Clinical Trials are En Route to Eliminating Inaccuracy and Waste by Leveraging AI and Machine Learning

In the traditional world of drug development, research happens in separate bubbles and exists in silos. Unfortunately, this separation of information slows overall progress and testing. The company VeriSIM Life is utilizing AI and machine learning to combine data and speed up drug development. 

“I always enjoyed challenging problems and how to solve them,” said Dr. Jo Varshney, Founder, and CEO of VeriSIM Life. Dr. Varshney became involved in drug development after feeling limited by the reactiveness of traditional medicine. “Testing in rats and then saying it will work in humans is a hard thing to feel OK about. That caution became a big driving factor in life,” said Dr. Varshney.  

The machine learning from VeriSIM is called BioSIM. It uses existing test results and predicts how different medicines and compounds will impact a test subject. It imitates the biological reality of the living. Dr. Varshney said they try to create virtual imprints of behaviors we see between different animals.  

An integrated approach is critical to rapid drug advances. According to Genetic Engineering and Biotechnology News, “in drug development, speed is cultivating collaborative relationships, enabling data-driven workflows.” With BioSIM, the data is collected, computed, and identifies patterns. The technology leverages the drug development strategy to predict the best next step.  

Even in the earliest stages of development, when there might not be any animal data, the system can predict outcomes based solely on compounds. “What we call the three Rs. Reduction, refinement, and replacement of certain animal experiments,” said Dr. Varshney. “That’s the beauty of machine learning and AI. It’s the computation, learning, and identifying patterns.” 

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