John Cumbers of SynBioBeta and Laura Jennings-Antipov of Riffyn discuss how the Riffyn SDE was used to accurately identify top producing strains for scale-up production.
Enabling Right-First-Time Scale Up Decisions
This video illustrates how the Riffyn SDE enabled a customer to select the right strain for scale-up the first time, saving months of wasted time and effort and accelerating product development cycles.
In this case, a customer was engineering yeast strains for maximum succinate production, then testing yield in small scale fermentation experiments, and then using these data to choose strains for scale-up. Top strains were being identified by manual inspection plots of yield vs. time, and initially all observed changes in yield were assumed to be due to the changes in strain from sample to sample. However, the selected top performing strains were not repeating their performance in downstream runs, creating frustration for the team on both sides of the selection decision.
The customer recognized that there may be other variables contributing to succinate yield, and wanted to identify those variables, but the data were fragmented; strain engineering data were in lab notebooks, fermentation data were stored in on-board instrument databases, and analysis data were stored in Excel spreadsheets. Datasets were hard to find, even harder to interpret, and deciphering connections between datasets was almost impossible.
Laura shows how the Riffyn SDE was used to pull all these data together into a globally accessible system, where data were contextualized and connected automatically into a single comprehensive data table ready for analysis. Using this Riffyn SDE data table, the customer was able to find an unexpected correlation between temperature and succinate yield, which changed the interpretation of their data. When the results were corrected for temperature variations, the true strain effect was revealed. The data analysis enabled by Riffyn SDE allowed the customer to select reliably improved strains for production, saving the customer months of wasted time and effort.
Uncovering Variation in Cell Culture
This video shows the use of Riffyn to assess and identify the sources of undesired variation in your experimental methods. This experiment examines a simple 3-step cell growth process with optical density as a final measurement. It is designed to assess the impact of suspected causes of growth variation (temperature, shaking speed, treatment concentration), and to isolate unknown causes of error (i.e., find the step that contributes the most error). It is structured with a full-factorial DoE on the first step, triplicate replication on the second step, and triplicate measurement on the third step. Riffyn integrates all experimental parameters and measured data into a data frame. Using ANOVA, variance components analysis and multivariate regression in JMP, the main contributors to experimental variation are identified.
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.
Drug Formulation Study
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.
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.
High Throughput Screening & Hit Picking
This video illustrates how you can use Riffyn for high-throughput screening activities including hit picking, sample tracking, and uploading of screening or plate map data. In this experiment, 384 cell lines are transformed with plasmid and screened in a three-tier screening process. Riffyn allows the scientist to select hits, pass the hits from tier to tier, track their parent-child lineage and plate position throughout the process. All the screening data is captured together alongside the sample tracking information for easy integration and analysis by Riffyn's analytical engine.
Animal Pharmacokinetics Study
This video illustrates the use of Riffyn to execute an Animal PK study, including multivariate data analysis. The aim of this study is to determine the impact of various animal and dosing characteristics on the absorption of a therapeutic compound into the bloodstream. Analytical data on blood concentration is collected into Riffyn alongside study data characterizing the animal physiology and dosing parameters. Riffyn is used to automatically integrate the data into a statistical data frame and then feed the data frame to JMP for multivariate analysis. Two factors are determined to have a statistically significant impact on adsorption, one is shown to be irrelevant.
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