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设计预测模型以优化非甾体抗炎药奥沙普秦的溶解度。

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

机构信息

Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.

Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia.

出版信息

Sci Rep. 2022 Jul 30;12(1):13106. doi: 10.1038/s41598-022-17350-5.

DOI:10.1038/s41598-022-17350-5
PMID:35907929
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9338975/
Abstract

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

摘要

如今，人们已经做出了许多努力来提高和开发新型治疗药物的溶解度和生物利用度。其中最可信的方法之一是超临界二氧化碳流体（SC-CO）的操作。由于其无色、经济高效和环保等显著优点，这种操作已被用作药理学中的独特方法。

本研究项目旨在通过人工智能对奥沙普秦在 SC-CO 中的溶解度进行数学计算。奥沙普秦是一种非甾体抗炎药，可用于关节炎疾病，以改善肿胀和疼痛。奥沙普秦是一种 BCS 类 II（生物药剂学分类系统）药物，具有低溶解度和生物利用度。为了优化和提高奥沙普秦的溶解度，本研究考虑了三种基于集成决策树的模型，包括随机森林（RF）、极端随机树（ET）和梯度提升（GB）。该模型使用了 32 个数据向量，以温度和压力作为输入，以药物溶解度作为输出。使用均方误差（MSE）指标，ET、RF 和 GB 的误差率分别为 6.29E-09、9.71E-09 和 3.78E-11。然后，使用 R 平方指标，它们分别显示了 0.999、0.984 和 0.999 的结果。GB 被选为最佳拟合模型，其最优值包括温度为 33.15（K），压力为 380.4（bar），溶解度优化值为 0.001242（摩尔分数）。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/05799e49da28/41598_2022_17350_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/7fa921615793/41598_2022_17350_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/a84e0d2ca326/41598_2022_17350_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/e67eb51cd7c3/41598_2022_17350_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/741969663842/41598_2022_17350_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/3ce2fb1e130a/41598_2022_17350_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/05799e49da28/41598_2022_17350_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/7fa921615793/41598_2022_17350_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/a8c3642431e5/41598_2022_17350_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/849756a15eb7/41598_2022_17350_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/a84e0d2ca326/41598_2022_17350_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/e67eb51cd7c3/41598_2022_17350_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/741969663842/41598_2022_17350_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/3ce2fb1e130a/41598_2022_17350_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5968/9338975/05799e49da28/41598_2022_17350_Fig8_HTML.jpg

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