Pharma innovation expert Bernard Munos captures the inherent inefficiency of drug development with two fascinating statistics he recently shared with me.
First, for large pharmas, the average cost of developing a new drug (simply based on the total R&D costs divided by the total number of new drugs approved for sale) works out to about $5B per drug. It’s an astronomical number, and one that keeps growing to a worrisome degree. The Munos analysis encompasses both the cost of failures and what he calls the cost of scale. In contrast, the actual cost to get a single drug approved for smaller companies – an analysis that omits the cost of failure because it doesn’t look at the many small companies that tried to advance drugs and failed – works out to a bit over half a billion dollars, or about 10-fold less.
One implication of these data is that in large pharmas, drug discovery seems terribly inefficient, with huge amounts of money going into products that never become approved drugs. Another implication, says Munos, is that large pharmas are, theoretically, quite vulnerable to disruption, since they “need every day of their patent life to recover that cost and fund an ever-growing R&D budget that keeps producing the same output.” That’s another way of saying their existing operating model requires extracting all available revenue from existing approved products.
It hasn’t escaped anyone’s notice that it would behoove pharma to make R&D more efficient, as even small increases in the rate of success at any stage would be expected to translate into improved overall R&D efficiency. However, achieving such efficiency gains has remained remarkably elusive, despite the hundreds of millions of dollars that have been spent on management consultants, and despite the execution of continuously refreshed restructuring initiatives generally driven by said consultants.
Two very different companies making news at JPM20 say they have an approach that could make a dent in the R&D statistics: Atomwise, the AI-for-drug discovery company led by Abraham Heifets, and EQRx, former VC Alexis Borisy’s ultra-buzzy, on-Zeitgeist fast-follower newco. Both seem to be focused on dramatically different aspects of drug development, yet they share a commonality in their approach that’s worth a closer look.
San Francisco-based Atomwise, founded in 2012, seeks to use AI to accelerate the identification of promising molecular compounds, with a particular emphasis on drugging the undruggable. In the last week, they’ve announced a new partnership with the accelerator BioMotiv, and the extension of a 2017 collaboration with Bayer.
Atomwise’s thesis is that while the overall probability of success (POS) for any early stage compound is quite low, the actual POS is naturally much higher if you remove a key aspect of the risk; one way of accomplishing this is by targeting something you are certain will have an impact on disease, if only you could access it. The thinking is that often, new disease targets represent, at best, hopeful, educated guesses, but still involve a huge amount of biological risk – as well as the many other risks (such as safety, tolerability, clinical efficacy) associated with getting a new chemical entity all the way to the point of FDA approval.
Heifets argues that his platform, like CRISPR, is valuable precisely because it enables drug developers to physiologically manipulate established targets in a way that was previously unachievable. As he writes, “the excitement around CRISPR, protein degradation, and RNA-targeting techniques is justified because these techniques offer us the chance to drug fundamentally new targets that were not otherwise attainable by other methods,” adding “The future of drug discovery is in using new technologies to drug the undruggable.”
Munos, for his part, worries that the targets Atomwise is attacking are not as de-risked as the company may assume. “There is no such thing as a validated, undruggable target,” he notes, explaining “the only validation that can be trusted is that which comes with a drug approval. Before that, targets may be interesting or promising, but they are not validated.” He adds, “Most of the clinical trials that fail aim at targets that are thought to be validated. Yet toxicity and insufficient efficacy are the most common causes of trial failure.” Munos’s comments echo the old pharma saw that the definition of a validated target is one where there’s already a drug with $1 billion in sales.
Cambridge, Mass.-based EQRx, announced this week, represents a response to the problem of costly drugs. Borisy, a former partner at Third Rock Ventures, says he sees a market opportunity in pursuing established targets, and essentially undercutting pricey first-to-market products. His thought is that by focusing on established mechanisms, you can make new drugs for much less money, because you anticipate a far lower failure rate (you know the target is both relevant and targetable) than the typical innovator company. This first requires making a new chemical entity that eludes the innovator’s original patents. Then, presumably, EQRx can perhaps also design more efficient clinical studies by leaning on established examples.
You can think of Borisy’s approach as “pre-generics,” perhaps (with apologies to the pre-cogs of Minority Report), although he aspires to make drugs that are somewhat better than first-in-class products. The economic argument is that his reduced costs and development time will enable him to get new molecules onto the market before the first-in-class product goes generic, and to sell this fast-follower at an aggressive low price, but that still allows for significant gross profit margins. Borisy expects to be able to do this for multiple products. As Luke described it earlier this week, “the idea at EQRx is to use the bursting knowledge of biological targets and new treatment modalities to make fast-follower patented drugs that are sold at radically cheaper prices – maybe 50, 60, 70 percent cheaper than others in a given class.”
While noting the profound transformative potential EQRx would have if successful, by cutting deeply into pharma’s anticipated revenue over the patent life of an approved drug, Munos nevertheless remains skeptical:
“Given the long lead time of drug R&D, in order to reach the market before the pioneer drug becomes generic, the ‘fast-follower’ must get going long before the drug it follows gets approved. And if the lead drug stumbles, so does the fast-follower. EQRx apparently thinks it can tweak the fast-follower model by waiting until a drug has been approved — thus validating its mechanism — before it gets going and still reach the market long enough before the lead drug loses its patent. This would require an improvement in the speed of drug R&D that has never been seen before despite pharma’s decades of relentless efforts at process improvement (e.g., six sigma). It would be a monumental achievement.”
A Shared Focus on De-risking
While Atomwise and EQRx are focused on very different problems, both are leveraging a similar strategy: improve the overall probability of success by attacking something that’s already (somewhat) de-risked. For Atomwise, this means creating a new compound for an established target that no one’s been able to drug, and drug it for the first time; for EQRx, this means creating a new molecule for an important target that’s already been drugged, and doing it faster/better/cheaper.
Each is betting that while the overall economics around new drug development are dispiriting, the value proposition for a candidate drug that’s derisked can be far more promising. Both companies, as Munos points out, face real challenges as they strive to deliver at the scale necessary to make the still-difficult math work.
In some ways, Atomwise may have the easier lift. Even if only a few compounds are ultimately successful, the individual drugs could support the growth of the company (assuming the company retains adequate economics in the products, which will apparently be developed by partners – this is a critical consideration). Atomwise could succeed even if the platform doesn’t meaningfully alter the grim R&D statistics for the industry as a whole.
EQRx has not gone into significant technical detail about how, exactly, it will go about achieving its needed gains in speed and cost. But whatever technologies it brings to bear will have to be remarkable to achieve its founding promise. EQRx has to deliver multiple fast-followers through all phases of compound development and clinical testing, with enough speed, enough economy, and a high enough success rate. That’s a very high hurdle, though also a worthy ambition.