QbD-Based Design Space Development for Honey-Containing Traditional Chinese Medicine Tablets Assisted by the SeDeM Expert System and Machine Learning

Background/Objectives: Oral solid dosage forms of traditional Chinese and ethnic medicines are currently undergoing modernisation. The objective of this study is to explore the scope for formulation variation arising from batch-to-batch fluctuations in intermediates, and to identify the factors influencing key quality attributes of honey-containing tablets. In this regard, a machine-learning-based predictive model is being formulated that will integrate and analyse formulation factors and the results characterised by the SeDeM expert system. Utilising the SeDeM index as a mediating variable, the study endeavours to establish a comprehensible and predictable stepwise research pathway to provide a foundation for industrial-scale upscaling. Methods: Twelve SeDeM expert systems were utilised to characterise honey-containing granules for formulation screening, to evaluate their suitability for use in traditional Chinese medicine honey-containing tablet systems, and to identify key limiting factors and the feasibility space affecting the quality of the final product; Based on the QBD philosophy, a TriAD (Tri-criterion Adaptive Design) design scheme was proposed, integrating the horizontal balance of orthogonal designs, the spatial coverage of uniform designs, and the parameter estimation efficiency of D-optimal designs into the experimental layout of the formulation feasibility space; Through further data aggregation, a multi-layer feature set comprising four formulation factors, six SeDeM indicators, and three critical quality attributes (CQAs) was constructed. The mediating effects of the SeDeM indicators were revealed through different pathways involving 37 combinations of simple, linear, and Bootstrap models. Furthermore, 180 linear and non-linear machine learning models (comprising 12 categories of algorithms) were trained to predict formulation and CQA outcomes, ultimately completing the design space mapping and validation. Results: The results show that the SeDeM parameters effectively bridge the CQA results of different honey formulations, with these indicators acting as selective mediators between formulation factors and CQAs. Compared with a pure data model relying solely on raw formulation variables, the introduction of SeDeM knowledge, combined with high-information-content samples obtained via TriAD, improved the predictive performance and robustness of the SeDeM–ML hybrid model in terms of disintegration time and hardness; its R2_LOO increased by 0.267 and 0.510, respectively, and the overall predictive space was significantly expanded. Experimental validation was conducted using formulations within the design space predicted by the optimal model; the results showed that both the prediction bias and the relative standard deviation were less than 5 percent. Conclusions: The present study demonstrates that SeDeM can not only be used to evaluate formulations of honey-containing TCM tablets but also serves as an intermediary bridge linking formulation factors, granule-mechanism variables, and tablet quality outcomes. TriAD, in turn, further translates the QbD philosophy into an actionable formulation space design, thereby providing a development pathway for honey-containing tablets that combines interpretability, predictability, and QbD consistency, and offers new insights for the industrial application of oral TCM preparations.