Division/Committee: [BIOT: Division of Biochemical Technology]

With diversified therapeutic pipelines, compressed timelines, and an evolving regulatory landscape, the pharmaceutical industry is in need of improved process understanding to facilitate molecular assessment, candidate selection, process development (PD), and characterization (PC) of more efficient and cost-effective manufacturing processes. One approach that can help meet these demands is the use of high-throughput methodologies, which can reduce time and resource requirements during development and commercialization stages. High-throughput methodologies allow for rapid exploration and characterization of a wide design or operational space through the automated testing of a large number of samples and/or conditions. These techniques enable researchers to quickly screen and evaluate numerous options, accelerating the discovery and optimization of processes. Incorporating high-throughput techniques into modeling development to generate large data sets or derive model parameters is of great interest to researchers. By applying mechanistic, data-driven, or hybrid modeling approaches in combination to the high-throughput data, researchers can gain deeper insights into the underlying processes, optimizations and accelerate timelines accordingly. Case studies highlighting the practical applications and benefits of employing high-throughput methodologies in pharmaceutical research and development, including monoclonal antibody (non-mAb) and non-mAb, are invited to submit abstracts.

Tiago Matos

Merck & Co

Appearances