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.
For example, Recursion uses biological experiments combined with machine learning to identify potential treatments faster than traditional methods. Additionally, it has created a platform with data and tools for biopharma and commercial users to utilize for drug discovery and development.
In exploring the various approaches, the real promise of Ai in drug discovery rests with knowledge creation. By enabling the efficient exploration of biological variability, Ai can dramatically increase the number of experiments by studying literally trillions of interactions between variables. This capability is particularly helpful for complex and costly maladies such as Alzheimer’s disease, Parkinson’s disease, autism, and for people with multiple chronic diseases. In other words, Ai can process vast amounts of biological data, uncover hidden causal relationships, and generate new actionable insights. The government should be focused on and encourage these capabilities because they hold the potential to improve the health of the nation’s most disadvantaged citizens and significantly cut the costs of providing care.
Healthcare Regulation Must Adapt to the AI Age
The potential for rapid advancement of artificial intelligence in the field of drug discovery requires a new regulatory model. Rather than applying the current regulatory process to the new Ai-driven research, the goal of the federal government should be to develop a regulatory process that accelerates effective cost-reducing combinations of multi-variable treatments.
For example, rather than discrete Phase I, II and III trials, all clinical work utilizing Ai should be collapsed into one elongated trial, given that Ai can be used to continually update and validate documentation. As participants are added to the trial, safety results can be examined and reported in real time. Once the trial surpasses a certain number such as 1000 participants with proven efficacy and meeting the specified safety protocols, it would be approved for roll-out. The role of the government in such an approach would be as auditor to validate the output of the trial. This function would include experimental validation, mechanistic understanding, and ethical oversight.
Summary
The healthcare industry has been failing the U.S. populace for many years with high costs and poor performance outcomes. The existing drug discovery process has offered relatively minor improvements to this equation.
On the other hand, the emerging AI discovery and development models are posed to beat traditional basic research projects to market by years – and at a fraction of the costs. To achieve the full potential of the new technology, an entirely new industry model is required. That is, the subsidies for basic research and the regulation of clinical trials using AI for discovery must change.
Any basic research project currently under review is at a distinct disadvantage to an AI-driven research project – and should not be funded. Rather the focus of government funds should be on AI-driven research, particularly those targeting Alzheimer’s, Parkinson’s, autism, and patients with multiple chronic diseases. These categories contribute to a majority of healthcare expenses in the U.S. and are the least likely to be cured by the traditional approach to research.
Additionally, regulation can leverage the documentation and continuous updating features of AI to collapse clinical trials into one continuous phase that can receive regulatory approval when the pre-set conditions for safety and efficacy are met after a specified number of participants have entered the trial.
Steve Zecola sold his web application and hosting business when he was diagnosed with Parkinson’s disease twenty three years ago. Since then, he has run a consulting practice, taught in graduate business school, and exercised extensively
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