Predictive Scale-up/Scale-down for Production of Pharmaceuticals and Biopharmaceuticals

Manufacturing in the pharmaceutical industry involves a series of chemical and physical processes to produce the desired product with intense focus on quality and reproducibility. The goal to meet quality standards with increasing process understanding in the pharmaceutical industry is made more challenging by the desire to increase speed-to-market for these products and to find more efficient approaches to develop processes. In order to meet these objectives, deeper understanding of, and novel approaches to, scale-up from lab scale to pilot-and commercial-scale processes is needed. The chemical and physical processes used by the pharmaceutical industry involve heat, mass, and momentum transfer as well as kinetic and thermodynamic driving forces, all of which have potential scale-dependence. Additionally, operational issues at scale (out of specification investigation, new impurity, raw material vendor change, new equipment, etc.) require a systematic scale-down approach to lab-scale to facilitate experimental investigation and develop solutions. These challenges are found in batch and continuous processes for the chemical and biological synthesis of drug substance and drug product. Predictive scale-up and scale-down, based on first-principle models and understanding, is essential to development and manufacturing in the pharmaceutical industry. The fundamental insight provided by these models and their application to scale-up/down can reduce development costs and provide increased knowledge of at-scale process performance, leading to improved process control and product quality. This session invites papers that cover both theoretical approaches to and demonstrated implementation of scale-up/scale-down analysis across drug substance and drug product processes for small molecules, antibody-drug conjugates, and biological products.