Biotech visionary. Looking for best drugs with goose data. Retweets mean honking endorsement.

Joined April 2022
🤔 Would Rina-S be better if it was more or less stable?
26%Better
21%Worse
54%About the same
39 votes • 20 hours
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Almost got away with it! If it wasn't for them pesky scientist!
MRG007 (CDH17 ADC) #ESMO26 Nice to see PK... ...but the exposure comment is ridiculous 🤯! Calculated in ng/mL based on Cmax, but MW are very different (150000 vs 435). In reality exatecan exposure is high (6% of ADC AUC), especially for a DAR4 ADC! Here replotted (in nM) 👇
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It's interesting to think about, when we hand new technologies to scientist, how do we evaluate their impact down stream in actual new drugs/therapies? 🤔
> AlphaFold worked, after all. did it though? alphafold was created 8 years ago now and has led to 0 new cures for diseases and 0 clinical trial candidates downstream of it's predicted structures.
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Topo1i ADCs in Folate are very exiting right now!
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I'm beginning to think there is a pattern here. 🤔
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Sometimes, when I talk with med chemist, they only know a single word.
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So much to learn from talking with successful BD teams.
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Mr. @DaveARicks , @EliLillyandCo seems to have the momentum of a runaway freight train. Why are you so successful?
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We've trained a Claude agent on a million @benchling ELN entries with the goal of making a model to optimize our experimental design process. Our learning is that people haven't been doing their ELNing.
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Goose Data (wa/wa) retweeted
people arguing developability is some magic sauce these companies have should look at the literature. you have a series of criteria which are hard coded and very trivial (no cysteines in CDRs, reduce possible glycan sites...etc) to internalize and you have a few softer metrics which are open source on the back end. twist, genscript, thermo.....these guys don't have any functional readouts downstream so they have *zero* inference to the utility of the expressed molecules and very limited data on the runs that they do put in their workflows for things like aggregation or thermal stability there is really genuinely nothing special here. i know many pharmas and CROs that have internalized high throughput antibody expression workflows. TWST and companies like them are selling to a specific crowd (cash rich AI x Bio people) who have shown no ability to do this basic thing (make molecules and test them in vitro) and these companies will eventually run out of money propping up this revenue. likely without any drugs to show for it. that said, this means the trade is short term bullish but long term bearish. there is no $100B+ or $1T TAM for this work. drug discovery is not being 'revolutionized'. it is being pushed forward incrementally by very smart people with S-tier judgement in translational science and a real ability to execute. not new people who can't even internalize in vitro work with hundreds of millions in cash
A remarkable overestimation of CRO revenue on the back of a slight improvement to in silico pipelines. Genuinely wreckless and very ignorant of market realities. Yes the AI trade has tailwinds right now but a lot of this post is pure hyperbole and if you're actually putting money on these companies you're better served hedging than putting your foot on the gas of this trade
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The bottleneck is and always has been the lack of core team meetings to discuss the pre-read slide deck. Starting a working group to figure out a path forward.
either way discovery isn't the bottleneck
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The biggest effect of AI will be making drug discovery...
12%Cheaper
31%Faster
15%Better Drugs
42%nothing will change
599 votes • Final results
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Really is the bottle neck of the healthcare industry! Imagine how much more successful Purdue would have been with such a thing!
An AI agent that can write actual prescriptions! This team is building the future of healthcare and it's inspiring to watch. There is such a massive opportunity to improve healthcare with AI and it requires bold work like this. A huge congratulations to the Nolla team! Well deserved. I'm biased, we got to work with Nolla at @TrainLoop_ai post training models for use cases like this. There might even be more models on the way, who knows.
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My take on $GMAB topo FRa ADC
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One failure of AI is that the ads I see on this site are still trash. No, I don't have chronic lymphocytic leukemia, I don't need a BTK inhibitor. Thanks you very much 😤
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Drug development is about the molecules we synthesized along the way.
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Just because you hit statistical significance once, doesn't mean you'll hit it again. 😔 At least management is positive!!!
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There's a lot of lingo here. But when scientist and clinicians say something is "unimpressive", what they mean is that it's bad. When they do say "bad", it probably means it's literally poison.
In July, AZD9592 (AZ's EGFR-cMET TOPO1i ADC) got discontinued due to "unimpressive PK and PD" A patent published today reported preliminary clinical and PK data for 150+ patients (not uncommon to see in patents ahead of conference disclosures) "unimpressive" was the right word!
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Branding is where the real innovation lives in our field.
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