Fouad Marwa A, Serag Ahmed, Tolba Enas H, El-Shal Manal A, El Kerdawy Ahmed M
Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St, P.O. Box 11562, Cairo, Egypt.
Department of Pharmaceutical Chemistry, School of Pharmacy, Newgiza University (NGU), Newgiza, km 22 Cairo-Alexandria Desert Road, Cairo, Egypt.
BMC Chem. 2022 Nov 3;16(1):85. doi: 10.1186/s13065-022-00874-2.
Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.
喹诺酮类和磺胺类是两类具有丰富药物化学特征历史的抗菌剂,这些特征影响着它们的抗菌谱、疗效、药代动力学和不良反应情况。对它们的迫切需求,再加上其耐药率不断上升,使得开发合适的分析方法以加速和促进其分析成为必要。在本研究中,先进的萤火虫算法(FFA)与支持向量回归(SVR)相结合,用于选择最重要的描述符,并分别使用一系列11种选定的喹诺酮类药物和13种磺胺类药物构建两个定量结构-保留关系(QSRR)模型,以预测它们在高效液相色谱(HPLC)中的保留行为。具体而言,分别研究了流动相中pH值和乙腈组成对喹诺酮类和磺胺类药物保留行为的影响。所获得的QSRR模型在内部和外部验证中均表现良好,证明了它们的稳健性和预测能力。Y随机化验证表明,所获得的模型并非由统计偶然性导致。此外,所获得的结果揭示了在当前色谱条件下影响这两类药物保留行为的分子特征。