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7317250 
Journal Article 
Editorial 
Application of advanced in silico methods for predictive modeling and information integration 
Valerio, LG 
2012 
Yes 
Expert Opinion on Drug Metabolism & Toxicology
ISSN: 1742-5255
EISSN: 1744-7607 
8 
4 
395-398 
English 
22432718 
WOS:000301836700001 
INTRODUCTION: In silico predictive methods are well-known tools to the drug discovery process. In recent years, these tools have become of strategic interest to regulatory authorities to support risk-based approaches and to complement, and potentially strengthen evidence when considering product quality and safety of human pharmaceuticals.

AREAS COVERED: This editorial reviews how chemically intelligent systems and computational models using structure-based assessments are important for providing predictive data on drug toxicity and safety liabilities considered at the FDA. The example of regulatory interest in application of in silico systems for mutagenicity predictions of drug impurities is discussed.

EXPERT OPINION: The importance of information integration is emphasized toward the application of in silico predictive methods and enhancing data mining capabilities for safety signal detection. Modeling for cardiovascular drug safety based on human clinical trial data is one area of active testing of predictive technologies at the FDA. The FDA has taken appropriate steps in its strategies and initiatives aimed to enhance and support innovation for regulatory science and medical product development by developing and implementing the use of in silico predictive models and medical toxicity databases. This science priority area will ultimately help improve and protect public health. 
Computational toxicology; Drug impurities; Drug safety; Genotoxicity; in silico methods; in silico toxicology; QSAR; SAR; cardiovascular agent; chemical structure; computer model; data base; drug impurity; drug safety; drug toxicity; food and drug administration; mutagenicity; review; Animals; Artificial Intelligence; Computer Simulation; Drug Discovery; Drug Toxicity; Forecasting; Humans; Information Management; Mutagenicity Tests; Mutagens; Risk Assessment; Structure-Activity Relationship; United States; United States Food and Drug Administration