I N D U S T R Y I N V E S T I G A T I O N
Pharma R & D has been heading in the wrong direction , becoming progressively less efficient over the last few decades . Large pharma companies now spend more than US $ 6 billion on R & D per approved drug , compared to just US $ 40 million ( in today ’ s dollars ) in the 1950s , with approximately 85 % of that spending coming after discovery . The inequality between near-zero-cost AIdriven drug discovery and skyrocketing costs for clinical trials and regulatory approval will create a bottleneck , stalling promising drugs from reaching patients unless AI is equally applied across the entire R & D lifecycle . As co-founders of Benchling , a technology company focused on life science R & D , we ’ ve been giving this issue a lot of thought .
To be sure , the biopharma industry has long known it needs to evolve its R & D systems . The paper describing the rapid decline in R & D efficiency , often known as Eroom ’ s Law , is more than a decade old . AI , now being used across nearly every industry as a force for disruption , bringing with it automation , scalability and intelligence , should be used to improve every part of the R & D lifecycle , not just discovery , to increase throughput and improve efficiency .
Rethinking the R & D lifecycle with AI
This is no small lift – applying AI will involve rethinking how R & D organisations operate , rather than simply applying AI to how things work today . Just look at how AI-driven drug discovery is already putting new and different pressures on lab-based experimentation . Drug candidates discovered with AI need to be tested through experiments in the lab , which in turn generate experimental data that is used to further refine AI models through a “ lab in a loop ” process . An enormous influx of new
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