ABSTRACT
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.
Subject(s)
Anti-Infective Agents/chemistry , Anti-Infective Agents/pharmacokinetics , Dengue/drug therapy , Drug Design , Malaria/drug therapy , Neural Networks, Computer , Tuberculosis/drug therapy , Animals , Anti-Infective Agents/pharmacology , Anti-Infective Agents/toxicity , Computer Simulation , Computer-Aided Design , Dengue Virus/drug effects , Ether-A-Go-Go Potassium Channels/antagonists & inhibitors , Ether-A-Go-Go Potassium Channels/metabolism , Humans , Mice , Mycobacterium/drug effects , Plasmodium/drug effectsABSTRACT
The one-dimensional model of Hann et al. (JChem Inf Comput Sci 41(3):856864) has been extended to include reverse binding and wrap-around interaction modes between the protein and ligand to explore the complete combinatorial matrix of molecular recognition. The cumulative distribution function of the MaxwellBoltzmann distribution has been used to calculate the probability of measuring the sensitivity of the interactions as the asymptotic limits of the distribution better describe the behavior of the interactions under experimental conditions. Based on our model, we hypothesized that molecules of lower complexity are preferred for target based screening campaigns, while augmenting such a library with moieties of moderate complexities maybe better suited for phenotypic screens. The validity of the hypothesis has been assessed via the analysis of the hit rate profiles for four ChemBL datasets for enzymatic and phenotypic screens.
Subject(s)
Combinatorial Chemistry Techniques , Drug Design , Drug Discovery , Pharmaceutical Preparations/analysis , Small Molecule Libraries/chemistry , Drug Evaluation, Preclinical , Humans , Small Molecule Libraries/metabolism , Structure-Activity RelationshipABSTRACT
Crystal structure analysis of Flavivirus methyltransferases uncovered a flavivirus-conserved cavity located next to the binding site for its cofactor, S-adenosyl-methionine (SAM). Chemical derivatization of S-adenosyl-homocysteine (SAH), the product inhibitor of the methylation reaction, with substituents that extend into the identified cavity, generated inhibitors that showed improved and selective activity against dengue virus methyltransferase (MTase), but not related human enzymes. Crystal structure of dengue virus MTase with a bound SAH derivative revealed that its N6-substituent bound in this cavity and induced conformation changes in residues lining the pocket. These findings demonstrate that one of the major hurdles for the development of methyltransferase-based therapeutics, namely selectivity for disease-related methyltransferases, can be overcome.
