Bringing a new biopharmaceutical product to market is a lengthy and costly process. Cutting every possible bit of time and money out of that process is therefore paramount. When it comes to accelerating the drugs in the pipeline that show the most promise, however, biopharmaceutical manufacturers have faced a chronic challenge. The length of time it takes to compare data from various R&D experiments and pilot batches is so great it is faster and easier to simply conduct new experiments.
A large molecule pharmaceutical company was finding it difficult to reduce the time involved in pinpointing pilot drug batches that showed the best opportunity to be produced commercially. Batches do not always maintain integrity as they scale from R&D to pilot to commercial production. The company’s scientists were struggling to predict cell growth at scale, especially using laboratory and pilot data from different historians and databases.
Previously, the engineers would export all the data from the different sources into Excel spreadsheets and try to overlay it – a highly time-consuming process. Then, they would calculate scale-up factors such as agitator power per unit volume or oxygen sparging rate. Using spreadsheets for this analysis was too difficult and cumbersome. The pharmaceutical company needed a way to accelerate the scale-up process for its promising new monoclonal antibodies.
Continue reading to learn how implementing a solution that allowed them to combine lab and pilot plant data from different historians to visualize trends and perform advanced analytics resulted in faster process development, which meant the pharmaceutical company was better able to meet clinical timelines.