In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques

• Published in: Carbohydrate polymers 2022-01, Vol.275, p.118712-118712, Article 118712
• Main Authors: Li, Junjun, Gao, Hanlu, Ye, Zhuyifan, Deng, Jiayin, Ouyang, Defang
• Format: Article
• Language: English
• Subjects: Benzimidazoles - chemistry; Cyclodextrins - chemistry; Drug Compounding; Hydrocortisone - chemistry; Machine Learning; Models, Molecular; Molecular modeling; Polymers - chemistry; Quinolones - chemistry; Random forest; Solubility prediction; Ternary cyclodextrin complexes
• ISSN: 0144-8617; 1879-1344
• DOI: 10.1016/j.carbpol.2021.118712

Ternary cyclodextrin (CD) complexes (drug/CD/polymer) can effectively improve the solubility of water-insoluble drugs with large size than binary CD formulations. However, ternary formulations are screened by a trial-and-error approach, which is laborious and material-wasting. Current research aims to develop a prediction model for ternary CD formulations by combined machine learning and molecular modeling. 596 ternary formulations data were collected to build a prediction model by machine learning. The random forest model achieved good performance with R2 = 0.887 in ST prediction and R2 = 0.815 in ST/SB prediction. Two ternary formulations (Hydrocortisone/β-CD/HPMC and dovitinib/γ-CD/CMC) were used to validate the prediction model. Molecular modeling results showed that HPMC not only warped around hydrocortisone but also prevented CD molecules from self-aggregation to increase solubility. In conclusion, a prediction model for the ternary CD formulations was successfully developed, which will significantly accelerate the formulation screening process to benefit the formulation development of water-insoluble drugs. [Display omitted] •Random forest model did well in ternary cyclodextrin formulation prediction.•Factors that may impact solubilization was ranked by random forest model.•Molecular dynamic simulation was performed to investigate molecular mechanism.