I like how the author has discovered that a problem that costs pharma companies billions of dollars actually has a quick fix: simply model how livers react to infinite compounds.
Since all livers behave the same and there is no variation based on gene expression or disease, this is a trivial task! The reason why this hasn’t been Solved is because none have been Disruptive enough to dream of doing research in this area.
> 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 statement seems overly optimistic. Predictive Pharmacokinetics that obviates most trials would need to model all possible drugs for all people. The computational complexity of that problem seems out of reach. The best way to get to know complex adaptive systems (which can’t be properly simulated most of the time) is to test them empirically.
This is very interesting, and I think massively underestimates the variability in how small molecular drugs are affected by the body. Small differences in molecules can have drastic effects on where, how, and how quickly they're adsorbed, and what systems they affect.
More than anything, I think this just underestimates the unpredictability of it all. To extend the author's metaphor, if you measure the 100 data points from shooting 10 different projectiles out of 10 different devices, on 10 different celestial bodies, you're still not going to have a lot of predictive power that can be generalized.
I'm surprised the article didn't mention anything about pharmacogenetics. For example, drug-metabolizing enzyme polymorphisms can often lead to a 10+ fold difference in Cmax (or name the parameter) between a poor and an ultrarapid metabolizer of some enzyme.
As someone who has worked in pharma, this article is pretty damn ignorant. There’s a ton of funding for pharmacokinetics work. For liver and the gut, it is widely agreed that 2-D tissue culture is completely non-predictive, so the last couple of decades have been building 3-D tissue culture and nowadays, companies commercially purchase organs on a chip for both and even multi-organ systems[0]. Plenty of companies have internal teams that have built similar technology and they all have teams working hard trying to build computational models on the generated data. But small changes in molecules have huge ADMET impacts and these organs are just a subset of the organs that can impact pharmacokinetics. Plus, toxicology can tank a drug by impacts on nearly any organ, so whole animal models always still win.
The entire article just yells ignorance of what drug companies work on and what they’re investing in.
There's a company called VeriSIM life trying to make the physiological models mentioned in the post more accessible [1]. They apparently fit their models across a bunch of publicly available and proprietary data. I found some peer-reviewed publications (e.g. [2]), but I am not sure how widely they are used.
[1] https://www.verisimlife.com/our-platform [2] https://www.tandfonline.com/doi/full/10.2147/DDDT.S253064?ro...
The problem is even more complicated than the author presents. Much of the chemistry that happens within the body happens within cells. Serum levels are a poor proxy for absorption into organ, tissue, and ultimately cell types. In fact, the problem is even more complex: the spatial organization of a cells within a tissue is heterogeneous; thus we can’t even reliably predict how well a drug will reach a particular cell type since it depends on the specific cell location. This is a reason why cancer is so difficult to treat: cancer cells often barricade themselves behind other cells and the tissue stroma, making it difficult to get a drug at a high enough concentration to kill the cancer cell.
Biology is fiendishly complex and empirical. One regularly sees Silicon Valley types come along and propose to ‘disrupt’ the field using the latest fad from the field of computer science, only to slink away years later having been humbled by biology. The current fad is AI, which despite having added some extraordinary tools to the biologist’s tool chest, will also not live up to its hype.
I like the optimism of the author, and if their overall point is for there to be more open sharing of data in pharmacokinetics research, that is a valid and worthwhile topic of discussion. One that is worth advocating for, as the potential results it would yield for future research and for the progress of science.
That said, their view that it is simply a matter of collaboration and coordination is entirely wrong. Sharing of data and collaboration would absolutely be worthwhile (though it runs opposite to the direction of incentives in profit-driven drug development) but it's like saying we could start building a Dyson Sphere tomorrow and solve the worlds energy problems if we just pooled our talent and resources. In contrast to what the author claims, we need HUGE advances in technology and our understanding of the human body, pharmaceutical sciences, drug development, etc. before this is possible. To use their example of GLP-1 agonists, prior to their development and wide-spread usage, the psychological effects of these drugs were completely unknown. Both positive and negative, clinically. But what if those effects were much more dangerous? Many SSRIs have a black box warning, which is mostly applicable to specific age groups. Negative side effects that we see in teenage patients are much less common in other age groups. These kinds of effects are why medicine moves very slowly and experimental work is costly, because ultimately we are talking about peoples lives and not a machine that is easily replaced if we break it during the testing phase.
Millions of animals would be the first to rejoice and praise a model that didn't require in-vivo testing but, we may sadly never see that day. I'm skeptical that even the development of an ASI would be enough to get us there.
I did find that the author has a knack for explaining difficult concepts with simple and illustrative metaphors. As a clinician and scientist in the pharma research space, this is one of the few articles I would send to a friend that finds the topic interesting but lacks the background knowledge to understand most literature about drug research.
The problem is actually much worse for topical drugs, where there is often very little information as to the how, where, when, and how much of drug permeation through the skin. It’s a huge gap in our understanding not just for brand name drugs but for generics as well.
Currently, this problem is already mostly addressed by animal trials. Granted that that the pharmacokinetics of a given drug in mice, or even monkeys, won't be identical to that in humans, it's more predictive than using isolated tissue or cell preparations.
>It would just require the raw data from a variety of pharmacokinetic trials...
By and large, such trials are likely poorly relevant to the pharmacokinetics of your drug candidate, unless its very similar to an existing drug. And even then, you will still have to test your actual drug in actual people during Phase 0 trials.
Maybe there could be a publicly funded virtual liver project.
Yes, pharma is empirical, not predictive. We are very far from understanding biology well enough to be predictive (except on TV).
Look are Loewe’s recent column in Science: Target-based drug programs seem like an obviously sensible approach but in practice have not been fruitful: https://www.science.org/content/blog-post/target-based-drug-...
Biology is still art and luck.
Note: I’m a former small molecule pharma developer myself.