Application of advanced in silico methods for predictive modeling and information integration

Expert Opinion on Drug Metabolism and Toxicology

Volumn 8, Issue 4, 2012, Pages 395-398

  Valerio, Luis G ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

Computational toxicology; Drug impurities; Drug safety; Genotoxicity; in silico methods; in silico toxicology; QSAR; SAR

Indexed keywords

CARDIOVASCULAR AGENT;

CHEMICAL STRUCTURE; COMPUTER MODEL; DATA BASE; DRUG IMPURITY; DRUG SAFETY; DRUG TOXICITY; FOOD AND DRUG ADMINISTRATION; MUTAGENICITY; REVIEW;

EID: 84858858717     PISSN: 17425255     EISSN: 17447607     Source Type: Journal    
DOI: 10.1517/17425255.2012.664636     Document Type: Review

References (15)

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5. Valerio LG Jr, Yang C, Arvidson KB, et al. A structural feature-based computational approach for toxicology predictions. Expert Opin Drug Metab Toxicol 2010;6(4):505-18 A technology evaluation of a computational software used at the FDA.
6. Valerio LG Jr, Prous J, Valencia A, et al. Computational Modeling for QT Prolongation: A Drug Cardiovascular Endpoint of Paramount Importance. In: Society of Toxicology, editor. The Toxicologist Annual Meeting of the Society of Toxicology. Oxford Journals; Washington, D.C, USA: 2011. p. 126 A preliminary report on a collaborative project between FDA and a software developer where predictive models were constructed for QT prolongation and Torsade de Pointes based on human clinical trial data.
7. Arvidson KB, Chanderbhan R, Muldoon-Jacobs K, et al. Regulatory use of computational toxicology tools and databases at the United States food and drug administration's office of food additive safety. Expert Opin Drug Metab Toxicol 2010;6(7):793-6 Describes research activity and regulatory use of predictive QSARs for assessing the genotoxicity and carcinogenicity of indirect food additives at FDA/CFSAN.
8. FDA. Guidance for Industry. Genotoxic and Carcinogenic Impurities in Drug Substances and Products: Recommended Approaches. Draft Guidance. US Depart. Of Health and Human Services. Ed.: Office of Communications, Division of Drug Information, CDER, FDA; 2008;p. 1-13
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10. DIA. DIA/FDA Quantitative Structure-Activity Relationship (Q)SAR Approaches to Assessing Genotoxic Impurities in Pharmaceuticals. Drug Information Agency; Rockville, MD USA: 2011
11. Mostrag-Szlichtyng A, Zaldivar Comenges JM, Worth AP. Computational toxicology at the European commission's joint research centre. Expert Opin Drug Metab Toxicol 2010;6(7):785-92 An excellent concise review from the European Commission's computational toxicology group and describes the software tools the JRC has been developing.
12. Hillebrecht A, Muster W, Brigo A, et al. Comparative evaluation of in silico systems for ames test mutagenicity prediction: Scope and limitations. Chem Res Toxicol 2011;24(6):843-54 This paper is a research study comparing predictive performances of commonly used in silico programs against public and proprietary in-house data sets. The authors find that the commercial methods did better with public data than their proprietary in-house data.
13. Valerio LG, Yang C, Arvidson KB, et al. A structural feature-based computational approach for toxicology predictions. Expert Opin Drug Metab Toxicol 2010;6(4):505-18
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