Jacob Trefethen

OpenAI Foundation

science!

[Jacob Trefethen @JacobTref]

Alzheimer’s is one of medicine's hardest unsolved problems, and one of the most devastating.

At the OpenAI Foundation, we believe AI is well suited to its complexity. We're directing over $100M to scientists mapping the disease, designing drugs, & more.

I wrote about it here:
https://t.co/wOkiE78KUo

[Evan Riggs @EvanRiggss]

We are also looking for a (remote optional) podcast producer to take Hard Drugs with @salonium and @JacobTref to the next level.

If you’re eager to turn long, deeply researched discussions on medical innovation into a full-fledged podcast listened to by some of the smartest people around, DM me as well!

[stacy palmer @stacypalmer]

A.I. Fortunes Could Bring Critical Funds to Global Health. The Scramble Is On. https://t.co/dr7DHrtq5F

[Jacob Trefethen @JacobTref]

HT @satvik_93

Source: https://t.co/6E5DPCLTBC

[Jacob Trefethen @JacobTref]

Makes you think

[Jacob Trefethen @JacobTref]

I believe that 1) siRNA will change medicine, with new treatments for tens of diseases, 2) tens of millions of people will take an siRNA drug in the 2030s, with some chance of >100M people, and 3) I will have taken at least one siRNA drug within a decade.

I have never worked on siRNA, and I also don't think of myself as a hypester. So, why the confidence? There's the technical answer, then the structural answer.

The technical answer is that siRNA has several attractive properties as a drug modality, and enough of the chemistry problems have been solved that I'm confident more drug approvals are on the way (though there are many important unsolved problems left, e.g. in delivering siRNA to organs other than the liver). The four most attractive properties are that with siRNA you can reduce production of previously undruggable proteins by going after the RNA that codes for them; they’re “programmable” to a gene/protein you’re going after; siRNA can last long, with slow release inside cells; and siRNA drugs have the potential to be relatively cheaply made and self-administered.

The structural answer, I believe, is biotechnology’s combination of long timelines with particularly un-juicy, hard-to-remember concepts. If something takes 20 years, you can often spot it 10 years in -- but when it’s an acronym and involves chemical conjugation steps, it’s hard to remember to care. Many important ideas in biology are messy and technical and, unless you've caught the bug (intellectual or literal), boring. That means there are many important secrets lying in plain sight, as per @nayafia's last book.

While disagreements and unknowns remain, it is not particularly controversial among scientists that siRNA holds a lot of promise (alongside some realised promise already!) -- and, yet, most people who will benefit from siRNA drugs in the 2030s have never heard of it.

(PS: given my job involves stewarding charitable dollars related to science, I don’t invest in individual pharma/biotech stocks personally; this post isn’t about the financial prospects of particular companies.)

[Ethan Epperly @ethanepperly]

John Urschel just showed that the growth factor for Gaussian elimination with partial pivoting is ~n^{1/2} with high probability, resolving a long-standing conjecture of Nick Trefethen! https://t.co/FraoYI6yh2

[Jack O'Meara @Jack_O_Meara]

Hear, hear! Let's switch off misbehaving genes and kick some diseases' butts over the next decade

[Jacob Trefethen @JacobTref]

@Jack_O_Meara skip to 21:50

[Charlie Petty @incredutility]

I am very siRNA-pilled. Looking forward to seeing forthecoming data on intranasal siRNA for the prevention of infection by respiratory pathogens.

PS - siRNA is also very promising for biosecurity! It's well-suited for sequence-to-antiviral approaches and has many advantages over traditional antivirals and vaccines.

[Saloni @salonium]

@JacobTref Our YouTube thumbnail amuses me very much

[Devan @dshahh]

It’s true but but I’d drop the “si” and say RNA drugs will change medicine since this criteria applies to mRNA already, and likely will apply to other forms of RNA medicine in dev

[Jacob Trefethen @JacobTref]

I believe that 1) siRNA will change medicine, with new treatments for tens of diseases, 2) tens of millions of people will take an siRNA drug in the 2030s, with some chance of >100M people, and 3) I will have taken at least one siRNA drug within a decade.

I have never worked on siRNA, and I also don't think of myself as a hypester. So, why the confidence? There's the technical answer, then the structural answer.

The technical answer is that siRNA has several attractive properties as a drug modality, and enough of the chemistry problems have been solved that I'm confident more drug approvals are on the way (though there are many important unsolved problems left, e.g. in delivering siRNA to organs other than the liver). The four most attractive properties are that with siRNA you can reduce production of previously undruggable proteins by going after the RNA that codes for them; they’re “programmable” to a gene/protein you’re going after; siRNA can last long, with slow release inside cells; and siRNA drugs have the potential to be relatively cheaply made and self-administered.

The structural answer, I believe, is biotechnology’s combination of long timelines with particularly un-juicy, hard-to-remember concepts. If something takes 20 years, you can often spot it 10 years in -- but when it’s an acronym and involves chemical conjugation steps, it’s hard to remember to care. Many important ideas in biology are messy and technical and, unless you've caught the bug (intellectual or literal), boring. That means there are many important secrets lying in plain sight, as per @nayafia's last book.

While disagreements and unknowns remain, it is not particularly controversial among scientists that siRNA holds a lot of promise (alongside some realised promise already!) -- and, yet, most people who will benefit from siRNA drugs in the 2030s have never heard of it.

(PS: given my job involves stewarding charitable dollars related to science, I don’t invest in individual pharma/biotech stocks personally; this post isn’t about the financial prospects of particular companies.)

[Saloni @salonium]

Can we switch off diseases?

siRNA drugs are, in my view and @JacobTref’s too, one of the most exciting developments in medicine. So we decided to do an episode!

It’s a technology that can ‘silence’ harmful genes in the body. And there are lots of cases where you might want to silence genes:

- Infectious diseases, where you can silence pathogen proteins or human proteins that pathogens depend on
- Rare genetic diseases, where you can silence the production of mutated proteins
- Cardiovascular diseases, by silencing proteins that raise cholesterol
- Cancers, by silencing proteins that help tumour cells grow, survive, metabolize, metastasize, or resist treatment
- and more!

What’s even better is that siRNA drugs are:

• Potentially tunable, meaning you could reduce the production of a protein, rather than silencing it entirely
• Programmable, meaning you can quickly switch out which gene it targets by changing its sequence
• Long-lasting: some drugs last for months, even half a year, with a single injection
• Off-the-shelf: they typically don’t involve complicated surgeries or procedures, because of the way they’re delivered in the body

Today, siRNA drugs are largely used to treat liver diseases, partly because that’s the easiest starting point.

And they are extremely effective at it. One example is as a cholesterol drug: siRNAs can reduce PCSK9 levels by around 70%, reducing cholesterol levels by 50-60%, beyond the effect of statins.

But we think it’s just a matter of time before they can be used to target other organs too.

Will siRNA treat diseases of other organs? And what’s the catch?

Listen to our latest episode of HARD DRUGS to find out!

Chapters:
00:00 Introduction
09:58 siRNA trivia
28:14 The 20 year journey to make siRNA drugs
36:07 Which diseases could siRNA treat?
50:45 Programmable, long-lasting, scaleable drugs
54:32 The limits of siRNA
1:08:23 Conclusion

[Benjamin Vincent @BenjaminGVincen]

You’re wrong in this case. OpenAI Foundation is funding @iskander and I to do genomics, immunopeptidomics, antigen-specific T cell response measurement, TCR profiling of antigen-specific T cells, and TCR-T performance comparison on samples from 200 cancer patients. All data will be made freely available and anyone will be able to use them to train better models of peptide presentation by HLA and immunogenicity in vivo. They are also funding a clinical trial which is a comparison of neoantigen vaccine formulations , with immunological potency across standard assays as the endpoint, a trial that biotech/pharma will never do because they don’t want their proprietary method(s) to lose. This is both serious and rigorous.

[Jacob Trefethen @JacobTref]

@Diplomatt42 Thanks for sharing. Love the specificity!

[Jacob Trefethen @JacobTref]

https://t.co/W6Dc0lyUvy

[Jacob Trefethen @JacobTref]

More details on our plans in the linked interview. But, this is the gist:

[Naomi Handly @lnhandly]

@JacobTref nails the challenge of drug disco on the head

"...we collectively don’t know enough to deliver treatments that are excellent for patients"

Looking forward to seeing what else comes out of @FoundationOAI

https://t.co/tfYNQj7Nzq

[Enlli McAleese @EnlliMcA]

It was a pleasure working with @asteriskmgzn on this piece, which looks at a case study where regulatory reliance works well. Thank you @chengela and @clarabcollier for making the editing process wonderful!

[Jacob Trefethen @JacobTref]

@eric_is_weird Congratulations Eric!

[Jacob Trefethen @JacobTref]

@seemaychou 😊

[leonardo pegollo @LeonardoPegollo]

I really like how this is thought out

[Jacob Trefethen @JacobTref]

Making sure data is accessible to researchers

Finally, across all of our grants, we are ensuring the resulting datasets are as accessible as possible to researchers around the world.

For much of scientific history, testing hypotheses was a primary bottleneck on discovery. There was only so much “intelligence” available to throw at problems—a set number of graduate students and career scientists, who generated data and analyzed that data around a problem. AI’s continuing progress in coding and data analysis breaks that assumption. Analyzing data may become vastly more scalable, with insights emerging from collaborations that are hard to foresee at the time the data are generated.

Our mission is to ensure that artificial general intelligence benefits all of humanity. Scientific data created with our support should be made broadly available, while maintaining individual privacy and consent wherever human data are involved. But accessibility goes beyond just the raw release of the data. We encourage grantees to publish their analyses via preprints, share data regularly rather than only at the end, and, if a grantee’s data is not “connected”, work in tandem with users of the data to assess its utility on biologically valuable problems.

I'd like to thank @seemaychou, @PracheeAC, and @beckypferdehirt in particular for helping us think through what this can mean specifically, based on their experience, as well as inspiring us to think big.

[Jacob Trefethen @JacobTref]

Here's more on our initial strategy behind these grants, and other grants we're investigating in Public Data for Health, that may be of interest to researchers:

Our starting hypotheses for areas OpenAI Foundation support can be most useful are: connected data, scarce data, and direct data. Datasets we fund will usually have one or two of these properties, described in more detail in the next tweets, and in rare cases will share all three properties.

Across all datasets, we believe making data broadly accessible, while ensuring privacy whenever that relates to patient data, will become increasingly important. This will allow researchers to build on each other’s work, and scientific progress to emerge from unexpected places.

[Jacob Trefethen @JacobTref]

At the OpenAI Foundation, we believe breakthroughs against disease will come from pairing intelligence with more observations of the world – in other words, more data.

Today, we're announcing over $125M in grants, launching our second science program:
https://t.co/otD2TRJCyu

[Jacob Trefethen @JacobTref]

3. Direct data: Measuring biological and clinical states closest to what matters 🔬

Biology has spent much of its history measuring what was easy or cheap to measure. For instance: measuring individual cell data over human clinical data, or RNA expression rather than proteins. These proxies have been enormously useful, but they can also become detached from the goals of understanding biology, and of preventing and treating disease. In cases like this, we want to give researchers the resources they need to assemble datasets that measure what matters most. It may also be the case that the necessary instruments to measure “biological truth” do not yet exist, or are prohibitively expensive to use at scale. Within this program, we hope to support both initiatives – new data, and new tools.

For example, our grant to @UNC to establish the Initiative for Generative Immunotherapy will allow the team at UNC Lineberger and UNC Health to produce the human-grounded data necessary to improve the next generation of cancer vaccines. That means measuring tumor cells’ surface proteins directly, as well as patients’ T cell responses, rather than “only” sequencing the tumor. This data is expensive to create; by doing it comprehensively this time, and making the results open, we hope all future cancer vaccine efforts will be able to benefit. In particular, if this project is successful, future vaccine candidates will be able to enter human dosing with a stronger set of targets. That should improve their chances of working, and increase what can be learned when they do not.