Modeling Clobetasol Propionate Solubility in Supercritical CO₂ Using Enhanced Machine Learning Techniques
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By
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Nidal H. Abu-Hamdeh
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Mohammed N. Ajour
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September 7, 2026
Clinical Scorecard: Modeling Clobetasol Propionate Solubility in Supercritical CO₂ Using Enhanced Machine Learning Techniques
At a Glance
| Category | Detail |
|---|
| Condition | Clobetasol Propionate Solubility |
| Key Mechanisms | Machine learning models including Decision Tree, Lasso, and Gaussian Process Regression for solubility prediction. |
| Target Population | Pharmaceutical researchers and formulators. |
| Care Setting | Pharmaceutical engineering and manufacturing. |
Key Highlights
- Adaptive Boosting (AdaBoost) enhances predictive performance of solubility models.
- FB-GPR model achieved the highest accuracy with an R2 score of 0.977.
- Solubility data derived from experiments under supercritical CO₂ conditions.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Patient & Prescribing Data
Not applicable; study focuses on solubility modeling.
Insights into solubility can inform drug formulation processes.
Clinical Best Practices
- Utilize machine learning techniques for solubility prediction in drug development.
- Incorporate temperature and pressure as key variables in solubility modeling.
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