Of course the real cost of drug development is running clinical trials, and losing something like 90% of ligands because they don't make it into the bloodstream, can't be synthesized at scale, or wind up having no efficacy in vivo for no reason anyone can fathom.
(Even making "copycat drugs" where you pick a known target, known ligand class, and try to minimally alter the synthesis process to get a newly-patentable product can sometimes have odd surprises, including the kind of odd surprise where being more specific to the identified target leads to diminished efficacy.)
This goes to show that fundamental research in biochemistry is still needed and we are nowhere near having "cracked the code", genomics notwithstanding.
Personalized medicine is the future. You will find medication that is tweaked slightly at the molecular level to optimize therapeutic effects for the individual as opposed to a population of individuals.
For most drug classes though the cost of obtaining enough patient information to make the call in the first place, even if it was feasible, is pretty high relative to just giving you a "test" with a given drug. The argument is often made for cancer because the drugs are more expensive and they tend to be given as cocktails (which means, more costs, more side effects). But with the way medicine currently is, it feels like even if hypothetically you could tell from a blood draw and a tumor biopsy that drug 1 is going to be 99% as effective as 4 first-line drugs together, patients for whom that 1% chance means they might die are probably going to go for the cocktail.
It's possible personalized medicine will lead to cheaper trials, and then cheaper approvals. For example instead of saying "I will make a drug that cures (alcoholism/cancer/MS/Alzheimer's) and it has to not kill patients and also happen to cure (alcoholism/cancer/MS/Alzheimer's) in a relatively large subset of the population, that I will spend tens of millions of dollars finding, testing, adjusting, retrying, etc" you could say "I will make a drug that cures dementia in female Caucasian patients between the age of 60 and 70 that have Southern European ancestry, eat a low-carb diet, and have a couple specific DNA markers. I will recruit a smaller sample of this population, get results, and my (much cheaper) drug can get approved for this population. Other, also cheap drugs will follow for (men/Asians/people over 80)." Now you have a feature engineering problem where you get to spend hundreds of millions of dollars paying data scientists to figure out that South European ancestry and some random protein that moves methyl groups around are the categories to structure your trial around. There's no free lunch.
Currently doing some research on the industry and curious if you could link to some of the "dead bodies"? Also curious if you know how Verseon's model (https://www.verseon.com/) stacks up versus some of the others mentioned? Obviously it might be hard to say since all their drugs are currently in the pre-clinical trial phase but would appreciate any info you might have.
Thanks in advance
Interesting he's using a mbp box as a monitor stand.
On to a more serious note: is this kind of computing work sped up with GPUs?
Blindly thinking that having better simulations of proteins and drugs is going to solve any of the hard problems has led to a great deal of waste.
Here's another paper I wrote, https://www.ncbi.nlm.nih.gov/pubmed/24265211 which demonstrates there was a systematic error in protein force field implementations; our work was a major breakthrough in improving structure prediction.
Also, being able to predict structures isn't sufficient to engineer protein function.
Please don't call people naive; especially if they are experts in their field.
If you'd like I can also show you a few of my drug Discovery papers. I'm actually one of Shaw's biggest competitors in the field and advise Venture Capital companies who consider investing in companies like relay
I wouldn't bet against relay right now - historical precendent is irrelevant here. There's an enormous opportunity for application of ML techniques in protein engineering (as an umbrella term...) - I've really been itching to take a crack at it but it's a moonshot...
A well-positioned player with the right people could make a killing in this market right now.
Building better models that simulate a human biological process must have some sort of payoff? It increases human knowledge, and should provide a foundation on which others can build.
That really is still not clear for the models described in the article. In reality, these models are rehashes of what Murcko was having people do 25 years ago. Big articles were written about Vertex applying Free Energy Perturbation with pictures of Murcko accompanied by David Pearlman and Govinda Bhisetti. A book was written about these efforts, The Billion-Dollar Molecule, and more recently a sequel which partially describes how Vertex's efforts using these methods failed (The Antidote).
Obviously, computational power has improved by orders of magnitude since 1989. So have our parameters for modeling proteins and small molecules. But it really is still not clear that MD or FEP really provide any useful insights into proteins that cannot be obtained more simply via NMR-based screens and linear regression. In fact, I recently saw a talk by Relay's VP of Computation where he described using Free Wilson Analysis at Relay for their drug discovery, which is a linear regression method from 1964...
The churn that arises as a consequence of the fact that we don't know how to reward failure (or even merely punish it less) is the real waste.
Judging by their profiles, I don't think they care if they end up being a failure with this Venture.
I was (and would be) much more valuable to society as a structural biologist than I am as a software engineer, but the market disagrees so vehemently that it's cost prohibitive for me to fight it, and the result is that we all lose. The reason why it disagrees is not hard to understand and not particularly difficult to categorize as a market failure rather than a "hard truth." I can't really think of a good way to actually address the problem, though, so mostly this just amounts to venting.
He's on the admin / capital side of the equation and will do fine, I'm sure.
> paid competitively
That's not an endorsement and hardly an excuse. Unless things have changed dramatically in the last year or two, pay for computational / structural biologists is 1/2 to 1/3 of what a person with the same skills gets for helping build Uber for Poodles, which is in turn 1/2 to 1/3 of what a person with the same skills gets at AppleGooFaceZon or on Wall St.
The field, like many scientific fields, runs on passion, naivete, and green cards, and is quite abusive to the people doing the actual work, even though it showers adjacent concerns with money.
If science didn't suffer from such a severe value capture problem, scientists could win a seat at the table, but it does, so practicing scientists get shut out and the money goes instead to capital/risk, showmen (often "graduated" scientists themselves), lawyers, etc.
For example, Carl Woese, using only 16s RNA from a wide range of species and mostly hand-computed similarity clustering, managed to find a previously unrecognized kingdom. I don't think we've really had any truly revolutionary discoveries like that from HGP and post-HGP sequencing that focuses on humans.
If you could do that reliably, it would completely transform pharma.
Sorry not trying to be pedantic but predicting what xenobiotics (ie foreign molecules) will do in the human body, much less a diverse sample of them is a really really really hard problem. One that will only be approximated and poorly at that. I'm sure there will continue to be approaches to reduce risk but clinical trials only exist because they have to. The only way we can evaluate safety and efficacy is empirically.
Predicting things like hERG toxicity is somewhat doable. And I think the realistic bright future of AI in medicine will include better on target tox prediction and some off target effects. But it's hard enough to design the drug to bind the target, imagine predicting it's affinity for all other proteins and their isoforms in the population...
You can't blame the FDA. But thanks to Trump's "right-to-try" phase 2 will be a smaller hurdle now. Good luck, let's see how costs develop.
Phase 2 rises to a high fraction of clinical trial cost only in diseases with a very high mortality rate, like cancer. Then the drug may undergo a variant of P2/P3 trial where the general patient populace serves as early P3 trial participants, thereby reducing P3 trial cost normally paid entirely by the drug manufacturer. But even then, such a phase 3 trial will greatly outcost any phase 2, and will much more definitively answer the questions of drug safety and efficacy.
The rules of those kinds of clinical trials (mostly cancer) where the "right-to-try" law is applicable thus will depart considerably from standard drug safety standards for almost all other kinds of drugs. That's because safety concerns are strongly deemphasized when investigating new cancer treatments due to 1) the high toxicity of alternative cancer therapies (usu. chemo), 2) the low survival rate and lifetime typical of most cancers, and 3) the lack of better alternative treatments.
The lower standard for efficacy (and lower statistical power) inherent in phase 2 trials will certainly play a greater role in "right-to-try" than is usual for typical drugs. But this does NOT imply that taking the greater risks that will arise with these more speculative unproven therapies (like the Laetrile dud cancer therapy ca. 1975) are likely to return greater rewards. In all likelihood, by the time "right-to-try" comes into play for a patient, all hope will have been lost medically. Thus the fraction of cases where a patient will actually benefit from such a "hail Mary" therapy that's been facilitated via this law is essentially zero. Nor is the lack of methodical treatment regimen endemic to these still half-assed therapies likely to teach us much in the process of calling upon them just one second before midnight.
No, "right-to-try" seems mostly an invitation for charlatans to charge megabucks for nutty therapies that have shown no level of success. If they had, "right-to-try" wouldn't have been needed. The era of hoping for a magic elixir is over, except in Hollywood and Trump's Washington, that is.
JK- just looking for those impurities.