Nick

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Nick

Nick

@Lawbitrage

I am an investor. In my free time, I read sovereign bond contracts and the New England Journal of Medicine.

เข้าร่วม Ekim 2013
302 กำลังติดตาม1.1K ผู้ติดตาม
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Nick
Nick@Lawbitrage·
A few years back I started a journey to learn more about biopharma and drug discovery. Here is a list of the books I have found helpful (loosely grouped by topic) with the outstanding books highlighted by (+++). Feel free to add your own recommendations, I'll keep adding mine.
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Crémieux
Crémieux@cremieuxrecueil·
I love this. These researchers took participants from two trials, where the people in either trial would have been eligible to be in the other, making it possible to compare tirzepatide to placebo shots for heart attacks. Tirzepatide lowered all-cause mortality *39%*. HUGE!
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Professor Oak
Professor Oak@Prof_Oak_·
Thrilled to hear of @JMaraganore’s forthcoming book chronicling the $ALNY story I remember, early in undergrad, discovering & then devouring the seminal stories in biotech history - Billion-Dollar Molecule, Genentech, Her-2, and so on. Those books profoundly shaped my early path & trajectory. I’m so grateful they were there for me to discover in the first place I’m certain John’s book will do the same for future generations of biotech leaders… and I’m equally excited to read it myself. Thank you for investing the time & energy, John
Brad Loncar@bradloncar

This is a great discussion about the state of drug development. Also, I was excited to hear @JMaraganore mention that he’s writing a book!

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Nick@Lawbitrage·
@PatrickHeizer And there will still be people that look at the OS chart and complain about the early crossing of the curves...
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Nick@Lawbitrage·
Off the Scales: The Inside Story of Ozempic and the Race to Cure Obesity -> First book on the GLP-1 story, well written and a good story, but unfortunately the book gets some of the pharma terminology wrong.
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Nick@Lawbitrage·
Managing Discovery in the Life Sciences - Harnessing Creativity to Drive Biomedical Innovation -> Great collection of case studies for various drugs and modalities. Recommended reading.
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Nick@Lawbitrage·
From Breakthrough to Blockbuster – The Business of Biotechnology by Donald Drakeman -> Good overview of the industry, especially for new those new to this field.
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Samuel Hume
Samuel Hume@DrSamuelBHume·
How outcomes in relapsed/refractory multiple myeloma changed, from 1986 to 2026
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Nick@Lawbitrage·
@EricTopol @AADskin This is correct. However keep in mind that Eli Lilly's psoriatic arthritis trial for example only enrolled overweight patients. Hence there seems to be a signal, but its far from conclusive in my opinion.
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Eric Topol
Eric Topol@EricTopol·
The list of conditions for which GLP-1 drugs provide benefit independent of weight loss keeps growing. Add psoriatic arthritis #AAD26 @AADskin
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MV Chandrakanth
MV Chandrakanth@ChandrakanthMv·
Phase 3 (PRESERVE-003): Gotistobart vs Docetaxel Post PD-(L)1 resistant sqNSCLC Early overlap → late separation (~6 months) HR 0.46 | Durable OS benefit 👉 Classic immunotherapy effect #MVOnco #NSCLC #Immunotherapy #LungCancer
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Balazs Halmos
Balazs Halmos@BalazsHalmosMD·
💯 Most exciting “sleeper” study at #ELCC26 waking us and CTLA-4 inhibition up again for a patient subset in great need for new options! One that if holds true in larger experience would definitely pass @PTarantinoMD ‘s 4-wheeled T-test!
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Alessio Cortellini@ACortelliniMD

Great to see eventually these initial (strong) signals of benefit for gotistobar vs docetaxel in 2nd L SqNSCLC just presented at #ELCC26 A space where we're clearly struggling to raise the bar! Any other CTLA4i believer out there? @IASLC @myESMO

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Roberto Ferrara
Roberto Ferrara@RobertoFerrara_·
Blocking PD-1 & PD-L1 at the same time has strong biological rationale because with an anti-PD1 alone, PD-L1 continues to inhibit B7.1 (the 2nd immuno signal). But data with PD-1/PD-L1 bispecific not so clinically encouranging. Food for thoughts from @BenjaminBesseMD #ELCC2026
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Matthew Herper
Matthew Herper@matthewherper·
As someone who has covered cancer drug development for 25 years, one of the few things I am sure of is that the odds of technology folks thinking they understand biology are much higher than the odds they actually do.
Egan Peltan@EganPeltan

This is totally out of control: There’s 0 - I repeat 0 - evidence any of the LLM work did anything meaningful for Rosie’s cancer I’m sorry to rain on the parade here. I know we want to believe. But, it’s possible to do a lot of things and have nothing happen @paul_conyngham co-administered α-PD-1 (conventional immunotherapy) with a TKI and the mRNA. It’s probably the most effective cancer immunotherapy of all time. This isn’t a small detail! There’s no evidence his process (beyond FDA approved doggie α-PD-1) had any impact on disease progression. The most parsimonious explanation is a partial response to α-PD-1 I get it. The chat bots make for a great story (although checking multiple LLMs isn’t validation), but it’s really just a neat story. It’s fundraising copy. Before he starts selling the “custom neoantigen mRNA vax” story to consumers, he should provide some evidence it did anything! That’s responsible citizen science This is just storytelling for the AGI true believers. Specifically, a story in search of venture money

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Samuel Hume
Samuel Hume@DrSamuelBHume·
How modern therapies dislodged chemotherapy in advanced urothelial carcinoma
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Nick@Lawbitrage·
17m OS! Pumitamig in ES-SCLC looking really strong. These are from the latest China-only phase II with mature OS data released today; especially interesting here is that the PFS and OR data have been replicated in the global phase II part of the ongoing global phase II/III study.
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Nick@Lawbitrage·
This is a very good take.
Brett Caughran@FundamentEdge

AI won't kill fundamental investing because more information doesn't kill alpha. We have decades of priors here (Excel, Bloomberg, alt data...all democratized analysis & information gathering, and didn't kill alpha). As measured by factor volatility, stocks are less efficient and more alpha-rich than ever (and empirically, the ability of multi-eight figure market neutral multi-managers to consistently grind out 10-15% returns in an idio-maximized way proves this point...15 years ago a $10bn hedge fund was considered to be impossibly large). Innovations in investment process have shifted alpha pools, for sure, and systematic investors have arbitraged many old, reliable fundamental alpha pools. But as the players at the poker table have shifted, the constraints of those new players have created new alpha pools. Long duration fundamental investing has been gutted, and definitionally competing against a group of non-fundamental (quants, factor/thematic investors, indexers) and duration-constrained (multi's) investors should be a huge competitive advantage, long term (however frustrating in the near term). To wit, a 9-month thesis where I "look through" the next two prints is now considered a long-term thesis. Rigorous investment process serves investment judgment, but the real alpha generation fits a power-law distribution and there is some ineffable "nose for money" that the great investors have, that cannot be trained necessarily. Investing is a very hard game, that cannot be distilled to a reinforcement learning sandbox (by the time it is, the regime will have shifted and new drivers move stocks). AI has no sense of materiality, no true discernment, and the lack of context of N of 1 situations (if you haven't noticed, we are living in an N of 1 world!). There is a irreducible element of humanness that is critical to success in fundamental investing, and that won't change. What does this all mean? In my opinion, there is no better time to be starting a careers as an investor. My first year on the desk, I spent a lot of time doing grunt work: updating Nielsen files, updating models for my PM, creating same store sales master files, building question lists for CEO meetings, etc. This is grunt work. I can automate this all now, and get more quickly to the deep, value added parts of learning the investment process. Will AI drive alpha? This is a debate people are having, which I find sort of silly. When used correctly, by the right investor, of course it will. Ask any great investor if they had another 4 hours of research time per day whether the quality of their research would improve? That's kind of a dumb question...of course it will. Compressing the mechanical part of your job to focus more on the artisanal part of the job is Step 1, and with agentic systems accelerating fast is now in the strike zone of possibility. This is before we start to layer in a broader monitoring net and use cases to go deeper and build more rigor, finding signals in unstructured data that were missed before, as well as turning your investment genius into a co-pilot pattern recognition system. The future is very bright for fundamental investing, in my opinion.

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Brett Caughran
Brett Caughran@FundamentEdge·
AI won't kill fundamental investing because more information doesn't kill alpha. We have decades of priors here (Excel, Bloomberg, alt data...all democratized analysis & information gathering, and didn't kill alpha). As measured by factor volatility, stocks are less efficient and more alpha-rich than ever (and empirically, the ability of multi-eight figure market neutral multi-managers to consistently grind out 10-15% returns in an idio-maximized way proves this point...15 years ago a $10bn hedge fund was considered to be impossibly large). Innovations in investment process have shifted alpha pools, for sure, and systematic investors have arbitraged many old, reliable fundamental alpha pools. But as the players at the poker table have shifted, the constraints of those new players have created new alpha pools. Long duration fundamental investing has been gutted, and definitionally competing against a group of non-fundamental (quants, factor/thematic investors, indexers) and duration-constrained (multi's) investors should be a huge competitive advantage, long term (however frustrating in the near term). To wit, a 9-month thesis where I "look through" the next two prints is now considered a long-term thesis. Rigorous investment process serves investment judgment, but the real alpha generation fits a power-law distribution and there is some ineffable "nose for money" that the great investors have, that cannot be trained necessarily. Investing is a very hard game, that cannot be distilled to a reinforcement learning sandbox (by the time it is, the regime will have shifted and new drivers move stocks). AI has no sense of materiality, no true discernment, and the lack of context of N of 1 situations (if you haven't noticed, we are living in an N of 1 world!). There is a irreducible element of humanness that is critical to success in fundamental investing, and that won't change. What does this all mean? In my opinion, there is no better time to be starting a careers as an investor. My first year on the desk, I spent a lot of time doing grunt work: updating Nielsen files, updating models for my PM, creating same store sales master files, building question lists for CEO meetings, etc. This is grunt work. I can automate this all now, and get more quickly to the deep, value added parts of learning the investment process. Will AI drive alpha? This is a debate people are having, which I find sort of silly. When used correctly, by the right investor, of course it will. Ask any great investor if they had another 4 hours of research time per day whether the quality of their research would improve? That's kind of a dumb question...of course it will. Compressing the mechanical part of your job to focus more on the artisanal part of the job is Step 1, and with agentic systems accelerating fast is now in the strike zone of possibility. This is before we start to layer in a broader monitoring net and use cases to go deeper and build more rigor, finding signals in unstructured data that were missed before, as well as turning your investment genius into a co-pilot pattern recognition system. The future is very bright for fundamental investing, in my opinion.
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Fabian Theis
Fabian Theis@fabian_theis·
Great to be at Roche’s new Institute for Human Biology (IHB) in Basel. Spoke on 'virtual organoids' & predictive models. Inspiring talks from Paola Arlotta, Amy Shyer & Mark Fishman. Exciting convergence of biology, engineering & AI - congrats to Roche on this bold initiative!
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Curiosity
Curiosity@CuriosityonX·
BREAKING🚨: ALL FIVE types of nucleic acid bases, the building blocks of LIFE 'DNA and RNA', have been found in samples collected from asteroid Ryugu
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Nick@Lawbitrage·
@jeuasommenulle The emphasis is *in mice*. Very different story in human and most cancer vaccines in humans have failed so far (it's also far from certain of the dog vaccine actually did anything).
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