Founder & CEO of BioSyft
Every drug that reaches a patient has first been tested in animals. Animal models remain one of the most important tools we have for understanding disease and evaluating potential therapies before they ever reach a human. Despite their central role in drug development, the way we measure animal behavior, the primary signal researchers use to understand neurological disease, has barely changed in decades.
In many laboratories, behavioral analysis is still performed manually. Scientists watch hours of video and record what they see: walking, grooming, rearing. The process is slow, subjective, and limited by what a human observer can realistically track. As a result, much of the behavioral information contained in these experiments is never captured.
I encountered this problem directly during my PhD in pharmacology while studying how drugs alter brain circuits and motor behavior. Like many behavioral scientists, I spent an absurd amount of time analyzing animal videos by hand. The tools available to us were limiting not just our efficiency, but the depth of insight we could extract from our experiments.
BioSyft was created to modernize how behavioral research is performed. Our technology uses computer vision and deep-AI modelling to automatically analyze animal behavior with far greater scale and precision than traditional methods. Instead of relying on manual scoring and a handful of behavioral labels, researchers can capture rich, quantitative behavioral signatures directly from video.
This matters because drug development depends on these signals. More than 90% of drugs that enter human clinical trials ultimately fail, and that number is even higher for neurological diseases. If we want to improve how therapies are developed, we need better ways to understand what is happening in the biological systems we study before those drugs ever reach humans.
BioSyft’s deep learning-powered platform automates this process and goes further to detect subtle behaviors that you or I may not even realize are important. Giving scientists and pharmaceutical companies more informative data with less effort on both the scientist and the animals.