Dharshini Maru

co-founder @adam_biotech | @fdotinc | building the best bioprinting system
👾🧬

[Dharshini Maru @dharshinimaru]

We got tired of broken clinical trial systems, so we built Adam Biotech.

Preclinical testing wasn't built for the speed AI designs drugs at now.

90% of drugs that pass preclinical still fail in clinical trials.

We're not slowing drug discovery down to fix that. We're building 3D-bioprinted human tissue, on demand, engineered to catch the failures your animal models and 2D models miss.

https://t.co/PC8PYrzO2w

[Adam Biotech @Adam_Biotech]

As higher throughput methods develop to design and manufacture drugs, higher throughput methods to test and eliminate candidates before expensive in-vivo trials are needed.

[Dharshini Maru @dharshinimaru]

@NatalieLeviii thanks!!

[Dharshini Maru @dharshinimaru]

AI has made it possible to build a future where no one has to suffer from disease again, and people are still more interested in using AI to make brainrot memes.
We won’t stop building at Adam Biotech until we bring accessible and safe medication to all.

[Dharshini Maru @dharshinimaru]

It could be fun if you tried bioprinting synthetic, 3D tissue models with @Adam_Biotech 👀

[lada @ladanuzhna]

Preclinical stage is an escape room you only have 4 years to escape from

[Dharshini Maru @dharshinimaru]

You don’t have to test on animals to do preclinical testing though.
Try organoids and bioprints if you don’t want to kill mice.

[Dr Alexander D. Kalian @AlexanderKalian]

You really can't make this up.

Yesterday, I accused tech circles of overconfidence about AI in biology - e.g. blurting "AI will cure cancer in 5 years!" - without understanding clinical trials or basic pharmacology.

The result? A mass of comments from tech bros downplaying the need for clinical trials!

They basically doubled down on their overconfidence about a field they have not seriously studied or researched.

Well, bad news, guys - biology is not code. It is highly complex, high-noise, high-failure, R&D-heavy for wet labs, and full of painful unknowns and edge cases. Your Python skills and vibe coding don't give you insight into the nuances of drug development.

Thank goodness these people are not in charge of any serious biomedical research programs.

Many of them even advocate going straight from AI discovery to human trial, without the years of slow, expensive preclinical studies on mice.

Well, ~100 million mice die each year, for these studies. Most are euthanised for analysis of their tissues, but a substantial subset directly die as a result of candidate drugs being toxic.

Millions of people would literally perish every year, instead, if these overconfident tech X guys were to be in charge.

[Dharshini Maru @dharshinimaru]

@nisargxj @NatalieLeviii @obotiq wow!!

[Dharshini Maru @dharshinimaru]

Do I have to yap more about how important it is to accelerate drug discovery to make it to T10 of the leaderboard? BECAUSE I’LL DO IT

[Shengkun @shengkun_ye]

introducing the founders inc yap leaderboard

yap yap

[Dharshini Maru @dharshinimaru]

@helvetica24pt 💯

[Dharshini Maru @dharshinimaru]

Engineering Life.

[Ojas Kandhare @ojaskandy]

they named it adam and they're doing biotech

playing god's role

[Dharshini Maru @dharshinimaru]

The best way to make biology SCALABLE is to engineer cells to grow faster & not need as many expensive ingredients as it currently does.

[Dharshini Maru @dharshinimaru]

revolutionizing preclinical trials by providing better synthetic tissue models.

#biotech #startup

[Abraham Hengyucius @ahengyuc]

Adam Biotech is officially launching!

Learn more by checking out our website: https://t.co/9g6Dxw6ZVs

[Dharshini Maru @dharshinimaru]

@pupscub thanks!!

[Dharshini Maru @dharshinimaru]

We got tired of broken clinical trial systems, so we built Adam Biotech.

Preclinical testing wasn't built for the speed AI designs drugs at now.

90% of drugs that pass preclinical still fail in clinical trials.

We're not slowing drug discovery down to fix that. We're building 3D-bioprinted human tissue, on demand, engineered to catch the failures your animal models and 2D models miss.

https://t.co/PC8PYrzO2w

[Dharshini Maru @dharshinimaru]

@riaarora_ 🚀🚀

[Dharshini Maru @dharshinimaru]

@daleverett thanks!

[Dharshini Maru @dharshinimaru]

visit us at: https://t.co/auKTb3rDlk

[Dharshini Maru @dharshinimaru]

@bryan_johnson You should be bioprinting your own tissues. organ replacement before organ failure for longevity.

[Dharshini Maru @dharshinimaru]

GPT-5.6’s capabilities in medical knowledge-based reasoning is super impressive!

language and measurement are just different problems though. one’s about generating good text, one’s about generating trustworthy data. both are equally important to tackle.

worth keeping these separate as “AI in medicine” claims stack up.

#biotech

[Karan Singhal @thekaransinghal]

♥️ GPT-5.6 is a major step forward for health, both at the frontier and at cost.

These models push the frontier of performance per dollar, bringing the best health intelligence to all. The smallest variant, GPT-5.6 Luna, evaluated at the lowest reasoning effort, outperforms GPT-5.5 at the highest reasoning effort–despite costing 25x less. The largest variant, GPT-5.6 Sol, sets a new high bar at cost.

Another especially cool result: physicians found fewer flaws in GPT-5.6 responses than physician-written responses.

We collected diverse tasks that remain difficult for recent OpenAI models, across patient-facing and clinician-facing use cases. We asked speciality-matched physicians to write responses to these tasks with unlimited time and web access. We then asked other physicians to compare responses side-by-side, blinded to their source. Physicians were asked to comment on areas of improvement across five axes: accuracy, communication, completeness, instruction following, and health decision helpfulness. We then reported the fraction of responses across sources rated perfectly across all axes, across 20,000 total axis ratings. GPT-5.6 Sol appeared strongest, although all GPT-5.6 models performed significantly better than physicians.

[Dharshini Maru @dharshinimaru]

Imagine spending years developing a faster engine on a car that doesn't even have wheels yet. That's what big pharma is doing when it comes to drug discovery.

So much time is put into "AI drug discovery" and optimizing protein pathways that we have forgotten why it takes a decade for a drug to come out of trials in the first place. Our primitive & ancient (as in even the Ancient Greeks used animal testing) pre-clinical and clinical infrastructure.

Everyone wants to build the engine. Nobody wants to build the wheels.

That's why we at Adam Biotech are focusing on the physical layer.

You can't AI your way out of bad ground truth. Fix the wheels first.

#biotech #startup #pharma