Exploring the potency of 6-bromo-3-methylquinoline analogues as prostaglandin F2α inhibitors
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Kaushik Sarkar
Abstract
Prostaglandin F2α (PGF2α) is associated with preterm labor and preterm birth. PGF2α inhibitors have thus proven to be a promising target in the development of lead compounds to prevent preterm birth. In this work, Quantitative Structural Activity Relationship (QSAR) was implemented on a dataset of 77 compounds of 6-bromo-3-methylquinoline analogues using statistical approach and random selection in the QSARINS software. The Genetic Algorithm-Multiple Linear Regression (GA-MLR) approach was used to predict the best model (R2 = 0.8943 and Q2LOO = 0.8836). The inclusion of descriptors FNSA-2 and WV.mass resulted in a well‐fitted and highly predictable model. Artificial neural network (ANN) analysis was also carried out to validate the model effectiveness. Twenty eight new molecules with better predicted biological activity (pIC50) were designed. The binding energy from the docking study of seven compounds have shown higher binding activity than P10 into prostaglandin F synthase protein (PDB ID: 2F38). The stability of protein–ligand complex was further validated by 100 ns molecular dynamics simulation and MM-PBSA binding free energy. DFT and ADME-toxicity analysis also confirmed their drug-likeness properties. Collectively, our findings highlight novel quinoline derivatives as promising lead candidates, warranting further validation through collaborative in vitro and in vivo studies.
Funding source: Princess Nourah bint Abdulrahaman University
Award Identifier / Grant number: PNURSP2025R419
Funding source: Science and Engineering Research Board
Award Identifier / Grant number: EEQ/2023/000692
Acknowledgment
All authors are thankful to the High Performance Computing (HPC) cluster of University of North Bengal for computational facility. We would like to mention our sincere thanks to Dr. Paolo Gramatica, QSAR Research Unit, Insubria University, Italy, for the academic license software.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Rajesh Kumar Das is grateful to SERB, DST New Delhi for the project file no. EEQ/2023/000692. The authors Ammena Y. Binsaleh and Nawal Al Hoshani greatly acknowledge and express their gratitude to the Researchers Supporting Project number (PNURSP2025R419), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/znc-2025-0116).
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