Abstract
The pharmaceutical industry is placing growing emphasis on sophisticated process modeling to enhance the efficiency of drug design and production pipelines. Optimal control over these models can significantly improve manufacturing performance by lowering costs, boosting productivity, and ensuring rigorous quality compliance. However, the intricate nature and heavy computational load of these models often require the adoption of more practical or simplified alternative strategies for optimisation such as simulation-based approaches. In this work, we introduce a simulation-based framework including a "top-level" gradient-based mathematical programming optimisation model coupled with a "low-level" simulation scheme, to optimise multi-scale drug substance manufacturing flowsheets. The proposed framework optimises critical quality attributes, such as yield and purity, including green metrics such as process mass intensity, aligning with digital platforms (e.g. gPROMS) used in the pharmaceutical industry. By constructing Pareto fronts, we capture the balance between conflicting objectives, providing valuable insight for informed decision-making. The results indicate that this simulation-based approach can effectively optimise complex process behaviors while improving computational efficiency as no preliminary data is needed. By applying the framework to a real-world case study, we demonstrate its potential in optimising complex pharmaceutical manufacturing flowsheets.