Shweta Singh Chauhan , E. Azra Thaseen , Ramakrishnan Parthasarathi
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引用次数: 0
Abstract
Bacterial infections caused by resistant strains, especially those conferring multi-drug resistance (MDR), have become a severe health problem worldwide. Novobiocin (NB) is a widely used antibiotic that inhibits the action of DNA gyrase in Escherichia coli (E. coli). The drug's efficiency is hindered by its strong binding with the resistance causing efflux pump AcrAB-TolC on recurrent exposure. Consequently, the discovery of alternate/substitute analogue compounds for the parent drug with higher selectivity could counter drug resistance. In this work, we identified potent analogues of drug NB against the gyrase B enzyme by performing high throughput virtual screening of forty analogues that includes drug-likeness properties, pharmacokinetic parameters analysis, molecular docking, and molecular dynamics (MD) simulations. Our comprehensive pharmacological profiling with intrinsic analysis of selectivity and safety resulted in the identification of four potential compounds, C4 (ZINC218812366), C6 (ZINC221968665), C8 (ZINC49783724) and C10 (ZINC49783727), have better inhibitory and binding capacity against the primary target gyrase B subunit and reduced interaction with the counterpart of resistant target AcrB. These findings provide proof of concept for developing lead compounds targeting gyrase B and help in combatting AcrB-mediated drug resistance.
耐药菌株,尤其是具有多重耐药性(MDR)的耐药菌株引起的细菌感染已成为全球严重的健康问题。新生物素(NB)是一种广泛使用的抗生素,可抑制大肠杆菌(E. coli)中 DNA 回旋酶的作用。该药物在反复接触时会与导致耐药性的外排泵 AcrAB-TolC 发生强结合,从而影响其疗效。因此,发现具有更高选择性的母药替代/替代类似化合物可以对抗耐药性。在这项工作中,我们通过对 40 种类似物进行高通量虚拟筛选,包括药物相似性、药动学参数分析、分子对接和分子动力学(MD)模拟,确定了 NB 药物对回旋酶 B 的强效类似物。我们通过对选择性和安全性的内在分析进行了全面的药理学分析,最终确定了 C4(ZINC218812366)、C6(ZINC221968665)、C8(ZINC49783724)和 C10(ZINC49783727)这四种潜在化合物,它们对主要靶标回旋酶 B 亚基具有更好的抑制和结合能力,并减少了与抗性靶标 AcrB 的相互作用。这些发现为开发靶向回旋酶 B 的先导化合物提供了概念证明,有助于对抗 AcrB 介导的耐药性。
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