official support or endorsement by the US Food and Drug Administration is intended or should be inferred.