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.
However, predicting the quality of a batch has traditionally been a challenge for drug manufacturers. The usual process is to take samples 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. Inadequate lab results can require time consuming changes or expensive reworks if it is even possible to recover the batch. If the batch does not meet the quality requirements, the manufacturer can lose anywhere from hundreds of thousands to millions of dollars for a lost batch.
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 without optimizing the process, resulting in the potential of wasted energy and raw materials or reduced product quality and yield. The company needed a better way to predict batch quality, enabling process optimization.