IDENTIFYING LEAD COMPOUNDS FOR POTENTIAL ANTI-TUBERCULOSIS DRUGS BY IN SILICO MYCOBACTERIUM TUBERCULOSIS SHIKIMATE KINASE INHIBITORS SELECTION OF CHEMICAL LIBRARY
Objective: The present study aimed to identify Shikimate Kinase (SK) inhibitors as antitubercular agents from a chemical library by the utilization of molecular docking simulation, pharmacophore evaluation, and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction approaches. Methods: A molecular docking study by Molecular Operating Environment (MOE) was used to screen 400,000 compounds from the Mcule ULTIMATE Express 1 chemical library. This docking study used a rigid docking technique to simulate the interaction between receptors and compounds. The screened compounds were then validated by pharmacophore and ADMET analyses to show the presence of positive characteristics. Results: The result of molecular docking simulation identified N-[2-(diethylamino)ethyl]-2-(pyrrolidin-1-yl)acetamide as the most promising candidate for targeting Mycobacterium tuberculosis Shikimate Kinase (MtSK), due to its binding energy score (-11.3412 kcal/mol) and suitability of interacting residues (Asp34 and Gly80). Moreover, this compound also shared similar pharmacophores with shikimate, and it had positive drug-like and ADMET properties. Conclusion: This work identified one candidate for SK inhibitor from a pool of five drug-like hit compounds. These inhibitors show promise as prospective candidates for the development of a new anti-tuberculosis therapy and warrant additional experimental investigation.
Authors
- Sri Teguh Rahayu (ORCID: https://orcid.org/0000-0002-2810-160X)
- Eka Siska (ORCID: https://orcid.org/0000-0002-7360-4054)
- Adjar Wibowo
- Nuki Bambang Nugroho (ORCID: https://orcid.org/0000-0002-7748-2218)
- BELLA ETIKA
Institutions
- Universitas Esa Unggul (ID)
- National Research, Development and Innovation Office (HU)
Publication Details
- Journal
- International Journal of Applied Pharmaceutics
- Published
- 2025-03-07
- DOI
- https://doi.org/10.22159/ijap.2025v17i2.52759
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50