Experimental Confirmation of New Drug–Target Interactions Predicted by Drug Profile Matching

We recently introduced Drug Profile Matching (DPM), a novel affinity fingerprinting-based in silico drug repositioning approach. DPM is able to quantitatively predict the complete effect profiles of compounds via probability scores. In the present work, in order to investigate the predictive power of DPM, three effect categories, namely, angiotensin-converting enzyme inhibitor, cyclooxygenase inhibitor, and dopamine agent, were selected and predictions were verified by literature analysis as well as experimentally. A total of 72% of the newly predicted and tested dopaminergic compounds were confirmed by tests on D1 and D2 expressing cell cultures. 33% and 23% of the ACE and COX inhibitory predictions were confirmed by in vitro tests, respectively. Dose-dependent inhibition curves were measured for seven drugs, and their inhibitory constants (<i>K</i>_i) were determined. Our study overall demonstrates that DPM is an effective approach to reveal novel drug–target pairs that may result in repositioning these drugs

Drug Effect Prediction by Polypharmacology-Based Interaction Profiling

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug–protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation