Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Kashipur, India.
Department of Pharmaceutical Chemistry, Teerthanker Mahaveer College of Pharmacy, Moradabad, India.
SAR QSAR Environ Res. 2022 Apr;33(4):289-305. doi: 10.1080/1062936X.2022.2066175.
Tuberculosis (TB) is a global threat with a large burden across the continents in terms of mortality, morbidity, and financial losses. The disease has evolved into multi-drug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) tuberculosis owing to numerous factors ranging from patients' non-compliance to demographical implications. There have been very few new drugs for resistant TB. Resistance has already been reported even for the newly introduced drug bedaquiline. An attempt has been made to integrate both structure-based and QSAR drug design techniques (QSAR-SBDD) for the identification of novel leads. The docking scores normally do not correlate with the activity. Hence, the docking results have been analysed in terms of the number of interactions rather than docking scores. The parameters derived from interactions have been used in developing the QSAR models. The best model shows a good correlation ( = 0.908) between the activity and interaction parameter 'C' describing the sum of all the interactions with each amino acid residue. This model also predicts external dataset with a good correlation ( = 0.851) and can be used for the identification of novel chemical entities (NCEs) and repurposed drugs for TB therapeutics.
结核病（TB）是一种全球性威胁，在各大洲的死亡率、发病率和经济损失方面都带来了巨大负担。由于从患者不遵医嘱到人口统计学影响等诸多因素，该疾病已经演变为耐多药（MDR-TB）和广泛耐药（XDR-TB）结核病。对于耐药结核病，几乎没有新的药物。即使是新引入的药物贝达喹啉，也已经出现了耐药性报告。我们尝试将基于结构和定量构效关系（QSAR）药物设计技术（QSAR-SBDD）相结合，以确定新的先导化合物。通常情况下，对接评分与活性不相关。因此，我们根据相互作用的数量而不是对接评分来分析对接结果。我们从相互作用中提取参数，并将其用于开发 QSAR 模型。最佳模型显示了活性与描述与每个氨基酸残基所有相互作用总和的相互作用参数“C”之间的良好相关性（r=0.908）。该模型还可以很好地预测外部数据集（r=0.851），可用于鉴定新型化学实体（NCEs）和用于结核病治疗的再利用药物。
