Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors.

Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.

A Novel Selective Inverse Agonist of the CB2 Receptor as a Radiolabeled Tool Compound for Kinetic Binding Studies.

The endocannabinoid system, and in particular the cannabinoid type 2 receptor (CB2R), raised the interest of many medicinal chemistry programs for its therapeutic relevance in several (patho)physiologic processes. However, the physico-chemical properties of tool compounds for CB2R (e.g., the radioligand [3H]CP55,940) are not optimal, despite the research efforts in developing effective drugs to target this system. At the same time, the importance of drug-target binding kinetics is growing since the kinetic binding profile of a ligand may provide important insights for the resulting in vivo efficacy. In this context we synthesized and characterized [3H]RO6957022, a highly selective CB2R inverse agonist, as a radiolabeled tool compound. In equilibrium and kinetic binding experiments [3H]RO6957022 showed high affinity for human CB2R with fast association (kon) and moderate dissociation (koff) kinetics. To demonstrate the robustness of [3H]RO6957022 binding, affinity studies were carried out for a wide range of CB2R reference ligands, spanning the range of full, partial, and inverse agonists. Finally, we used [3H]RO6957022 to study the kinetic binding profiles (i.e., kon and koff values) of selected synthetic and endogenous (i.e., 2-arachidonoylglycerol, anandamide, and noladin ether) CB2R ligands by competition association experiments. All tested ligands, and in particular the endocannabinoids, displayed distinct kinetic profiles, shedding more light on their mechanism of action and the importance of association rates in the determination of CB2R affinity. Altogether, this study shows that the use of a novel tool compound, i.e., [3H]RO6957022, can support the development of novel ligands with a repertoire of kinetic binding profiles for CB2R.