There’s a blog post that floats around the Internet about the metaphor of the broken stair. In this blog post, the author talks about how certain social groups have a person who before you talk with them, someone will pull you aside and say, “Hey, listen, be careful around John. He can be touchy, so you probably shouldn’t let yourself be alone around him.”
> All of this stuff is way harder than it sounds, because semaglutide and liraglutide are large fatty acids that can’t be crystallized. I think the best analogy would be designing a magnetic pool toy that’s designed to connect to two other separate, magnetic pool toys simultaneously, but all of the pool toys are transparent and slippery, and the only way you have of connecting them is by stirring a bathtub with a spoon. You can see how questions like, “How do I make these pool toys connect better?” or even “How can I tell if they’re connecting?” get really, really difficult.
Why do some people respond to certain drugs but not others? Frequently the answer is “nobody knows, let’s try a different treatment.” I suspect this usually means there’s at least one hidden variable or mechanism nobody has discovered yet, let alone measured…
In your ping pong ball example: imagine we recorded a bunch of height/distance data but without being aware of concepts like drag, temperature, barometric pressure, wind speed and direction, the coriolis effect, etc. My layman’s intuition is that this is our current state of pharmacokinetics — we don’t have a complete model but we also don’t even know what data we need to build one, and this a complicated system with hundreds or thousands of things going on and millions of variables. Which ones are important? It’s hard to say what approach would be best for tackling this
Nov 2, 2023·edited Nov 2, 2023Liked by Trevor Klee
Speaking as a physician in the era of NP-run medicine, half-lives and Vd are foreign concepts to most people allowed to prescribe drugs. And to those of us who understand the concepts, it is as you said, not always useful given the unknowns; polypharmacy, co-morbidities, and pathologies.
You could do all the work in the world in the pre-clinical steps, it doesn't mean much if I need to explain to a CRNA that Versed in a cirrhotic patient might not be the best idea as a person not even trained in Anesthesia.
I'm proud to say that specific instance was the first and only time I pushed flumazenil.
> We could fix this stair. It would just require the raw data from a variety of pharmacokinetic trials, some in-depth experiments on human liver and gastric membranes, and some simulation of the physics of how different drugs diffuse into the bloodstream and across membranes. This would be difficult, but not impossible, and would not require any huge scientific advances. If it were done, it would likely save hundreds of millions, if not billions of pharma dollars each year, improve or even save the lives of the thousands of people who depend on therapeutic dose monitoring (e.g. every organ transplant recipient), and get us way closer to obviating healthy human trials altogether.
I've been exploring similar & adjacent hypotheses in the biopharma pipeline. I'm curious though, why is there a coordination problem in biopharma considering that it tackles a well established cause for high failure rates?
hm. so what you're proposing is like "get some data so we can make decent pK models" and then...what?
"we won't have to run the trials"? no, that sounds wrong, you're not going to get *that* much faith in your models
"we can generate lots of different versions of a drug in silico, use models to predict which ones will have good pK, and thus run our animal & human experiments on candidates that have better odds of working"?
> All of this stuff is way harder than it sounds, because semaglutide and liraglutide are large fatty acids that can’t be crystallized. I think the best analogy would be designing a magnetic pool toy that’s designed to connect to two other separate, magnetic pool toys simultaneously, but all of the pool toys are transparent and slippery, and the only way you have of connecting them is by stirring a bathtub with a spoon. You can see how questions like, “How do I make these pool toys connect better?” or even “How can I tell if they’re connecting?” get really, really difficult.
Loved this, and excellent essay.
Why do some people respond to certain drugs but not others? Frequently the answer is “nobody knows, let’s try a different treatment.” I suspect this usually means there’s at least one hidden variable or mechanism nobody has discovered yet, let alone measured…
In your ping pong ball example: imagine we recorded a bunch of height/distance data but without being aware of concepts like drag, temperature, barometric pressure, wind speed and direction, the coriolis effect, etc. My layman’s intuition is that this is our current state of pharmacokinetics — we don’t have a complete model but we also don’t even know what data we need to build one, and this a complicated system with hundreds or thousands of things going on and millions of variables. Which ones are important? It’s hard to say what approach would be best for tackling this
Speaking as a physician in the era of NP-run medicine, half-lives and Vd are foreign concepts to most people allowed to prescribe drugs. And to those of us who understand the concepts, it is as you said, not always useful given the unknowns; polypharmacy, co-morbidities, and pathologies.
You could do all the work in the world in the pre-clinical steps, it doesn't mean much if I need to explain to a CRNA that Versed in a cirrhotic patient might not be the best idea as a person not even trained in Anesthesia.
I'm proud to say that specific instance was the first and only time I pushed flumazenil.
Wonderful post
> We could fix this stair. It would just require the raw data from a variety of pharmacokinetic trials, some in-depth experiments on human liver and gastric membranes, and some simulation of the physics of how different drugs diffuse into the bloodstream and across membranes. This would be difficult, but not impossible, and would not require any huge scientific advances. If it were done, it would likely save hundreds of millions, if not billions of pharma dollars each year, improve or even save the lives of the thousands of people who depend on therapeutic dose monitoring (e.g. every organ transplant recipient), and get us way closer to obviating healthy human trials altogether.
I've been exploring similar & adjacent hypotheses in the biopharma pipeline. I'm curious though, why is there a coordination problem in biopharma considering that it tackles a well established cause for high failure rates?
hm. so what you're proposing is like "get some data so we can make decent pK models" and then...what?
"we won't have to run the trials"? no, that sounds wrong, you're not going to get *that* much faith in your models
"we can generate lots of different versions of a drug in silico, use models to predict which ones will have good pK, and thus run our animal & human experiments on candidates that have better odds of working"?