Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Abstract

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R²_train = 0.8646, R²_test = 0.8864, Q² = 0.5773) and the predicted inhibitory activity (pIC₅₀) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.

Communicated by Ramaswamy H. Sarma

Keywords:

• Leishmaniasis
• molecular docking
• molecular dynamics
• organoselenium compounds
• pharmacokinetics

Acknowledgments

All authors are well acknowledged for their immense contributions to this study.

Authors’ contributions

Study design: FAU, GAS, AU; Software: FAU, IA, MA; Figures: FAU, MA, YW, WIA; Data analysis: FAU, GAS, STA; Drafting: FAU, IA; Critical review and supervision: GAS, AU.

Disclosure statement

The authors declare no competing interests.

Data availability statement

Data shall be made available on request.

Additional information

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R39), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.