Development of a Hybrid Machine Learning Algorithm for Impurity Reduction in Nitrosamine Formation Under Variable Pharmaceutical Processing Conditions Compared with Gradient Boosting and Artificial Neural Networks

Nitrosamine impurity formation remains a significant challenge in pharmaceutical manufacturing because of the complex, nonlinear interactions among processing temperature, reaction pH, nitrite concentration, amine precursor availability, reaction duration, solvent composition, moisture content, and storage conditions. Conventional experimental optimization and standalone machine learning algorithms frequently encounter limitations in modelling coupled physicochemical interactions, generalizing across variable manufacturing conditions, and identifying impurity-minimizing operating parameters under pharmaceutical quality constraints. This study proposes the development of a novel Physics-Informed Gradient–Neural Optimization Algorithm (PIGNOA) that integrates Extreme Gradient Boosting (XGBoost), an Artificial Neural Network (ANN), physicochemical reaction constraints, adaptive ensemble learning, and Bayesian optimization into a unified predictive and decision-support framework for nitrosamine impurity reduction. The proposed algorithm combines the structured-feature modelling capabilities of XGBoost with the nonlinear representation capabilities of multilayer neural networks, while incorporating nitrosation-relevant chemical information and uncertainty-aware optimization to improve predictive reliability and identify feasible low-impurity processing conditions. A multivariable pharmaceutical processing dataset comprising experimental conditions, raw-material characteristics, formulation attributes, and analytically measured nitrosamine concentrations will be used for model development, with targeted impurity quantification supported by validated liquid chromatography–tandem mass spectrometry methods. The proposed PIGNOA framework will be comparatively evaluated against standalone XGBoost, Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Support Vector Regression (SVR), and conventional ANN models using coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), predictive uncertainty, computational efficiency, and experimentally verified impurity reduction. Comparative performance graphs, predicted-versus-observed concentration plots, residual distributions, sensitivity analyses, and processing-condition response surfaces will be developed to investigate prediction accuracy, model stability, and process optimization effectiveness. Statistical comparisons will assess whether the proposed algorithm achieves significant improvements over existing approaches across independent test datasets and pharmaceutical processing scenarios. The anticipated contribution is an interpretable, constraint-aware hybrid learning framework capable of supporting reliable nitrosamine risk prediction, identification of lower-risk manufacturing conditions, and pharmaceutical impurity control consistent with applicable regulatory requirements.