Suppr超能文献

结核分枝杆菌β-CA 抑制机制的元宇宙生理学建模。

Physiological modeling of the metaverse of the Mycobacterium tuberculosis β-CA inhibition mechanism.

机构信息

Department of Neuroscience, Psychology, Drug Research, and Child's Health, Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Via Ugo Schiff 6, 50019, Sesto Fiorentino, Italy.

Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

出版信息

Comput Biol Med. 2024 Oct;181:109029. doi: 10.1016/j.compbiomed.2024.109029. Epub 2024 Aug 21.

Abstract

Tuberculosis (TB) is an infectious disease that primarily affects the lungs of humans and accounts for Mycobacterium tuberculosis (Mtb) bacteria as the etiologic agent. In this study, we introduce a computational framework designed to identify the important chemical features crucial for the effective inhibition of Mtb β-CAs. Through applying a mechanistic model, we elucidated the essential features pivotal for robust inhibition. Using this model, we engineered molecules that exhibit potent inhibitory activity and introduce relevant novel chemistry. The designed molecules were prioritized for synthesis based on their predicted pKi values via the QSAR (Quantitative Structure-Activity Relationship) model. All the rationally designed and synthesized compounds were evaluated in vitro against different carbonic anhydrase isoforms expressed from the pathogen Mtb; moreover, the off-target and widely human-expressed CA I and II were also evaluated. Among the reported derivatives, 2, 4, and 5 demonstrated the most valuable in vitro activity, resulting in promising candidates for the treatment of TB infection. All the synthesized molecules exhibited favorable pharmacokinetic and toxicological profiles based on in silico predictions. Docking analysis confirmed that the zinc-binding groups bind effectively into the catalytic triad of the Mtb β-Cas, supporting the in vitro outcomes with these binding interactions. Furthermore, molecules with good prediction accuracies according to previously established mechanistic and QSAR models were utilized to delve deeper into the realm of systems biology to understand their mechanism in combating tuberculotic pathogenesis. The results pointed to the key involvement of the compounds in modulating immune responses via NF-κβ1, SRC kinase, and TNF-α to modulate granuloma formation and clearance via T cells. This dual action, in which the pathogen's enzyme is inhibited while modulating the human immune machinery, represents a paradigm shift toward more effective and comprehensive treatment approaches for combating tuberculosis.

摘要

结核病(TB)是一种传染病,主要影响人类的肺部,病因是结核分枝杆菌(Mtb)细菌。在这项研究中,我们引入了一个计算框架,旨在识别对 Mtb β-CAs 有效抑制至关重要的重要化学特征。通过应用机械模型,我们阐明了对稳健抑制至关重要的基本特征。使用该模型,我们设计了具有强大抑制活性的分子,并引入了相关的新化学物质。根据 QSAR(定量构效关系)模型预测的 pKi 值,对设计的分子进行了优先排序,以用于合成。所有合理设计和合成的化合物均在体外针对来自病原体 Mtb 表达的不同碳酸酐酶同工酶进行了评估;此外,还评估了脱靶且广泛存在于人体中的 CA I 和 CA II。在所报道的衍生物中,2、4 和 5 表现出最有价值的体外活性,是治疗结核病感染的有前途的候选药物。所有合成的分子都根据计算机预测表现出良好的药代动力学和毒理学特性。对接分析证实锌结合基团有效地结合到 Mtb β-Cas 的催化三联体中,支持这些结合相互作用的体外结果。此外,根据先前建立的机械和 QSAR 模型具有良好预测准确性的分子被用于深入研究系统生物学领域,以了解它们在对抗结核发病机制中的作用机制。结果表明,这些化合物通过 NF-κβ1、SRC 激酶和 TNF-α 来调节免疫反应,从而参与调节肉芽肿的形成和清除,以调节 T 细胞。这种双重作用,即抑制病原体的酶,同时调节人体免疫机制,代表了一种向更有效和全面的治疗方法转变,以对抗结核病。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验