A model validation and consensus building environment

Abstract

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll®. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a ‘consensus’ model that may be a much better model than any of the individual predictive model. The KnowItAll® system can handle two types of predictions: real number and categorical classification.

Keywords:

• Model
• Validation
• Consensus
• Intrinsic water solubility
• Plasma-protein-binding
• Mutagenicity

Acknowledgement

Many thanks to Dr. Ferenc Darvas and Dr. Laszlo Ürge (CompuDrug International, Inc.) 10, Dr. Joseph Votano (ChemSilico, LLC) 9, 14, 20, Dr. Philip Howard, Dr. Bill Meylan, and Dr. Jay Tunkel (Syracuse Research Corporation) 17, Dr. Hamilton Hitchings (Equbits, LLC) 15, 16, and Dr. Kas Subramanian (Strand Life Sciences) 18, 19 for contributing predictive models. Thanks to Professor John C. Dearden (Liverpool John Moores University), Dr. Joseph Votano (ChemSilico, LLC) and Dr. Kas Subramanian (Strand Life Sciences) for sharing datasets 9, 13, 14 18–20.