From QbD to Explainable AI: Predictive Design Space Mapping of Lecithin/Chitosan Nanoparticles

Abstract Quality by Design (QbD) studies generate structured formulation datasets, but response surface models may not fully describe nonlinear or response-specific behavior in nanoparticle systems. This study reanalyzed a published silymarin-loaded lecithin/chitosan nanoparticle QbD dataset to determine whether independent explainable machine learning could add predictive and interpretive value to conventional QbD outputs. Twenty-nine formulation runs, comprising six formulation/process inputs and three critical quality attributes—particle size, polydispersity index (PDI), and entrapment efficiency—were extracted and modeled. A model-zoo of 105 regression variants per response was evaluated using leave-one-out cross-validation, followed by optimized-checkpoint comparison with reported QbD/RSM predictions, permutation feature importance, partial dependence analysis, and predictive design space mapping. Model performance was response dependent: XGBoost provided the best global particle-size prediction (R² = 0.926; RMSE = 27.52 nm), Extra Trees was best for PDI (R² = 0.695; RMSE = 0.0379), and distance-weighted KNN was best for entrapment efficiency (R² = 0.938; RMSE = 1.72%). At the optimized checkpoint, Gaussian Process regression predicted particle size with lower error than RSM (0.789 vs. 8.804 nm), and checkpoint-compatible Random Forest predicted PDI more closely than RSM (0.0004 vs. 0.032 absolute error), whereas RSM remained slightly closer for entrapment efficiency. Explainable AI identified lecithin:chitosan ratio and drug amount as recurring formulation drivers, while the predicted acceptable region was concentrated within intermediate formulation ranges. These findings support explainable machine learning as a complementary layer for QbD-based nanoparticle development rather than as a replacement for response surface modeling.