Case Study

Batch Quality Prediction In Pharmaceutical Manufacturing

Source: Seeq Corporation

Quality is the most critical metric in pharmaceutical manufacturing – after all, nothing is more important than protecting patient health. Drug companies need to test each batch to ensure it meets quality standards.

But predicting the quality of a batch has traditionally been a challenge for drug manufacturers. The usual process is to take a sample while a process is running and send it to the lab for analysis. But waiting for lab results adds time – often several hours – to the process. And once the results come back, nothing more can be done to save a batch that was going bad. The quality is either acceptable or not. If the batch does not meet the quality requirements, the manufacturer will lose anywhere from hundreds of thousands to millions of dollars.

A large molecule pharmaceutical manufacturer was struggling to predict batch quality results in near real time. Delayed lab results made it difficult for the company to optimize process inputs to control the batch yield. The company’s process inputs were set with a known value, resulting in the potential of wasted energy and raw materials. The company needed a better way to predict batch quality, enabling process optimization.

Learn more about a solution that allowed the scientists to build a model of process quality based on data from the OSIsoft PI data historian. The model was then used to predict the quality of future batches, enabling modifications during production before a batch needs to be scrapped for a quality issue.

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