The Role of AI and ML in Clinical Testing of New DrugsJo Varshney, CEO of VeriSIM Life, on Overcoming Drug Development Hurdles
Developing new drugs takes huge investments of money and time. To help pharmaceutical companies make the right bets, AI tools can identify potential issues with drug candidates at an earlier stage in development. Researchers then can address those problems before starting costly and time-consuming clinical trials, which reduces the overall risk of failure, said Jo Varshney, founder and CEO of VeriSIM Life.
"There's a lot of success we see at an earlier stage before the drug candidate makes it to the patient trials. But unfortunately, 95% of the times they fail," said Varshney, whose company provides a virtual drug development engine, BIOiSIM, which incorporates traditional statistical modeling with artificial intelligence and several types of machine learning.
"We combine mathematical and knowledge-based models to represent the biology to predict what could be the red flags before getting into the clinic" to trial-test a new drug, she said in an interview with Information Security Media Group.
The BIOiSIM platform evaluates drugs and assigns a "translational index score," which is similar to a credit score, Varshney said. "Just like when you receive a credit score, you know your financial health, our clients receive a detailed report with this translational index score that tells them the confidence we have in the model's predictions and ultimately tells them how or whether or not they should proceed with the drug candidate."
The AI-enabled technology could potentially help pharmaceutical firms improve drug candidates. For example, Varshney said, it could help predict how patients with various genome profiles would respond to a new drug, which can help the drug developers target a broader range of patients.
In this video interview with Information Security Media Group, Varshney also discussed:
- How to overcome challenges involved with drug development and testing;
- Examples of drug development projects that use VeriSIM Life's BIOiSIM platform;
- Other promising uses of AI in healthcare.
Prior to founding VeriSIM Life in 2017, Varshney earned a doctorate in veterinary medicine and a Ph.D. in comparative oncology/genomics from the University of Minnesota. She also holds a degree in comparative pathology from Penn State and one in computational sciences from UC San Francisco.