Assessment of Structural Basis for Thiazolopyridine Derivatives as DNA Gyrase-B Inhibitors.

Q3 Pharmacology, Toxicology and Pharmaceutics
Vishal P Zambre, Nilesh N Petkar, Vishal P Dewoolkar, Swapnali V Bhadke, Sanjay D Sawant
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引用次数: 0

Abstract

Background: Tuberculosis (TB) is one of the leading causes of death in the post-COVID- 19 era. It has been observed that there is a devastating condition with a 25-30% increase in TB patients. DNA gyrase B isoform has proved its high potential to be a therapeutically effective target for developing newer and safer anti-TB agents.

Objective: This study aims to identify minimum structural requirements for the optimization of thiazolopyridine derivatives having DNA gyrase inhibitory activities. Moreover, developed QSAR models could be used to design new thiazolopyridine derivatives and predict their DNA gyrase B inhibitory activity before synthesis.

Methods: 3D-QSAR and Group-based QSAR (G-QSAR) methodologies were adopted to develop accurate, reliable, and predictive QSAR models. Statistical methods such as kNN-MFA SW-FB and MLR SW-FB were used to correlate dependent parameters with descriptors. Both models were thoroughly validated for internal and external predictive abilities.

Results: The 3D-QSAR model significantly correlated steric and electrostatic descriptors with q2 0.7491 and predicted r2 0.7792. The G-QSAR model showed that parameters such as SsOHE-index, slogP, ChiV5chain, and T_C_C_3 were crucial for optimizing thiazolopyridine derivatives as DNA gyrase inhibitors. The 3D-QSAR model was interpreted extensively with respect to 3D field points, and the pattern of fragmentation was studied in the G-QSAR model.

Conclusion: The 3D-QSAR and G-QSAR models were found to be highly predictive. These models could be useful for designing potent DNA gyrase B inhibitors before their synthesis.

噻唑吡啶衍生物作为DNA gyase - b抑制剂的结构基础评价。
背景:结核病是后covid - 19时代的主要死亡原因之一。据观察,结核病患者增加了25-30%,这是一种毁灭性的情况。DNA回转酶B异构体已被证明具有很高的潜力,可作为开发更新和更安全的抗结核药物的治疗有效靶点。目的:确定具有DNA旋切酶抑制活性的噻唑吡啶衍生物的最低结构要求。此外,所建立的QSAR模型可用于设计新的噻唑吡啶衍生物,并在合成前预测其DNA旋切酶B抑制活性。方法:采用3D-QSAR和Group-based QSAR (G-QSAR)方法建立准确、可靠、可预测的QSAR模型。使用kNN-MFA SW-FB和MLR SW-FB等统计方法将依赖参数与描述符关联起来。两个模型的内部和外部预测能力都得到了彻底的验证。结果:3D-QSAR模型预测立体和静电描述符的相关系数为q2 0.7491,预测r2 0.7792。G-QSAR模型表明,ssohe指数、logp、chiv5链和T_C_C_3等参数对噻唑吡啶衍生物作为DNA回转酶抑制剂的优化至关重要。针对三维场点对3D- qsar模型进行了广泛的解译,并对G-QSAR模型中的破碎模式进行了研究。结论:3D-QSAR和G-QSAR模型具有较高的预测能力。这些模型可用于在DNA回转酶B抑制剂合成前设计有效的DNA回转酶B抑制剂。
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来源期刊
Current drug discovery technologies
Current drug discovery technologies Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
3.70
自引率
0.00%
发文量
48
期刊介绍: Due to the plethora of new approaches being used in modern drug discovery by the pharmaceutical industry, Current Drug Discovery Technologies has been established to provide comprehensive overviews of all the major modern techniques and technologies used in drug design and discovery. The journal is the forum for publishing both original research papers and reviews describing novel approaches and cutting edge technologies used in all stages of drug discovery. The journal addresses the multidimensional challenges of drug discovery science including integration issues of the drug discovery process.
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