Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China.
Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China; School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Eur J Pharm Biopharm. 2024 Mar;196:114201. doi: 10.1016/j.ejpb.2024.114201. Epub 2024 Feb 2.
Cocrystal engineering, which involves the self-assembly of two or more components into a solid-state supramolecular structure through non-covalent interactions, has emerged as a promising approach to tailor the physicochemical properties of active pharmaceutical ingredient (API). Efficient coformer screening for cocrystal remains a challenge. Herein, a prediction strategy based on machine learning algorithms was employed to predict cocrystal formation and seven reliable models with accuracy over 0.890 were successfully constructed. Imatinib was selected as the model drug and the models established were applied to screen 31 potential coformers. Experimental verification results indicated RF-8 is the optimal model among seven models with an accuracy of 0.839. When the seven models were combined for coformer screening of Imatinib, the combinational model achieved an accuracy of 0.903, and eight new solid forms were observed and characterized. Benefiting from intermolecular interactions, the obtained multicomponent crystals displayed enhanced physicochemical properties. Dissolution and solubility experiments showed the prepared multicomponent crystals had higher cumulative dissolution rate and remarkably improved the solubility of imatinib, and IM-MC exhibited comparable solubility to Imatinib mesylate α form. Stability test and cytotoxicity results showed that multicomponent crystals exhibited excellent stability and the drug-drug cocrystal IM-5F exhibited higher cytotoxicity than pure API.
共晶工程涉及通过非共价相互作用将两个或多个组件自组装成固态超分子结构，已成为一种有前途的方法，可以调整活性药物成分 (API) 的物理化学性质。有效的共晶形成共晶体筛选仍然是一个挑战。在此，采用基于机器学习算法的预测策略来预测共晶形成，并成功构建了七个准确性超过 0.890 的可靠模型。伊马替尼被选为模型药物，并应用所建立的模型筛选了 31 种潜在的共晶形成剂。实验验证结果表明，在这七个模型中，RF-8 是最佳模型，准确性为 0.839。当将这七个模型组合用于伊马替尼的共晶形成剂筛选时，组合模型的准确性达到了 0.903，观察到并表征了八种新的固体形式。得益于分子间相互作用，获得的多组分晶体显示出增强的物理化学性质。溶解和溶解度实验表明，所制备的多组分晶体具有更高的累积溶解速率，显著提高了伊马替尼的溶解度，并且 IM-MC 的溶解度与伊马替尼甲磺酸盐 α 型相当。稳定性测试和细胞毒性结果表明，多组分晶体表现出优异的稳定性，药物-药物共晶 IM-5F 表现出比纯 API 更高的细胞毒性。
