Literature DB >> 16445115

Computational toxicology: heading toward more relevance in drug discovery and development.

Dale E Johnson1, Amie D Rodgers.   

Abstract

Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact the attrition rate of compounds in early research and development, and prove successful in predicting adverse drug reactions (ADRs) in patients enrolled in clinical trials, and particularly prior to the marketing of drugs. The impact of such tools to date, however, has been modest and relatively narrow in scope. It is important to note that advances within medical science and newer approaches in clinical development will require predictive toxicology applications to be viable, and therefore efforts must be directed into making these tools relevant to the goal of preventing undesired toxicity in patients. In this Editorial Opinion, the current status of computational toxicology within industry is reviewed and areas in which advances can be made are highlighted. While predicting the potential of a compound to induce specific ADRs continues to be a formidable task, the field of computational biology is now heading in a direction more relevant to human disease and adverse outcomes.

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Year:  2006        PMID: 16445115

Source DB:  PubMed          Journal:  Curr Opin Drug Discov Devel        ISSN: 1367-6733


  4 in total

1.  Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools.

Authors:  Malcolm J D'Souza; Fumie Koyoshi; Lynn M Everett
Journal:  Pharm Rev       Date:  2009 Sep-Oct

2.  Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Authors:  Amie D Rodgers; Hao Zhu; Denis Fourches; Ivan Rusyn; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2010-04-19       Impact factor: 3.739

3.  Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification.

Authors:  Pedro J Ballester; Martina Mangold; Nigel I Howard; Richard L Marchese Robinson; Chris Abell; Jochen Blumberger; John B O Mitchell
Journal:  J R Soc Interface       Date:  2012-08-29       Impact factor: 4.118

4.  In silico toxicology - non-testing methods.

Authors:  Hannu Raunio
Journal:  Front Pharmacol       Date:  2011-06-30       Impact factor: 5.810

  4 in total

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