Presented at The Bioprocessing Summit 2018, Boston, MA, USA
Knowledge of the properties and characteristics of large-molecule drug candidates needs to be widely available to development scientists, who use it to make a collective R&D decision on drug candidates and to properly follow the Quality-by-Design approach by defining the Quality Target Product Profile and identifying the critical product quality attributes to be monitored. A vast amount of analytical data needs to be assessed due to miniaturized screening approaches and new analytical methods, including process analytical techniques. A typical bottleneck in developability and manufacturability assessments is the timely availability of the heterogeneous analytical data and the difficulties to put them into the right context (e.g., understanding which sample was analyzed at which step in a process). We designed a workflow system which captures and structures all product quality attributes and process attributes acquired during the biologics R&D process. These data are associated to molecules and cell lines, as well as to samples with corresponding key process parameters describing the sample generation process. We show examples illustrating the importance of such a highly integrated and highly structured data management approach for a comprehensive and systematic assessment of the developability and manufacturability risks of large-molecule drug candidates in the early stage of development.