Integrating Artificial Intelligence into Biopharmaceutical Formulation and Development: A Critical Review.

OBJECTIVE: This review critically examines the integration of artificial intelligence (AI) methods into biopharmaceutical formulation science, with emphasis on core formulation challenges including solubility, stability, aggregation, viscosity, excipient compatibility, nanocarrier optimization, release behaviour, and manufacturability. METHOD: A structured literature review was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Relevant peer-reviewed studies were selected using predefined inclusion and exclusion criteria and evaluated based on methodological quality and reported outcomes. RESULT: The reviewed studies demonstrate that AI improves the prediction of critical formulation properties, optimizes formulation design, and enhances manufacturing processes. However, its performance depends on data quality, model validation, and interpretability. DISCUSSION: AI has the potential to accelerate formulation development and improve product quality through predictive modelling and intelligent process optimization. Nevertheless, challenges related to data availability, regulatory acceptance, and model transparency remain barriers to broader implementation. CONCLUSION: AI represents a transformative enabler for biopharmaceutical formulation science. Realizing its full potential requires FAIR data ecosystems, explainable AI (XAI) frameworks, and adaptive regulatory guidance aligned with ICH Q8-Q12 and GAMP 5 standards.