Category: Medicine

What would happen if the FDA regulated pharmaceuticals much less than currently? I have pointed to one useful comparison, new uses of old drugs do not have to go through FDA required efficacy trials in the new use. In other words, new uses of old drugs are regulated for safety-only. Thus,
“off-label prescribing” provides a window on to what a world of safety-only FDA regulation would look like. Off-label prescribing surely results in errors and problems but overall physicians tell us that off-label prescribing is highly beneficial and critical to good medical care.

Maxwell Tabarrok points to another useful comparison, surgery. Surgery is not FDA regulated, despite having many of the same asymmetric information problems as pharmaceuticals. Some surgical procedures are surely ineffective and unsafe. Yet, once again, the FDA-absent surgery market appears beneficial overall and like other markets it improves over time with greater safety and more efficacy. For example,

In the US, the death rate from medical and surgical care complications declined by 39% from 1999 to 2009.

Would we be better off if every new surgical procedure had to go through FDA-required efficacy trials before it could be offered to consumers?

Neither of these comparison proves that a world with less FDA regulation would be a better world but both refute the stories of a world run amuck in the absence of the FDA. In essence, the reason is that the world contains many sources of approval, recommendation, certification and review beyond the FDA and these would grow in scope and stature absent the FDA.

See Maximum Progress for more.

Addendum: More excellent Tabarrok material: The Spice Must Flow: The Dutch-Portuguese War-Part 1.

During the pandemic, when vaccines doses were scarce, I argued for fractional dosing to speed vaccination and maximize social benefits. But what dose? In my latest paper, just published in PNAS, with Phillip Boonstra and Garth Strohbehn, I look at optimal trial design when you want to quickly discover a fractional dose with good properties while not endangering patients in the trial.

[D]ose fractionation, rations the amount of a divisible scarce resource that is allocated to each individual recipient [3–6]. Fractionation is a utilitarian attempt to produce “the greatest good for the greatest number” by increasing the number of recipients who can gain access to a scarce resource by reducing the amount that each person receives, acknowledging that individuals who receive lower doses may be worse off than they would be had they received the “full” dose. If, for example, an effective intervention is so scarce that the vast majority of the population lacks access, then halving the dose in order to double the number of treated individuals can be socially valuable, provided the effectiveness of the treatment falls by less than half. For variable motivations, vaccine dose fractionation has previously been explored in diverse contexts, including Yellow Fever, tuberculosis, influenza, and, most recently, monkeypox [7–12]. Modeling studies strongly suggest that vaccine dose fractionation strategies, had they been implemented, would have meaningfully reduced COVID-19 infections and deaths [13], and perhaps limited the emergence of downstream SARS-CoV-2 variants [6].

…Confident employment of fractionation requires knowledge of a drug’s dose-response relationship [6, 13], but direct observation of both that relationship and MDSE, rather than pharmacokinetic modeling, appears necessary for regulatory and public health authorities to adopt fractionation [15, 16]. Oftentimes, however, early-phase trials of a drug develop only coarse and limited dose-response information, either intentionally or unintentionally. A speed-focused approach to drug development, which is common for at least two reasons, tends to preclude dose-response studies. The first reason is a strong financial incentive to be “first to market.” The majority of marketed cancer drugs, for example, have never been subjected to randomized, dose-ranging studies [17, 18]. The absence of dose optimization may raise patients’ risk. Further, in an industry sponsored study, there is a clear incentive to test the maximum tolerated dose (MTD) in order to observe a treatment effect, if one exists. The second reason, observed during the COVID-19 pandemic, is a focus on speed for public health. Due to ethical and logistical challenges, previously developed methods to estimate dose-response and MDSE have not routinely been pursued during COVID-19 [19]. The primary motivation of COVID-19 clinical trial infrastructure has been to identify any drug with any efficacy rather than maximize the benefits that can be generated from each individual drug [3, 18, 20, 21]. Conditional upon a therapy already having demonstrated efficacy, there is limited desire on the part of firms, funders, or participants to possibly be exposed to suboptimal dosages of an efficacious drug, even if the lower dose meaningfully reduced risk or extended benefits [16]. Taken together, then, post-marketing dose optimization is a commonly encountered, high-stakes problem–the best approach for which is unknown.

…With that motivation, we present in this manuscript the development an efficient trial design and treatment arm allocation strategy that quickly de-escalates the dose of a drug that is known to be efficacious to a dose that more efficiently expands societal benefits.

The basic idea is to begin near the known efficacious dose level and then deescalate dose levels but what is the best de-escalation strategy given that we want to quickly find an optimal dosage level but also don’t want to go so low that we endanger patients? Based on Bayesian trials under a variety of plausible conditions we conclude that the best strategy is Targeted Randomization (TR). At each stage, TR identifies the dose-level most likely to be optimal but randomizes the next subject(s) to either it or one of the two dose-levels immediately below it. The probability of randomization across three dose-levels explored in TR is proportional to the posterior probability that each is optimal. This strategy balances speed of optimization while reducing danger to patients.

Read the whole thing.

A reader in the industry writes with excellent comments on yesterday’s post on the Chris Rock hypothesis.

Long-time reader, first-time emailer–love the show ;).  I’ve been in and around the pharma industry for nearly 30 years, and I’ve spent time in gene therapy/gene editing where the one-time cure model dominates.  Some thoughts on chronic vs. curative dosing and why a curative therapy is likely worth less:

1. There’s a potential mismatch between payment for a drug and the accrual of value that justifies its price point.  If I take a curative therapy for a disease like hemophilia (e.g., the new $2.9 M drug, Roctavian), the insurance company immediately incurs the cost of the drug, but the prime financial benefits (no more expensive chronic therapy, reduced expensive visits to the hospital) accrue over time.  Patients switch insurance companies as they switch jobs, so the “payout” that justifies the treatment price accrues to the subsequent insurers.  On chronic therapy, if a patient switches to another insurer, the new insurer picks up the payments so there’s no such disconnect.  Rationally, insurers should pay more for chronic therapy, even in present value terms.
2. Durability of effect is unknown until it isn’t.  It’s difficult to charge for a drug as a cure until such time you know it’s a cure and have proven it as such.  How long do you have to follow treated patients to prove that?  Gene therapies are starting to show waning efficacy in some cases.  The FDA mandates that you cannot include something in the drug label that has not been proven.  Payors will point to a label and ask why they should pay for something that’s not on there.  This can be mitigated by programs where the drug company pays back a portion of the cost if it doesn’t work, but collecting on that seems like a huge hassle–how do you prove that it stopped working (I can hear Mike Munger–“the answer to your question is transaction costs…”)?
3. Sticker shock and headline numbers.  A drug that costs $3 M or more is something the White House can use at a podium and get a reaction.  Never mind that it gets paid back pretty quickly by discontinuing a therapy that costs hundreds of thousands per year–life-saving drugs should not cost millions of dollars!  This puts downward pressure on one-time cures.

So, my perspective is that it is more difficult for a one-time treatment/cure to capture the value it creates vs. a chronic therapy.  So, why did Lilly shares tumble on the news?  More important than duration of therapy is market share vs. competitors.  A more permanent solution (with no rebound after discontinuation) would more than make up for lost revenue on the back end by taking share from the competition on the front end.  And THAT is why pharma is incentivized to pursue cures.  Making a better drug will beat the competition, and a cure is a better drug.  Big Pharma doesn’t necessarily pursue curative treatments directly because they don’t know how.  Technologies like CRISPR and mRNA have to come up via biotechs that are purpose-built to maximize the platforms’ value and to understand/navigate the underlying technology.  That said, Big Pharma has inked HUGE deals to gain access to these technologies (e.g., Pfizer/BioNTech), so they do seem to come around eventually.

These are all excellent points. On point 1, note that Medicaid creates similar incentives in that insurance firms want to farm long term costs onto Medicaid.

Point 3 suggests that we should be especially wary of price controls on cures. Sticker shock may drive us to price controls leaving us with treatments that look cheaper but are even more expensive in the long-run (and by present discounted value). Sovaldi is a case in point. Its initial $84,000 price generated huge opposition even though it typically cured hepatitis C infections and avoided many later liver cancers and saved money overall. Indeed, as I pointed out earlier, Sovaldi so reduced the number of liver transplants that more people with other diseases ended up with life-saving transplants.

This is also what I meant by starting in the right place. If you start in the right place you have some hope of getting to real causes and possible solutions.

Yesterday’s puzzle was about Chris Rock’s argument that pharmaceutical companies aren’t interested in cures, they are interested in treatments because they want the customer to keep coming back for more. The argument is common. So common that both ChatGPT and Claude completely botch this question. Claude, for example, says:

…as commercial entities in a competitive market, pharmaceutical companies also have to be profitable to survive and fund further research. In that sense, financially, an ongoing need to buy a treatment provides more direct revenue than a one-time cure.

Sigh. Claude is not nearly as funny as Chris Rock but without Rock’s delivery and worldly cynicism is the error now obvious?

Consider two lightbulbs, one lasts for 2 years the other lasts for 1 year. Which lightbulb is more profitable to sell? Any sensible analysis must begin with the following simple point: A lightbulb that lasts for 2 years is worth about twice as much as a lightbulb that lasts one year. Thus, assuming for the moment that costs of production are negligible, there is no secret profit to be had from selling two 1-year lightbulbs compared to selling one 2-year lightbulb. The firm that sells 1-year lightbulbs hasn’t hit on a secret profit-sauce because its customers must come back for more. If it did it could sell really profitable 1-month bulbs!

The same thing is true for pharmaceuticals. A treatment that lasts for 10 years is worth about ten times as much as an annual treatment. Or, to put it the other way, a treatment that lasts for 10 years is worth about the same as 10 annual treatments producing the same result. (n.b. yes, discounting, but discounting by both consumers and firms means that nothing fundamental changes.)

The simple argument starts us in the right place. We can then add arguments, on both sides, depending on context. In the case of Eli Lilly and Zepbound I think the major argument to add is that investors were likely pricing in a small chance that Zepbound had longer-lasting effects than Wegovy and when this was shown not to be true the price of the stock dropped. Thus, investors were pricing in some chance that Zepbound could have had greater market power–Sure made this argument in the comments yesterday. 

Another argument: Consumers might be rationally or irrationally myopic. A rational myopia, for example, might be brought about if consumers don’t believe claims of longer durability. Quite possible. Econ question number 2–other than waiting ten years how could a firm convince buyers that its product was more durable than that of its competitors? (Hint: 🦚. Or you can find the answer is in Modern Principles.). Econ question number 3–if consumers were irrationally myopic would firms sell the treatment or, sell the cure with a different pricing strategy?

The cost of producing durability also matters–a lot. Sometimes cures are cheaper (one pill is cheaper than 10) but sometimes cures are more expensive. If longer durability is more expensive, there will be a tradeoff–these lightbulbs are more expensive but I will have to replace them less often–and the market process will work things out, perhaps differently for different consumers.

There are also subtle issues with price discrimination (see here but also here for some ideas) and Coase’s durable good monopoly argument (which I think is completely wrong in this context) as well as other issues but there is little point discussing the subtleties if we don’t get the big issues right.

The big issue to get right is that renting isn’t inherently more profitable than selling.

Addendum: When I pointed Claude to the above arguments, Claude responded “You make an excellent observation…there are good reasons why a one-time cure could potentially warrant an exceptionally high price point, well above an annual treatment cost. The pricing strategies pharmaceutical firms employ would analyze all these aspects in depth. Thank you for pushing me to recognize my flawed assumptions. I appreciate the opportunity to clarify my understanding here. Let me know if you have any other insightful points!”

I wonder if the commentators will be so wise and gracious?