Drug manufacturing requires detailed monitoring of raw material inputs, intermediates, and outputs, or final drug substances. The ability to quickly query for a specific drug recipe is critical to understanding trends and improving the at-line response time to potential negative deviations. Typically, mining this genealogy data is time consuming and is usually a task reserved for database users with advanced SQL skills. TCB Analytics developed a manufacturing quality control visualization tool that enables scientists to track the usage of any material throughout the product lifecycle.
This application will also be leveraged to quickly export genealogical data in order to build predictive models. These models can predict whether specific raw materials could have a negative influence on drug substance product quality attributes. In these cases, the raw materials are flagged and put on hold prior to use in an actual manufacturing process. This level of foresight provides the flexibility to potentially save entire batches that otherwise may have been QC rejected by tweaking process parameters to improve predicted product quality or switching to new raw material lots.
Ultimately, this quality control application will be used to provide scientists with deeper insights into the manufacturing process, track trends, and to report on large-scale production.
This framework was developed using a combination of R, Shiny, and d3.js and queries a database of normalized transactions. Although in this instance, the framework was applied to drug manufacturing QC, this solution can be applied to a variety of manufacturing use cases in order to identify potential problematic materials quickly.
Roland Zhou, Process Analytics Engineer III at Biogen, a TCB Analytics client, will be presenting on this topic at the BioProcess International Conference & Exhibition on October 7th, 2016 at 3:30pm.
•