(282e) Transforming Pharmaceutical Development through Predictive Science and Computational Modelling

Responding to the challenge of bringing lifechanging treatments and medicines with the utmost quality standards to the patients world-wide, the pharmaceutical industry is undergoing a transformational change to reduce the development timelines. Two major transformations which are driving these changes are the transition from batch to continuous manufacturing technology and the digitalisation of product and process design activities. The digital twin of continuous process plays an important role from early to late stages of process development. It allows extensive analysis of the process while minimising experimentation at scale and progress using less material. This is especially relevant to: (i) create process operating space and obtain process information in early stages with reduced API availability, (ii) test new prototypes and changes in equipment, (iii) de-risk the process and investigate extreme and edge points without risk, (iv) understand the process performance and dynamics in cases where it is difficult or even impossible to measure internal variables experimentally, and (v) execute simulations at any time and independently of material availability. The digital twin is further used to investigate the impact of operating conditions on the critical quality attributes of final drug product. It integrates comprehensive process knowledge generated from early to late stage and simulates a range of processing conditions to design an optimal set of experiments. The process knowledge generated from digital twin and other models are getting integrated with the experimental data in the relevant sections of regulatory filing dossier to further strengthen the overall scientific and technical content.