Senceroglu Sait, Ayari Mohamed Arselene, Rezaei Tahereh, Faress Fardad, Khandakar Amith, Chowdhury Muhammad E H, Jawhar Zanko Hassan
Faculty of Pharmacy, Ege University, Izmir 35040, Turkey.
Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar.
Pharmaceuticals (Basel). 2022 Nov 14;15(11):1405. doi: 10.3390/ph15111405.
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.
本研究构建了一种机器学习方法，用于同时分析多种聚合物-药物体系的热力学行为。从建模角度分析了对乙酰氨基酚、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、磺胺二甲嘧啶、磺胺甲嘧啶和磺胺噻唑在1,3-双[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、固体分散体中的溶解温度。最小二乘支持向量回归（LS-SVR）旨在根据聚合物和药物类型以及聚合物中的药物载量来近似药物在聚合物中的溶解温度。通过对核类型（即高斯核、多项式核和线性核）以及用于调整LS-SVR系数的方法（即留一法和10折交叉验证方案）进行反复试验，对该机器学习模型的结构进行了优化。敏感性分析结果表明，高斯核和10折交叉验证是为给定任务开发LS-SVR的最佳选择。所构建的模型得到的结果与文献中报道的278个实验样本一致。事实上，在训练和测试阶段分别实现了8.35%和7.25%的平均绝对相对偏差百分比。在最大可用数据集上的性能证实了其适用性。这样一个可靠的工具对于监测聚合物-药物体系的稳定性和可递送性至关重要，特别是对于聚合物中难溶性药物，未来可通过将其应用于实际实施进一步验证。
