Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media.

Sait Senceroglu, Mohamed Arselene Ayari, Tahereh Rezaei, Fardad Faress, Amith Khandakar, Muhammad E H Chowdhury, Zanko Hassan Jawhar
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引用次数: 3

Abstract

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer-drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer-drug systems' stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

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构建基于支持向量回归的智能模型模拟药物在高分子介质中的溶解度。
本研究构建了一种机器学习方法来同时分析多种聚合物-药物体系的热力学行为。对乙酰氨基酚、塞来昔布、氯霉素、d -甘露醇、非洛地平、布洛芬、布洛芬钠、吲哚美辛、伊曲康唑、萘普生、硝苯地平、扑热息痛、磺胺嘧啶、磺胺嘧啶、磺胺嘧啶和磺胺噻唑在1,3-二[2-吡咯烷酮-1-基]丁烷、聚醋酸乙烯酯、聚乙烯吡咯烷酮(PVP)、PVP K12、PVP K15、PVP K17、PVP K25、PVP/VA、PVP/VA 335、PVP/VA 535、PVP/VA 635、PVP/VA 735、Soluplus中的溶解度从建模角度进行了分析。最小二乘支持向量回归(LS-SVR)设计从聚合物和药物类型以及聚合物中的药物负载来近似药物在聚合物中的溶解温度。通过对核类型(即高斯,多项式和线性)和用于调整LS-SVR系数的方法(即,遗漏1和10倍交叉验证场景)进行试验和错误,可以很好地调整该机器学习模型的结构。灵敏度分析结果表明,高斯核和10倍交叉验证是开发给定任务的LS-SVR的最佳候选。所建立的模型得到的结果与文献中报道的278个实验样本一致。在训练阶段和测试阶段,平均绝对相对偏差率分别达到8.35和7.25。在最大可用数据集上的性能证实了它的适用性。这种可靠的工具对于监测聚合物药物系统的稳定性和可交付性至关重要,特别是对于聚合物中难溶性药物,可以通过在未来的实际实施中采用它来进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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