Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing.

The development of continuous pharmaceutical manufacturing is crucial and can be analyzed via advanced computational models. Machine learning is a strong computational paradigm that can be integrated into a continuous process to enhance the drugs' solubility and efficacy. In this research, a simulation method for estimating pharmaceutical solubility was considered in green solvents to develop the idea of continuous pharmaceutical manufacturing. Artificial intelligence strategies were utilized to apply models for fitting several solubility datasets. Using machine learning techniques, the solubility of Clobetasol Propionate (CP) was modeled at temperature values between 308 K and 348 K, and pressures in the range of 12.2 MPa to 35.5 MPa. In this research, two models-a neural network-based model called MLP (Multilayer Perceptron) and a probabilistic model called GPR (Gaussian Process Regression)-along with an ensemble voting model based on these two, were considered for modeling. A GWO (Grey Wolf Optimization) method was also used to tune their hyperparameters. All three models have significant performances on estimation of CP solubility. But the voting model, which is a combination of the other two models, is better than the other two models in terms of accuracy. The ensemble voting model, integrating MLP and GPR with GWO optimization, offers superior accuracy for predicting CP solubility, advancing continuous pharmaceutical manufacturing.