Upstream processes are one of the most evolving technologies available for the production of medicinal products. There is a constant need to upscale medicinal products from a few liters to thousands to cover population needs anywhere from chemical entities and recombinant proteins to different types of antibodies or viral vaccines (like COVID-19)… and these are just some examples. This webinar will address how the urgency for creating safe, efficient and quality vaccines for critical situations, such as COVID-19, is paramount and will outline how production and control challenges can be overcome with the support of mechanisms like augmented intelligence.
The systematic application of AI orchestrating the complexity associated with vaccine manufacturing leads to better knowledge and allows operators to act quickly before potential anomalies can occur, thereby improving drug safety and manufacturing process. Controlling all of the process parameters can be difficult and very time consuming for process development or pharma teams due to the large amount of data registered in real time. Pharma companies often lack the technology to measure all the variables to pay close attention to all critical factors which could directly or indirectly affect the safety, the potency, the impurity profile and the quality of medicinal products. In addition, working with biological entities in bioreactors implies a real challenge for modern manufacturing since critical process parameters and critical factors inherently present high variability in these systems. For this reason, the acquisition of real-time knowledge is increasingly necessary in continuous manufacturing to avoid process deviations which could lead to low efficiency or rejected batches.
In this webinar, join Toni Manzano and Raul Alba as they show what useful aspects to monitor in upstream processes, detect early deviations using AI algorithms and discuss classical statistical analysis and advanced analytics using AI tools. The presenters will show how easy it can be to perform root cause analysis of possible deviations and convert data into knowledge so that challenging scenarios can be predicted and avoided before they occur.