Faris Abdelmoujoud, Cacciatore Ivana, Alnajjar Radwan, Hanine Hadni, Aouidate Adnane, Mothana Ramzi A, Alanzi Abdullah R, Elhallaoui Menana
LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Department of Pharmacy, University 'G. d'Annunzio' of Chieti-Pescara, Chieti, Italy.
Front Mol Biosci. 2024 Mar 7;11:1348277. doi: 10.3389/fmolb.2024.1348277. eCollection 2024.
The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.
杂环化合物具有多种功能,在抑制Janus激酶(JAKs)方面特别有效。因此,识别其复杂结构与生物活性之间的相关性对于开发治疗类风湿性关节炎(RA)和癌症的新药至关重要。在本研究中,使用了一组针对JAK1和JAK3具有选择性的28种杂环化合物,采用多元线性回归(MLR)构建定量构效关系(QSAR)模型。在QSAR模型的开发中采用了人工神经网络(ANN)模型。通过内部和外部方法,包括适用域(DoA),评估了模型的稳健性和稳定性。纳入模型的分子描述符与X射线晶体学中观察到的JAKs受体-配体复合物结构表现出令人满意的相关性,使模型具有可解释性和预测性。此外,为每个JAK1和JAK3设计了药效团模型ADRRR和ADHRR,证明在区分活性化合物和诱饵方面有效。两个模型在识别新化合物方面均表现出良好性能,ADRRR模型的ROC为0.83,ADHRR模型的ROC为0.75。使用药效团模型,根据与每个JAK1和JAK3研究系列中最具活性的化合物相比的强亲和力,选择了最有前景的化合物。值得注意的是,所选化合物(如化合物ZINC79189223和ZINC66252348)的药代动力学、物理化学性质和生物活性与其在RA中的治疗效果一致,这归因于它们无毒、具有胆碱能性质、不存在P-糖蛋白、高胃肠道吸收以及穿透血脑屏障的能力。此外,评估了ADMET性质,分子动力学和MM/GBSA分析表明这些分子具有稳定性。