Bruce Booth

Early stage biotech VC. Recovering scientist. Opinions expressed are solely my own and do not express the views or opinions of Atlas Venture.

[Bruce Booth @LifeSciVC]

Undoubtedly true in some diseases. The surrogate biomarker of blood pressure was well-established for follow-on ACEi. Today ~10 yrs is average, as you know.
Big long term outcomes like Alzheimer's are generally not fast (Leqembi ~12.5 years from FIH to acc approval).
The recent big ZEUS and HORIZON studies took 5-7 years to readout for single Ph 3s; those drugs started clinical testing in 2012 and 2015, respectively. Long development programs, only to fail in the end.

[Bruce Booth @LifeSciVC]

For diseases to be “relics of the past” in 10 years… since clinical development timelines are 8-10 years… the drugs entering development right now need to be the ones curing everything… I’m a biotech optimist, but that feels unlikely.

I do appreciate the value of conveying excitement about new technologies and their possible impact, but the trough of disillusionment after over-hyped expectations is often a deep dark place for investors and innovators alike.

[Bruce Booth @LifeSciVC]

@VargasMissing Without wholesale changes to regulatory requirements and clinical protocols, it’s very unlikely there’s more than 10-20% timeline compression from AI. A 52-week study requires 52-weeks of treatment regardless of how much AI you throw at it…

[Bruce Booth @LifeSciVC]

@john_evans3 and sometimes the "fix it" process will take a few decades!

[Bruce Booth @LifeSciVC]

Nimbus reached another important milestone: first-in-human dosing with its selective SIK2 inhibitor for autoimmune disease

CEO @AbbasRKazimi blogs on the context: "Molecules to Medicines: The Pursuit of Repeatable Drug Discovery" https://t.co/bmbq4wceM1

@NimbusTx

[Bruce Booth @LifeSciVC]

Impact of COINS Act on Biotech: this is the summary image, and its spot-on:

[Peter Kolchinsky @PeterKolchinsky]

Having waded through 163 pages of the draft COINS rule published by Treasury, my team and I can now respond with a simple graphic.

It will blind US companies and undermine our biopharma capabilities and national security, exactly opposite to what the policy is aiming to achieve. It does nothing to actually block a Chinese biotech from commercializing a drug in the US, with a European partner, European investors, or on its own. We value medicines highly and will now be paying others for them. Why would we do this to ourselves? We shouldn’t.

But you can’t submit a comic to Treasury so we wrote out a detailed comment that both lays out how the draft rule would backfire on American biotech and what revisions would allow Treasury to better achieve their stated national security objectives.

Have a look. Biotech investors and executives are welcome to sign onto our comment but the main purpose of publishing it now is to drive discussion that helps everyone sharpen their thinking and informs their own comment to Treasury, due Dec 7th. Anyone is welcome to crib from this. No need to ask.

We wrote it after considerable input from legal experts and peers. Since coins draft rule is itself unclear on many points, reasonable people can disagree as to its intent and meaning, so some of our comments address broader points meant to encompass a range of possibilities.

If you read what Treasury wrote, you might get the sense that they know this will backfire on America and are looking for support to revise their language to avert harm. So give this thought and submit your own analysis. We provide links in the article to the submission page.

And thanks to all who have already signed on.

@NPLB_org

https://t.co/oD6YsQiDHe

[Bruce Booth @LifeSciVC]

I'm a few weeks late, but our "Biotech Bait & Boats 2026" fishing season is over in the Boston Hahbah. We had tight lines, great catches, and superb biotech discussions while out on the water. We did a few dozen outings, 50+ guests, and lots of striped bass in the boat... Biotech is all about reeling in the big one!

[Bruce Booth @LifeSciVC]

Fun engagement on this crazy thought experiment.

Might be hard to do this kind of big M&A productively, but looking fwd to seeing (if) the frontier labs move boldly in the life sciences

[Bruce Booth @LifeSciVC]

It's Halloween. Here's a trick-or-treat "thought experiment" for Anthropic (or OpenAI) going big into the Life Sciences. Probably more trick, than treat, but here goes...

Both have made making medicines a top priority, as described in Anthropic's S1.  Right now they are taking some “small” bets in the space (~0.1% of expected market cap's).  These are welcome moves and we are hopeful for more engagement in #biotech in general from the frontier lab companies.

But if you are a $2T public company (as they are likely to be post-IPO), how could you achieve scale in this life science domain?  What’s the best way for maximum impact on bringing drugs to patients? [assuming that's the goal for their efforts in the life science]

I’d argue it's buying a large Pharma company and then radically changing the operating model to put compute and integration in the center.

Sort of like retrofitting a big Pharma R&D enterprise into a @NimbusTx model on steroids with compute and tech integration in the center, intercolated with deep biology and drug hunting capabilities, with operational execution on the periphery (impt, but not the driver). Own the learning loops, compress the DMTA cycle across a huge portfolio. Nucleate and empower teams around great potential medicines in a decentralized R&D model...

Let me get vaguely specific, for illustrative purposes.

Anthropic could buy $BMY for less than 10% of its market cap, an all-stock deal.

BMS has about 30,000 people.  Trim commercial (a distraction to the radical change mission) by selling the long list of tail products (top 5 products are 70% of revenues).

Then apply frontier AI models to the business across the board - commercial, manufacturing, and R&D. Tons of data sits in these silos. With data, figure out where the bloat is in the corporate bureaucracy and remove it. Be especially aggressive on scaling back G&A and the lawyers, replacing headcount in those functions with AI-enablement.  In the business functions, allow change-makers to focus on operational efficiency.

In R&D, BMS has about 3000 scientists/clinicians and operators on their R&D team.  Keep or even grow this group - its where the magic has to start.

BMS has a legacy of great R&D advances and has tons of historical data.  Frankly, they probably have some of the deepest datasets on mAb discovery (Mederex mouse one of the most prolific mAb platforms ever), induced proximity (CelMod and related datasets are without peer), cell therapy (Juno was a pioneer with one of largest CART datasets), radiopharma (more recently), and so much more. Deeply mine these datasets.

Take all the "me-too" R&D spend on crowded targets and dial it out of the portfolio...  Refocus all that energy on building deep biology datasets to complement the historical modality data. Invest in deep cell and tissue profiling, all the ‘omics, at scale and in an integrated way.  Put AI into the center of the operational workflow as an enabling tool for drug hunters, not replacing them. Work on fewer projects as a whole, but focus on “holy grail” drug targets and first-in-class/first-in-modality programs aiming for step-changes in treatment. And let AI operational efficiency compress clinical timelines as much as possible (10-20%).

Unleash teams of drug hunters with AI-powered R&D at scale on any/all therapeutic areas, not restricted to where commercial wants you to focus.  A true science-first enterprise at scale.  “A great medicine makes a commercial franchise, a commercial franchise doesn’t make great medicines” rings true to me here. Commercial voices, I'm told, dominate at BMS, even back in R&D. Maybe that's not optimal?

This would be a radical overhaul of a large R&D-intensive company.  Give the experiment 5-7 years to alter the vector of R&D output.  Long enough to see discovery-led changes percolate into early development and clinical PoC...

This kind of ongoing R&D investment would be tiny fraction of Anthropic’s revenues during that period, offset in part by the profits from the big commercial brands.  A rounding error for them, but if it worked, this would fundamentally alter the industry’s trajectory.

Probably can’t be done - changing large legacy groups is hard - but seems like the only near-term way to truly test “AI at scale in drug R&D” is thru something audacious like this, rather than just thru small (and beautiful) case studies of program-focused biotech …

With $5T+ of anticipated market cap between the handful of frontier lab companies, this seems like a relatively small bet on a big prize.

And, lastly, make @Prof_Oak_ head of the BMS business unit of $ANT.

Trick or treat? Boo!!

[Bruce Booth @LifeSciVC]

@rtnarch Would require dramatic big pharma corporate change, which may be impractical, I don’t disagree

Just hard to see Anthropic-relevant change (or bigger industry-level change) with 10-20 biotech deals of a few $100Ms each, would need to be more impactful

[Bruce Booth @LifeSciVC]

It's Halloween. Here's a trick-or-treat "thought experiment" for Anthropic (or OpenAI) going big into the Life Sciences. Probably more trick, than treat, but here goes...

Both have made making medicines a top priority, as described in Anthropic's S1.  Right now they are taking some “small” bets in the space (~0.1% of expected market cap's).  These are welcome moves and we are hopeful for more engagement in #biotech in general from the frontier lab companies.

But if you are a $2T public company (as they are likely to be post-IPO), how could you achieve scale in this life science domain?  What’s the best way for maximum impact on bringing drugs to patients? [assuming that's the goal for their efforts in the life science]

I’d argue it's buying a large Pharma company and then radically changing the operating model to put compute and integration in the center.

Sort of like retrofitting a big Pharma R&D enterprise into a @NimbusTx model on steroids with compute and tech integration in the center, intercolated with deep biology and drug hunting capabilities, with operational execution on the periphery (impt, but not the driver). Own the learning loops, compress the DMTA cycle across a huge portfolio. Nucleate and empower teams around great potential medicines in a decentralized R&D model...

Let me get vaguely specific, for illustrative purposes.

Anthropic could buy $BMY for less than 10% of its market cap, an all-stock deal.

BMS has about 30,000 people.  Trim commercial (a distraction to the radical change mission) by selling the long list of tail products (top 5 products are 70% of revenues).

Then apply frontier AI models to the business across the board - commercial, manufacturing, and R&D. Tons of data sits in these silos. With data, figure out where the bloat is in the corporate bureaucracy and remove it. Be especially aggressive on scaling back G&A and the lawyers, replacing headcount in those functions with AI-enablement.  In the business functions, allow change-makers to focus on operational efficiency.

In R&D, BMS has about 3000 scientists/clinicians and operators on their R&D team.  Keep or even grow this group - its where the magic has to start.

BMS has a legacy of great R&D advances and has tons of historical data.  Frankly, they probably have some of the deepest datasets on mAb discovery (Mederex mouse one of the most prolific mAb platforms ever), induced proximity (CelMod and related datasets are without peer), cell therapy (Juno was a pioneer with one of largest CART datasets), radiopharma (more recently), and so much more. Deeply mine these datasets.

Take all the "me-too" R&D spend on crowded targets and dial it out of the portfolio...  Refocus all that energy on building deep biology datasets to complement the historical modality data. Invest in deep cell and tissue profiling, all the ‘omics, at scale and in an integrated way.  Put AI into the center of the operational workflow as an enabling tool for drug hunters, not replacing them. Work on fewer projects as a whole, but focus on “holy grail” drug targets and first-in-class/first-in-modality programs aiming for step-changes in treatment. And let AI operational efficiency compress clinical timelines as much as possible (10-20%).

Unleash teams of drug hunters with AI-powered R&D at scale on any/all therapeutic areas, not restricted to where commercial wants you to focus.  A true science-first enterprise at scale.  “A great medicine makes a commercial franchise, a commercial franchise doesn’t make great medicines” rings true to me here. Commercial voices, I'm told, dominate at BMS, even back in R&D. Maybe that's not optimal?

This would be a radical overhaul of a large R&D-intensive company.  Give the experiment 5-7 years to alter the vector of R&D output.  Long enough to see discovery-led changes percolate into early development and clinical PoC...

This kind of ongoing R&D investment would be tiny fraction of Anthropic’s revenues during that period, offset in part by the profits from the big commercial brands.  A rounding error for them, but if it worked, this would fundamentally alter the industry’s trajectory.

Probably can’t be done - changing large legacy groups is hard - but seems like the only near-term way to truly test “AI at scale in drug R&D” is thru something audacious like this, rather than just thru small (and beautiful) case studies of program-focused biotech …

With $5T+ of anticipated market cap between the handful of frontier lab companies, this seems like a relatively small bet on a big prize.

And, lastly, make @Prof_Oak_ head of the BMS business unit of $ANT.

Trick or treat? Boo!!

[Bruce Booth @LifeSciVC]

@geneman10 Beyond compute and cash, they bring the urgent biz model change imperative? Legacy internal inertia of Big Pharma keeps the status quo.

[Bruce Booth @LifeSciVC]

@geneman10 Haha! Best response yet. .

[Bruce Booth @LifeSciVC]

@matthewherper @rplenge @Prof_Oak_ Great point about if they do/don’t do something at scale

[Bruce Booth @LifeSciVC]

@matthewherper Certainly true, would need to wall off the distraction… esp since it would be a 5+ year corporate experiment. I’m sure @rplenge and @Prof_Oak_ would figure it out!

[Bruce Booth @LifeSciVC]

.@JasonPaulRhodes is spot on here 👇

Leaders of successful virtual biotechs, like @Prof_Oak_ , are able to bring great teams together to deliver the mission, and the best do it with the leanest possible capital efficiency required to meet the goal.

[Jason Rhodes @JasonPaulRhodes]

On Mithril, AI, biotech venture capital...a few (more) thoughts.

First, big congratulations again to Cody @Prof_Oak_, who's terrific and keeps hitting it out of the park. He's been a vital colleague at @atlasventure

And big congratulations to @AnthropicAI on the acquisition and bringing Cody on board.

Mithril is a story about an intriguing new pharmacologic mechanism, a virtual team with deep disease biology expertise and killer track records in new therapeutic modalities, and AI for operational efficiency.

If I had to reduce the secret of success to one thing, in biotech and in life more broadly, it would be to surround yourself with great, great people.

Great biotechs pull the right people together for a common purpose, whether employees, consultants, advisors, or at CROs. Our companies have all of them along with all of the people and resources at Atlas that lean in, push, pull, sweat, and strive to build them.

Capital intensity and speed are critical issues in biotech. Anything that reduces the first and increase the latter with rigor is a good thing, including the clever application of AI to orchestrate teams of remarkable, real people.

[Bruce Booth @LifeSciVC]

This is both not new, and not right.

We've had "virtual" biotech teams for decades - small nimble groups of seasoned drug hunters working in our companies. AI had little to do with it. You assemble the right full-time team internally, and bring large numbers of part-time experts onto the real "team" to get it done.

Credible VC firms have no religion about needing huge numbers of full-time FTEs. In fact, we'd always love fewer hires than not. But we always want to staff to get the job done. Has nothing to do anything but the right execution.

[Bruce Booth @LifeSciVC]

@AppleHelix This is utter rubbish. We try build high quality teams in every case - it takes a team, a village in fact, to bring a drug forward to patients.

[Bruce Booth @LifeSciVC]

@Atelfo How does that data square with nearly 40% of NDAs this year being first in class?

[Bruce Booth @LifeSciVC]

Let's just run thru a few drug industry assumptions...

40% of new drug approvals in 2026 were first-in-class (FDA data)

Novel biology is generally riskier, so have to assume more than 40% of the INDs are first-in-class (well accepted)

1200 INDs from commercial sponsors last year (FDA data)

70%+ of initial clinical starts last year were from R&D stage biotechs, private and public (IQVIA data)

Implies at least ~500 were for first-in-class programs from biotech companies.

With preclinical failure rates, 500 INDs would require about 800 Development Candidates

800 DCs probably require 2000 new hit-finding discovery programs.

So you really think starting and funding 2000 discovery campaigns is only 1% of biotech invested capital?

The math just doesn't compute.

I suspect it's easily more like 20-30% of biotech invested capital today.

Since $25B has gone into biotech equity financings this year, that's at least $5-8B into first in class programs..

But we both agree that whatever it is, it should be an even larger proportion of industry activity!

[Bruce Booth @LifeSciVC]

The Fruits of AI Drug Abundance: Indigestion In The Near Term

While the long term impact of AI is likely transformational, in the near term more drugs against known targets will likely exacerbate the crowding challenge dragging down industry R&D productivity

https://t.co/ynj0uqZ4MS

[Bruce Booth @LifeSciVC]

@AAMortazavi @drrichjlaw Disagree with this thread. Lots of VC-backed biotechs are pursuing first-in-class drugs, both w/in our portfolio & others - involving novel targets as well as new modalities. Ofc there are many me-too companies out there, but the ecosystem is 1000's of biotechs