Riffyn helps advance EMA and FDA goals to reduce animal testing
Improved data analytics produced an improved protocol
EMA and FDA seek to reduce animal testing. Ultimately, the goal is to eliminate it altogether.
Repeating history with no other alternatives. In an example case, study scientists believed that multiple 100-animal trials were needed to test the weight gain caused by a pro-biotic treatment because this was their historical approach.
Lacking personnel and systems to measure each animal. Scientists did 100-animal trials but did not have enough personnel, nor the data systems and data science tools, to track, measure, and analyze each animal each day. As a result, they could only capture mean pen growth instead of mean animal growth each day.
Missing crucial metrics. Animal trials were based on allocating an average mass of animals to the trial. Some trials had large/small animals but were still judged on the basis of the overall mass gain. Scientists were unable to get the metric they really wanted: percent mass gain per animal. The averaging across animals “washed out” the signal variety that would have been observable in individual animals. The inability to track individual animals forced excessive use of animals to gain sufficient signal from the washed-out average data.