Case Study: Drug Tablet Formulation


Riffyn’s Director of Science, Laura Jennings-Antipov, shows how a formulation sciences team used Riffyn SDE to improve a drug tableting process.

* Note: This demo is an excerpt from a webinar presentation which affects the audio quality.

Analyzing tablet properties

This video illustrates how the Riffyn SDE was used in a drug tablet formulation group to pull together fragmented data sets. This allowed the customer to quickly determine which experimental variables had the largest effect on tablet integrity and ultimately improve their formulation process.

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.


Current methods use manually recorded data shared on paper or individual spreadsheets.

Riffyn collects data and structures it according to experimental design parameters.

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.


    Once your data is in Riffyn there’s no more searching for it, cleaning, organizing it, or aggregating.
    — Laura Jennings-Antipov, Riffyn Director of Science

    Riffyn concatenates the dozens of data tables into a single flattened statistical data frame. This data frame can be imported into data analysis software and analyzed for insights.

    Key Features

    Design of experiments

    Data collection & joining

    Connection to Data Analysis Applications