QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

Abdelmoujoud Faris; Ibrahim M Ibrahim; Radwan Alnajjar; Hanine Hadni; Mashooq Ahmad Bhat; Muhammad Yaseen; Souvik Chakraborty; Nada Alsakhen; Israa M Shamkh; Fazal Mabood; Ahmed M Naglah; Ihsan Ullah; Noha Ziedan; Menana Elhallaoui

doi:10.1080/07391102.2023.2283168

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

J Biomol Struct Dyn. 2023 Dec 7:1-30. doi: 10.1080/07391102.2023.2283168. Online ahead of print.

Authors

Affiliations

¹ LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
² Biophysics Department, Faculty of Science, Cairo University, Cairo, Egypt.
³ Department of Chemistry, Faculty of Science, University of Benghazi, Benghazi, Libya.
⁴ Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
⁵ Institute of Chemical Sciences, University of Swat, Main Campus, Charbagh, Swat, Pakistan.
⁶ Department of Physiology, Bhairab Ganguly College, Belghoria, Kolkata, West Bengal, India.
⁷ Department of Chemistry, Faculty of Science, The Hashemite University, Zarqa, Jordan.
⁸ Botany and Microbiology Department, Faculty of Science, Cairo University, Cairo, Egypt.
⁹ Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
¹⁰ Department of Physical, Mathematical and Engineering Science, Faculty of Science, Business and Enterprise, University of Chester, Chester, UK.

PMID: 38059345
DOI: 10.1080/07391102.2023.2283168

Abstract

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin.Communicated by Ramaswamy H. Sarma.

Keywords: ADMET; JAK3; MM/GBSA; cancer; covalent docking; molecular dynamics; rheumatoid arthritis.