By Petter Mörée and Joe Reckamp, Seeq
As machine learning and increasingly automated monitoring techniques continue to transform the pharmaceutical manufacturing space, the need for fit-for-purpose modeling technologies that combine empirical data with the chemistry, engineering, biology, and physics that define manufacturing advanced therapeutics is critical to furthering their innovation.
Multivariate analysis, which allows operators to model processes with several signals, can afford manufacturers a holistic, interconnected view of their operations in near real-time. Despite the challenges, the value of integrating multivariate analysis, particularly in combination with hybrid modeling approaches, can prove invaluable in achieving greater process understanding for expensive, difficult-to-produce therapeutic modalities.
Often, individual managers within a pharmaceutical manufacturing organization may adopt modeling strategies on their own, convinced of their value in monitoring a given process. While this approach may be valuable for the business unit it impacts, failing to scale and optimize across an organization represents a missed opportunity to deploy flexible, effectual models capable of facilitating greater process control, improving process understanding, and achieving meaningful process gains.