Literature DB >> 16182212

Reengineering the pharmaceutical industry by crash-testing molecules.

Peter W Swaan1, Sean Ekins.   

Abstract

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.

Mesh:

Year:  2005        PMID: 16182212     DOI: 10.1016/S1359-6446(05)03557-9

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  4 in total

1.  Virtual target screening: validation using kinase inhibitors.

Authors:  Daniel N Santiago; Yuri Pevzner; Ashley A Durand; MinhPhuong Tran; Rachel R Scheerer; Kenyon Daniel; Shen-Shu Sung; H Lee Woodcock; Wayne C Guida; Wesley H Brooks
Journal:  J Chem Inf Model       Date:  2012-07-23       Impact factor: 4.956

2.  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

Review 3.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

Review 4.  In silico pharmacology for drug discovery: applications to targets and beyond.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

  4 in total

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