Literature DB >> 17229485

Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products.

Naomi L Kruhlak1, Joseph F Contrera, R Daniel Benz, Edwin J Matthews.   

Abstract

Active ingredients in pharmaceutical products undergo extensive testing to ensure their safety before being made available to the American public. A consideration during the regulatory review process is the safety of pharmaceutical contaminants and degradents which may be present in the drug product at low levels. Several published guidances are available that outline the criteria for further testing of these impurities to assess their toxic potential, where further testing is in the form of a battery of toxicology assays and the identification of known structural alerts. However, recent advances in the development of computational methods have made available additional resources for safety assessment such as structure similarity searching and quantitative structure-activity relationship (QSAR) models. These methods offer a rapid and cost-effective first-pass screening capability to assess toxicity when conventional toxicology data are limited or lacking, with the potential to identify compounds that would be appropriate for further testing. This article discusses some of the considerations when using computational toxicology methods for regulatory decision support and gives examples of how the technology is currently being applied at the US Food and Drug Administration.

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Year:  2006        PMID: 17229485     DOI: 10.1016/j.addr.2006.10.008

Source DB:  PubMed          Journal:  Adv Drug Deliv Rev        ISSN: 0169-409X            Impact factor:   15.470


  6 in total

1.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

Authors:  Natalja Fjodorova; Marjan Vračko; Marjan Tušar; Aneta Jezierska; Marjana Novič; Ralph Kühne; Gerrit Schüürmann
Journal:  Mol Divers       Date:  2009-08-15       Impact factor: 2.943

2.  In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Authors:  Fuxing Li; Defang Fan; Hao Wang; Hongbin Yang; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2017-07-31       Impact factor: 3.524

Review 3.  Genetic toxicology in the 21st century: reflections and future directions.

Authors:  Brinda Mahadevan; Ronald D Snyder; Michael D Waters; R Daniel Benz; Raymond A Kemper; Raymond R Tice; Ann M Richard
Journal:  Environ Mol Mutagen       Date:  2011-04-28       Impact factor: 3.216

4.  Extracting Relevant Information from FDA Drug Files to Create a Structurally Diverse Drug Database Using KnowItAll®

Authors:  Malcolm J D'Souza; Fumie Koyoshi
Journal:  Pharm Rev       Date:  2009-05-08

5.  Increasing the Value of Data Within a Large Pharmaceutical Company Through In Silico Models.

Authors:  Alessandro Brigo; Doha Naga; Wolfgang Muster
Journal:  Methods Mol Biol       Date:  2022

6.  The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding.

Authors:  Xiang-Wei Zhu; Alexander Sedykh; Hao Zhu; Shu-Shen Liu; Alexander Tropsha
Journal:  Pharm Res       Date:  2013-04-09       Impact factor: 4.200

  6 in total

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