A new serotonin 2A receptor antagonist with potential benefits in Non-Alcoholic Fatty Liver Disease

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors

Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutagenicity of the commonly used hydroxamates (zinc-binding groups, ZBGs) are also increasing. Considering these concerns, non-hydroxamate ZBGs offer a promising alternative for the development of non-mutagenic HDAC3 inhibitors. Unfortunately, the quantum chemical space of non-hydroxamates has not been studied in detail. This study has three primary goals: (i) to perform semiempirical quantum chemical calculations, examining AM-1 model parameters relevant to zinc binding, (ii) to develop supervised mathematical learning models to train a diverse set of non-hydroxamate-based HDAC3i, and (iii) to apply fragment-based approaches to identify sub-structural fragments (fingerprints) that promote or hinder HDAC3 inhibitory activity through classification-based QSARs. In addition, flexible molecular docking analysis, 200 ns MD simulation, and free energy landscape (FEL) analysis further established the importance of identified fingerprints in the modulation of HDAC3 inhibitory activity. This comprehensive analysis of structural variations among non-hydroxamate HDAC3i provides valuable insights, contributing to the design of potential non-mutagenic HDAC3i.

PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management

Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by two main objectives: (i) a ligand-based approach to explore the chemical space of PPARγ modulators followed by molecular docking ensembles (MDEs) to investigate ligand-binding interactions, (ii) the development of a supervised ML model for a large dataset of compounds targeting PPARγ. Additionally, the combination of chemical space networks with ML models enables the rapid screening and prediction of PPARγ modulators. These modeling analyses will assist medicinal chemists in designing more potent PPARγ modulators. To further enhance accessibility for the scientific community, we developed an online tool, "PGMP_v1," aimed at prospective screening for PPARγ modulators. The tool "PGMP_v1" is available at the provided link https://github.com/Amincheminfom/PGMP_v1 . The integration of these computational methods has uncovered crucial structural motifs that are essential for PPARγ activity, advancing the development of more effective modulators in the future.

Discovery of a peripheral 5HT2A antagonist as a clinical candidate for metabolic dysfunction-associated steatohepatitis

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is currently the leading cause of chronic liver disease worldwide. Metabolic Dysfunction-Associated Steatohepatitis (MASH), an advanced form of MASLD, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Based on recent findings by our team that liver 5HT2A knockout male mice suppressed steatosis and reduced fibrosis-related gene expression, we developed a peripheral 5HT2A antagonist, compound 11c for MASH. It shows good in vitro activity, stability, and in vivo pharmacokinetics (PK) in rats and dogs. Compound 11c also shows good in vivo efficacy in a diet-induced obesity (DIO) male mice model and in a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) male mice model, effectively improving histologic features of MASH and fibrosis. According to the tissue distribution study using [14C]-labeled 11c, the compound was determined to be a peripheral 5HT2A antagonist. Collectively, first-in-class compound 11c shows promise as a therapeutic agent for the treatment of MASLD and MASH.