Poor pharmacokinetic and toxicity profiles are major reasons for the low rate of advancing lead drug candidates into efficacy studies. The In-silico prediction of primary pharmacokinetic and toxicity properties in the drug discovery and development process can be used as guidance in the design of candidates. In-silico parameters can also be used to choose suitable compounds for in-vivo testing thereby reducing the number of animals used in experiments. At the Novartis Institute for Tropical Diseases, a data set has been curated from in-house measurements in the disease areas of Dengue, Tuberculosis and Malaria. Volume of distribution, half-life, total in-vivo clearance, in-vitro human plasma protein binding and in-vivo oral bioavailability have been measured for molecules in the lead optimization stage in each of these three disease areas. Data for the inhibition of the hERG channel using the radio ligand binding dofetilide assay was determined for a set of 300 molecules in these therapeutic areas. Based on this data, Artificial Neural Networks were used to construct In-silico models for each of the properties listed above that can be used to prioritize candidates for lead optimization and to assist in selecting promising molecules for in-vivo pharmacokinetic studies.
Keywords: Neural Networks, computational ADME, computer aided drug design, molecular diversity, pharmacokinetics, mathematical models, molecular modeling.