基于喹啉抑制恶性疟原虫药效核心的 2D/3D-QSAR 模型开发:通过实验验证的硅学方法

Pharmaceuticals Pub Date : 2024-07-04 DOI:10.3390/ph17070889
Marcos Lorca, Gisela C. Muscia, S. Pérez-Benavente, José M. Bautista, Alison Acosta, Cesar González, Gianfranco Sabadini, J. Mella, S. Asís, Marco Mellado
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摘要

疟疾是一种由疟原虫引起的传染病,大多数抗疟疾药物都具有广泛的耐药性。我们报告了基于比较分子场分析(CoMFA)、比较分子相似性指数分析(CoMSIA)和二维 QSAR 模型的两个三维 QSAR 模型的开发情况,使用的数据库包含 349 种对恶性疟原虫 3D7 菌株有活性的化合物。这些模型经过了内部和外部验证,符合所有指标(q2 > 0.5、r2test > 0.6、r2m > 0.5 等)。最终模型的统计值如下:r2test CoMFA = 0.878,r2test CoMSIA = 0.876,r2test 2D-QSAR = 0.845。通过合成十种喹啉衍生物并对其进行抗恶性疟原虫 3D7 的生物学评价,对上述模型进行了实验检验。CoMSIA 和 2D-QSAR 模型的预测能力优于 CoMFA 模型(MAE 分别为 0.7006、0.4849 和 1.2803)。所选的三种喹啉衍生物的理化和药代动力学特性与氯喹相似。最后,这些化合物对人类 HepG2 细胞的细胞毒性较低(IC50 > 100 µM)。这些结果表明,QSAR 模型能准确预测毒理学特征,与体内实验数据密切相关。
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2D/3D-QSAR Model Development Based on a Quinoline Pharmacophoric Core for the Inhibition of Plasmodium falciparum: An In Silico Approach with Experimental Validation
Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.
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