Predicting disintegration time in fast-disintegrating tablets using machine learning: a data-driven framework based...

Fast-disintegrating tablets (FDTs) are widely used oral dosage forms in which disintegration time is a critical quality attribute influencing drug release and patient compliance. However, formulation development is challenging due to complex and often non-linear interactions between excipient composition, physicochemical properties, and tablet characteristics. Conventional trial-and-error approaches are therefore time-consuming and inefficient.

This study aimed to develop a data-driven framework for predicting disintegration time in FDT formulations and to investigate how different representations of excipient information affect predictive performance.

A dataset of 1982 FDT formulations was analyzed using three alternative excipient representations: (i) identity-based encoding of excipients by chemical name and quantity, (ii) excipient-specific functional representation preserving both identity and functional role, and (iii) functionally aggregated representation summarizing quantities by excipient class. Regression models were used as the primary predictive approach to estimate continuous disintegration time. Classification models were additionally explored by discretizing disintegration time into formulation-relevant intervals. Model performance was evaluated using regression metrics and classification measures including weighted F1-score and the Matthews correlation coefficient (MCC).

Deep neural networks achieved the highest predictive performance across all representations, with the best results obtained using the excipient-specific functional dataset (R² = 0.86, MAE = 8.53 s). Random forest models also demonstrated stable performance. Functional excipient representation improved prediction compared with identity-based encoding by reducing dataset sparsity while preserving formulation-relevant information.

Functional excipient representation provides an effective data abstraction strategy that enhances predictive modeling in pharmaceutical formulation datasets and supports more efficient data-driven decision making in early-stage FDT development.