Small-molecule drug manufacture involves the batch processing of chemical compounds with the final step being their crystallization into a solid format. During a commercial campaign, a small-molecule pharmaceutical company needed to investigate the cause of crystallization deviations at the end of its batch processing. The time required for filtration exceeded the specifications – in one case more than doubling the filtration time -- resulting in a deviation that suggested a quality problem.
The situation was serious, as deviations in crystallization threaten the integrity of the entire batch. Once a major deviation occurs, it can take days or weeks for the process to get back up and running as the team needs to prove they understand what happened and how it won’t happen it again.
The manufacturer’s engineering team was experiencing difficulty in trying to get to the root of the problem using spreadsheets. Differences in signal trends are difficult to spot with visualization and require extensive calculations that are difficult to perform with spreadsheets. For example, it is difficult to determine the energy balance on the crystallization profile using Excel. And the engineer would need to rebuild the calculations each time she wanted to evaluate a different time period. Spreadsheets were insufficient to meet the need for ease of use and speed of calculation.
Read how implementing a root cause analysis to determine the changes that might explain the circumstances surrounding the slow-filtering batch they were able to dramatically shorten the analysis time for the engineering team through integrative calculations and data analytics.