Predicting ADME properties and side effects: the BioPrint approach.

Computational methods are increasingly used to streamline and enhance the lead discovery and optimization process. However, accurate prediction of absorption, distribution, metabolism and excretion (ADME) and adverse drug reactions (ADR) is often difficult, due to the complexity of underlying physiological mechanisms. Modeling approaches have been hampered by the lack of large, robust and standardized training datasets. In an extensive effort to build such a dataset, the BioPrint database was constructed by systematic profiling of nearly all drugs available on the market, as well as numerous reference compounds. The database is composed of several large datasets: compound structures and molecular descriptors, in vitro ADME and pharmacology profiles, and complementary clinical data including therapeutic use information, pharmacokinetics profiles and ADR profiles. These data have allowed the development of computational tools designed to integrate a program of computational chemistry into library design and lead development. Models based on chemical structure are strengthened by in vitro results that can be used as additional compound descriptors to predict complex in vivo endpoints. The BioPrint pharmacoinformatics platform represents a systematic effort to accelerate the process of drug discovery, improve quantitative structure-activity relationships and develop in vitro/in vivo associations. In this review, we will discuss the importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities.

N-terminal tyrosine residues within the potassium channel Kir3 modulate GTPase activity of Galphai.

trkB activation results in tyrosine phosphorylation of N-terminal Kir3 residues, decreasing channel activation. To determine the mechanism of this effect, we reconstituted Kir3, trkB, and the mu opioid receptor in Xenopus oocytes. Activation of trkB by BDNF (brain-derived neurotrophic factor) accelerated Kir3 deactivation following termination of mu opioid receptor signaling. Similarly, overexpression of RGS4, a GTPase-activating protein (GAP), accelerated Kir3 deactivation. Blocking GTPase activity with GTPgammaS also prevented Kir3 deactivation, and the GTPgammaS effect was not reversed by BDNF treatment. These results suggest that BDNF treatment did not reduce Kir3 affinity for Gbetagamma but rather acted to accelerate GTPase activity, like RGS4. Tyrosine phosphatase inhibition by peroxyvanadate pretreatment reversibly mimicked the BDNF/trkB effect, indicating that tyrosine phosphorylation of Kir3 may have caused the GTPase acceleration. Tyrosine to phenylalanine substitution in the N-terminal domain of Kir3.4 blocked the BDNF effect, supporting the hypothesis that phosphorylation of these tyrosines was responsible. Like other GAPs, Kir3.4 contains a tyrosine-arginine-glutamine motif that is thought to function by interacting with G protein catalytic domains to facilitate GTP hydrolysis. These data suggest that the N-terminal tyrosine hydroxyls in Kir3 normally mask the GAP activity and that modification by phosphorylation or phenylalanine substitution reveals the GAP domain. Thus, BDNF activation of trkB could inhibit Kir3 by facilitating channel deactivation.