Artifical Intelliegence And Machine Learning in Solid Dosage Formulation

Abstract

Solid dosage forms, tablets, capsules, pellets, and powders, remain the most widely manufactured medicines worldwide, yet their development has long relied on slow, trial-and-error experimentation that contributes to the 10–15 year, multi-billion-dollar cost of bringing a new drug to market. This review examines how artificial intelligence (AI) and machine learning (ML) are reshaping solid dosage formulation across its full lifecycle. The review surveys documented applications spanning pre-formulation screening, formulation design, manufacturing process control, dissolution and stability modeling, analytical method development, and emerging generative AI approaches, supported by published results demonstrating high predictive and detection accuracy. Real-world case studies, including Pfizer’s AI-assisted oral formulation work, a Merck convolutional neural network for tablet coating-defect detection, and autonomous formulation platforms such as Intrepid Labs, demonstrate that these methods have moved from research promise toward industrial deployment. The review then examines the data-related, model-related, regulatory, industry, and ethical challenges still limiting broader adoption, including proprietary data scarcity, black-box interpretability, and a regulatory landscape only beginning to formalize AI-specific validation requirements through the FDA’s 2025 draft guidance and the FDA-EMA’s January 2026 joint guiding principles. Future directions considered include federated learning for cross-industry data sharing, explainable AI for regulatory-ready models, digital twins for end-to-end manufacturing, and AI-guided 3D printing for personalized dosage forms. Rather than replacing the formulation scientist, AI/ML is reshaping that role, and this review concludes that closing the remaining gap depends less on further technical advances than on coordinated progress among academia, industry, and regulators.