Cholesteryl ester transfer protein inhibitors (CETPi) entered clinical trials with a seemingly solid biological hypothesis and great clinical promise. This class of drugs raises high-density lipoprotein (HDL), the so-called “good cholesterol”. The hope was that raising HDL with a CETPi would lead to a reduction in cardiovascular risk, similar to how lowering LDL cholesterol with a statin is associated with a reduction in risk.
Unfortunately, one after the other, the CETPi’s failed to show benefit in clinical trials. One member of this class (torcetrapib) even increased the risk of serious cardiovascular events. However, in 2017, after more than a decade of CETPi trials and thousands of patients enrolled, Merck’s trial of anacetrapib found a small, but statistically significant benefit. Yet, shortly after that trial’s result was published, Merck announced that they were abandoning the drug anyway.
While there is a lot to unpack from this episode in drug development, the topic of interest here is what we can learn from this case about evidence-based life science investment. As this portfolio evolved, and one drug after the other was abandoned, when is the earliest that we could have identified that this portfolio was a bad investment? Was it only in 2017 that we could confidently say that this was a bad bet? Or was there sufficient data before then that would have allowed an investor to predict that this drug class was not going to pan out?
The animated graph below helps to shed light on these questions, showing how the portfolio of clinical trials for CETPi’s evolved over time. Each row in this figure corresponds to a particular drug/phase combination. Each bubble corresponds to a trial, appearing along the timeline in the year it is initiated, and then it grows (representing the numbers of patients enrolled) until its year of completion, at which it point the bubble stops moving/growing and changes color to reflect its final outcome. Click the play button to watch the animation. You can also drag the scrubber to advance or reverse the timeline to “freeze the frame” and examine how the portfolio looked at a particular point in time.
What does this show?
It is quite unusual for a company to not pursue a regulatory approval for a product that has been shown to be beneficial in a large randomized clinical trial. The decision to abandon anacetrapib is therefore puzzling, especially when one considers how much the pivotal clinical trial must have cost (it ran for about 6 years and enrolled more than 30,000 patients, so we’re talking hundreds of millions of dollars if not billions). But even though we do not have access to Merck’s reasoning, there are some clues as to why they might have chosen not go forward with trying to license anacetrapib. First, in 2016 the FDA withdrew indications for two other drugs that raised HDL (niacin and fenofibrate), and in that decision, the FDA stated that they no longer believed that increasing HDL was a sufficient biomarker for cardiovascular benefit. Thus, over the course of the CETPi program, the FDA’s position appears to have shifted to be more skeptical of the underlying biological hypothesis here.
Second, given the prior failures and safety risks with another members of the CETPi class, it is possible that the FDA was going to require another large clinical trial before they would license anacetrapib. And indeed, this would seem like a prudent regulatory decision in light of this total body of evidence. One positive result does not automatically outweigh the other largely negative findings in this class. If faced with having to conduct another long and expensive trial (and as always, the possibility that a subsequent trial would not confirm the positive finding), it could be that Merck decided it was not worth the additional investment.
Taking all of the evidence (and the FDA’s position) into account in 2016, things were clearly not looking good for this class. But let’s roll the clock back to 2012: That was year, Roche’s negative, phase 3 dalcetrapib came out (making it the second member of the class to fail, after Pfizer’s torcetrapib). Eli Lilly and Merck had just started their phase 3 trials of evacetrapib and anacetrapib, respectively. Hindsight is 20:20, of course. And the existence of 2 failed members of a class does not scientifically rule out the possibility that another member might succeed… but were these really good bets at the time? It was clear that torcetrapib did hit its biological target (i.e., raising HDL). And Roche’s phase 1 and phase 2 trials had suggeted that dalcetrapib was promising (just like evacetrapib and anacetrapib’s phase 1 and phase 2 trials). Was there good reason to think that the outcomes for these later two entrants would be different? Or was this continued development the result of something like institutional or scientific inertia? (e.g., The companies had the product in their pipeline, the trial machinery was ready to go, the market opportunity was big, so they went ahead with it.)
Obviously, this animation and the underlying data do not tell us what was going on in the heads of Lilly’s or Merck’s executives. But a smart investor, armed with this data and an understanding of how the landscape is evolving, is now in a much stronger position to ask the right questions. For example, in 2012, an investor could have reached out to an expert at Pfizer or Roche and asked them to explain or annotate the figure (e.g., “Can you explain to me why these trials by your competitors might be justified? Is this just throwing good money after bad?”)
Or in our vision for the future of life science investing, an investor can zoom out from this one drug class, and survey the surrounding landscape to get a broader perspective about patterns in the drug development enterprise. How did the other cholesterol drug portfolios evolve? Are they littered with failures before eventual success or did they mostly find winners right away? How many other examples are there of initial failures within a class followed by later successes? If the answers to these last two questions are “No, success in a class are typically found early on” and “Rarely", then this is valuable insight for whether to invest (or hold or sell).