Repurposing of FDA approved ring systems through bi-directional target-ring system dual screening

An RORα agonist, ODH-08, inhibits fibrogenic activation of hepatic stellate cells via suppression of SMAD3

AIMS

Hepatic fibrosis is a dynamic process characterized by the net accumulation of an extracellular matrix resulting from chronic liver injury such as nonalcoholic steatohepatitis. Activation of hepatic stellate cells (HSCs) plays a role in transdifferentiation of quiescent cells into fibrogenic myofibroblasts. We aimed to examine the function of retinoic acid receptor-related orphan receptor alpha (RORα) and its novel agonistic ligand, 1-(4-benzyloxybenzyl)-3-(2-dimethylaminoethyl)-thiourea (ODH-08) against activation of HSCs using hepatic fibrosis mouse models.

MAIN METHODS

Chemical synthesis, a reporter gene assay, surface plasmon resonance analysis, and a docking study were performed to evaluate ODH-08 as a ligand of RORα. In vivo experiments with mice fed a Western diet were performed to evaluate the effect of ODH-08. The human HSC line, Lx-2, and primary mouse HSCs were employed to identify the molecular mechanisms underlying the antifibrogenic effect of ODH-08.

KEY FINDINGS

A novel RORα-selective ligand, ODH-08, was developed based on modification of JC1-40, an analog of N-methylthiourea. Administration of ODH-08 to the Western diet-fed mice reduced hepatic collagen deposition and expression levels of fibrogenic markers such as α-smooth muscle actin and collagen type I alpha 1 chain. Activation of RORα-either by transient overexpression of RORα or treatment with ODH-08-suppressed the expression of fibrogenic proteins in HSCs. The activation of RORα suppressed the activity of SMAD2 and 3, which are the primary downstream proteins of transforming growth factor β.

SIGNIFICANCE

RORα and its agonist ODH-08 have a potent antifibrotic effect, which could provide a novel antifibrotic strategy against hepatic fibrosis.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Prediction of chemical warfare agents based on cholinergic array type meta-predictors

Molecular insights into chemical safety are very important for sustainable development as well as risk assessment. This study considers how to manage future upcoming harmful agents, especially potentially cholinergic chemical warfare agents (CWAs). For this purpose, the structures of known cholinergic agents were encoded by molecular descriptors. And then each drug target interaction (DTI) was learned from the encoded structures and their cholinergic activities to build DTI classification models for five cholinergic targets with reliable statistical validation (ensemble-AUC: up to 0.790, MCC: up to 0.991, accuracy: up to 0.995). The collected classifiers were transformed into 2D or 3D array type meta-predictors for multi-task: (1) cholinergic prediction and (2) CWA detection. The detection ability of the array classifiers was verified under the imbalanced dataset between CWAs and none CWAs (area under the precision-recall curve: up to 0.997, MCC: up to 0.638, F1-score of none CWAs: up to 0.991, F1-score of CWAs: up to 0.585).

Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.