Abstract

Future alternatives to the presently accepted in vitro paradigm of prediction of intrinsic clearance, which could be used earlier in the drug discovery process, would potentially accelerate efforts to identify better drug candidates with more favorable metabolic profiles and less likelihood of failure with regard to human pharmacokinetic attributes. In this study we describe two computational methods for modeling human microsomal and hepatocyte intrinsic clearance data derived from our laboratory and the literature, which utilize pharmacophore features or descriptors derived from molecular structure. Human microsomal intrinsic clearance data generated for 26 known therapeutic drugs were used to build computational models using commercially available software (Catalyst and Cerius²), after first converting the data to hepatocyte intrinsic clearance. The best Catalyst pharmacophore model gave an r of 0.77 for the observed versus predicted clearance. This pharmacophore was described by one hydrogen bond acceptor, two hydrophobic features, and one ring aromatic feature essential to discriminate between high and low intrinsic clearance. The Cerius² quantitative structure activity relationship (QSAR) model gave an r² = 0.68 for the observed versus predicted clearance and a cross-validatedr² (q²) of 0.42. Similarly, literature data for human hepatocyte intrinsic clearance for 18 therapeutic drugs were also used to generate two separate models using the same computational approaches. The best Catalyst pharmacophore model gave an improved r of 0.87 and was described by two hydrogen bond acceptors, one hydrophobe, and 1 positive ionizable feature. The Cerius² QSAR gave anr² of 0.88 and aq² of 0.79. Each of these models was then used as a test set for prediction of the intrinsic clearance data in the other data set, with variable successes. These present models represent a preliminary application of QSAR software to modeling and prediction of human in vitro intrinsic clearance.