Target-based drug discovery is a waste of time, says a systematic review of 32,000 articles and patents from the last 150 years:

…only 9.4% of small-molecule drugs have been discovered through “target-based” assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery.

So it would seem. And even those drugs initially developed to target a single protein or mutation end up having many more unanticipated effects. Back to the jungle and the ocean depths, then?

(ᔥDerek Lowe)

As a prolific child fabulist, I very much appreciated @ayjay’s reminiscence. Most people are, in fact, reflexive embellishers, but not everyone can recognize it in themselves or turn it into a healthy skepticism. At least that’s what I tell myself!

The NIH Clinical Center used to commission artwork for some of their lectures. Here is one for “Phobias and Panic Disorder” from 1985.

In retrospect, that friend worried about an eagle flying away with his toddler as prey wasn’t completely bonkers.

Derek Lowe writes about a recent Cancer Cell paper pitting glioblastoma cells against each other in a mouse model:

A single clonal line that hit on high Myc expression could outcompete fifteen thousand others from a standing start!

As someone who’s treated patients with Burkitt lymphoma, the Myc-dependent cancer, I can absolutely believe this.

From the archive: the author presenting some preclinical work on the cell cycle at the AACR annual meeting in Washington DC, circa 2017. Little did I know that six years later I’d be living just a few blocks down the street.

For your (and mine, time permitting) weekend reading, listening, and/or viewing pleasure: the Tim Ferris interview with Nassim Taleb and Scott Patterson. I sure hope you are familiar with the former; the latter is a reporter for The Wall Street Journal who also wrote a book about “black swan traders”, which, yes, is probably going to the pile the antilibrary. It is a 2-hour discussion rightfully described as a feast.

It’s been a while, but school is back in session and so are interesting online lectures. Here are a few I plan on attending, time permitting:

• Alpha Spending for Clinical Trials by Dr. James F. Troendle of NHLBI (Thursday, September 7, 3pm EDT)
• Varicella Zoster Virus in Glioma Etiology and Survival: Evidence of a Beneficial Virus? by Dr. Stephen Francis, UCSF (Tuesday, September 12, 2pm EDT)
• 2023 Judith H. Greenberg Early Career Investigator Lecture by Dr. Akhila Rajan, FHCC, and I know the title doesn’t tell you much but per the description “Dr. Akhila Rajan will discuss her research in a 30-minute talk on investigating how fat cells communicate with the brain, followed by a 30-minute live Q&A” (Wednesday, September 27, 1pm EDT)

One I absolutely must attend is held tomorrow (Thursday, September 7) at 6pm EDT, when I will talk about RNA cell therapy as the keynote speaker at the Maryland BioNetworking Summit, held at the BSE Facility at the Universities at Shady Grove in Rockville. It is in-person only and free to attend, if you register here.

Reading Adam Mastroianni’s latest article, about the vacuousness of psychology, and it looks like the world is ready for psychohistory. Now if only we had an intelligence greater than ours to develop it…

Figures from the which-arm-for-the-second-shot study I wrote about yesterday reminded me of the delightfully titled “An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability” published in PNAS. ↬Dean Eckles To be clear, this is not the error study authors made, but see for yourself the chart that got them a USA Today head nod:

So much variability! And even with 143 samples, still so much uncertainty. But increasing the sample size would help the variability much, as noted in PNAS:

You can squish the error bars, but you can’t squish your study population. Anyway, I thought it was interesting.

But now that we have that figure from the vaccine article up, let’s note two more things:

• The percentages in question are absolutely tiny. Median is 0.06% in the ipsilateral and <0.03% in the contralateral group, if we just eyeball it. At that point you are looking at what, 2–3 dots on the flow cytometry scatterplot? Interoperator variability must be huge.
• Their cutoff for saying that someone “has” spike-directed CD8 T cells was, I presume from the dotted horizontal line labeled “DL”, 0.03%. They based this off of historic values of unvaccinated healthy controls from this paper from the same group. I may be getting into the weeds too much here, but the text of that article says that “0.03% of reactive T cells was set as the detection limit based on the distribution of T cell frequencies after control stimulations”. This is so vague that it’s perfectly useless, but looking at Figure 1 gives it away: they picked one standard deviation away from the mean for the CD4-positive cells as the detection limit for both CD8-positive and CD4-positive spike-specific T cells. And even in that experiment, the variability of spike-specific CD8 T cells is so much greater than that of the CD4+ ones (0.02% versus >1% maximum in the healthy, unvaccinated and unexposed-to-SARS-CoV-2 controls). So they are not only extrapolating old experimental data into the new one, they are extrapolating one cell type into another when no such extrapolation should be made. Put hey, it came out in Nature Medicine, so it must be OK. Sigh.