Document Detail


In-silico ADME Models: A General Assessment of their Utility in Drug Discovery Applications.
MedLine Citation:
PMID:  21320065     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
ADME prediction is an extremely challenging area as many of the properties we try to predict are a result of multiple physiological processes. In this review we consider how in-silico predictions of ADME processes can be used to help bias medicinal chemistry into more ideal areas of property space, minimizing the number of compounds needed to be synthesized to obtain the required biochemical/physico-chemical profile. While such models are not sufficiently accurate to act as a replacement for in-vivo or in-vitro methods, in-silico methods nevertheless can help us to understand the underlying physico-chemical dependencies of the different ADME properties, and thus can give us inspiration on how to optimize them. Many global in-silico ADME models (i.e generated on large, diverse datasets) have been reported in the literature. In this paper we selectively review representatives from each distinct class and discuss their relative utility in drug discovery. For each ADME parameter, we limit our discussion to the most recent, most predictive or most insightful examples in the literature to highlight the current state of the art. In each case we briefly summarize the different types of models available for each parameter (i.e simple rules, physico-chemical and 3D based QSAR predictions), their overall accuracy and the underlying SAR. We also discuss the utility of the models as related to lead generation and optimization phases of discovery research.
Authors:
M Paul Gleeson; Anne Hersey; Supa Hannongbua
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Current topics in medicinal chemistry     Volume:  11     ISSN:  1873-4294     ISO Abbreviation:  Curr Top Med Chem     Publication Date:  2011  
Date Detail:
Created Date:  2011-02-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101119673     Medline TA:  Curr Top Med Chem     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  358-81     Citation Subset:  IM    
Affiliation:
Department of Chemistry, Faculty of Science, Kasetsart University, 50 Phaholyothin Rd, Chatuchak, Bangkok 10900, Thailand. paul.gleeson@ku.ac.th.
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