Hybrid rule‐based and optimization‐driven framework for the synthesis of end‐to‐end optimal pharmaceutical processes

Abstract The modernization of pharmaceutical manufacturing is driving a shift from traditional batch processing to continuous alternatives. Synthesizing end‐to‐end optimal (E2EO) manufacturing routes is crucial for the pharmaceutical industry, especially when considering multiple operating modes—such as batch, continuous, or hybrid (containing both batch and continuous steps). A major challenge is the ability to compare these manufacturing alternatives across different operating modes, hindering optimal superstructure synthesis. To bridge this gap, this study introduces a hierarchical framework for the synthesis of E2EO manufacturing routes, employing a hybrid rule‐based and optimization‐driven approach. This method optimizes flowsheets modeled using PharmaPy through a simulation‐optimization technique with modest computational requirements. The effectiveness of the proposed framework is demonstrated through a case study on the manufacturing of the cancer therapy drug Lomustine. Two distinct manufacturing scenarios are analyzed to generate E2EO manufacturing campaigns tailored to the specific chemistries and process configurations, considering process efficiency and sustainable manufacturing.