Bridging data and theory: hybrid modelling for pharmaceutical crystallisation process development

Crystallisation remains a critical unit operation in pharmaceutical manufacturing, yet process development is often constrained by empirical approaches and data limitations. Mechanistic models such as population balance frameworks provide physical interpretability but require extensive parameterisation and struggle with complex, multiscale phenomena. Data-driven machine learning models offer strong predictive performance but lack transparency, limiting regulatory acceptance. Hybrid modelling, which integrates mechanistic theory with data-driven components, has emerged as a promising strategy to address these limitations. This review surveys the current modelling landscape and critically assesses recent case studies across batch cooling, antisolvent, and continuous crystallisation modes, covering population balance and machine learning combinations. Key open problems are identified, including the absence of agreed modelling and experimental design frameworks, data sparsity, poor scale-up transferability, unresolved regulatory requirements around uncertainty quantification and model. Practical technical recommendations and a proposed workflow are provided to guide future work, with the aim of moving the field beyond isolated case studies toward predictive, explainable, deployable and published crystallisation process development.