%0 Journal Article
%T Machine Learning Across the Amorphous Solid Dispersion Design Space for Predicting Miscibility, Stability, Drug Release, and Formulation Failure
%A Michael Roberts
%A Sarah Thompson
%A James Anderson
%A Robert Smith
%J International Journal of Pharmaceutical And Phytopharmacological Research
%@ 2250-1029
%D 2026
%V 16
%N 4
%R 10.51847/uN3XDXLimx
%P 40-49
%X Amorphous solid dispersions (ASDs) are frequently treated in predictive modeling as formulations whose success can be represented by a single endpoint, such as miscibility, glass-transition temperature, physical stability, or dissolution. This abstraction is increasingly inadequate because an ASD is a dynamic material system whose behavior changes with composition, molecular interactions, manufacturing history, temperature, humidity, aging, aqueous exposure, and the timescale on which failure is defined. This state-of-the-art review examines how machine learning can operate across that multidimensional design space while remaining grounded in the physicochemical evidence that defines meaningful prediction targets. Current studies demonstrate useful task-specific prediction of properties including polymer-dependent thermal behavior, drug–polymer compatibility, phase behavior, ASD formation, physical stability, and dissolution-related performance. However, these successes largely arise from distinct datasets, endpoint definitions, representations, and validation regimes. The central synthesis proposed here is therefore a failure-aware view of ASD modeling in which intermediate material states, manufacturing context, performance objectives, and failure outcomes remain analytically separable before being integrated. Such a framework requires models to distinguish thermodynamic compatibility from kinetic persistence, storage stability from aqueous release, process feasibility from long-term robustness, and internal predictive performance from transfer to new chemical, polymeric, manufacturing, or laboratory domains. Machine learning can strengthen ASD development when it organizes rather than erases these distinctions. Its principal current limitations are sparse negative evidence, inconsistent labels, restricted external validation, heterogeneous process histories, and incomplete representation of time- and condition-dependent formulation failure.
%U https://eijppr.com/article/machine-learning-across-the-amorphous-solid-dispersion-design-space-for-predicting-miscibility-stab-nup2hwjqkosjrnb