By Alexander Kele, Senior Global Product Manager – Digital, Cytiva
The primary role of a process development (PD) scientist is to develop robust processes that can successfully transition from laboratory scale to a safe and reliable commercial manufacturing plan. Data management plays a critical role in doing this, as the decisions process developers make about critical quality attributes, process parameters, yield, purity, and other factors are based on information collected from a wide range of sources. Traditional ways for capturing, storing, querying, and managing data are time-consuming, prone to error, and can hinder collaboration. They also make it harder to find and access data already created.
With much of the drug development process dependent on collaboration and sharing of knowledge, relying on tools that create executional bottlenecks and lower the quality of insights can lead to unnecessary repetition of experiments and incomplete transfer between internal or external teams. Additionally, there are expectations from regulators that effective strategies for managing data integrity and traceability will be implemented during drug development. These internal and external drivers put a burden on pharmaceutical companies to rapidly address data management challenges. However, doing so calls for a deeper understanding of the shortcomings of today’s methods and the factors you need to consider when adopting a solution to successfully overcome them.