While it’s no big surprise that Big Pharma is quickly incorporating AI into the drug R&D efforts, recent research shows that startups are wasting little time, as well.
As a recent article in a financial publication described the pace of AI investment from startups, “In the first half of 2021, investments flooded into AI in life sciences R & D startups, with their funding increasing by almost 3x compared to the same period in 2020. The startup landscape raked in USD 2.5B (H1 2021). Three major IPOs in this space have already taken place in 2021, including Adgene, Valo Health, and Recursion Pharmaceuticals.”
The article offers a global perspective on AI R&D investment, noting that 56% of global funding this year has been in the US, while China and the UK come in with 16% and 12.3% respectively.
But what’s perhaps more interesting is how AI investment is occurring all along the development chain, “from screening drug compounds to estimating their success rates and assuring accuracy during clinical trials.” In addition speeding up the discovery of new therapies, the article notes the introduction of AI is allowing for a more personalized approach to treating patients.
Specifically, the article explains that in early drug development, which “involves everything from reading and interpreting existing literature to examining how potential medications react with targets,” AI “can increase drug research success rates by 8-10%, finish it at a quicker rate, and also reduce costs by up to 80% resulting in billions of dollars in savings for the industry.”
Once researchers have settled on a set of molecules for preclinical development, they must be optimized in terms of the qualities that govern their behavior within an organism. The advantage AI brings to this stage involves its ability to “help trials run more efficiently and allow researchers to forecast how a drug will behave. It can also help in decoding open and closed-access data on reagents such as antibodies and present published figures with actionable insights.”
Finally, during clinical development, “AI algorithms allow a steady stream of clinical trial data to be cleansed, consolidated, coded, stored, and maintained, while reducing the influence of human error in data collection and allowing for smooth integration with other databases.”
Despite the benefits of AI, the article notes pharma startups face a number of challenges, “including unsuitable skill sets, cybersecurity concerns, and complex procedures.” Still, AI “will offer tremendous benefits to drug development and public health as knowledge and experience improve and technology develops.”
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