Drug Tablet Formulations
Integrating fragmented process data for cause-and-effect analysis
The customer’s data were fragmented across non-mineable PDF batch records, on-board instrument databases, and Excel spreadsheets stored on individual computers. The impact of their fragmented data was that experimental results were inaccessible, data files were incomprehensible to colleagues, and connections between datasets were unclear. As a result, datasets could not be pulled together for analysis, and the customer could not determine which experimental variables were having the largest impact on the quality of the tablets produced.
Laura shows how Riffyn SDE was used to pull all the data together into a globally accessible system, where it was automatically annotated, relationships between datasets were defined, and related data were automatically joined into a single, comprehensive data table ready for analysis. Using this Riffyn SDE data table, the customer was quickly and easily able to determine which experimental variables were affecting the quality of their tablets, and this information was used to improve their formulation process.
Process and experiment design were created, and data were captured and prepared with Riffyn. Plots were created with JMP® Software. Copyright 2018, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. Reproduced with permission of SAS Institute Inc., Cary, NC.
"What if I could look at the data together from all experiments? Riffyn makes that easy."
Highlighted in the Video
Capturing data from spreadsheets
Capturing time-series data
Combined analysis of multiple experiments