Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling.

The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.

Series of (([1,1'-Biphenyl]-2-yl)methyl)sulfinylalkyl Alicyclic Amines as Novel and High Affinity Atypical Dopamine Transporter Inhibitors with Reduced hERG Activity.

Currently, there are no FDA-approved medications for the treatment of psychostimulant use disorders (PSUD). We have previously discovered "atypical" dopamine transporter (DAT) inhibitors that do not display psychostimulant-like behaviors and may be useful as medications to treat PSUD. Lead candidates (e.g., JJC8-091, 1) have shown promising in vivo profiles in rodents; however, reducing hERG (human ether-à-go-go-related gene) activity, a predictor of cardiotoxicity, has remained a challenge. Herein, a series of 30 (([1,1'-biphenyl]-2-yl)methyl)sulfinylalkyl alicyclic amines was synthesized and evaluated for DAT and serotonin transporter (SERT) binding affinities. A subset of analogues was tested for hERG activity, and the IC50 values were compared to those predicted by our hERG QSAR models, which showed robust predictive power. Multiparameter optimization scores (MPO > 3) indicated central nervous system (CNS) penetrability. Finally, comparison of affinities in human DAT and its Y156F and Y335A mutants suggested that several compounds prefer an inward facing conformation indicating an atypical DAT inhibitor profile.

Modifications to 1-(4-(2-Bis(4-fluorophenyl)methyl)sulfinyl)alkyl Alicyclic Amines That Improve Metabolic Stability and Retain an Atypical DAT Inhibitor Profile.

Atypical dopamine transporter (DAT) inhibitors have shown therapeutic potential in the preclinical models of psychostimulant use disorders (PSUD). In rats, 1-(4-(2-((bis(4-fluorophenyl)methyl)sulfinyl)ethyl)-piperazin-1-yl)-propan-2-ol (JJC8-091, 3b) was effective in reducing the reinforcing effects of both cocaine and methamphetamine but did not exhibit psychostimulant behaviors itself. Improvements in DAT affinity and metabolic stability were desirable for discovering pipeline drug candidates. Thus, a series of 1-(4-(2-bis(4-fluorophenyl)methyl)sulfinyl)alkyl alicyclic amines were synthesized and evaluated for binding affinities at DAT and the serotonin transporter (SERT). Replacement of the piperazine with either a homopiperazine or a piperidine ring system was well tolerated at DAT (Ki range = 3-382 nM). However, only the piperidine analogues (20a-d) showed improved metabolic stability in rat liver microsomes as compared to the previously reported analogues. Compounds 12b and 20a appeared to retain an atypical DAT inhibitor profile, based on negligible locomotor activity in mice and molecular modeling that predicts binding to an inward-facing conformation of DAT.