Today’s Washington Post has a good write-up on how genetic engineering of the near-extinct American chestnut tree to make it more resistant to infection went wrong:

After he enlisted the help of Ek Han Tan, a geneticist at the University of Maine, to analyze the chestnut’s genome, they made their discovery this fall: The plants they were working on were, in fact, not Darling 58 trees.

Instead, they found they were working with a different chestnut line — called the Darling 54 — where the gene was inserted in another chromosome entirely, potentially corrupting one of the tree’s existing genes.

…

In a phone interview, Newhouse, the SUNY ESF director, acknowledged the mix-up but said he wasn’t sure what transpired.

“As far as exactly how it happened, we don’t know,” he said. “It must have been a label swap between these two trees that we were working with at the same time” in or around 2016.

The brilliant minds who think engineering mosquitos is a good idea can’t foresee that even a seemingly innocuous clerical error can lead to disaster, never mind the second-order effects to nature if your project succeeds. Whoever’s read Taleb’s Incerto (or some of his tweets) knows better.

If you think yourself a scientist or physician-scientist, please stop what you are doing and dedicate 5 minutes to reading Darren Dahly’s tip for recording data in a spreadsheet. Your future self, your statistician, and the general scientific community will thank you.

Adam Mastroianni is out with another long essay about science and how statistics are not the be-all and end-all of finding the truth, because:

… the whole ritual of “run study, apply statistical test, report significance” is only about 100 years old, and the people who invented it were probably drunk.

So a bunch of drunks built modern statistics on the foundations of probability theory created by a bunch of gamblers. Sweet.

Last year we showed that “better” cancer drugs don’t necessarily cost more. In a follow-up analysis just out in JAMA Network Open, it looks like novelty doesn’t have much to do with the price either. Then what does? To paraphrase № 46: drugs cost whatever the market will bear.

If you’ve haven’t heard of Big Biology until today, well, welcome to the club. It’s a podcast, and it describes itself thusly:

Scientists talking to scientists, but accessible to anyone. We are living in a golden age of biology research. Big Biology is a podcast that tells the stories of scientists tackling some of the biggest unanswered questions in biology.

Right up my alley! I started with the latest episode, on invasive species, and the intro seems a bit too scripted, but the focus is on the interviews, and those delivered. It’s already on my Overcast list of regulars. (ᔥRobin Sloan)

Not too much this month, for understandable reasons:

• Novel Insights Into Heart Brain Interactions and Neurobiological Resilience by Dr. Ahmed Tawakol; Wednesday, December 6 at 12pm EST.
• A Broken System: American Health Care Needs Combination Therapy by Dr. Martin Shapiro; Thursday, December 7 at 2:30pm EST.
• Investigating Stem Cells as Quantum Sensors by Dr. Wendy Beane; Monday, December 11 at 12pm EST.
• Harnessing Technology and Social Media to Address Alcohol Misuse in Adolescents and Emerging Adults by Drs. Maureen Walton and Mai-Ly Steers; Wednesday, December 13 at 12pm EST.

There is both a science and an art to medicine. The “art” part usually comes into play when we talk about bedside manner and the doctor-patient relationship, but recognizing and naming diseases — diagnosis — is also up there. José Luis Ricón wrote recently about a fairly discrete entity, Alzheimer’s disease, and how several different paths may lead to a similar phenotype. This is true for most diseases.

But take something like “cytokine release syndrome”, or “HLH”, or any other syndromic disease that is more of a suitcase phrase than anything, and that can present as a spectrum of symptoms. Different paths to different phenotypes, with only a sleigh of molecular storytelling to tie them together. Yet somehow it (mostly) works. It’s quite an art.

So, who wants to dismantle the FDA, you ask? Some patient advocacy groups, among others, aided by a few senators:

We need the FDA to be more insulated from these forces. Instead, every few years, legislators offer bills that amount to death by a thousand cuts for the agency. The latest is the Promising Pathways Act, which offers “conditional approval” of new drugs, without even the need for the preliminary evidence that accelerated approval requires (i.e., some indication that biomarkers associated with real outcomes like disease progression or survival are moving in the right direction in early drug studies).

…

This bill is being pushed by powerful patient groups and has the support of Democratic senators like Kristin Gillibrand and Raphael Warnock, who should know better.

The bill would codify using “real-world data” and unvalidated surrogate endpoints for something called “provisional approval”, a level below the already tenuous accelerated approval.

I can see how it may appeal to patients: you may get a promising new drug for your life-threatening, debilitating disease sooner via this pathway. On the other hand, there are already mechanisms in place that enable access to these: a clinical trial, for one. Or expanded access (a.k.a. “compassionate use”) for those who may not be eligible for a trial.

So how would “provisional approval” help? If anything, wouldn’t it transfer the risks and — importantly — costs of drug development from the drug manufacturer/sponsor/study investigator to the patient?

Ultimately, the reason why there aren’t many cures for rare, terminal diseases is not because the big bad FDA is keeping the already developed drugs away from patients but rather because they are devilishly difficult to develop at our current level of technology. Wouldn’t it then make more sense to work on advancing the technology ⊕ The careful reader will note that the opposite is being done, and I write this as no great fan of AI. that would lead to those new cures? I worry that the Promising Pathways Act would solve a problem that doesn’t exist by adding to the already skyrocketing costs of American health care. But that could be just me.

(↬Derek Lowe)

Our clinical trials course at UMBC is well under way, and we are getting some terrific questions from students. Here is one!

Q: Are outcomes surrogate endpoints or is there a distinction between the two?

The terms “outcome” and “endpoint” are not strictly defined and some people use them interchangeably. However:

• Outcomes are broader, and include any change in health that are considered important enough to measure in a patient (such as “overall survival” — the amount of time between enrolling onto the trial and death, or “quality of life” — a certain score on a specified scale that a patient fills out or the doctor performs).
• Endpoints are more specific than outcomes, consider the whole study population instead of individual patients, and need to have a precisely defined way of measurement and time points when they are measured (e.g. “median overall survival”, “3-year overall survival rate”, and “5-year overall survival rate” are three different endpoints that are different ways of aggregating and evaluating the same individual patient outcome — overall survival).

It reminds me of the confusion between efficacy and effectivness, only it’s worse: there is no agreed-upon text that describes the distinction, so it is a really terminological free-for-all. Indeed, what I wrote above may end up not being true — caveat lector! As always, it is always best to ask people to clarify what they meant when they said this or that. Regardless, if someone tells you that “overall survival” (or, worse yet, “survival”) was the primary endpoint, it clearly can’t be the case. Endpoints need to be more specific than that.

Surrogate outcomes and surrogate endpoints are those which are stand-ins for what we actually care about. ⊕ Here is a good video on surrogate endpoints in oncology.E.g. when we give chemotherapy to someone with cancer, we do it so that they would live longer and/or better. However, it is quicker and easier to measure if the tumor shrinks after chemotherapy (i.e. “responds” to treatment), and we believe that the tumor shrinking will lead to the patient living longer or better (which may not necessarily be the case!), so we use the response as a surrogate outcome for survival and quality of life (by how much did the tumor shrink? was it a complete or a partial response according to pre-specified criteria?). Study level surrogate endpoints would be the overall response rate, partial response rate, complete response rate, etc.

We have created so much confusion here that it is a small miracle we can communicate amongst ourselves at all.

As We May Think is one of the greatest essays ever written, and I am all for popularizing it, but one thing about the most recent mention just rubbed me the wrong way in how it presented its author, Vannevar Bush:

Bush was part of the Oppenheimer set; he was an engineer whose work was critical to the creation of the atomic bomb.

This paints the picture of an engineer working at Los Alamos under Oppenheimer to make the bomb, when in fact Bush was leading the United States' nuclear program for two whole years before Oppenheimer became involved. Oppenheimer’s predecessor? Sure. Part of his set? Misleading.

I suspect it was presented this way because of that movie; the more I keep seeing these kinds of distortions as a result, the less I think of it. This is why I will keep recommending The Making of the Atomic Bomb to everyone and anyone who was tickled by the Los Alamos scenes — the only ones worth watching.