Deliver New Drugs Faster
AIRobotics finds hidden success patterns in your existing data
Pharmaceutical companies are
EVERY DAY of delay for
EVERY NEW DRUG approval.
According to FDA regulations, all new antineoplastic drugs should undergo standardized
Cell Culture expansion often fails or gives unpredictable results as two many parameters can affect the outcome.
With AI, in-vitro preclinical trials take less time, reducing Pharma Companies revenue loss by millions of dollars.
Data Analysis
AI analyses historical cell expansion data and finds parameters combination that can predict the new expansion process outcome.
Parameter Weighing
All parameters are considered, from cell type to medium batch number, from flask size and orientation to passage dates.
Success Mapping
The Expansion Process is standardized and optimized to a level, where failures are reduced tenfold and reproducibility becomes a reality.
Use-case
Cell Expansion Prediction with AI
Cell Expansion Process is often unpredictable and has a vast number of parameters. Some secondary parameters can have a serious effect on the outcome. That includes parameters responsible for a complete failure of the process and that can change for every particular Cell Lot. Those parameters can be inherited that is also taken into account.
The task
We were given the full historical data for training the AI and the task was to predict the outcome for the process that we were not aware of.
Results
Even with the simplified set, we achieved 86% accuracy in determining the possible outcome. It was predicted to the level that can reduce failures or result in the rejection of new expanded cell cultures by the Quality Control.
The video demonstrates a small part of the technology capability.
Live Cell Culture expansion prediction was done using data that was provided under strict NDA. Therefore we removed identifiable information and non-public details and concentrated on the technology and methods used for the analysis.
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