Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining both side effects and efficacy. As many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here, we compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one such, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

Rationale Amisulpride is approved for clinical use in treating schizophrenia in a number of European countries and also for treating dysthymia, a mild form of depression, in Italy. Amisulpride has also been demonstrated to be an antidepressant for patients with major depression in many clinical trials. In part because of the selective D2/D3 receptor antagonist properties of amisulpride, it has long been widely assumed that dopaminergic modulation is the proximal event responsible for mediating its antidepressant and antipsychotic properties. Objectives The purpose of these studies was to determine if amisulpride’s antidepressant actions are mediated by off-target interactions with other receptors. Materials and Methods We performed experiments that: (1) examined the pharmacological profile of amisulpride at a large number of CNS molecular targets and (2) after finding high potency antagonist affinity for human 5-HT7a serotonin receptors, characterized the actions of amisulpride as an antidepressant in wild-type and 5-HT7 receptor knock-out mice. Results We discovered that amisulpride was a potent competitive antagonist at 5-HT7a receptors and that interactions with no other molecular target investigated here could explain its antidepressant actions in vivo. Significantly, and in contrast to their wildtype littermates, 5-HT7 receptor knockout mice did not respond to amisulpride in a widely used rodent model of depression, the tail suspension test. Conclusions These results indicate that 5-HT7a receptor antagonism, and not D2/D3 receptor antagonism, likely underlies the antidepressant actions of amisulpride.

There is a high demand for potent, selective, and brain-penetrant small molecule inhibitors of leucine-rich repeat kinase 2 (LRRK2) to test whether inhibition of LRRK2 kinase activity is a potentially viable treatment option for Parkinson's disease patients. Herein we disclose the use of property and structure-based drug design for the optimization of highly ligand efficient aminopyrimidine lead compounds. High throughput in vivo rodent cassette pharmacokinetic studies enabled rapid validation of in vitro-in vivo correlations. Guided by this data, optimal design parameters were established. Effective incorporation of these guidelines into our molecular design process resulted in the discovery of small molecule inhibitors such as GNE-7915 (18) and 19, which possess an ideal balance of LRRK2 cellular potency, broad kinase selectivity, metabolic stability, and brain penetration across multiple species. Advancement of GNE-7915 into rodent and higher species toxicity studies enabled risk assessment for early development.

Mutations in the genetic sequence of leucine-rich repeat kinase 2 (LRRK2) have been linked to increased LRRK2 activity and risk for the development of Parkinson's disease (PD). Potent and selective small molecules capable of inhibiting the kinase activity of LRRK2 will be important tools for establishing a link between the kinase activity of LRRK2 and PD. In the absence of LRRK2 kinase domain crystal structures, a LRRK2 homology model was developed that provided robust guidance in the hit-to-lead optimization of small molecule LRRK2 inhibitors. Through a combination of molecular modeling, sequence analysis, and matched molecular pair (MMP) activity cliff analysis, a potent and selective lead inhibitor was discovered. The selectivity of this compound could be understood using the LRRK2 homology model, and application of this learning to a series of 2,4-diaminopyrimidine inhibitors in a scaffold hopping exercise led to the identification of highly potent and selective LRRK2 inhibitors that were also brain penetrable.

The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.