Learn to invest in biotech like the professionals from a former biotech executive and investment banker. For education purposes only. Course on Thinkific
Joined June 2021
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Investors, remember that the FDA puts out a wealth of information about drug candidates ahead of a potential approval
A serious investor MUST read these!
They are the ultimate arbiter for approval in the US
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
An important model for biotechs is an epidemiology build: an #epibuild
It takes a disease prevalence (or incidence) and turns it into annual revenue. This is what is plugged into other models
Here's an example:
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Since most biotechs aren't profitable they often use warrants (including pre-funded) to raise capital while mitigating short term dilution
But what is a warrant and what are their implications?
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
A warrant is a security that gives the holder the right, but not the obligation, to buy (typically) stock at a certain price
Conceptually similar to an option, they both have a strike price and expiry date, but with a few differences (not exhaustive)
Many recent biotech follow-ons issue common equity along with "pre-funded" warrants. Why?
Investors have ownership concentration limits and a warrant isn't direct ownership before its exercised
But pre-funding means the company still gets the cash
Either way the warrants are still dilutive and investors should calculate their impact
Biotech Investor retweeted
ChatGPT has now a big problem.
Researchers at Oxford exposed a massive threat to large language models.”
It’s called "Model Collapse."
And it explains why the internet is about to become a toxic waste dump for Large Language Models.
Right now, AI companies are scraping the entire web to train their next-generation models.
But there’s a massive problem.
The internet is no longer just human data. It is rapidly filling up with AI-generated text, images, and code.
What happens when an AI trains on data generated by another AI?
A new paper in Nature just gave us the answer. And it is terrifying.
When AI models are recursively trained on AI-generated data, they suffer from irreversible brain damage.
The researchers found that the model starts forgetting the "tails" of the original data distribution. The rare, weird, unique, and complex human nuances completely disappear.
It's like making a photocopy of a photocopy.
Every generation loses resolution. The output gets flatter, blander, and more generic, until the model completely collapses in on itself.
For years, we thought the biggest bottleneck for AI was compute, getting enough H100 GPUs from Nvidia.
This paper proves the real bottleneck is us.
If we keep flooding the internet with synthetic AI slop, we will poison the very data pool required to build smarter models.
The ultimate irony? We thought AI was going to make human creators obsolete.
Instead, this research proves that verified, genuine human data is about to become the most valuable commodity in the tech industry.
Investors should beware of drug revenue forecasts. They're often wildly off, either too high or too low
That will impact your analysis
This is from 2013. If there's more recent data, please cite the source below
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Product differentiation is more important than classical drug models indicate
First-in-class will retain larger share if later entrants are not differentiated while a differentiated drug will gain outsized share even if a later entrant
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Biotech investors should understand how contingent value rights #CVR work
They're more common in biopharma than other industries due to the binary nature of drug development
From May 2022-April 2023 CVRs were more common across all industries, including life sciences
However, in life sciences 29% of deals had a CVR component
In life sciences, CVRs are more common the smaller the deal value. Almost half (45%) of CVRs were for transactions under $500m
Over 2/3rds of CVR triggers are for approvals (37%) or sales milestones (32%)
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
Drug launches are hard, especially for companies with no experience. Yet its becoming more common
First timers underperform expectations vs experienced peers ~75% vs ~55% of the time
@McKinsey correlates this to greater & earlier commercial investment from experience
Do you agree?
Biotech investors take note: Blockbuster drugs drive #pharma profitability
From 2011-2020, all blockbuster were profitable but no low selling drugs were
No immunology blockbusters in the time frame but there are now
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
Pharma has varying levels of success developing drugs. That's a boon for biotech
Here are the biopharma companies ranked by likelihood of first approval (LoA%) from 2006-2022
$AMGN, $NVO, #Eisai top the list
$GSK, #Astellas, $ABBV are at the bottom
#learnbiotechinvesting #BiotechPrometheus
A key factor in understanding how a biotech drug is reimbursed is assessing where it sits on the formulary (its position)
This is a drastic simplification but useful to know
Be aware of the perverse incentives #PBM
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
Reminder: Drug development is hard. People think it's linear, but it's not
Here are schematics of what most people think, what it is in reality, & modifications for small or large molecules
Investors take note: all must work to MAKE a drug, but almost any step can BREAK a drug!
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
It's important for investors to assess the clinical meaningfulness of a result
A "positive" result sometimes isn't enough if it doesn't significantly change the treatment landscape. But this can be subjective
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Biotech Investor retweeted
Surprise 1: Anthropic acquired a one-person biotech
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.
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
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Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Investors must continually assess the ever-changing Standard of Care landscape. It could change and make a positive clinical result less relevant
Also keep an eye on the competition
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
Why does T-DXd stop working? Paired biopsies before and after ADCs, presented by @PTarantinoMD at ESMO 2026, give a clear first answer: the tumor rarely loses HER2.
Instead, several mechanisms coexist in the same tumor:
Drug pumps up: ABCB1 rose in 5 of 7 T-DXd-treated tumors, ABCC1 in 4 of 7.
Uptake down: MAN2A2, a glycosylation enzyme, fell in 5 of 7. In cell models, silencing it reduced T-DXd binding and internalization.
After sacituzumab, no recurrent pattern at all.
For clinic: a repeat biopsy that only re-checks HER2 will usually come back "still positive" and explain nothing. The resistance happens after the drug reaches the cell.
Seven patients, hypothesis-generating. But it tells us what the next biopsy should measure. #bcsm #ESMO26
@myESMO
Another reason a P3 study can fail while a P2 was successful is that P2 placebo groups, with narrower eligibility criteria & fewer patients, often have more variable placebo-adjusted effects
Use Phase 3 data, often on drug labels, to make comparisons; They're more consistent
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
Approved drugs can have a "black box" (aka boxed) warning, the highest safety-related warning that FDA can put on an approved drug
Usually applied to multiple drugs in the same class it can hamper revenue potential if alternatives exist
Investors should be aware
#learnbiotechinvesting #biotech #investing #BiotechPrometheus
An important reminder for #investors about the competitive landscape of drug development; there is often lots of competition
Slicing & dicing the patient population adds levels of complexity
In the end, clinical data, patient need, and payer dynamics always win
H/T @andrewpannu
#learnbiotechinvesting #BiotechPrometheus
In ~2.5 years, MASH has gone from its first FDA approval to a ~$1.5B annualized drug launch, $8B+ in upfront FGF21 acquisitions and a crowded 500+ asset landscape. But what's fascinating is the commercial / clinical split is happening much earlier than usual.
By all accounts, the commercial market is barely penetrated. Madrigal (while biased) estimates 1% penetration - the prescribing and diagnostic infrastructure needed to identify the ~410K untreated patients in the US is in its infancy.
Against this backdrop, we historically used to see a number of fast followers with near-identical trial designs and target patient populations, even in mid-stage development.
Instead, even later-stage R&D is already planning for a more fragmented, multi-drug treatment environment canonically associated with more mature markets. Many trials are segmented by fibrosis stage, metabolic comorbidities and, importantly, response to emerging frontline treatments, including GLP1s (Wegovy was approved in MASH in August 2025).
Planning for future treatment sequencing has always been part of the playbook, but the degree of competition in every corner of drug development these days has pushed forward the need to have a differentiated patient angle. Every shift in the SOC creates a new patient phenotype that did not exist before.
In this case, GLP-1 exposed MASH patients will differ histologically from the untreated population and the bet is that the broad reach of GLP1s will flip these ratios over time (Novo indicated 1/3 of patients in efruxifermin's Ph2 study were already on GLP1s), as it may be a more natural discovery point for undiagnosed patients.
And that means the next drug will need different comparators, patient selection criteria and, potentially, endpoints to answer emerging treatment questions, like: what additional benefit should a liver-directed drug deliver? When do you add it?
An asset's competitive position isn't fixed the moment it enters clinical development, so staying nimble to skate where the puck is going is critical.
Going back to MASH, we pulled together an interactive landscape covering mechanisms, fibrosis stages, efficacy, deals, China and upcoming readouts: sleuthinsights.com/analysis/…
Some takeaways from that work:
• F4 is the next expansion opportunity - current approvals cover F2/F3, but ~250K additional US patients have compensated cirrhosis
• The next 12 months are packed with catalysts, including Inventiva's pivotal lanifibranor readout, Phase 3 data from Novo and Roche's FGF21s, and the expected Rezdiffra cirrhosis outcomes readout
• GLP1s haven't yet boxed out liver-directed drugs. Rezdiffra is still adding ~6-7K net patients per quarter despite Wegovy's entry
I've included a snapshot below but it's definitely worth playing with yourself!
For those working in MASH, what are we missing? Particularly interested in which patient segments and treatment combinations you think will matter most over the next few years.
Biotech Investor retweeted
🚨WOW! TIME magazine has chosen the recent approval of RASONQUE - Revolution Medicines $RVMD once-daily oral tablets for the treatment of pancreatic cancer patients, as part of its 2026 best inventions list! According to RevMed’s #ESMO26 presentation some 67,000 people in the U.S. are diagnosed with pancreatic cancer every year and almost seven out of eight patients die within five years of diagnosis - many within a year!! So far Pancreatic cancer patients had very limited treatment options and typically resorted to chemotherapy and that is exactly why RASONQUE’s approval is a truly historical milestone in humanity’s battle against cancer!
ALT WOW! TIME magazine has chosen the recent approval of RASONQUE - Revolution Medicine’s once-daily oral tablets for the treatment of pancreatic cancer patients, as part of its 2026 best inventions list! According to RevMed’s ESMO26 presentation some 67,000 people in the U.S. are diagnosed with pancreatic cancer every year and almost seven out of eight patients die within five years of diagnosis - many within a year!! So far Pancreatic cancer patients had very limited treatment options and typically resorted to chemotherapy and that is exactly why RASONQUE’s approval is a truly historical milestone in humanity’s battle against cancer!
Here is an excellent WSJ chart which wonderfully illustrates the mechanism of action (MOA) of $RVMD RASONQUE (daraxonrasib) - a groundbreaking treatment for pancreatic cancer🧵👇. RASONQUE MOA involves locking onto and blocking the active "switched-on" mode of RAS proteins, which are key drivers of tumor growth in most pancreatic cancers. The way RASONQUE works is that it first binds to a normal cellular protein called cyclophilin A. This pairing later creates a two-part unit that targets and locks onto the active “on" state of RAS proteins and by forming this three-part structure, it prevents RAS from passing growth signals down the chain to other proteins, while helping push RAS back into its inactive “off" state.
ALT Here is an excellent WSJ chart which wonderfully illustrates the mechanism of action (MOA) of RASONQUE (daraxonrasib) - a groundbreaking treatment for pancreatic cancer. RASONQUE MOA involves locking onto and blocking the active "switched-on" mode of RAS proteins, which are key drivers of tumor growth in most pancreatic cancers. The way RASONQUE works is that it first binds to a normal cellular protein called cyclophilin A. This pairing later creates a two-part unit that targets and locks onto the active “on" state of RAS proteins and by forming this three-part structure, it prevents RAS from passing growth signals down the chain to other proteins, while helping push RAS back into its inactive “off" state.