Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.
Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, Taif, 21944, Saudi Arabia.
Sci Rep. 2023 Jan 24;13(1):1313. doi: 10.1038/s41598-022-25562-y.
Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).
粒径、形状和形态可被视为最重要的功能参数，它们在提高口服固体制剂的性能方面的作用是毋庸置疑的。超临界二氧化碳流体（SCCO）技术是控制口服固体制剂上述参数的有效方法。在这项研究中，基于人工智能模型，使用二氧化碳作为常见的超临界溶剂，在不同的压力和温度（308-338 K）下，研究了药物溶解度的测量。结果表明，压力对药物溶解度有很强的影响。在这项研究中，首次使用决策树（DT）、自适应提升决策树（ADA-DT）和 Nu-SVR 回归模型作为一种新的模型，这些模型有两个输入，包括压力，X1=P（bar）和温度，X2=T（K）。此外，输出为 Y=溶解度。DT、ADA-DT 和 Nu-SVR 的 R-squared 评分为 0.836、0.921 和 0.813。此外，在 MAE 方面，它们的误差率分别为 4.30E-06、1.95E-06 和 3.45E-06。另一个度量标准是 RMSE，其中 DT、ADA-DT 和 Nu-SVR 的误差率分别为 4.96E-06、2.34E-06 和 5.26E-06。由于分析输出，ADA-DT 被选为最佳和新颖的模型，并且可以通过向量显示最佳输出：（x1=309，x2=317.39，Y1=7.03e-05）。
