Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models

Abstract

Production of solid-dosage drug nanoparticles was assessed by theoretical models to investigate the possibility of drug treatment via supercritical green processing. Nanonization can enhance drug solubility and consequently its bioavailability which is of great importance for pharmaceutical industry. This research presents a comparative study of three different regression models including Gaussian process regression, k-nearest neighbors, and multi-layer perceptron for predicting solvent density and solubility of Hyoscine drug. The models optimized using political optimizer (PO) algorithm. The results showed that all three optimized methods were able to predict density and solubility with high accuracy. PO-GPR achieved the highest R2 score for solubility (0.9984) and same for density (0.9999). The PO-MLP model achieved the high R2 score for density (0.9997) and the second-highest score for solubility (0.9945). PO-KNN also showed good performance for density (R2 = 0.9557) and solubility (R2 = 0.9783) but was outperformed by the other two models. In terms of RMSE and AARD%, PO-GPR and PO-MLP achieved lower error rates compared to PO-KNN. Overall, the results suggest that PO-GPR and PO-MLP are promising methods for predicting density and solubility of values. The models were useful for the application of drug nanonization and can be used to optimize the process.

Keywords

• Drug nanoparticles
• Machine learning
• Pharmaceutical manufacture
• Artificial intelligence
• Green technology