AI-Based Prediction of Particle Size-Dissolution Relationships in Solid and Suspension Dosage Forms

Particle size distribution (PSD) is a critical quality attribute influencing dissolution, bioavailability, and manufacturability of pharmaceutical solid and suspension dosage forms. Traditional experimental approaches for particle size optimization and dissolution evaluation are time-consuming, resource-intensive, and often retrospective, limiting predictive capability. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer transformative solutions by enabling data-driven prediction of PSD and its impact on dissolution behavior. Machine learning algorithms, including ensemble methods such as Light Gradient Boosting Machine (LightGBM) and artificial neural networks, can integrate formulation variables, process parameters, and analytical data to accurately predict particle size and polydispersity. These AI-based models facilitate early identification of critical formulation risks, support Quality by Design (QbD) principles, and reduce experimental workload. Moreover, coupling PSD predictions with dissolution modeling allows estimation of in vitro drug release profiles, particularly for poorly soluble drugs, without extensive repetitive testing. The integration of in-line and at-line particle size monitoring techniques with AI-driven models further enables real-time quality prediction and proactive process control. Despite these advantages, challenges remain in model interpretability, generalizability, regulatory acceptance, and dependence on high-quality datasets. Addressing these limitations through explainable AI, standardized validation, and robust data strategies is essential for broader industrial adoption. Overall, AI-based prediction of PSD–dissolution relationships represents a promising pathway toward knowledge-driven pharmaceutical development, offering accelerated formulation optimization, improved product quality, and alignment with regulatory and Pharma 4.0™ initiatives.