A Hybrid Statistical and Machine Learning Approach for Optimizing Critical Quality A Hybrid Statistical and Machine Learning Approach for Optimizing Critical Quality

This paper advances pharmaceutical Quality by Design (QbD) by empirically demonstrating the structural failure of traditional linear OLS regression ($R_{adj}^2 = 0.00\%$) in multi-stage oral solid dosage (OSD) manufacturing due to severe multicollinearity (VIF up to 29.40), while establishing a robust machine learning benchmarking framework tailored for small industrial datasets ($N=30$). Utilizing a Leave-One-Out Cross-Validation (LOOCV) protocol to prevent data leakage, the study identifies Random Forest as the optimal predictive architecture, achieving the lowest Mean Absolute Error ($\text{MAE} = 0.8023\%$) and an RMSE of $1.1646\%$ for predicting finished tablet Assay (%). Crucially, the authors quantify cross-stage parameter propagation through feature importance metrics, isolating film coating Weight Gain ($25.6\%$), Water Content ($18.1\%$), and Moisture Content ($12.2\%$) as the primary drivers of active ingredient potency—delivering a regulatory-compliant tool to proactively mitigate Out-Of-Specification (OOS) risks in commercial production.