Identification of novel DNA gyrase inhibitor by combined pharmacophore modeling, QSAR analysis, molecular docking, molecular dynamics, ADMET and DFT approaches.

Autor: Moulishankar A; Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, India., Sankaranarayanan M; Medicinal Chemsitry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani 333031, Rajasthan, India., Thirugnanasambandam S; Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, India. Electronic address: chemistrysundar@gmail.com., Dhamotharan J; Department of Pharmaceutical Analysis, Sri Venkateswara College of Pharmacy, Rvs Nagar, Tirupati Road, Chittoor 517127, Andhra Pradesh, India., Mohanradja D; Department of Pharmaceutical Analysis, SMVEC Pharmacy College, Madagadipet 605107, Puducherry, India., Sivakumar PM; Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam. Electronic address: sivamedchem@gmail.com.
Jazyk: angličtina
Zdroj: Acta tropica [Acta Trop] 2024 Dec; Vol. 260, pp. 107460. Date of Electronic Publication: 2024 Nov 10.
DOI: 10.1016/j.actatropica.2024.107460
Abstrakt: DNA gyrase, an ATP-dependent enzyme, plays a critical role in DNA replication, transcription, and recombination in Mycobacterium tuberculosis (MTB). While fluoroquinolones are effective antibacterial agents targeting DNA gyrase, their clinical use is often limited due to side effects and the emergence of bacterial resistance. In this study, we developed a quantitative structure-activity relationship (QSAR) model to predict the anti-tubercular activity of Quinoline-Aminopiperidine derivatives targeting the DNA gyrase enzyme, using a dataset of 48 compounds obtained from the literature. The QSAR model was validated using both internal and external validation metrics. Model 4, the best predictive model, demonstrated a strong fit with an R² of 0.8393, an adjusted R² (R²adj) of 0.8010, and a lack of fit (LOF) parameter of 0.0626. The QSAR results revealed that DNA gyrase inhibition is significantly influenced by factors such as partition coefficient, molecular flexibility, hydrogen bonding potential, and the presence of fluorine atoms. Twelve quinoline-aminopiperidine derivatives were designed, and their anti-tubercular activity was predicted using QSAR model-4. These compounds were further assessed for pharmacokinetic properties, toxicity, and binding affinity to DNA gyrase. Pharmacophore modeling was also performed and validated using MOE software. The final pharmacophore model includes the features of two aromatic hydrophobic features, one hydrogen bond acceptor, and one hydrogen bond donor. The results indicated that designed compounds QA-3 and dataset compounds C-34 exhibit favorable drug-likeness properties. Molecular dynamics simulations confirmed the stable binding of compounds QA-3 and C-34 to the DNA gyrase protein, highlighting their potential as promising anti-tubercular agents. The developed QSAR Model-4 will facilitate the prediction of anti-tubercular activity in Quinoline-Aminopiperidine derivatives.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
Externí odkaz: