Prediction of enhanced drug solubility related to clathrate compositions and operating conditions: Machine learning study

Abstract

Although complexation technique has been documented as a promising strategy to enhance the dissolution rate and bioavailability of water-insoluble drugs, prediction of the enhanced drug solubility related to clathrate compositions and operating conditions is still a challenge. Herein, clathrate compositions (drug content (DC), drug molecular weight (M) and molar ratio (Ratio)), operating conditions (drug concentration (C), pH, pressure (P), temperature (T) and dissolution time (t)) under the different excipients (PEG, PVP, HPMC and cyclodextrin) as main solubilizers of the clathrates condition as input parameters were used to predict two indexes (drug dissolved percentage and dissolution efficiency) simultaneously through machine learning methodfor the first time. The results show that PVP as the main solubilizer of clathrates had higher prediction accuracy to the drug dissolved percentage, and HPMC as the main solubilizer of clathrates had higher prediction accuracy to the drug dissolution efficiency. In addition, the influence of various factors and interactions on the target variables were analyzed. This study affords achievable hints to the quantitative prediction of the drug solubility affected by various compositions and different operating conditions.