The last five years have seen large innovations throughout drug development and clinical trial life cycles—from finding a target and designing the trial, to getting a drug approved and launching the drug itself. The recent use of mRNA vaccines to combat covid-19 is just one of many advances in biotech and drug development.Whether in preclinical stages or in the commercialization of a drug, AI-enabled drug development is now used by an estimated 400 companies and has reached a $50 billion market, placing AI more f...
The last five years have seen large innovations throughout drug development and clinical trial life cycles—from finding a target and designing the trial, to getting a drug approved and launching the drug itself. The recent use of mRNA vaccines to combat covid-19 is just one of many advances in biotech and drug development.
Whether in preclinical stages or in the commercialization of a drug, AI-enabled drug development is now used by an estimated 400 companies and has reached a $50 billion market, placing AI more firmly in the life sciences mainstream.
“Now, if you look at the parallel movements that are happening in technology, everyone’s in consensus that the utility of what AI can do in drug development is becoming more evident,” says senior vice president at Medidata AI, Arnaub Chatterjee.
The pandemic has shown how critical and fraught the race can be to provide new treatments to patients, positioning the pharmaceutical industry at an inflection point, says Chatterjee.
And that’s because drug development usually takes years. Evidence generation is the industry-standard process of collecting and analyzing data to demonstrate a drug’s safety and efficacy to stakeholders, including regulators, providers, and patients.
The challenge, says Chatterjee, becomes, “How do we keep the rigor of the clinical trial and tell the entire story, and then how do we bring in the real-world data to kind of complete that picture?”
To build more effective treatments faster, drug and vaccine companies are using data iteratively to improve understanding of diseases that can be used for future drug design. Bridging gaps between clinical trial and real-world data creates longitudinal records. AI models and analytics can then be used to enable feedback loops that are key for ensuring safety, efficacy, and value, says Chatterjee.
“We want to create safe and expeditious access to therapy,” says Chatterjee. “So we really have to meet this moment with innovation. With all the new advances happening in drug development, there’s no reason why technology and data can’t be there.”
This episode of Business Lab is produced in association with Medidata.
Related resources
● Integrated evidence, Medidata
● Why artificial intelligence could speed drug discovery, Morgan Stanley
