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The astonishingly rapid development of successful COVID-19 vaccines grew partly out of a clinical failure seven years earlier of a promising HIV vaccine. When COVID came along, Moderna and Pfizer-BioNTech pursued follow-on research to produce their highly effective inoculations.
That’s just one example of how failure isn’t always pointless in research and development. Even unsuccessful drug R&D can have indirect value, according to Chicago Booth’s Alexander P. Frankel, Harvard’s Joshua L. Krieger, MIT’s Danielle Li, and Northwestern’s Dimitris Papanikolaou. They constructed a model to analyze the value of learning that spills over from R&D, showing how ignoring its potential could hobble the development of innovative new treatments.
In drug development, the stakes are unusually high. A breakthrough can be a gold mine, as the mean expected value of an approved drug is $1.63 billion. But the average cost of creating a new drug is $1.4 billion, and more than 90 percent of drugs that enter clinical trials never make it to market.
In new-drug R&D, drugmakers must choose whether to “explore or exploit,” the researchers write. Pharmaceutical companies can try to exploit what they already know, pursuing incremental advances on existing treatments. Or they can explore novel drugs. The potential success of a drug candidate can be evaluated with greater confidence when the advance is only incremental, but completely novel drugs have the highest potential for insights that might lead to something like the mRNA COVID shots, the researchers find. Even if a novel drug never makes it to market, the research can lead to future drugs that do.
The researchers collected a sample of drug candidates from Clarivate Analytics’ Cortellis Investigational Drugs database and tracked revenue generated by approved new drugs using data from Evaluate Pharma, a provider of drug sales information. They used their two-step model to calculate the value of knowledge spillovers from exploratory research.
In the first step, the model estimated the benefits of developing incremental versus novel drugs. It predicted that once incremental drugs have begun development, they are more likely to proceed at every stage. This is largely because of the initial informational advantage. Companies can quickly screen out candidates that appear unlikely to succeed.
In the second step, the researchers calculated the delayed benefits from novel drug development. Even though these potential treatments are less likely to make it to market than incremental drugs, their potential successor drugs are likely to generate greater future revenues than incremental advances do, according to the model.
“Our results indicate that drug candidates regularly inspire the development of successor drugs,” the researchers write, adding that the majority of successor revenues accrue to drugs whose own development failed. “Novel drug candidates generate greater successor revenues than incremental drug candidates, despite being significantly less likely to reach the market.”
And yet drugmakers invest less in novel-drug development, even though the direct revenue from those that make it to market is substantially higher. That’s because pharmaceutical companies worry that competitors will benefit from spillover learning derived from published reports of clinical findings, the researchers suggest. They give the example of Japan’s Sankyo, which experimented with a statin drug in the late 1970s. The trials failed in animal testing because of adverse effects. But the US drugmaker Merck picked up the ball and developed lovastatin as the first approved such cholesterol treatment in 1987, and then the wildly successful multibillion-dollar pill simvastatin.
According to Frankel, the findings suggest ways that policy makers could encourage more R&D focused on novel treatments. Clinical trial disclosure rules could be changed to allow companies to keep information about novel drugs private for longer. Changes to patent laws could also help. Allowing “reach-through” patent claims could grant protection for a chemical product and its function on a biological target and could allow companies to collect revenue from rivals’ products that are spawned by their own R&D investments.
Alexander P. Frankel, Joshua L. Krieger, Danielle Li, and Dimitris Papanikolaou, “Evaluation and Learning in R&D Investment,” NBER working paper, May 2023.
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