Decision-Centric Explainable AI for QbD Optimization of Ultrasound-Triggered Drug- Loaded Microbubbles and Control Strategy Development

A decision-centric workflow was developed to link a QbD-defined design space with interpretable surrogate modeling for rapid selection of ultrasound-triggered doxorubicin-loaded chitosan microbubbles. A 15-run Box–Behnken design spanning chitosan, palmitic acid, and Pluronic F68 was used to quantify three critical quality attributes (CQAs)—mean size, encapsulation efficiency (EE), and burst release at 40 s (Burst40)—and to train response-specific surrogates evaluated by leave-one-out cross-validation (LOOCV). The DoE produced a broad performance envelope (size 2.35–4.85 µm; EE 60–82%; Burst40 55.6–95%) with clear composition-driven trade-offs, notably between EE and acoustic responsiveness. A regularized polynomial surrogate (PolyRidge) provided strong LOOCV performance for size (R²=0.885; RMSE = 0.264 µm) and EE (R²=0.871; RMSE = 2.18%), whereas Burst40 was only moderately predictable (R²=0.501; RMSE = 8.61%), consistent with threshold-like cavitation and microstructure sensitivity. Digital screening with multi-objective desirability and explainability (SHAP, permutation importance, partial dependence, LIME) shortlisted manufacturable candidates; experimental validation showed QbD/RSM was better calibrated for absolute size and EE, while both modeling approaches remained similarly limited for Burst40. Importantly, shortlisted formulations preserved suppressed baseline release and pronounced ultrasound-enhanced release, yielding trigger-dependent cytotoxicity and increased apoptotic commitment in MCF-7 cells.