Predictive ADMET studies, the challenges and the opportunities.
Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.
Davis
Andrew M
AM
Department of Physical and Metabolic Science, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire, LE11 5RH, UK. Andy.davis@astrazeneca.com
Riley
Robert J
RJ
D000818:Animals; D017076:Computer-Aided Design; D016208:Databases, Factual; D015195:Drug Design; D006801:Humans; D021281:Quantitative Structure-Activity Relationship