Al Hagbani Turki, Alshehri Sameer, Bawazeer Sami
Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, Saudi Arabia.
Front Med (Lausanne). 2024 Jul 22;11:1435675. doi: 10.3389/fmed.2024.1435675. eCollection 2024.
This research is an analysis of multiple regression models developed for predicting ketoprofen solubility in supercritical carbon dioxide under different levels of T(K) and P(bar) as input features. Solubility of the drug was correlated to pressure and temperature as major operational variables. Selected models for this study are Piecewise Polynomial Regression (PPR), Kernel Ridge Regression (KRR), and Tweedie Regression (TDR). In order to improve the performance of the models, hyperparameter tuning is executed utilizing the Water Cycle Algorithm (WCA). Among, the PPR model obtained the best performance, with an R score of 0.97111, alongside an MSE of 1.6867E-09 and an MAE of 3.01040E-05. Following closely, the KRR model demonstrated a good performance with an R score of 0.95044, an MSE of 2.5499E-09, and an MAE of 3.49707E-05. In contrast, the TDR model produces a lower R score of 0.84413 together with an MSE of 7.4249E-09 and an MAE of 5.69159E-05.
本研究分析了多个回归模型，这些模型是为预测酮洛芬在超临界二氧化碳中的溶解度而开发的，以不同水平的T（K）和P（巴）作为输入特征。药物的溶解度与压力和温度这两个主要操作变量相关。本研究选择的模型有分段多项式回归（PPR）、核岭回归（KRR）和 Tweedie 回归（TDR）。为了提高模型的性能，利用水循环算法（WCA）进行超参数调整。其中，PPR 模型表现最佳，R 分数为 0.97111，均方误差（MSE）为 1.6867E - 09，平均绝对误差（MAE）为 3.01040E - 05。紧随其后的是 KRR 模型，其表现良好，R 分数为 0.95044，MSE 为 2.5499E - 09，MAE 为 3.49707E - 05。相比之下，TDR 模型的 R 分数较低，为 0.84413，MSE 为 7.4249E - 09，MAE 为 5.69159E - 05。
