Faris Abdelmoujoud, Ibrahim Ibrahim M, Al Kamaly Omkulthom, Saleh Asmaa, Elhallaoui Menana
LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.
Biophysics Department, Faculty of Science, Cairo University, Cairo 12613, Egypt.
Molecules. 2023 Aug 6;28(15):5914. doi: 10.3390/molecules28155914.
Rheumatoid arthritis (RA) remains one of the most prevalent autoimmune diseases worldwide. Janus kinase 3 (JAK3) is an essential enzyme for treating autoimmune diseases, including RA. Molecular modeling techniques play a crucial role in the search for new drugs by reducing time delays. In this study, the 3D-QSAR approach is employed to predict new JAK3 inhibitors. Two robust models, both field-based with R = 0.93, R = 0.96, and Q = 87, and atom-based with R = 0.94, R = 0.97, and Q = 86, yielded good results by identifying groups that may readily direct their interaction. A reliable pharmacophore model, DHRRR1, was provided in this work to enable the clear characterization of chemical features, leading to the design of 13 inhibitors with their pIC values. The DHRRR1 model yielded a validation result with a ROC value of 0.87. Five promising inhibitors were selected for further study based on an ADMET analysis of their pharmacokinetic properties and covalent docking (CovDock). Compared to the FDA-approved drug tofacitinib, the pharmaceutical features, binding affinity and stability of the inhibitors were analyzed through CovDock, 300 ns molecular dynamics simulations, free energy binding calculations and ADMET predictions. The results show that the inhibitors have strong binding affinity, stability and favorable pharmaceutical properties. The newly predicted molecules, as JAK3 inhibitors for the treatment of RA, are promising candidates for use as drugs.
类风湿性关节炎（RA）仍然是全球最普遍的自身免疫性疾病之一。 Janus激酶3（JAK3）是治疗包括RA在内的自身免疫性疾病的关键酶。分子建模技术通过减少时间延迟在寻找新药方面发挥着至关重要的作用。在本研究中，采用3D-QSAR方法预测新的JAK3抑制剂。两个稳健的模型，基于场的R = 0.93、R = 0.96和Q = 87，以及基于原子的R = 0.94、R = 0.97和Q = 86，通过识别可能易于指导其相互作用的基团产生了良好的结果。这项工作提供了一个可靠的药效团模型DHRRR1，以清晰地表征化学特征，从而设计出13种具有pIC值的抑制剂。DHRRR1模型产生的验证结果的ROC值为0.87。基于对其药代动力学性质的ADMET分析和共价对接（CovDock），选择了五种有前景的抑制剂进行进一步研究。与FDA批准的药物托法替布相比，通过CovDock、300 ns分子动力学模拟、自由能结合计算和ADMET预测分析了抑制剂的药学特征、结合亲和力和稳定性。结果表明，这些抑制剂具有很强的结合亲和力、稳定性和良好的药学性质。新预测的分子作为治疗RA的JAK3抑制剂，有望成为有潜力的药物候选物。
