Sarder Arifuzzaman MS , Md. Harun-Or-Rashid PhD , Farhina Rahman Laboni M. Pharm. , Mst Reshma Khatun MS , Nargis Sultana Chowdhury PhD
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引用次数: 0
摘要
肝X受体(LXRs)在调节脂质代谢和炎症中起关键作用,其活性的改变与几种代谢性疾病有关。虽然已经确定了几种LXR激动剂,但由于其不良反应,其临床应用受到限制。在这项研究中,我们首先利用多个生物数据库(包括RNA-seq, Human Protein Atlas, DisGeNET和WebGestalt)来检查LXRs在mRNA和蛋白质水平上在不同组织中的表达。我们进行了网络和通路分析,以重新定义LXRs的生理作用和疾病关联。我们的研究结果强调了LXR的多种功能,并强调了小分子药物调节LXR活性以达到治疗目的的潜力。在第二阶段,我们进行了新型LXR调节剂的计算机搜索,从先前在临床前或临床环境中测试过的11种配体的分子对接研究开始。基于对接评分和化学药代动力学特性,我们确定T0901317和AZ876分别对LXR-α和LXR-β具有最高的结合亲和力。在最后一步,我们扩展了我们的筛选,以T0901317和AZ876的化学结构为导向,发现新的LXR配体。我们的对接和分子动力学(MD)模拟显示,ZINC000095464663和ZINC000021912925具有最强的结合亲和力,并且具有良好的药代动力学特征。总之,我们的计算机方法结合了网络分析、虚拟筛选、分子对接、MD模拟和化学药代动力学评估,发现了两种有前景的口服配体,为未来针对LXRs的治疗干预提供了潜力。
Revisiting the Role of Liver X Receptors (LXRs) in Disease: In-silico Discovery of Novel Modulators Through Molecular Docking and Chemico-Pharmacokinetic Profiling
Liver X Receptors (LXRs) play a critical role in regulating lipid metabolism and inflammation, with their altered activity linked to several metabolic diseases. Although several LXR agonists have been identified, their clinical use has been limited due to adverse effects. In this study, we first leveraged multiple biological data repositories (including RNA-seq, Human Protein Atlas, DisGeNET, and WebGestalt) to examine the expression of LXRs at both the mRNA and protein levels across various tissues. We performed network and pathway analyses to redefine the physiological roles and disease associations of LXRs. Our findings emphasize the diverse functions of LXRs and highlight the potential for small molecules to pharmacologically modulate LXR activity for therapeutic purposes. In the second phase, we conducted an in-silico search for novel LXR modulators, beginning with molecular docking studies of eleven ligands that have been previously tested in preclinical or clinical settings. Based on docking scores and chemico-pharmacokinetic properties, we identified T0901317 and AZ876 as leading candidates, showing the highest binding affinity for LXR-α and LXR-β, respectively. In the final step, we extended our screening to discover new LXR ligands guided by the chemical structures of T0901317 and AZ876. Our docking and molecular dynamics (MD) simulations revealed that ZINC000095464663 and ZINC000021912925 exhibited the strongest binding affinities, alongside favorable pharmacokinetic profiles for both LXR subtypes. In conclusion, our in-silico approach, combining network analysis, virtual screening, molecular docking, MD simulations, and chemico-pharmacokinetic assessments, has uncovered two promising ligands for oral administration, offering potential for future therapeutic interventions targeting LXRs.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs