
By STEVEN ZECOLA
Artificial intelligence (“AI”) has taken root in the field of drug discovery and development and already has shown signs of running past the traditional model of doing research. Congress should take note of these rapid changes and: 1) direct the Department of Health and Human Services (“HHS”) to phase down the government’s basic research grant program for non-Ai applicants, 2) require HHS to redirect these monies to fund nascent artificial intelligence applications, and 3) require HHS to revamp the roadmap for drug approvals of AI-driven trials to reflect the new capabilities for drug discovery and development.
Background
There are four distinguishing features of the U.S. healthcare industry.
First, the industry’s costs as a percentage of GNP have increased from 8% in 1980 to 17% today, and are expected to exceed 20% by 2030. The federal government subsidizes roughly one-third of these costs. These subsidies are not sustainable as healthcare costs continue to skyrocket, especially in the face of an overall $37 trillion federal deficit.
Second, the industry is regulated under a system that results in an average of 18 years of basic research and 12 years of clinical research for each drug approval. The clinical cost per newly approved drug now exceeds $2 billion. The economics of drug discovery are so unattractive to investors that the federal government and charitable foundations fund virtually all basic research. The federal government does so to the tune of $44 billion per year. When this cost is spread among the 50 or so drug approvals per year, it adds a cost of roughly $880 million to each drug, bringing the total cost to over $3 billion per drug approval. Worse yet, the process is getting slower and more costly each year. As such, drug discoveries under the current research approach will not be a significant contributor to lowering the overall healthcare costs.
Third, the Trump administration has undercut the federal government’s role in healthcare by firing several thousand employees from HHS. Thus, the agency can no longer effectively administer its previously adopted rules and regulations, and therefore, cannot be expected to shepherd drug discovery into lowering healthcare costs.
Fourth, on the positive side, artificial intelligence software combined with the massive and growing computational capacity of supercomputers have shown the potential to dramatically lower the cost of drug discovery and to radically shorten the timeline to identify effective treatments.
Enter Artificial Intelligence (AI) into Drug Discovery
For the past decade, a handful of companies have been exploring advanced automation techniques to improve the many facets of the drug discovery process. Improvements can now be had in fulfilling regulatory documentation requirements, which today add up to as much as 30% of the cost of compliance. More significantly, Ai can be used to accurately create comprehensive clinical documents from raw data with citations and cross-references – and continually update and validate the documentation.
The top Ai drug discovery companies include Insilico Medicine, Atomwise, and Recursion, which leverage Ai to accelerate various stages of drug development, from target identification to clinical trials. Other notable companies are BenevolentAI, Insitro, Owkin, and Schrödinger, alongside technology providers like Nvidia that supply critical Ai infrastructure for the life sciences sector.
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