Abstract
PURPOSE: Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, identify key predictive features, and arrive at formulations with improved flow properties. METHODS: A dataset of 410 blends, composed of 9 active pharmaceutical ingredients (APIs) and 18 excipients with varying silica dry-coating parameters, was analyzed. Supervised ML models were trained to predict various flowability categories (very cohesive, cohesive, semi-cohesive, well-flowing, and free-flowing). Particle size, morphology, surface properties, and coating parameters were used as features. Classification algorithms, including Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were evaluated. Unsupervised clustering identified natural groupings within flowability data. RESULTS: The best-performing models achieved up to 85% accuracy for predicting flowability regimes of individual components and 87% for blends. Individual components generally showed higher accuracy than blends, except in the uncoated scenario with 2 flow regimes, where blends outperformed with 94.67%. SHapley Additive exPlanations (SHAP) and Feature Importance analysis indicated dry coating parameters as the most influential factors, followed by particle size and morphology. ML models effectively identified category transitions between flow regimes, offering insights into blend optimization. CONCLUSION: Integrating ML with mechanistic approaches effectively predicted powder blend flowability across diverse categories and elucidated feature-property relationships. These outcomes can facilitate the rational design of blends having enhanced flow properties at reduced experimental effort through judiciously selected dry coating of a blend constituent; making this approach promising for advancing pharmaceutical process and product development.