From Predictions to Understanding: Explainable Machine Learning in Pharmaceutical CMC Development

Abstract

Supervised Machine Learningmodels are often regarded as black boxes, as they are frequently designed to give accurate predictions without disclosing how individual features contribute to those outcomes. Explainable Machine Learning (XML) offers methods such as feature importance and SHAP value analysis to make the relationships between features and target variables visible, thereby enhancing the transparency of the model principles. In this perspective, we present XML within the context of the fourth paradigm and highlight its relevance for pharmaceutical Chemistry, Manufacturing and Control development, where both predictive accuracy and a deep understanding of process mechanisms are crucial. Using examples from biologics manufacturing and formulation chemistry, we illustrate how XML enables deeper insight into the mechanisms of protein solubilization in combination with ion pair formation and thus paves the way for more rational process optimization and formulation strategies in alignment with regulatory requirements.