Sixty percent of the total U.S. population takes at least one prescription drug. Separately, but not coincidentally, 60 percent of the population has a negative view of the pharmaceutical industry.
The reason may often be a result of the sticker shock and frustration patients experience when a pharmacist reveals the price of the prescription. Questioning the cost of drugs is valid, especially for life-saving medications like insulin and epinephrine. It raises many ethical concerns, especially when you consider that the global pharmaceutical industry is valued at approximately $1.6 trillion. However, when you pull back the curtain on how drug prices are determined, you’ll find that the reasons for their high cost are much more complex than many realize. As the price of drugs continues to dictate the public’s sentiment toward the pharmaceutical industry, it’s critical to understand all the factors at play and what needs to be done to truly make medications more affordable.
Research and development efforts consume nearly a quarter of the revenue generated by pharmaceutical companies, making the economics of drug development vastly different from almost every other industry. On average, developing a single successful medicine takes over a decade and costs between $2.6 billion and $6.7 billion. The odds of success are low: one in every 10 drugs that enter clinical trials becomes an approved therapy. Pharmaceutical companies waste an estimated $45 billion yearly on failed clinical trials.
Drug development is a lot like oil drilling: each successful drug needs to fund the dozens of failed drugs that came before it. This cost is passed on to the consumer, and this is the unfortunate reality behind the economics of drug development.
The question is: can this economic reality continue? Over the last two decades, the number of clinical trials per year has quadrupled, while their success rates have continued to drop. As a result, drug development costs have gone up nearly fivefold, and this trend is expected to escalate. Clinical trials are becoming more complex, especially as the number of unvetted drugs discovered by A.I. makes their way into the development stage, which further raises the risk of life sciences companies investing and chasing drug combinations that are ultimately destined to fail.
While A.I. has already made an enormous impact on drug discovery, it now has the potential to make a revolutionary impact on clinical trials by increasing the volume of real-world data leveraged in a study and significantly reducing the number of patients required for comprehensive results. This is where the industry can make huge efficiency gains. As our understanding of biology evolves, we can map out entire biological systems and combine that with advanced A.I. and computational frameworks. We can start to make much more educated guesses about clinical trials, dramatically reduce their failure rates and make them faster and cheaper.
I have seen A.I.-simulated clinical trials shorten study duration by 11 months and reduce enrollment by 40 percent while enabling drug developers to increase success rates by nearly 20 percent. A.I. clinical simulations have also improved the accuracy rate of oncology-focused trials by 58.3 percent and respiratory-focused clinical trials by 52.6 percent. This has translated into hundreds of millions of dollars saved in drug development costs and billions gained in future revenue. A.I. trial simulations have the potential to completely change the economic model behind clinical trials and drug development as a whole.
As clinical trials become smarter and more efficient with A.I., we can expect everyone from small biotech startups strapped for resources to large pharmaceutical companies with substantial R&D budgets to adopt this technology. This will result in increasing success rates, more drug approvals and a decline in the overall cost of development. The industry finally has a chance to reverse the trend of increasing drug costs that has been plaguing it for decades. Reduced drug development costs and reduced regulatory oversight over the use of A.I. from the FDA will create economic pressure that ultimately leads to increased R&D budgets for additional drugs and treatments, as well as more affordable drugs for consumers everywhere.
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