Clinical Report: Modeling Clobetasol Propionate Solubility in Supercritical CO₂

Overview

This study employs machine learning techniques to model the solubility of Clobetasol Propionate (CP) in supercritical CO₂. The FB-GPR model demonstrated an R² score of 0.977 and an RMSE of 1.11 × 10⁻².

Background

Understanding the solubility of active pharmaceutical ingredients (APIs) like Clobetasol Propionate is crucial for drug formulation and efficacy. Accurate solubility predictions can streamline the development of pharmaceutical processes, particularly in the context of supercritical fluid applications.

Data Highlights

Key Findings

• Adaptive Boosting (AdaBoost) was utilized to enhance the performance of solubility prediction models.
• The FB-GPR model achieved the highest accuracy with an R² score of 0.977.
• The models were optimized using the firefly algorithm to determine unknown coefficients.
• Clobetasol Propionate solubility was modeled as a function of temperature and pressure.

Clinical Implications

The findings suggest that machine learning can effectively predict the solubility of Clobetasol Propionate, which may aid in the formulation of nanomedicines and other pharmaceutical applications. Accurate solubility modeling can potentially reduce the need for extensive experimental trials in drug development.

Conclusion

The study demonstrates the application of machine learning in enhancing solubility predictions for Clobetasol Propionate in supercritical CO₂.

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