Digital twins built using mechanistic models are playing an increasingly significant role in helping pharmaceutical industries develop robust and more economically efficient manufacturing processes. Building a digital twin for an end-to-end drug manufacturing process allows exploration of the individual and combined effects of numerous process parameters during the active ingredient and drug product manufacturing stages on the final drug product performance. This allows for the development of a more robust drug manufacturing process and greater assurance of product quality while reducing process development timelines and resources.
This work will outline the quantitative analysis performed on the manufacturing process for mefenamic acid by building the end-to-end mechanistic flowsheet model Each element of the drug substance production step, including synthesis step, crystallization and separation are validated using process data individually. Once each unit operation step in the production step is validated, a systems model was configured to provide a quantitative representation of the end-to-end production process including drug substance, drug product and product performance elements. This end-to-end model was subsequently utilized to develop a quantitative understanding of the effect of process disturbances, raw material variability, process parameters, formulation parameters and model uncertainty on CQAs (e.g. tablet properties) and manufacturability. Along with the analysis, the relative influence of those factors (sensitivity indices) on the desired CQAs will also be presented. Furthermore, the impact of batch and continuous operational elements of the process will also be probed. This work helps to identify any existing gaps in the scientific understanding of the effects of some of the API attributes on the desired KPIs during this end-to-end flowsheet analysis will also be discussed.
