Anhydroparthenin as a dual-target inhibitor against Sterol C-24 methyltransferase and Sterol 14-α demethylase of Leishmania donovani: A comprehensive in vitro and in silico study.

Parthenium hysterophorus plant has a diverse chemical profile and immense bioactive potential. It exhibits excellent pharmacological properties such as anti-cancer, anti-inflammatory, anti-malarial, microbicidal, and anti-trypanosomal. The present study aims to evaluate the anti-leishmanial potential and toxicological safety of anhydroparthenin isolated from P. hysterophorus. Anydroparthenin was extracted from the leaves of P. hysterophorus and characterized through detailed analysis of 1H, 13C NMR, and HRMS. Dye-based in vitro and ex vivo assays confirmed that anhydroparthenin significantly inhibited both promastigote and amastigote forms of the Leishmania donovani parasites. Both the cytotoxicity experiment and hemolytic assay revealed its non-toxic nature and safety index in the range of 10 to 15. Further, various mechanistic assays suggested that anhydroparthenin led to the generation of oxidative stress, intracellular ATP depletion, alterations in morphology and mitochondrial membrane potential, formation of intracellular lipid bodies, and acidic vesicles, ultimately leading to parasite death. As a dual targeting approach, computational studies and sterol quantification assays confirmed that anhydroparthenin inhibits the Sterol C-24 methyl transferase and Sterol 14-α demethylase proteins involved in the ergosterol biosynthesis in Leishmania parasites. These results suggest that anhydroparthenin could be a promising anti-leishmanial molecule and can be developed as a novel therapeutic stratagem against leishmaniasis.

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods.

In this work we investigated the Pks13-TE domain, which plays a critical role in the viability of the mycobacteria. In this report, we have used a series of AI and Physics-based tools to identify Pks13-TE inhibitors. The Reinvent 4, pKCSM, KDeep, and SwissADME are AI-ML-based tools. AutoDock Vina, PLANTS, MDS, and MM-GBSA are physics-based methods. A combination of these methods yields powerful support in the drug discovery cycle. Known inhibitors of Pks13-TE were collected, curated, and used as input for the AI-based tools, and Mol2Mol molecular optimisation methods generated novel inhibitors. These ligands were filtered based on physics-based methods like molecular docking and molecular dynamics using multiple tools for consensus generation. Rigorous analysis was performed on the selected compounds to reduce the chemical space while retaining the most promising compounds. The molecule interactions, stability of the protein-ligand complexes and the comparable binding energies with the native ligand were essential factors for narrowing the ligands set. The filtered ligands from docking, MDS, and binding energy colocations were further tested for their ADMET properties since they are among the essential criteria for this series of molecules. It was found that ligands Mt1 to Mt6 have excellent predicted pharmacokinetic, pharmacodynamic and toxicity profiles and good synthesisability.