<![CDATA[Frameshifts with Benjamin Arya]]>https://frameshifts.biohttps://substackcdn.com/image/fetch/$s_!0By8!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8ba0b601-f4e8-4ff2-9511-20eb2435ba44_540x540.pngFrameshifts with Benjamin Aryahttps://frameshifts.bioSubstackMon, 18 May 2026 05:12:34 GMT<![CDATA[Building Science's Missing Infrastructure — Adam Marblestone | Frameshifts Episode #11]]>https://frameshifts.bio/p/building-sciences-missing-infrastructurehttps://frameshifts.bio/p/building-sciences-missing-infrastructureThu, 02 Apr 2026 04:07:39 GMTMany of science’s most important projects fall through the cracks between academia and industry. These are the foundational tools that could accelerate entire fields, and yet don’t fit neatly into a PhD thesis or a venture-backed startup.

And to humanity’s credit, there has been growing interest in the last few years in building alternative systems for accelerating science beyond academia and industry.

Groups like Episteme, the Arc Institute and Astera differ in structure and ambition, but they share a common premise: some of the most important work in science requires institutions that neither universities nor venture-backed companies are built to support.

They also share something else. None of them would exist without billionaire philanthropy: Altman and Masa in the case of Episteme, Jed McCaleb in the case of Astera, and the Collison brothers in the case of the Arc Institute.

And yet when I bring these organizations up with friends and colleagues in science, I often sense the same underlying skepticism: impressive as they are, can any of them really become durable drivers of scientific progress or are they structurally incapable of becoming anything more than donor-dependent experiments? Bell Labs changed the world, but it was built on an economic foundation, a telecommunications monopoly, that no longer exists.

So, what’s left in humanity’s armament for progress? Well, there’s the NIH, DARPA and ARPA-H in America, ARIA in the UK, university-affiliated research institutes around the world, a dense ecosystem of startups concentrated in the major entrepreneurial hubs, and then a handful of billionaire-backed nonprofit research orgs.

But there is another model that has been gaining traction in recent years: the Focused Research Organization, or FRO. These are nonprofit research organizations built around tightly scoped scientific milestones, typically with 10 to 30 person teams and budgets in the $20-30 million range.

Adam Marblestone is the founder of Convergent Research and the architect of the FRO model.

Late last year, in what many saw as validation of the model, the National Science Foundation announced a new initiative to “launch and scale a new generation of transformative independent research organizations to advance breakthrough science”.

In my chat with Adam, he traces his path from graduate training in George Church’s lab to DeepMind’s neuroscience team. He came to believe that science needs a third institutional model, one that complements rather than replaces academia and industry.

We discuss the idea of “intellectual dark matter”, the promising ideas researchers have but rarely get the chance to pursue. Adam explains why mathematicians need robust software infrastructure just as much as astronomers need telescopes, and how Convergent Research is systematically identifying more than 100 missing “Hubble Space Telescopes” across scientific fields. Adam argues that many breakthrough ideas remain invisible not because they are wrong, but because the shared infrastructure needed to test them does not yet exist.

Topics we cover include:

  • Why progress in fields like mathematics and neuroscience is often bottlenecked by missing shared infrastructure (e.g. proof verification, connectome mapping, ultrasound brain interfaces)

  • How “intellectual dark matter” exposes systemic blind spots in the way science is funded, evaluated, and organized

  • How the Gap Map is systematically cataloging hundreds of missing foundational capabilities across scientific disciplines

  • Why building scientific infrastructure often requires industry-style execution inside nonprofit structures

  • Why some of the most ambitious deep-tech efforts are too infrastructural for venture capital, yet too operational for academia

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]]><![CDATA[Why 92% of Your Proteins Are Invisible — Parag Mallick | Frameshifts Episode #10]]>https://frameshifts.bio/p/why-92-of-your-proteins-are-invisiblehttps://frameshifts.bio/p/why-92-of-your-proteins-are-invisibleWed, 17 Dec 2025 16:01:27 GMTParag Mallick is a Stanford Professor and Chief Scientist at Nautilus Biotechnology, a publicly traded biotech company. He’s also a professional magician and circus performer, which might sound random until you realize that his company does the closest thing to proteomics magic I’ve ever seen. The things that make you weird, he argues, are exactly what let you see data differently than anyone else.

Here’s what caught me off guard: we can only measure ~8% of human proteins with current mass spectrometry tools. The other 92%—what Parag calls the “dark proteome”—is essentially invisible to us.

Why is proteomics so much harder than genomics? Three reasons. First, protein concentration. DNA is basically uniform—roughly the same amount per cell. Proteins range from one copy to a billion copies in a single cell, and no analytical tool can handle that spread. Second, dynamics. Your genome is relatively static over your lifetime. Your proteome changes every second of every day. Third, there’s no equivalent of amplifying proteins like PCR can do. You get what you get.

Nautilus is tackling this with single-molecule detection. The technical approach is fascinating: they use DNA origami nanoparticles. Picture a Ritz cracker with a flagpole sticking out the top. That flagpole has exactly one attachment point. They coat sample proteins with one half of a click chemistry reagent (methyltetrazine), the nanoparticle has the other half (trans-cyclooctene), and they bind together. One protein per nanoparticle. These then self-assemble onto a flow cell with 100-nanometer landing pads, creating a hyperdense array of billions of individual protein molecules.

Now comes the really clever part: protein identification. Traditional proteomics tries to build one highly specific antibody per protein—an intractable problem when you’re dealing with millions of proteoforms. Nautilus does the opposite. They intentionally build cross-reactive affinity reagents. The system uses ~300 different affinity reagents, each one recognizing just a three-amino-acid epitope. That’s deliberately non-specific. Then they run 300 cycles of iterative mapping. The primary data looks like fluorescent NGS—you get a light-up at each location on the array or you don’t. Binary. Yes or no.

The median protein only needs ~12 epitopes to be uniquely identified, but each protein gets touched 10-30 times across the 300 cycles for high confidence. It’s exactly like playing Guess Who: “Do you have glasses? Brown hair? A hat?” Each question alone tells you almost nothing, but together they pinpoint exactly who you are. Same principle here: “Do you have this 3-amino-acid sequence? What about this one?”

The 300 binary measurements create a point in 300-dimensional space. Each protein has a characteristic signature in this space. The machine learning layer compares your observed pattern against the reference proteome and asks: what protein is compatible with this specific binding pattern? If you find a totally new protein, it’ll occupy a new point in that space—something not in the database.

But here’s the thing about building something this audacious: you can’t prove it works before you start. Parag shared what the early days were actually like—renting a single lab bench at Stanford’s StartX incubator, trying to convince people to join when he couldn’t demonstrate single-molecule deposition yet, couldn’t show them they could run 180 cycles because they didn’t have an instrument. The first automation system was literally called “Parag” because he was pipetting by hand. How do you hire people to believe in something impossible? You share the vision of what it could mean—bringing the proteome to everyone—and see if that resonates. Some people thrive in that uncertainty. Others are brilliant at early-stage innovation but different people excel at scaling and productization. That evolution isn’t failure, Parag argues. It’s healthy. It’s part of the journey.

We’re really just at the beginning of understanding biology. The genomics revolution, as transformative as it’s been, was the opening act. The era where we can actually see what proteins are doing—that’s what comes next.

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Building the Wyss Institute, Organs-on-Chips, and Fighting for Science — Don Ingber | Frameshifts Episode #9]]>https://frameshifts.bio/p/don-ingber-building-the-wyss-institutehttps://frameshifts.bio/p/don-ingber-building-the-wyss-instituteTue, 02 Dec 2025 15:43:55 GMTThe future of human biology isn’t about better animal models - it’s about building human organs on chips that think like the body does.

Don Ingber, founding director of Harvard’s Wyss Institute, has one of the most unconventional origin stories in the field. In 1975, as a Yale undergrad, he took a sculpture class where students built floating structures held together only by tension; no rods touching, no rigid frames. At the same time, he watched cells in culture flatten, round up, and then flatten again.

The connection clicked: cells aren’t rigid blocks; they’re dynamic tensegrity structures, governed by forces, tension, and mechanical cues. That conceptual shift reshaped his view of biology.

Fast-forward, and Ingber’s team would eventually build something that changed the definition of a “model system”: organ-on-chip devices the size of a thumb drive, lined with living human cells, that recapitulate organ-level physiology with astonishing fidelity.

Key Takeaways:

  • Animal testing has a low success rate: 70% of clinical trials fail, with neurology hitting 95% failure rate. Organs-on-chip technology offers an alternative method that can help predict patient treatment responses

  • How a chip mimics an organ: Two microfluidic channels with different tissue types separated by a porous membrane. Cyclic suction mimics breathing or peristalsis

  • The microbiome breakthrough: Culturing complex bacterial communities for days using flow and mechanical forces creates a microbiome-like culture compared to static cultures

  • Personalized medicine economics: Test 100 patient-derived chips, identify the 50 responders, screen for toxicity, run focused clinical trials on the 35 most promising candidates

  • The Wyss origin story: A donor who walked away for two years, and a Martha’s Vineyard meeting that led to a $125 million gift

  • Why institutional structure matters: Independent governance, no deans, separate finances from Harvard and MIT

  • The Boston/Cambridge singularity: Universities, hospitals, VCs, pharma all in walking distance. You can’t recreate this by distributing talent globally

  • What’s at stake now: Immigration restrictions and funding cuts threaten the human capital that drives American innovation

This conversation goes beyond the science. We get into what it actually takes to build an institution like the Wyss, and how we’re watching the American scientific leadership face an existential threat with immigration restrictions and ideological constraints.

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Shelby Newsad: Why Platforms Fail, What Founders Need, and Where Biotech Wins | Frameshifts Episode #8]]>https://frameshifts.bio/p/shelby-newsad-why-platforms-failhttps://frameshifts.bio/p/shelby-newsad-why-platforms-failTue, 25 Nov 2025 17:10:56 GMTThis week, I sat down with Shelby Newsad, Partner at Compound VC, to break down what makes a great biotech investment and why so many platform stories fall short.

Shelby lays out a playbook forged across therapeutics, diagnostics, and lab automation. We dive into why mechanistic understanding predicts success, how diagnostics evolve into data businesses, and why the next wave of value may come from closed loop automated labs that combine hardware and software to finally link the whole bench.

We explore faster paths to human evidence through compassionate use, phase 0 trials, and cassette microdosing, and how biobanks move discovery closer to patients. Shelby also explains how RNA secondary structure targeting with covalent small molecules could merge phenotypic and target based discovery inside one company.

Then things get wonderfully weird. We discuss manufacturing as value capture in a world where intelligence designs drugs, gene edited flowers as a low regulation high margin venture canvas, and what it means when biohackers and LLMs push healthcare outside traditional borders.

Key Takeaways

  • Mechanism > platform: Companies prosecuting mechanistic disease biology have a 3–4× higher chance of success than platform first bets with fuzzy “why.”

  • Diagnostics as engines: Tests that are profitable and build biobanks create data and network effects and decouple cost from revenue via services and licensing.

  • Human evidence faster: Use compassionate use, phase 0, and cassette microdosing to de risk earlier with real PK PD and binding data.

  • Automation gap: Most lab robots lack vision systems. Closed loop labs that are miniaturized and observable are the usability unlock.

  • RNA covalency: Covalent binders to RNA secondary structures offer clean transcriptome readouts blending phenotypic and target based discovery.

  • Value capture shift: If AI designs the drug, manufacturing of proteins, chemicals, and gene therapies could own the margin stack.

  • Global arbitrage: The United States leads, but Australia and China accelerate timelines. Trials and approvals follow speed and clarity.

  • Weird is good: Gene edited flowers that are non edible and simpler to regulate marry beauty, margins, and scale, unexpected but venture real.

But in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Space Travel, AI, and Immortality — Peter Diamandis | Frameshifts Episode #7]]>https://frameshifts.bio/p/longevity-ai-and-the-future-of-humanhttps://frameshifts.bio/p/longevity-ai-and-the-future-of-humanTue, 18 Nov 2025 16:02:47 GMTWhat happens when the man who launched commercial spaceflight decides humanity needs to stop aging?

Peter Diamandis is building the infrastructure to tackle aging. He founded the XPRIZE Foundation, created Singularity University with Larry Page and Ray Kurzweil, and wrote the playbook on exponential thinking with Abundance and Bold. However, somewhere between flying Stephen Hawking into zero gravity and recruiting Elon Musk to his board, he realized something: if we’re building an unlimited future, we need to be around to see it.

We talk about why Peter walked away from space (his first love) to focus on longevity. He shares his story on how he raised $101 million for the XPRIZE Healthspan, the largest incentive prize he’s ever launched, and why 730 teams are now racing to reverse functional aging by 2030. Peter also talks about what it means to restore cognition, immune function, and muscle capacity to what you had 20 years ago, measurably and reproducibly.

We also get into the economics of abundance where Peter states that artificial intelligence, the most powerful technology in human history, is becoming free for 8 billion people. Intelligence as a service is demonetizing at 79% per year. Ultimately, are we heading toward Star Trek (exploring the universe with godlike capabilities) or WALL-E (sitting back while robots feed us grapes)?

Key Takeaways:

  • XPRIZE Healthspan: $101M prize with three targets: reverse cognitive decline, immune exhaustion, and sarcopenia. Winner announced by 2030.

  • The six D’s of exponentials: Digitize → Dematerialize → Demonetize → Democratize → Deceptive → Disruptive. Every industry that touches ones and zeros follows this curve.

  • Why biotech is broken: Wall Street stopped valuing potential and started demanding revenue. Peter thinks high-fidelity AI cell models (the biology equivalent of physics-based simulations of SpaceX’s Falcon 9) could restore confidence before Phase 3 trials.

  • Healthcare will be free: AI diagnosticians already outperform human doctors (92% vs 72% accuracy). Data collection will be ambient: sensors in your watch, ring, breath, typing patterns. Companies will pay to keep you healthy because catching disease early is cheaper than treating it late.

  • The cost curves: Full-body MRI machines that don’t need helium cooling, cost $200K instead of $2M, and scan in 10 minutes instead of 90. Genome sequencing headed toward free. Grail’s multi-cancer detection becoming a volume play.

  • Why Moore’s Law mattered more than anyone realized: In 1958, Gordon Moore noticed transistors per dollar doubled every 12-18 months. That gave us 60+ years of predictable exponential compute. Everything else, including AI, biotech, and nanotech, runs on top of that curve.

  • The transition from space to longevity: Peter realized rockets and satellites were incremental, but if AI, 3D printing, and nanotech go exponential, space becomes easy downstream. So he went deep into the enabling technologies first.

  • What “solving everything” looks like: Enough compute, enough algorithms, enough data to uncover the secrets of math, physics, chemistry, biology. Imagine what happens when every day you wake up to a scientific breakthrough?

This is one of those conversations where you realize the future isn’t decades away, it’s deceptively close. The tools exist, the compute exists and intelligence is free. The only question is whether you’ll build something with it, or watch from the sidelines.

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Minimally Invasive Neurosurgery, Brain-Computer Interfaces, and Continuous Pupil Monitoring — Theodore Schwartz]]>https://frameshifts.bio/p/minimally-invasive-surgery-continuoushttps://frameshifts.bio/p/minimally-invasive-surgery-continuousTue, 11 Nov 2025 15:19:56 GMTWhat happens when one of the world’s leading neurosurgeons steps away from the operating room to become a first-time founder?

Dr. Theodore Schwartz pioneered the field of minimally invasive brain surgery (going through the nose and eyelid instead of opening the skull). He’s published over 200 papers, written a bestselling book on neurosurgery history, and is now building a MedTech startup to solve a problem that’s plagued him for decades: there’s no way to continuously monitor a patient’s pupils during brain surgery.

We talked about the psychological weight of knowing you might paralyze 3-4% of patients no matter how good you are. Why it took him 20 years to truly master neurosurgery. The physics problems that keep him up at night. And why he thinks AI will struggle with surgery for a long time, not because of intelligence, but because of how differently humans and computers evolved.

We also get into the future of brain-computer interfaces. Ted sits on the scientific advisory board of Precision Neuroscience, and he breaks down exactly how their approach differs from Neuralink and Synchron. Spoiler: it’s about electrodes on the surface of the brain, not inside it, and why that might actually be better for getting high-bandwidth data out.

Plus: focused ultrasound opening the blood-brain barrier, targeted immunotherapies for glioblastoma, the doorman who changed his perspective on hard work, and what it’s really like to transition from 40 years of clinical practice to writing FDA guidelines and pitching to VCs.

In this episode, we discuss:

  • Why continuous pupillary monitoring through closed eyelids could revolutionize neurosurgery and ICU care

  • How focused ultrasound might eliminate the need for opening skulls entirely

  • The three competing BCI approaches: Synchron (stent-like electrodes in blood vessels), Neuralink (penetrating electrodes), and Precision Neuroscience (surface electrodes)

  • Why neurosurgery took 25 years for even the best hospitals to adopt minimall-invasive transorbital approaches

  • The decision algorithm surgeons use when removing a tumor that might blind someone, and why AI can’t make that call yet.

  • How Harvey Cushing reduced brain surgery mortality from 50% to 8% and essentially founded the field

  • Why grit and curiosity matter more than raw intelligence

  • The transition from practicing clinician to founder: patents, FDA meetings, grant writing, and learning to create value before raising VC money

This is one of those conversations where you realize how much frontier work is still happening in medicine, and how the people pushing those boundaries think about risk, responsibility, and impact.

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Michael Florea: Smart Cages, AAV Cocktails & The Future of Longevity Research | Frameshifts Episode #5]]>https://frameshifts.bio/p/michael-florea-smart-cages-aav-cocktailshttps://frameshifts.bio/p/michael-florea-smart-cages-aav-cocktailsTue, 04 Nov 2025 16:00:50 GMTThis week, I sat down with genome engineering prodigy Michael Florea.

I first met Michael at the Longevity Biotech Fellowship retreat in Tahoe National Forest, California (2.5hrs east of SF). I was in the final days of building an AI edtech startup, and looking for advice on what to do next.

Michael had been genetically engineering longevity since he was 16 years old. What he always wanted to do was extend the human lifespan. But he decided that the most impactful technology he could work on first was to remove the human bottleneck on in vivo experiments, namely for mice.

In vivo experiments are incredibly expensive, primarily because human beings are required to look after, feed, clean and collect data from mice living in old archaic mouse cages. If you’re doing a PhD, you’ll receive a literal email from a human when your mice die. It’s a brutally manual process.

The industry had not been disrupted in decades.

Now, as co-founder of Olden Labs, Michael is tackling this massive bottleneck in biomedical research: the manual, expensive nature of animal studies.

But we also reveal something that has not yet been discussed. In an as-of-yet unpublished paper, Michael and his colleagues in the Amy Wager’s lab at Harvard, have made a massive breakthrough in whole-organism gene delivery.

In this episode, we dive deep into:

  • The hidden crisis in animal research: Why a single aged mouse costs $700 (and is often sold out), how manual measurements create irreproducible results, and why the person running your experiment might be the biggest variable in your data

  • Olden Labs’ smart cages: How computer vision and AI can track 23 different health metrics continuously, eliminate the “technician effect,” and reduce study timelines from 4 years to 6 months

  • Breaking the gene delivery barrier: Michael’s breakthrough combining multiple AAV serotypes into a linear mathematical “cocktail” that achieves greater than 80% organism-wide, uniform-tissue expression - something nobody has achieved in AAV’s 60-year history

  • The economics of drug development: Why improving model predictiveness by just 1% equals screening 16x more drug candidates, and how Regeneron’s 25-year investment in humanized mice led to 8 consecutive drug successes

  • From automation to AI: How collecting standardized phenomics data at scale could enable the first true biological foundation models - imagine predicting drug effects in silico before any animal testing

Michael shares his journey from discovering Aubrey de Grey’s “Ending Aging” as a teenager in Estonia to developing what might be the most comprehensive whole-body gene delivery system ever created. We explore the technical challenges of AAV engineering, the game theory of data sharing in biology, and why solving these infrastructure problems might be the key to making humanity an immortal and disease-free species.

Whether you’re interested in gene therapy, drug development, research automation, or just understanding why biology moves so slowly, you should watch this episode.

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]]><![CDATA[Virtual Cells, Programmable Biology, and the Quest to Hack Human Aging — Omar Abudayyeh & Jonathan Gootenberg]]>https://frameshifts.bio/p/virtual-cells-programmable-biologyhttps://frameshifts.bio/p/virtual-cells-programmable-biologyThu, 09 Oct 2025 15:03:09 GMTThis week, I sat down with Omar Abudayyeh and Jonathan Gootenberg, the co-PIs who helped pioneer the field of genome engineering, and are now leading one of the most innovative and productive bioengineering labs in the world.

Omar and Jonathan met as graduate students in the lab of CRISPR pioneer, Feng Zhang. They have since co-authored more than 30 breakthrough papers that helped transform CRISPR-Cas from a basic research tool into a clinically relevant therapeutic and diagnostic modality. Their work has led to the founding of companies like Sherlock Biosciences, Proof Diagnostics, and Tome Biosciences.

Their mission is to make biology programmable, and here’s the progress they have already made towards that goal:

  • EvolvePro: To compress months of directed evolution into weeks by combining protein language models with experimental iteration

  • STITCHR: For scarless and efficient DNA insertions ranging from a single base to 12.7 kb

  • PRECISE: For writing RNA of arbitrary length and sequence into existing pre-mRNAs

  • Virtual Cell: To model cell states and perturbations to predict or control cellular behavior

  • Secretome Project: To systematically test human secreted peptides for desirable therapeutic phenotypes, like muscle gain and organ rejuvenation.

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Francisco LePort & Martin Borch Jensen: The Biotech Founders Testing 25 Drugs in One Horse | Frameshifts Episode #3]]>https://frameshifts.bio/p/francisco-leport-and-martin-borchhttps://frameshifts.bio/p/francisco-leport-and-martin-borchWed, 01 Oct 2025 15:03:10 GMTIn this week’s episode, I sit down with Francisco LePort and Martin Borch Jensen, the co-founders of Gordian Biotechnology. Gordian is revolutionizing drug discovery by testing where it matters most… directly in living organisms. We discuss their groundbreaking approach of pooled in vivo screening that transforms a single organism into thousands of parallel experiments, and how this could fundamentally change our approach to studying age-related diseases.

Francisco and Martin believe that traditional anti-aging drug discovery has been ignoring the biology of aging itself. Instead of testing drugs in mice (equivalent to studying osteoarthritis in 25-year-olds), they are using actual horses that developed arthritis naturally and thus better represent aging as it actually manifests. Their early results suggest we might be on the cusp of a new era where we can finally address the root causes of multiple age-related diseases simultaneously.

This episode covers:

  • Why aging research ignores actual aging biology, and how this kills drug development

  • How to do the work of 10,000+ animal studies in a single experiment using gene therapy and single-cell sequencing

  • How Francisco & Martin deliberately “killed their ambition” to go after aging directly

  • The policy disasters strangling US biotech innovation (and why China’s gaining ground)

  • How failed clinical trials waste billions in data that never gets analyzed

And in case you’re short on time, here’s a quick teaser:

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]]><![CDATA[Ron Weiss: Building Biological Neural Networks to Fight Cancer | Frameshifts Episode #2]]>https://frameshifts.bio/p/ron-weiss-building-biological-neuralhttps://frameshifts.bio/p/ron-weiss-building-biological-neuralWed, 24 Sep 2025 17:55:18 GMTIn this week’s episode, I sit down with Prof. Ron Weiss, the MIT pioneer who helped create the field of synthetic biology. We discuss the emergence of living therapeutics that can think, adapt, and make complex decisions inside the human body. The implications of this stretch from synthetic cells within implantable biosensors that can produce insulin based on real-time glucose sensing to self-amplifying RNAs encoding gene circuits that specifically detect and kill cancer cells.

Ron believes the future of medicine lies in treating biological systems as sophisticated analog computers… systems we can learn to program. And while this may still seem like a distant future, the first therapeutic fruits of this approach are already on their way to the market.

And in case you’re short on time, here’s a quick teaser on the “REACT” ARPA-H project that Ron’s lab is working on:

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]]><![CDATA[Kevin Esvelt: Sculpting Evolution, Engineered Viruses and Safeguarding Biotechnology | Frameshifts Episode #1]]>https://frameshifts.bio/p/kevin-esvelt-protein-evolution-genehttps://frameshifts.bio/p/kevin-esvelt-protein-evolution-geneThu, 11 Sep 2025 19:09:47 GMTIn this episode, I sit down with pioneering synthetic biologist Kevin Esvelt. Together, we explore how technologies like gene drives and phage-assisted continuous evolution could help tackle humanity’s toughest challenges, from eradicating malaria to protecting global food security. We also dig into the urgent need to safeguard biotechnology against misuse.

This is a conversation about the future of science, biosecurity, and society, and how we can harness biology safely to create a healthier, more equitable world.

And in case you’re short on time, here’s a 10-min teaser on the New World Screwworm, one of the world’s most terrifying parasitic species, and how we might be able to eliminate it:

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]]><![CDATA[Wall Street Is Wrong About Biotech]]>https://frameshifts.bio/p/wall-street-is-wrong-about-biotechhttps://frameshifts.bio/p/wall-street-is-wrong-about-biotechWed, 04 Jun 2025 01:55:54 GMTBiotech is having a really rough time right now.

Asset sensitive banks graph


The SPDR S&P Biotech ETF (XBI) is still 45% below its 2021 peak.
Investors have become conservative. The capital markets suck and IPO windows are effectively shut.

And it’s especially bad for longevity startups.

The 2024 IPO class has produced average returns of negative 52%. Most recent public biotechs are trading 70% below their offer price.
Small-molecule "asset plays" are back in vogue, marking another swing of the asset <-> platform pendulum.

Yet even these single-asset stories are struggling to close their Series-Bs.

And the big pharma companies are too occupied with geopolitical uncertainty and saving their own pipelines to save the promising startups that VCs are being forced to abandon.

Skepticism around gene editing, delivery, efficacy, safety, has resurfaced.

Yet the evidence tells a different story:
- 43 FDA-approved gene/cell therapies are already on the market, up from 8 just five years ago.
- CASGEVY became the first CRISPR-edited therapy to win FDA approval in late 2023... and is now reimbursed the US, UK and Bahrain.
- 250 gene-editing trials are active worldwide today, spanning oncology, metabolic, and rare disease.

The delivery tech stack is also compounding quietly.
- Organ-targeted LNPs now hit 40–60% editing in mouse heart/lung. 10x better than 2021 benchmarks.
- Engineered capsids, polymers, and exosomes have cut off-target liver uptake 2–3x in primates.

We’ve seen this movie. Remember when RNAi was “dead money”? Behind the scenes, the data looked good. Incremental progress was being made.

But in the capital markets of the time, sentiment trumped data.

Today, six siRNA drugs are on the market. They generate >$3B in annual sales.

The march of human (and AI-driven) progress in biology has not stopped.
- ML-guided capsid engineering has halved lead-to-candidate time in several pharma programs.
- GUIDE-seq-detectable off-target sites have dropped ~10-100x since 2015, i.e. ≈2× fidelity gain every 18–24 months.

Sounds to me a lot like the Moore’s law for engineering biology.

Capital is cyclical, but the tech stack (high-fidelity editors + ML-accelerated delivery) is keeping course on its exponential trajectory.

Betting against that curve was wrong for RNAi. It’s likely wrong for cell therapy and genome engineering today.

Do not ignore the data.

Over the next decade, these tools will open an uncharted frontier of therapeutics development.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[The Case for Transhumanism]]>https://frameshifts.bio/p/the-case-for-transhumanismhttps://frameshifts.bio/p/the-case-for-transhumanismMon, 07 Apr 2025 18:02:09 GMT

In Two Paths to the Future, Alvaro de Menard explores how humanity’s evolutionary next step may hinge on two intertwined technological races: advanced artificial intelligence and radical human enhancement. While much of his argument centers on how transhumanist breakthroughs might compete with super-intelligent AI, one especially pivotal but under-explored point concerns gene editing in adults—a prospect made feasible by recent developments in multiplex genomic engineering and the miniaturization of essential lab processes. The essay’s substance—and its echoes in wider transhumanist discourse—is best understood by tracing his arguments for AI’s swift arrival on the one hand and the promise of genetically enhanced (or cloned) humans on the other. Below is an overview and assessment of how these emerging gene-editing technologies might become an unstoppable force, changing both our physical and mental characteristics in ways that reduce our species-wide existential risks.

Disclaimer:

This essay presents speculative ideas about future technologies for informational and discussion purposes only. It does not endorse any non-consensual, unethical, or illegal applications of genetic engineering or artificial intelligence. Real-world implementation should proceed responsibly and with full consideration of ethical, social, and legal implications.

1. The Next S-Curve

Chalk Talk: S-Curve for Technology Investment - SKMurphy, Inc.

Alvaro frames historical progress as a series of stacked s-curves. Throughout history, once a major technology reaches a plateau of efficiency, a revolutionary breakthrough propels us to a higher s-curve—like the transition from stone tools to bronze, or muscle power to steam, or from steam to electricity. He argues that the present era feels suspiciously like the upper half of our industrial-informational s-curve: fertility is falling, economic growth is stalling, and current institutions appear unprepared to handle future shocks and existential risks. In other words, we’re living through the tail end of something, on the verge of a dramatic upswing.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

This sense of imminent disruption underscores his urgency. The next major paradigm will fundamentally differ from the existing order in the way the industrial era diverged from medieval society. Though no one can precisely foresee its shape, Alvaro suggests that superintelligent AI or large-scale human enhancement will usher it in.

Should superintelligent AI appear (and the goal posts keep shifting on this), it might displace humans in domains we believe are exclusively “ours,” including leadership, or decision-making. There is a geopolitical dimension as well: the creation of superintelligent AI has already become an arms race, with nations funneling enormous resources into building and controlling it. Alvaro warned back in 2021 (when OpenAI was only well known in developer and marketing communities for its GPT-3 API) that if a government takes AI development seriously, training costs may be no obstacle compared to its perceived strategic necessity. This was an accurate prediction in retrospect.

While the intelligence of AI systems continues to scale with the log of compute, genetic enhancement is racing to expand humanity’s natural capacity. These are the Two Paths to the Future. Each path addresses the limitations of the current paradigm but in fundamentally different ways.

2. The Human Path: Augmenting Our Genetic Endowment

Alvaro makes a detailed case for the second path: improving humanity by improving humans themselves. He discusses several methods:

  • Iterated Embryo Selection (IES): Using multiple rounds of in vitro fertilization to select embryos with superior genetic variants, thus compounding the gains across generations.

  • Gene Editing: Altering specific sequences in embryos (or potentially adults in the future) to enhance traits like intelligence and physical health.

  • Cloning Exceptional Individuals: Alvaro’s own thought-experiment suggests replicating a figure like John von Neumann (a Nobel Laureate known for being much more sharp-minded than Einstein) en masse as a shortcut to propagating genius.

He anticipates many decades might be needed for these approaches to bear fruit—children need years to mature and the technology must be perfected. Nonetheless, the potential yields are staggering: not just an increase in collective IQ, but also quantum leaps in innovation, economic growth, and perhaps even a more promising defense against AI risks, should truly novel minds enter the fray.

3. The Inevitability of Genomic Engineering

A recurring theme in both Alvaro’s essay and broader transhumanist discussions is the inevitability of new technologies, regardless of public sentiment or government regulation. In much the same way that nuclear weapons became a race among world powers once the underlying physics was discovered, genetic enhancement has reached a tipping point:

  1. Access to Tools

    • CRISPR-based methods: CRISPR-Cas9 and next-generation platforms allow relatively straightforward edits to the genome, guiding researchers (and, increasingly, hobbyists) to target or remove specific genetic sequences.

    • Diminishing Complexity: The “IQ required to clone humans” or engineer one’s own genome appears to fall quickly as knowledge spreads online and the cost of essential lab equipment drops. This is also in line with the more worrying Moore's Law of Mad Science: “Every eighteen months, the minimum IQ necessary to destroy the world drops by one point.”

    • Unregulated Spaces: Some are already performing cutting-edge gene editing in unconventional locations: small private laboratories, overseas institutes operating under relaxed regulations, and even “biohackers” in garages.

  2. Race vs. Collaboration

    As with previous technological races, countries and private entities will try to outrun one another, fearing strategic disadvantage. Whether it takes the form of an orchestrated government project or widespread private initiatives, there is minimal chance of halting progress at this stage.

  3. Geopolitical Considerations

    One of the most fascinating lines of the essay: “From a geopolitical perspective, He Jiankui's 3 year jail sentence [for the famous Chinese CRISPR babies] might be thought of as similar to Khrushchev removing the missiles from Cuba: Xi sending a message of de-escalation to make sure things don't get out of hand. Why does he want to de-escalate? Because China would get crushed if it came to a race between them and the US today. But in a decade or two?”

4. Lifespan Extension and Human Flourishing

While Alvaro’s essay focuses heavily on intelligence enhancement, the same gene-delivery and multiplex engineering techniques hold promise for entirely new dimensions of human flourishing:

  • Targeting Adult Somatic Cells

    Instead of focusing only on embryonic edits (which remain socially fraught), advanced adult genomic engineering aims to modify cells within a living organism. This approach could upgrade cognitive capacity, add new human capabilities like enhanced metabolism or cancer resistance, improve genomic integrity, and reduce age-related decline, without any need to conceive new embryos. Targeting somatic cells in already-grown people answers a key ethical critique: rather than reshaping life before it starts, it allows consenting adults to weigh the risks and benefits for themselves only, without impacting their germline.

  • Lifespan Extension

    Many researchers and futurists now view indefinite lifespan extension as a genuinely solvable engineering problem rather than an outlandish fantasy. Advances in genomic engineering, cellular biology, and regenerative medicine have already shown that improving or resetting core maintenance processes—such as DNA repair, protein turnover, and epigenetic markers—is likely to greatly enhance longevity. When these breakthroughs reach clinical application, they have the potential to do far more than extend the number of years people live; they may also lessen existential risks for humanity in multiple ways:

    1. Increased Resourcefulness: Lengthening an individual’s healthy lifespan not only preserves their cognitive agility but also accumulates more specialized expertise within society. A workforce that has decades of experience and continues to think with clarity can generate far more creative solutions to emergent crises. This sustained accumulation of knowledge has the potential to accelerate scientific progress, foster more resilient institutions, and reduce the odds of civilization being blindsided by sudden threats.

    2. Higher Collective Intelligence: Extending the prime working and research years of a population can create a compounding effect on innovation. Instead of losing experts to mandatory retirement or age-related decline, societies retain a reservoir of skilled thinkers for longer. The benefits extend beyond just prolonging the careers of brilliant scientists or visionary leaders. Teams and research institutes can maintain continuity in their long-term projects, ensuring less disruption when experienced members decide to shift focus or pass away. The result is a more cohesive, strategically oriented approach to tackling major global challenges.

    3. Avoiding Demographic Collapse: In many developed nations, aging populations and declining birth rates have triggered significant worries about labor shortages, pension burdens, and overall economic stagnation. By prolonging healthy lifespans, individuals remain active and productive longer, granting societies more time to adapt to new economic realities, retrain workforces, and restructure social support systems. Rather than spurring unsustainable resource usage, healthier and longer-living citizens can contribute their productivity and wisdom to mitigate the strains of demographic change. Indeed, without such measures, demographic collapse could pose an existential threat for many nations over the coming decades, particularly in the West and allied democratic countries. South Korea, for example, is heading toward a demographic collapse unlike anything the world has seen before (see South Korea is Over by Kurzgesagt).

5. Dispelling the Fears: “Genomic Vandalism” and Purist Ideologies

Apprehension around genetic engineering—particularly human genetic engineering—often emerges from two key areas:

  1. Technical Concerns:

    • George Church’s reference to “genomic vandalism” highlights the risk of off-target edits, where CRISPR or related technologies accidentally modify unintended regions of DNA. These errors can lead to harmful mutations or mosaic effects. Research into better targeting systems, prime editing, and advanced delivery vectors is addressing these uncertainties.

    • Even so, partial failures do not imply indefinite stagnation. Just as early computing and 19th century medicine had high error rates, gene editing will become more reliable over time, especially with improved molecular tools.

  2. Social or Ideological Fears:

    • 20th-century racial purists and eugenics movements cast a long shadow over discussions of genetic enhancement. However, modern applications need not revolve around superficial traits like height or facial symmetry. Instead, efforts can focus squarely on traits like intelligence that have wide-reaching social benefits, or on clinical health factors such as immune resilience or longevity.

  3. Enhancing Intelligence Without Erasing Diversity

    • There is growing recognition that diversity in personality and cognitive style benefits creative problem-solving. Enhancing average IQ does not necessitate a monoculture of mindsets, especially if different genetic lines and varied polygenic editing strategies are pursued. Intelligence is not like hair color; it can be cultivated alongside a vast range of other attributes and quirks. Alvaro’s broader argument is that higher intelligence combined with varied personal dispositions fosters the sorts of collisions and collaborations that drive scientific revolutions. More importantly, he notes that we need not replicate a single genetic template (though he uses von Neumann as an illustrative example); we can seek an ecosystem of enhanced individuals—each brilliant in different ways.

6. Why Higher Intelligence Is Good for Society

Numerous studies correlate higher intelligence with lower infant mortality, increased economic growth, and improvements in overall societal wellbeing. Alvaro and other authors have pointed out that as collective or national IQ rises, communities gain:

  1. Technological Innovation: More individuals can solve complex challenges in engineering, medicine, and software, leading to higher productivity.

  2. Better Governance: A cognitively sophisticated electorate—and a more capable set of policymakers—are likely to craft legislation and policy grounded in rigorous analysis. In such an environment, nuance becomes more common, as leaders and voters alike are better equipped to understand multifaceted problems and strike informed compromises.

  3. Reduced Threat of Exclusionary Tactics: Since a more informed society might better reject regressive ideals and promote multiplicity, the risk of returning to eugenics-style purity programs diminishes.

Additionally, emphasizing that increased intelligence does not require uniformity in other traits sidesteps the pitfalls of using genetic engineering purely to impose aesthetic or racial purity standards. Speaking as a Middle Eastern guy of below-average height, being short should not be seen as a trait in need of eradication, and advanced gene editing ought to remain open-ended. By focusing on cognitive enhancements without mandatory aesthetic or physical traits, we can harness the best of transhumanist advances—resilience, psychological balance, cooperation, and creativity—while preserving the heterogeneity that makes life dynamic and culturally rich.

7. Genome Engineering, Human Enhancement, and the Future

Alvaro posits a near future where humanity faces a fork in the road: rapidly accelerating AI or a growing movement in human enhancement. Yet it is plausible these two paths will converge. Adult gene editing might enable some individuals to integrate AI more seamlessly (for example through hyper-plastic brains adept at the man–machine interface).

The overarching effect is to reduce existential risks by fostering a society more capable of innovation, self-governance, and scientific literacy. Genetic engineering, wielded responsibly, might deliver its benefits at precisely the historical moment when humanity must adapt—or perish—in the face of exponential change.

Alvaro reminds readers that the world is poised to evolve beyond its current paradigm, and that this invites a choice. Some of that choice revolves around accepting or resisting artificial superintelligence; some revolves around whether to climb rapidly up the ladder of human modification.

But to me, these decisions are best reframed as inevitabilities. As long as the tools to accelerate human potential proliferate, somebody will use them. The nuclear age’s lessons on unstoppable technological arms races offer a powerful parallel. Neither government regulation nor public opinion can forestall the widespread adoption of adult somatic genomic engineering of genetic editing when the required knowledge and tools become sufficiently accessible.

If the race is inevitable, perhaps the safest option is to engage responsibly and with eyes wide open. Public opinion will matter less and less as these technologies inevitably mature. Reflect on how few people would have endorsed the development of human-level AI even a decade ago. It doesn’t matter anymore. Pandora’s box has been opened. Once such paradigm-shifting breakthroughs hover within reach, their adoption is nearly assured.

Far from increasing existential risk, a more scientifically literate, longer-lived, and cognitively enhanced population will likely be better equipped to solve the defining problems of our age.

Taken together, these developments suggest humanity stands on the cusp of a self-directed evolution—a threshold where our biology could soon be as malleable as our software. When multiplex genomic engineering converges with accelerating AI, the consequences could reshape governance, economics, and personal identity itself. Public opinion may attempt to moderate or redirect these forces, but as Alvaro notes, the dynamism of technological progress is relentless.

What remains is to ensure these capabilities serve the common good. If we use them to increase intelligence, lifespan, and individual freedom rather than constrain them, we may foster a more resilient civilization—one capable of confronting grand challenges and existential risks with insight, empathy, and creativity.

References and Further Reading

  • De Menard, A. (2021). Two Paths to the Future.

  • George Church, Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves.

  • Robertson, et al., Beyond the 100th Percentile: General Cognitive Ability & Life Outcomes, Journal of Intelligence.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[The Arcane is Yielding to the Computational]]>https://frameshifts.bio/p/the-arcane-is-yielding-to-the-computationalhttps://frameshifts.bio/p/the-arcane-is-yielding-to-the-computationalSat, 15 Feb 2025 20:20:19 GMTConstruction, manufacturing, and chemistry are no longer disciplines of science—they are disciplines of engineering.

We have developed precision, physics-based simulations and computational techniques that allow us to predict, with remarkable accuracy, the performance, stress factors, and long-term behavior of the materials and structures we design.

The same cannot yet be said for biology.

The Stupidity of Small Molecule Drug Discovery

Historically, the way we developed small-molecule drugs was brute-force trial and error, a process known as small molecule drug discovery.

Step one: Pick a biological target—an enzyme, receptor, or protein involved in a disease.

Step two: Use automation to screen hundreds of thousands (sometimes millions) of chemical compounds to see if any of them interact favorably with the target.

Most pharmaceutical companies maintain massive chemical libraries for this purpose. Pfizer has 4 million compounds in its database. AstraZeneca has 1.7 million.

While this process is magnificent in scale, it is also fundamentally non-systematic.

We close our eyes, pick a lead candidate, cross our fingers, and take it through clinical trials.

The result? A small arsenal of blunt weapons against various diseases—arrived at through almost pure trial and error.

Today, the process is slightly less random and marginally more systematic, but it is still far from engineered precision.

Phases of drug development

Rational Drug Design

Modern small-molecule drug development follows a structured approach known as rational drug design. Instead of relying on brute-force screening, scientists now use molecular insights, computational modeling, and iterative refinement to develop more precise therapies.

The process begins by identifying a biological target—typically a protein, enzyme, or receptor involved in a disease. The objective is to either activate or inhibit this target in a way that meaningfully improves patient outcomes. Once a target is selected, its molecular structure must be mapped to facilitate precise drug design. This is accomplished using X-ray crystallography, which determines atomic positions by analyzing how X-rays scatter through crystallized proteins; Nuclear Magnetic Resonance (NMR) spectroscopy, which uses magnetic fields to infer molecular structure in solution; or cryo-electron microscopy (cryo-EM), which captures high-resolution 3D images of large and complex biomolecules without requiring crystallization. Understanding the structure of a target is critical for designing molecules that fit precisely into its active site.

With this structural blueprint in hand, researchers begin the process of designing small molecules that interact with the target in a therapeutically beneficial way. Several approaches exist. High-throughput screening (HTS) allows scientists to test hundreds of thousands to millions of compounds, searching for those that bind effectively. A more selective method, focused screening, involves running assays on a hand-selected subset of molecules with known biological activity. Alternatively, computational modeling techniques such as molecular docking, molecular dynamics, and AI-driven chemistry enable researchers to predict and refine drug-target interactions in silico before conducting physical experiments. The effectiveness of each approach depends on the chemical properties of the target and the availability of data on similar molecules within the therapeutic class.

Once a promising compound is identified, it advances as a lead drug candidate. However, binding to a target in vitro does not guarantee that the molecule will be an effective or safe drug. Before entering clinical trials, the candidate undergoes optimization to refine its efficacy, specificity, and pharmacokinetics. Scientists must determine whether the drug is potent and selective, whether it can be administered orally without being destroyed in first-pass metabolism by the liver, and whether its therapeutic dose is distinct from its toxic dose. Additional factors include its solubility in blood, its propensity to accumulate in fat tissue, and its ability to cross the blood-brain barrier, which is crucial for neurological treatments. Long-term safety assessments seek to rule out risks of toxicity, carcinogenicity, or other adverse effects.

Each of these variables is tested through iterative cycles of design and refinement, progressively enhancing the drug’s properties before it ever reaches human trials. Yet, despite all these advancements, biology remains a science of trial and error.

Why Biology is Still Trial and Error

While chemistry has become an engineering discipline, medicine remains constrained by biological unpredictability.

Biology remains a science of discovery, governed by the scientific method—a slow, step-by-step process of hypothesis, experimentation, and refinement. Each experiment tests a single assumption, generates data, and informs the next iteration, gradually uncovering the fundamental truths of biological systems.

But this process is inherently blind—a one-step-at-a-time, trial-and-error binary search through an infinitely expansive tree of knowledge.

Progress feels fast because each breakthrough has a profound impact on quality of life and global living standards. Yet relative to the vastness of undiscovered biological knowledge, it remains excruciatingly slow. The true bottleneck is not our ingenuity, but the sheer volume of truths yet to be uncovered—and the transformative technologies that remain locked behind them.

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Why Computation Wasn’t Enough

For decades, scientists hoped that computational power alone would allow us to escape the slow, methodical process of experimentation. If we could simulate biology at a fundamental level, perhaps we could bypass the scientific method altogether—allowing us to test hypotheses in silico rather than in a lab.

But that vision never materialized.

Computers revolutionized engineering disciplines like construction, manufacturing, and materials science because these fields are governed by well-defined physical laws that can be modeled with high precision using classical physics. We can simulate stress factors on a bridge with finite element analysis, predict fluid dynamics with computational models, and even design new materials at the molecular level using quantum simulations.

Biology, however, is different. Simulating even the simplest biological systems has proven intractable. Modeling individual molecules—proteins, DNA, RNA, or lipids—demands quantum-level accuracy. Classical mechanics is insufficient to fully capture the intricacies of molecular interactions, making precise simulation of biological processes an extraordinary computational challenge.

The fundamental issue isn’t that biology is intrinsically more complex than physics or chemistry—it’s that biological complexity scales exponentially as the number of interacting atoms increases linearly.

Moreover, biology isn’t just about simulating individual molecules. It requires modeling entire networks of molecular interactions, spanning multiple levels of organization—from atoms to molecules, from molecules to cells, and from cells to tissues, organs, and complete organisms. Each layer introduces new emergent properties and feedback loops, further amplifying the computational burden.

This quickly becomes an intractable problem. As mentioned, the compute required to simulate increasingly complex systems also scales exponentially. The computational capacity of all of Earth’s semiconductors combined is in the range of 20-120 exaFlops (10^18 floating point operations per second).

Extrapolating from the compute power required for molecular dynamics and quantum simulations of smaller systems, perfectly simulating a single eukaryotic cell would demand zettascale computing—on the order of 10²¹ floating point operations per second. This surpasses the total compute capacity of the entire planet. Even Earth’s most powerful supercomputers fall several orders of magnitude short.

But perhaps brute-force simulation isn’t the answer.

Instead of reconstructing reality from first principles, can we distill the fundamental laws of biology into an efficient computational model—one that serves our purposes, rather than merely replicating nature?

Just as we have likely cracked the problem of human-like intelligence using omnimodal large language models, we are on the verge of another paradigm shift. We are beginning to reverse-engineer the intelligence embedded in biological systems—the structure, function, and design principles refined over billions of years of chaotic, meandering evolutionary exploration.

Computational Biology and Enabler Technologies

The key to turning biology into an engineering discipline is simple: data, compute, and deep learning.

Once we accumulate enough real-world biological data, we can develop deep learning algorithms that uncover the underlying rules governing life itself.

Take the case of protein folding.

Through enormous experimental effort and collaboration, scientists resolved and cataloged the three-dimensional structures of over 100,000 unique proteins. Though this represents only a fraction of the billions of known protein sequences, it was enough for DeepMind to train AlphaFold—a model capable of accurately predicting protein structure from amino acid sequence alone.

What biology needs is data, compute, and deep learning. That is all.

And the last two—compute and data—are on exponential growth curves with declining costs. Despite decades of warnings from armchair experts that “Winter is Coming” for Moore’s Law, there is no sign of slowdown—only acceleration.

Why Most of Us Fail to Grasp Coming Exponential Gains in AI

Improvements in deep learning algorithms tend to be incremental, but every so often, we witness a true breakthrough.

We had convolutional neural networks (CNNs) that revolutionized computer vision, transformer models that redefined natural language processing, and diffusion models that ushered in a new era of generative AI.

Biology is next.

Global investment and human capital allocated for the sole purpose of keeping these growth curves going, is itself, on a exponential growth curve.

A large focus of my writing over the coming months will be to explore how these forces—data, compute, and deep learning—are converging to transform biology.

Cracking the Protein Folding Code

The challenge is simple to define yet historically difficult to solve:

Can we predict a protein’s three-dimensional structure using only its amino acid sequence?

The evolution of protein structure prediction mirrors the early days of NLP. Just as NLP once relied on handcrafted heuristics and statistical rules before deep learning came along, protein structure determination followed a similar trajectory—moving from manual experimentation to computational inference.

Before deep learning, scientists resolved, and stored, the structures of proteins using experimental techniques, like X-ray crystallography, NMR spectroscopy and cryo-EM. The growing database of solved structures in the Protein Data Bank became an indispensable for molecular biology, but it also laid the groundwork for a fundamentally new approach: predicting protein structures directly from sequence data.

Without large-scale computational models, structural biologists relied on statistical rules derived from known protein structures, identifying which amino acids tended to form helices, sheets, or turns. Early approaches classified amino acids by polarity and side-chain bulkiness, noting that alternating non-polar (Ala/Leu) and charged (Glu/Lys) residues often formed alpha helices, while glycine and proline frequently appeared in beta turns. These empirical rules enabled rough secondary structure predictions, but they were inherently limited.

However, rule-based approaches failed to account for long-range interactions and complex folding dynamics, which determine a protein’s final shape. Structure is not dictated by individual residues in isolation but by how they interact across the entire sequence, and these heuristics-based models lacked the capacity to capture these dependencies, making them fundamentally inadequate for large-scale protein folding predictions.

Despite incremental improvements, progress stalled—not due to a lack of data, but because biological complexity outpaced classical modeling techniques. Only with deep learning and large-scale protein databases did the field experience a true paradigm shift.

How AlphaFold Works

In writing this newsletter, I finally had an excuse to read through all the AlphaFold papers, study the architectures of attention-based neural networks, convolutional neural networks (CNNs), and RFDiffusion.

But this post would become far too dense if I also attempted to explain deep learning from scratch, so if you’re unfamiliar with concepts like Neural Networks, Back-propagation, and Transformer Models, I highly recommend 3Blue1Brown’s crash course as a starting point. That being said, here’s a quick overview.

Just as NLP represents text as linear sequences of letters, protein sequences can be written as strings of amino acids—there are ~21 amino acids and each is represented by a letter of the alphabet. This linear sequence, known as the primary structure, encodes all the information needed for a protein to fold into its functional three-dimensional shape.

The linear sequence of amino acids folds into secondary structures, such as the alpha helix (think of a coiled slinky), the beta sheet (picture a pleated ribbon) or random turns (irregular loops connecting structured regions).

These secondary structures then fold together to form more complex arrangements, which we call the protein’s tertiary structure. Often, multiple folded proteins assemble into a larger complex, creating a quaternary structure.

A protein’s three-dimensional structure determines its function—how it interacts with other molecules, catalyzes reactions, or forms structural components of cells.

Protein Structure | BioNinja

Instead of relying on handcrafted rules, DeepMind and others recognized that protein structures could be learned directly from data. They encoded amino acid sequences as matrices and 3D spatial coordinates as corresponding matrices, allowing deep learning models to uncover structural patterns autonomously.

But predicting how an amino acid sequence folds requires more than just raw sequence data—it demands insight into spatial relationships. How does the model learn which residues are close in 3D space?

One key approach is Multiple Sequence Alignment (MSA). By searching large protein databases for homologous sequences across different species, researchers can align these sequences to identify evolutionary conserved positions and co-evolving residues. If two amino acids mutate together across evolution, it suggests they are likely structurally or functionally linked. This co-evolutionary signal allows the model to infer long-range interactions, helping it predict which residues will be adjacent in the final folded structure. In AlphaFold 2, DeepMind leveraged these MSAs as a critical input, guiding the model toward an accurate contact map or distance matrix.

Another breakthrough was the attention mechanism, introduced by eight researchers at Google in the landmark 2017 paper Attention Is All You Need. This innovation transformed the field of NLP and later proved to be just as revolutionary for protein structure prediction.

Rather than treating each amino acid independently, self-attention enables the model to contextualize each residue based on its interactions with all others. As a result, the model refines its understanding of the protein structure layer by layer, progressively encoding richer structural relationships.

More recent approaches, such as AlphaFold 3 and Meta’s ESMFold, are moving beyond MSAs. Instead of relying on homology-based constraints, these models utilize large-scale protein language models, trained on vast databases of sequences and structures. By learning the fundamental statistical and geometric properties of proteins, they can generalize more effectively—even for proteins without well-defined evolutionary homologs.

At a high level, AlphaFold’s process can be broken down into four key stages:

  1. Input: Sequence and Evolutionary Data
    The model takes an amino acid sequence as input. To infer structural constraints, it searches for evolutionarily related sequences from different species, aligning them in a Multiple Sequence Alignment (MSA). This helps the model detect which residues are conserved or co-evolve, providing clues about their spatial relationships.

  2. Prediction: Inferring 3D Structure

    Using a deep neural network, the model predicts the 3D coordinates of each residue. Instead of relying purely on local sequence information, it leverages an attention mechanism that helps it determine which residues are likely to interact, even if they are far apart in the sequence.

  3. Initial Guess & Error Calculation

    At first, the predictions are random and highly inaccurate. The correct experimental structure (from the Protein Data Bank) is used as a reference. A loss function computes the error between predicted and actual atomic positions.

  4. Learning & Optimization

    During training, the model adjusts its internal parameters (weights and biases) using gradient descent, gradually improving its predictions over many iterations. With each cycle, it refines its understanding of how proteins fold, using both evolutionary constraints (MSA-based insights) and contextual attention-based learning.

Through this process, AlphaFold learns to infer structure from sequence with ever-increasing accuracy, refining its understanding of protein folding in a generalizable manner.

But this is just the beginning.

In 2018, AlphaFold 1 laid the foundation. Two years later, AlphaFold 2 solved the structures of over 200 million proteins.

By 2021, RoseTTAFold demonstrated a new approach to protein structure prediction, and by late 2022, RFdiffusion introduced generative design capabilities—marking the shift from prediction to de novo protein engineering. Then came AlphaFold 3, Chai-1, and AlphaProteo in 2024, marking an unprecedented acceleration in the field.

Biology will no longer be governed by intuition and trial-and-error.

For the first time in history, life itself is becoming programmable. The arcane is yielding to the computational. What was once the domain of nature alone is now a design space.

Great tools empower people to create things even their makers could never have imagined.

So what will we create? How will we use these tools to rewrite our biology—and engineer immortality?

That’s what I’ll be exploring in the next newsletter.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[Extending Average Human Lifespan Using Drugs]]>https://frameshifts.bio/p/extending-average-human-lifespanhttps://frameshifts.bio/p/extending-average-human-lifespanTue, 01 Oct 2024 20:58:41 GMTThis should not come as a surprise: We are suffering, collectively, from an epidemic of obesity and metabolic syndrome. And it’s killing us.

Chart: Obesity Is Rising in the U.S. | Statista

This isn’t just limited to the US. Almost every country in the world (minus the notable exceptions of Germany and France) is continuing on a worryingly steep obesity trendline. Regardless of how many Sweetgreen and Equinox locations have opened up near you, and despite the growing popularity of the health and longevity-conscious movements, we are still getting fatter. This should worry you.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Line graph depicting obesity rates (BMI ≥ 30) across various countries from 1990 to 2016, including Egypt, USA, Saudi Arabia, Mexico, Argentina, South Africa, Australia, Brazil, UK, and others.

Obesity causes metabolic syndrome, a constellation of interrelated metabolic diseases that occur together and make each other worse.

Metabolic syndrome is the reason so many of us die from type II diabetes, cardiovascular disease, cerebrovascular disease, chronic kidney disease and fatty liver disease. It’s because all of these ailments egg each other on.

Addressing metabolic syndrome will not improve maximum human lifespan. The maximum human lifespan is currently ~120. However, solving metabolic syndrome very likely will radically extend average human lifespan.

Average vs Maximum Human Lifespan

Allow me to briefly draw a distinction between average human lifespan and maximum human lifespan.

The oldest person to have ever lived was Jeanne Calment, a French woman who died at the age of 122. The human body, with its current genomic design and under typical environmental conditions, can’t stretch much further than 120 years.

When most people think about drugs to extend human lifespan, they are thinking about supplements and pharmaceutical interventions that can help those of us who would otherwise die at age 60, get closer to Jeanne.

These kinds of drugs may increase healthspan and even delay death, but our body’s capacity to restore equilibrium to its myriad structural and biochemical systems still fades with time. Maximum human lifespan remains unchanged.

The Current Longevity Drugs

The approach thus far to developing longevity therapeutics has been to discover things that change with age, and to try to either supplement, preserve or degrade them. There are three main ways to do this.

Method 1

Conduct longitudinal biochemical analysis on serum and tissue samples to discover cell types or biomolecules that either deplete or accumulate over the course of mammalian lifespans, and then to either supplement those that decline or introduce compounds that break down/clear those that accumulate. Examples include:

  • NAD+ Boosters: NAD+ is a coenzyme for hundreds of enzymes that facilitate redox reactions, and central to energy metabolism. The coenzyme directly and indirectly influences many key cellular functions, including metabolic pathways, DNA repair, chromatin modeling, cellular senescence and immune cell function. All of these processes are critical for maintaining tissue and metabolic homeostasis. However, levels of NAD+ decline with age, and this is causally linked to numerous aging-associated diseases, including cognitive decline, cancer, metabolic disease, sarcopenia and frailty. There is evidence that taking NAD+ boosters (NMN, TMG), can combat this decline by increasing production of the coenzyme, and that the restoration of NAD+ levels can promote human healthspan and lifespan.

  • Sirtuin Activators: Sirtuins are a family of signalling proteins involved in metabolic regulation. Some, but not all of them are NAD+ dependent histone deacetylases, and others are protein deacetylases. These signalling proteins help protect against cellular stress and have been associated, albeit not without some controversy, with increased lifespan in some model organisms, like mice and fruit flies. At a cellular level, as NAD+ availability declines, sirtuin activity also decreases. Sirtuin activators like resveratrol have been found to interact with some sirtuin molecules (i.e. SIRT1) to promote their activity. However, there is now evidence that this activation is not direct, and only increases activity of SIRT1 towards certain substrates with bulky hydrophobic groups. That being said, the lifespan extension observed in mice and fruit flies is still promising. Time will tell if sirtuin activators can extend lifespan in humans.

  • Glutathione Supplements: Glutathione is an endogenous antioxidant produced through enzymatic reactions in the pentose phosphate pathway (PPP), which is also responsible for NADPH production. Levels of glutathione decline with age, contributing to increased oxidative stress. Now we have supplements that aim to restore this antioxidant defense system.

  • Senolytics: Senescent cells are cells that have stopped dividing but do not undergo apoptosis. These cells are abnormal in the sense that they are unable to proliferate owing to their stable cell-cycle arrest in the G1/G2 phase. This is a tumor-suppressive cell state, which is good. However, as senescent cells accumulate in tissues throughout the body, they contribute to the decline of our organ systems and secrete pro-inflammatory signals. Senolytics (e.g. Dasatinib and Quercetin) are compounds that attempt to target and clear senescent cells that accumulate with age.

Method 2

Study cellular processes and signalling that become dysfunctional over time, and to introduce molecules that attempt to preserve or enhance these cellular mechanisms into old age. Examples include:

  • mTOR inhibitors: The mechanistic target of rapamycin (mTOR) is a central protein kinase that regulates cell growth, metabolism and survival in response to nutrients, growth factors and cellular energy status. Highly simplified, it responds to various growth cues (e.g. growth factors, hormones and high nutrient loads) in order to initiate anabolic processes, including nucleotide, lipid and protein synthesis, while inhibiting catabolic processes. Over time, mTOR signalling often becomes excessively active due to chronic exposure to growth factors and overnutrition. This leads to the promotion of cellular senescence, neurodegeneration, cancer and metabolic disease through the accumulation of damaged proteins, dysfunctional organelles (like mitochondria) and cellular debris. mTOR inhibitors (e.g. rapamycin and its analogs) primarily inhibit mTOR complex 1 (mTORC1). This mimics the effects of caloric restriction by downregulating protein and lipid synthesis and upregulating autophagy. This has been shown to clear senescent cells, improve metabolic health and increase lifespan in several organisms. The inhibition of mTORC1 also leads to activation of AMPK, which acts as a cellular energy sensor, as well as PGC-1α, which orchestrates mitochondrial function and biogenesis. Indirect PGC-1α activation by rapamycin enhances mitochondrial function by increasing mitochondrial oxidative capacity and respiration rate while decreasing the production of mitochondrial reactive oxygen species. For this reason, mTOR inhibitors remain a promising longevity drug. Unfortunately, chronic mTOR inhibition leads to a sustained reduction in muscle protein synthesis, which can lead to muscle atrophy. It’s very important to cycle on and off mTOR inhibitors, and the timing of administration is crucial for minimizing this side effect. But even then, muscle wasting is an awful tradeoff for better metabolic health. Across 49 studies, muscle wasting has been associated with 1.3x higher mortality risks of all causes. It increases the risk of cardiovascular disease (RR = 1.29), cancer (RR = 1.14), respiratory disease (RR = 1.36) and is the number 1 cause of fatal falls in the elderly. This is why most active gym goers and resistance trainers, including myself, do not include rapamycin in their longevity stacks. Maintaining our muscle mass into our 80’s and 90’s is incredibly important if we hope to reach our personal longevity escape velocities.

  • Metformin: Originally a diabetes medication, metformin modulates a number of proteins involved in energy sensing and metabolism. There are too many proteins to mention here, so I will only mention a few. For one, metformin activates AMPK. AMPK is a key energy sensor that is activated when cellular energy is low. It does this by sensing high levels of AMP relative to ATP, and promotes catabolic processes that generate ATP, such as glucose uptake and fatty acid oxidation. It also inhibits anabolic processes that consume ATP, such as lipid and protein synthesis. Metformin also indirectly inhibits mTOR signalling, thereby promoting autophagy. Finally, metformin, impacts mitochondrial function by inhibiting Complex 1 in the electron transport chain (ETC). This initially reduces ATP production, but ultimately promotes mitochondrial biogenesis and turnover, and reduces ETC-mediated production of reactive oxygen species. As a result of these functions, metformin is one of the most promising longevity therapeutics. There is evidence that long-term use of metformin delays the onset of age-related diseases and extends average lifespan.

  • Coenzyme Q10 (CoQ10): Mitochondrial function declines with age, and leads to decreased cellular energy and increased oxidative stress. CoQ10, also known as ubiquinone, is a lipid soluble molecule found in almost all cell membranes, but is also an essential component of the mitochondrial electron transport chain (mETC), facilitating electron transfer between complexes I and II to complex III. CoQ10 levels directly affect the efficiency of the mETC and hence ATP synthesis. Adequate CoQ10 levels ensure optimal electron flow through the complexes, minimizing electron leakage and reactive oxygen species (ROS) production. By scavenging ROS, CoQ10 helps prevent mitochondrial DNA and proteins from oxidative damage, which prevents mitochondrial dysfunction. However, as you expected, the production of CoQ10 generally declines with age due to decreased activity of enzymes involved in its biosynthetic pathway. Aging cells also often produce more ROS, which leads to greater consumption of CoQ10. Studies in rodents and model organisms like C. elegans have shown that CoQ10 supplementation can extend lifespan and improve healthspan. And human studies have shown that CoQ10 supplementation both reduces mortality in about half of elderly patients with cardiovascular disease, and improves glycemic control in type II diabetes. And long-term CoQ10 supplementation, when combined with selenium, has been shown to improve health-related quality of life, and increase the number of days out of the hospital in elderly individuals. There are clearly metabolic and cardiovascular benefits to long-term supplementation, however direct lifespan extension in humans has not been proven yet.

Method 3

Examine the risk factors for all-cause mortality in the elderly, and then develop drugs that address these risk factors at the biomolecular level.

  • Statins (e.g., Atorvastatin, Rosuvastatin): HMG-CoA reductase is the rate-limiting enzyme in the mevalonate pathway of cholesterol biosynthesis. Statins competitively inhibit this enzyme, reducing endogenous production of cholesterol, and particularly low-density lipoprotein cholesterol (LDL-C). Reduced intracellular cholesterol production leads to up-regulation of LDL receptor expression on the surface of hepatocytes, allowing enhanced clearance of circulating LDL-C from the bloodstream into the liver for metabolism. By reducing LDL-C cholesterol, statins slow the progression of atherosclerosis, stabilise existing atherosclerotic plaques against potential rupture and inhibit the platelet aggregation that leads to thrombus formation. This all leads to a reduction in cardiovascular disease risk, including a reduction in the incidence of myocardial infarction, stroke and peripheral arterial disease. Studies of patients with coronary artery disease show that statins lead to a 30% reduction in all-cause mortality, and a 42% reduction in cardiovascular disease mortality. Given that these are leading causes of mortality in the elderly, it is safe to say that statins probably delay death in high risk populations.

  • Gastrointestinal Lipase Inhibitors (e.g., Orlistat): These drugs prevent fat absorption by binding to and inhibiting gastric and pancreatic lipases within the gastrointestinal tract. This inhibits the breakdown of fats into fatty acids that are small enough to cross the brush border membrane of the small intestine, which therefore inhibits fat absorption. The undigested fat then passes out of your body next time you have a bowel movement. This has the nice side effect of making your stools exceedingly stinky. But that’s better than being obese and metabolically unhealthy.

  • Antihypertensive Agents (e.g. ACE Inhibitors and Beta-Blockers): Antihypertensive agents are a broad category encompassing everything from diuretics to calcium channel blockers, ACE inhibitors and beta blockers. Each class lowers blood pressure through distinct mechanisms, which are not relevant to discuss here. What is important to note is that hypertension places increased shear stress on arterial walls, leading to structural damage and endothelial injury. Endothelial dysfunction promotes inflammatory processes, and oxidative damage, which further impairs vascular function. This allows lipid infiltration (particularly LDL-C) through damaged endothelium. The accumulation of lipids and associated recruitment of inflammatory cells leads to the development of atherosclerotic plaques, which grow, calcify and eventually obstruct blood flow or rupture to cause thromboembolic infarction of organs. Since hypertension is a significant risk factor for cardiovascular events, stroke, and other age-related conditions, it makes sense that anti-hypertensive agents increase average lifespan in patients who have it.

  • Hydroxymethylbutyrate (HMB): Muscle wasting, or sarcopenia is the progressive loss of muscle mass, strength and function that occurs over the course of your lifespan. From about the fourth decade of life, you will lose about 1% of muscle mass per year. By the time you reach your 70s or 80s, this loss compounds to severely impact your ability to maintain mobility, joint stability, balance and posture. This muscle loss directly leads to increased fall risk, and falls are the leading cause of fatal and non-fatal injuries among the elderly. They rank around 7th in terms of overall causes of death. Supplementation with HMB has been shown to reduce age-related muscle breakdown, especially when combined with resistance training in untrained older adults.

  • Antiplatelet Agents (e.g. Low-Dose Aspirin): Antiplatelet agents inhibit platelet aggregation–a key step in the formation of arterial clots. The most ubiquitous member of this class is aspirin, which is more commonly known as an over-the-counter pain reliever. Aspirin irreversibly inhibits the COX-1 enzyme in platelets by acetylating a residue at the enzyme’s active site. Since platelets lack a nucleus and cannot generate new proteins or enzymes, this effect lasts the entire 7-10 day lifespan of a platelet cell. Endothelial cells can regenerate COX enzymes because they have nuclei, and thus low-dose aspirin selectively inhibits platelet COX-1 without significantly affecting endothelial COX-2, preserving prostacyclin production and thus maintaining vasodilation. This prevents COX-1 from catalysing the production of prostaglandin from arachadonic acid. Prostaglandin is the precursor of thromboxane A2 (TXA2), which is a potent promoter of platelet activation and aggregation. By reducing the synthesis of TXA2, aspirin inhibits platelet clumping. Thus, long-term low-dose aspirin administration reduces the risk of myocardial infarction by preventing clot formation in coronary arteries. The same effect applies to other arterial blockages that cause ischemic stroke and pulmonary embolism. The evidence that aspirin reduces the incidence of these fatal thrombotic events is conclusive. It reduces cardiovascular disease risk and therefore increases healthspan in at-risk populations, but it doesn’t fundamentally increase maximum human lifespan.

There Are No True Longevity Drugs

The core conclusion of the evidence around these “longevity drugs” is that there are no true longevity drugs, at least as of yet. There are no “longevity genes” we can activate to increase lifespan. And there are no “longevity proteins” we can activate or inhibit to increase lifespan, especially in already healthy people.

There are only two true underlying killers apart from congenital genetic diseases: overnutrition and entropy.

We can prevent overnutrition with exercise, caloric restriction, fasting, and drugs to modulate the way our body absorbs, processes and uses nutrients for energy and metabolism (i.e. lipase inhibitors, metformin, mTOR inhibitors, etc). We can also combat overnutrition after it has already happened by manipulating biochemical pathways and cellular processes (i.e. statins, aspirin, ACE inhibitors, etc) to address the downstream consequences of metabolic dysfunction, and to prevent cardiometabolic syndrome-related illnesses.

Metabolic engineering to combat overnutrition is what I will be discussing in the rest of this article. Preventing entropy will come in a future article. It’s much more complicated and requires the convergence of multiple exponential growth trends.

Metabolic Syndrome

Metabolic syndrome can be broken down into:

  • Risk factors: Poor diet, a sedentary lifestyle, a family history of obesity and being emotionally distressed (aka chronic high cortisol). The most important factor here is overnutrition (a form of malnutrition) resulting from excessive intake of nutrients. Most people in developed countries are eating hyperpalatable foods that humans have not evolved to deal with. We are not able to properly regulate our nutrient intake in this environment of hyperabundance, particularly when confounded with the fact that we are living largely sedentary lifestyles and ingesting enormous quantities of “edible” seed oils, hydrogenated fats and sugars. Our survival mechanisms prompt us to store excess energy as fat when food is plentiful.

  • Overnutrition = Calories in > calories out for a long time + constant spikes in insulin due to a high energy intake from processed and high-carb foods. All this leads to accumulation of visceral fat that impairs health (i.e. become overweight/obese). You continue to accumulate fat, at first imperceptibly slowly. You diet, lose some fat, then gain it back (and a little bit more), then diet, lose fat, and gain it back (plus a little extra), but the overall trend is up and to the right.

  • To store the biomass of all this fat, your visceral fat cells undergo hypertrophy and hyperplasia (grow in size and number), and begin releasing free fatty acids and adipokines (e.g. leptin) into the blood.

  • Leptin is supposed to communicate to the satiety region in your hypothalamus that you have sufficient long-term energy stores, and that you should stop eating. However, most of us continue to accumulate fat as a consequence of our obesogenic environment and choices. High leptin levels lead to downregulated transport mechanisms, limiting leptin’s access to the hypothalamus. The leptin transporter system becomes saturated, and pro-inflammatory cytokines interfere with leptin signalling pathways. This leads to leptin resistance, and the inability of your hypothalamus to suppress your appetite and activate adaptive thermogenesis. Hence, your body weight set-point increases, and you become polyphagic and recalcitrantly fat.

  • Those same visceral fat cells (adipocytes) have reached their fatty acid uptake capacity and are now undergoing severe hypertrophy and hyperplasia. They become bloated and inflammatory, secreting pro-inflammatory cytokines (TNF-α, IL-6, etc) into the bloodstream.

  • This chronic, hyper-inflammatory environment disrupts metabolic signalling and leads to serine phosphorylation of the intracellular domain of the insulin receptor. This prevents your insulin receptors from being able to respond to their primary ligand, insulin. Now you have peripheral insulin resistance.

  • The β cells of your pancreas sense elevated glucose levels and upregulate insulin production. For the same amount of insulin that it used to release, your peripheral tissues (muscle and adipose tissue), are unable to take up enough glucose to maintain your blood sugar levels within the homeostatic range. You now have a pernicious spiral of elevated glucose levels, leading to elevated insulin secretion, leading to insulin resistance, leading to elevated glucose levels.

  • This causes hyperinsulinemia and pancreatic β cell exhaustion, ultimately leading to uncontrolled high blood glucose levels (aka hyperglycemia), all hallmarks of type II diabetes.

  • In addition to regulating glucose uptake, insulin also regulates fat metabolism. As your enlarged and dysfunctional adipocytes become resistant to insulin-mediated glucose uptake, they start to break down stored triglycerides into free fatty acids, and release even more free fatty acids into the bloodstream.

  • High levels of free fatty acids overload the liver, promoting fat accumulation and increased vLDL production. Your liver begins accumulating ectopic lipids. This is the beginning of non-alcoholic fatty liver disease (NAFLD).

  • The pro-inflammatory cytokines released by the adipocytes also lead to chronic inflammation, which leads to inappropriate central activation of the sympathetic nervous system. This, in turn, increases heart rate, which increases cardiac output, which, in turn, increases blood pressure.

  • The uncontrolled hyperglycemia and high blood pressure leads to shear stress on the endothelial membranes of your blood vessels and glycation of the thin capillary basement membranes. This leads to damage to the microvasculature in your brain (causing neuropathy), in your eyes (causing retinopathy) and in your kidneys (causing nephropathy, and eventually chronic kidney disease).

  • This endothelial dysfunction also extends to your macrovasculature. The endothelial membranes of your larger arteries and arterioles respond to chronic, low-level inflammation by becoming permeable, allowing lipids (particularly LDL-C cholesterol, triglyceride-rich lipoproteins, and lipoprotein a) to enter the arterial wall and accumulate in the sub-intimal space. The LDL particles become oxidised, and form fatty streaks beneath your blood vessels.

  • The chronic inflammation from these oxidised fatty streaks stimulates the release of cytokines, and promotes further endothelial dysfunction, which leads to the expression of chemokines that attract macrophages to the site. These macrophages attempt to phagocytose the lipids and LDL particles in an attempt to get rid of them. But there’s too many of them. There’s too much LDL, too many fatty streaks, too much inflammation. Your macrophages become frustrated, turn into “foam cells” and accumulate in the subintimal space. This is an atherosclerotic plaque, which can expand over the course of months, years and decades to constrict blood flow. And when it bursts, it’s called a thromboembolism.

  • It can happen in your brain, causing a stroke, in your coronary arteries, causing a myocardial infarction (aka a ‘heart attack’), in your lungs, causing a pulmonary embolism, and in plenty of other places in your body.

  • Eventually, you either die from an acute cardiovascular event, from liver failure, heart failure, or kidney failure.

If you want a full picture, you can check out this mechanism I made during my med school days.

This is all to say, most of us are metabolically unhealthy. We are eating too much of the wrong things, not exercising enough and accumulating too much visceral fat. Most of us are on track to develop heart disease, diabetes, chronic kidney disease or fatty liver disease during our lifetime. This is a pretty big cause of death, even if we solve the root causes of aging. Hence, the global effort by big pharma and biotech startups to counteract metabolic syndrome is warranted.

However, the vast majority of drug development efforts have focused on alleviating the symptoms and downstream consequences of prolonged metabolic dysfunction. Most companies and researchers are focused on inhibiting or activating various biomolecules in the attempt to counteract the symptoms of disease that emerge from long-term overnutrition.

For example, much of historical drug discovery methods have involved looking at biochemical pathways that are elevated or hyper-activated in disease states, and then attempting to develop small molecules that can bind to receptors, proteins or enzymes involved in these pathways, and allosterically inhibit them.

Using this logic, we’ve developed beta blockers, calcium channel blockers, ACE inhibitors, statins, diuretics, metformin, PCSK9 inhibitors and antiplatelet agents. These are all, mostly, good for humanity. But clearly something needs to change. Drug development needs to focus on addressing root causes of disease, in order to prevent disease, not to alleviate symptoms once the horse has left the station.

It wasn’t until recently that we developed GLP-1 receptor agonists (better known by their brand names Ozempic and Wegovy). This class of drugs is unique in the sense that they actually attempt to address the upstream causes of metabolic syndrome.

Ozempic and the Rise of the GLP-1 Agonists

This is how GLP-1 receptor agonists work:

  1. GLP-1 receptor agonists mimic the action of the endogenous hormone GLP-1. GLP-1 is an endogenous peptide hormone produced by the L-cells of the distal small intestine in response to nutrient ingestion – especially fats and carbohydrates.

  2. By activating GLP-1 receptors, GLP-1 receptor agonists artificially enhance insulin secretion from beta cells and suppress glucagon secretion from alpha cells. By lowering glucagon, these drugs decrease gluconeogenesis and glycogenolysis in the liver, thus reducing hepatic glucose production. GLP-1 receptor agonists also delay gastric emptying by modulating vagus nerve activity, leading to slower absorption of nutrients and prolonging the feeling of fullness. The prolonged digestion reduces the rate of glucose absorption, and therefore reduces the amplitude of blood glucose spikes.

  3. An important point to make is that unlike exogenous insulin administration and many other drug classes, the enhanced insulin secretion in patients who take GLP-1 receptor agonists is glucose-dependent. When these drugs bind to GLP-1 receptors on beta cells of the pancreas, they stimulate activity of adenylate cyclase, which increases levels of cyclic AMP (cAMP). Elevated cAMP enhances insulin gene expression and promotes insulin secretion only in the presence of elevated blood glucose levels. In other words, next time you eat a meal, your post-prandial blood glucose spike is tempered by elevated insulin secretion from your beta cells. If we were talking about exogenous insulin administration, you might be at risk of acute hypoglycemia, and worsening insulin resistance. However, in the context of reduced hepatic glucose production and tempered post-prandial blood glucose spikes due to delayed gastric emptying, insulin sensitivity gradually improves.

  4. But most importantly, the GLP-1 receptor agonists act centrally in the hypothalamus in brain regions involved in appetite regulation. If you remember earlier, I mentioned that it was visceral fat accumulation in obesity that led to chronically elevated leptin secretion and eventually leptin resistance. Leptin is the primary way that adipose tissue communicates long-term energy stores to the brain. So leptin resistance means that the brain is no longer sensitive to this communication and hence unable to downregulate appetite accordingly.

  5. By binding to GLP-1 receptors in the brain, GLP-1 agonists artificially enhance satiety signalling pathways, resulting in appetite suppression. In this way, this class of drugs acts almost at the level of the root causes of metabolic syndrome. You become less hungry, eat less, calories in < calories out, visceral fat is used for energy production, you experience considerable weight loss (mostly by losing a significant amount of visceral fat), your adipocytes stop releasing pro-inflammatory cytokines, low-level chronic inflammation resolves, insulin sensitivity partially recovers, ectopic lipid levels in your liver begin to deplete, serum triglycerides and LDL cholesterol levels go down, oxidative stress decreases, your endothelial function improves, the chronic activation of your sympathetic nervous system resolves, your blood pressure goes down, shear stress on the arterial walls returns to baseline and there is partial regression and stabilisation of your atherosclerotic plaques. You become metabolically well again.

This sounds incredible. It sounds almost too good to be true. It’s not. These drugs truly are amazing. So why isn’t everyone on them?

I recently spoke to an MD-PhD endocrinologist at UCSF, and asked him this same question. “Why isn’t everyone on them? Shouldn’t everyone be on them?”

“Well I certainly hope not! It’s a sad world to live in if everyone needs to be on Ozempic,” he retorted.

However, the unfortunate reality is that world is sad. Most people don’t have the self discipline to adhere to strict diet and exercise regimens for long enough to lose visceral fat and maintain the weight loss. And the truth is that the same leptin and insulin resistance we talked about earlier also shifts the hypothalamic body weight set-point, which makes obese people increasingly polyphagic, and means that weight loss is highly recalcitrant.

Most people should be on Ozempic.

So why aren’t they?

It’s Not That Easy.

GLP-1 receptor agonists have not cured metabolic syndrome.

15% of patients who try Ozempic fail to achieve any clinically significant weight loss. 30% of patients who take Ozempic for non-alcoholic fatty liver disease fail to achieve any resolution of underlying pathophysiology. A study published in Nature noted that 82 percent of participants taking a semaglutide drug reported adverse effects, generally mild to moderate.

The GLP-1 agonists class of drugs cause gastrointestinal upset, muscle wasting in the elderly and several other side effects associated with the on-target activity of mimicking the GLP-1 hormone. They are also (at least currently) mostly available by way of physician or self-administered weekly subcutaneous injection. About 20% of patients will fail to adhere to GLP-1 agonists precisely because of this mode of drug delivery.

The space is crowded with second and third generation GLP-1 receptor agonists in various stages of clinical development, some of which are orally bioavailable. There are also several clinical trials on combination therapy – that is pairing GLP-1 agonists with other drug classes, like metformin and PCSK9 inhibitors – to maximise therapeutic value while reducing side effects.

The GLP-1 receptor agonist class is also in various stages of clinical trials for dozens of other clinical indications. Which makes sense. A drug that reverses obesity will also relieve or reverse many other diseases associated with metabolic syndrome.

And yet, despite all of this progress, one third of Americans remain obese. The obesity epidemic persists. GLP-1 receptor agonists will not rid us of this scourge of metabolic ill-health.

This is not a class of drugs that all of us can take every day. The risk profile of Ozempic not conducive to a long-term use as a prophylactic, the mode of administration remains cumbersome, and the drug is at risk of bankrupting an already-broken American healthcare system.

We need new classes of drugs focused on various other – and currently neglected – metabolic pathways: cheaper, better tolerated, more effective drugs with minimal side effect profiles that can be taken orally by the entire population as a prophylactic to overnutrition and an obesogenic environment.

Herein lies the trillion dollar opportunity to end obesity and metabolic disease, to return the US workforce to its peak productivity, to unburden the taxpayer from a bankrupt federal healthcare system, and to help preserve America as the predominant global superpower.

Seriously. Drugs that combat metabolic syndrome will have far-reaching effects across every aspect of the economy.

This is the holy grail of metabolic engineering.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[How Do We Engineer Biological Immortality?]]>https://frameshifts.bio/p/longevity-escape-velocityhttps://frameshifts.bio/p/longevity-escape-velocityWed, 18 Sep 2024 23:59:42 GMTIntroduction

To start, I will say that this article will not be too scientific. I won’t be making references to any literature. What is to come will be my current and somewhat uninformed opinion, which I am open to changing if presented with persuasive evidence to the contrary.

Before I start delving into papers which demonstrate the recent progress humanity has made towards biological immortality, I need to talk about what this newsletter will be all about, and what it won’t be about.

A Brief Aside on the Importance of Urgency

First things first. Biological immortality is not inevitable. Technological progress in any field requires mobilised, focussed, high-efficiency effort driven by maniacal urgency and ruthless dedication to the cause.

Just as Elon mobilised humanity’s mechanical engineers and rocket scientists on a mission to mass produce reusable rockets, someone else needs to mobilise humanity’s biomedical researchers, synthetic biologists and nano/computational technologists to design and engineer humans with lifespan-extending and information-preserving cellular machinery.

We cannot take scientific and technological progress for granted. The wheel of progress spins ever faster only in the directions we focus it. And compounding growth curves only occur in the burgeoning technology stacks that our brightest minds work tirelessly to accelerate.

The natural progression of an advanced society is in the direction of stagnation and bureaucracy. As Isaac Asimov described in his science fiction novel Foundation, any sufficiently advanced empire is destined to collapse. Our Western Empire, which values individuality and scientific progress, like many historical empires, is also likely to fall. As empires age, they become increasingly difficult to govern efficiently. Bureaucratic inertia leads to poor decision-making machinery, inefficient capital allocation and barriers to innovation.

Every empire thus far has reached a technological peak, and eventually regressed. Over time, scientific knowledge is lost as a collapsing society focusses on maintaining the status quo rather than encouraging advancement. As we weaken, rival powers and internal factions gain influence, culture stagnates, infrastructure decays, the social order collapses and hard-won scientific and technological progress is lost.

This is all to say, we are not living at the end of history, and we don’t have time to waste. The world order is changing, and the American Empire is in the early stages of its collapse. I really hope this doesn’t happen. I hope Marc Andreessen is right. I hope America wins. But we cannot deny that these awesome exponential growth trends across multiple complementary and converging technologies may not last forever.

Longevity Escape Velocity and the Technologies That Matter

In order to make humanity an immortal and disease-free species, we need to reach what Aubrey de Grey has coined “Longevity Escape Velocity” before it’s too late.

Longevity escape velocity is the event horizon of the lifespan singularity. There will come a time when your remaining life expectancy is extended at a faster rate than the time that is passing. In other words, breakthroughs in biotech and longevity research will extend our remaining lifespan, buying more time for further breakthroughs, which, for some people, will eventually yield a J-curve in remaining lifespan.

Can You Live Forever? Part 1: Why Exceptional Longevity Is Closer Than You Think - Gowing Life

Depending on your age and risk factors, you have a higher or lower likelihood of achieving escape velocity compared to others. Each of you reading this will need to wait for a different maturity level of biotechnology before you will reach your personal escape velocity. Biological aging (disease manifestations and mortality risk) increases exponentially with time. If you’re older and metabolically unwell, then you will require a dramatically higher rate of progress before you can be confident that you will not die from age-related diseases.

If we are the first generation experiencing substantial increases in lifespan, it is also true that some of us might have to die for the next generation to figure out what went wrong. Biological immortality is not yet close to being an inevitability for any one individual; but on a population scale, we have a chance.

I believe that humanity will reach longevity escape velocity within our lifetimes due to converging exponential growth trends in (1) synthetic biology, (2) machine learning, (3) quantum computing, (4) genetic engineering, and (5) organism-wide gene delivery technology.

I will be publishing several articles on each of these fields in the coming weeks and months, so I won’t go into details now. But by this time next year, I hope we will have learned a great deal together about the science underpinning these revolutionary technologies, and will have some ideas on how we might use this emerging biological ‘tech stack’ in our quest for eternal youth.

What Engineering Immortality Will Not Be About

Now that we have a roadmap, let’s briefly touch on everything I will not be talking about.

  1. Metabolic engineering: Increasing average human lifespan by reversing metabolic syndrome (including insulin resistance, NAFLD, cardiovascular disease, etc.)
    Metabolic engineering will soon help us all live healthier, slightly longer and more productive lives. It’s what I’m working on right now. It’s longevity adjacent and I’m all for it. But it is a local maxima in the longevity landscape, and progress here is unlikely to translate into progress towards the global maximum of lifespan extension. That being said, the opportunity here is likely to be $1T+. And if you are (or know) someone with expertise in biotech, medicinal chemistry, drug development, clinical trials, biotech operations, computational biology, cell-based assays or are just super interested in this field, please reach out to me. The world’s first biotech startup that builds a real metabolic engineering drug to compete with big pharma will eventually reach a market cap bigger than Apple. And if this company is led by longevity mission-aligned founders who refuse to sell their drug to one of the four big biopharma companies, then these founders will have an gargantuan amount of capital they can deploy to fund moonshot longevity projects. We’re talking $100b+ real moonshots to solve bottlenecks in longevity science.

  2. Biostasis: Cryogenically preserving humans at very low temperatures after death with the hope that future medical technology might revive and treat them.

    Don’t get me wrong. I recently signed up to the Alcor Life Extension Foundation in hopes that if I legally die, my corpse and brain will be vitrified in liquid nitrogen. But this is a fail-safe. It relies on humanity achieving longevity escape velocity, and then achieving a completely different revival tech stack secondary to this. It then relies on future humans actually deciding to revive you, not just for a fun experiment but because they believe they are morally required to. Bringing people back from the dead just because you can is not an obvious moral conclusion of a sophisticated future society. It relies on the continued hegemony of both Western civilisation as well as the organisations (Alcor Foundation, Cryonics Institute, Tomorrow Bio, etc) that currently house and maintain the vitrified. It requires that future humans respect your decision to vitrify yourself and to take it upon themselves to bring you back to good health, restore your right and liberties, etc. There is no reason to have faith in all of these independent events lining up perfectly. Cryogenic preservation gives people who are longevity adjacent but not truly committed an excuse to keep doing what they are doing (a.k.a. working in tech and finance). I’m still going to get frozen if I die, and I recommend it for you as well. But this isn’t the answer.

  3. Replacement: Growing non-sentient human clones for organ transplants and full-body replacement.
    Again, I’m all for this. I really want someone to do it. In fact, I know some people who are working on it, and who are currently raising $30m for this project (reach out to me if you’d like to be connected). But the sociolegal landscape surrounding this field is fraught with challenges. Societal acceptance is not guaranteed. The legal precedent upon which this sits is far from solid. The US Supreme Court is likely to lean conservative for at least the next decade. If the general public can’t reach consensus on the issue of abortion and IVF, then they are very unlikely to reach consensus on growing non-sentient clones for organ harvesting. Manufacturing synthetic organs from induced pluripotent stem cells (iPSCs) is likely to face much less public backlash. But even if we were able to replace all of our visceral organs, the vast majority of humans will develop Alzheimer’s disease by age 120.

    Percentage of humans free from Alzheimer’s dementia stratified based on APOE genotype, a gene that codes for a protein that transports cholesterol and fats in the blood. Some APOE variants put people at more risk of both cardiovascular disease and Alzheimer’s disease than others. Humans homozygous for APOE2 have an exceptionally low likelihood of developing Alzheimer’s dementia during their expected lifespan, however most people in this low-risk population would eventually develop the disease if they lived to see their 120th birthday. (Reiman et al., 2020)

    That remains a glass ceiling on our life expectancy. We would still need to replace/preserve the human brain, which is complicated by the fact that the brain is the location from which our consciousness emerges and also by the fact that we don’t really know what consciousness is. That being said, the organ shortage is very real. If we can eliminate wait-times for organ transplants by growing perfectly compatible organs from your own iPSCs, then we will save millions of lives every year. And if we can eliminate the organ supply problem, then we can also grow immune-compatible tissues for elective purposes – to replace the aging parts of our bodies well before transitions to disease states. But replacement is not something I will be focussing on in this newsletter.

All Roads Lead Back to Advanced Bioengineering

The root cause of both aging and cancer is entropy. And this primarily manifests as genomic and epigenomic information loss that accumulates over the course of a human lifespan (Alan Tomusiak has a brilliant article on this here).

For either replacement, biostasis or metabolic drugs to enable biological immortality, humans eventually need to engineer therapeutics that can prevent and reverse the entropic decay of large organisms at the molecular and cellular level.

This requires advanced bioengineering, from therapeutic-grade genome engineering techniques and quantum modelling of human cells, to genome-length DNA synthesis and more accurate in-silico protein design.

As a society, we should focus most of our efforts on using advanced bioengineering to design enhanced/optimised cellular machinery that can better preserve genomic and epigenomic information at the cellular and molecular level against entropic disorganisation. This is the path toward biological immortality. It’s a fight against the second law of thermodynamics. And it requires cellular machinery that operates with higher fidelity than the primitive versions that evolution has blessed us with. This is Engineering Immortality.

See you next week.

Acknowledgements

Thank you to Robert Langer, Alan Tomusiak, Michael Florea and Alec Reiss for reviewing drafts of this article.

Engineering Immortality by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[What is Frameshifts?]]>https://frameshifts.bio/p/what-is-engineering-immortalityhttps://frameshifts.bio/p/what-is-engineering-immortalityWed, 11 Sep 2024 17:00:59 GMTAs many of you know, I believe that humanity can and should strive to become a disease-free and immortal species. I believe that we will reach longevity escape velocity within our lifetimes due to converging exponential growth trends in (1) synthetic biology, (2) machine learning, (3) quantum computing, (4) genetic engineering, and (5) gene delivery technology.

For most of my life, I have been disturbed by the fact that humans die. When I was young, I was a constant hypochondriac.

I forced my parents to take me to the cardiologist at age 9 because I was convinced I was having a heart attack.

Between the ages of 12 and 16, I visited multiple different doctors because I was convinced I had lymphoma.

Looking back, it’s obvious why I was (and still am) so anxious about the state of my health.

Humanity still has no control whatsoever over its mortal destiny. We are but tumbling peoples in the cosmic stream.

The best medical inventions of our time: CAR T-cell therapy, partial reprogramming and gene therapy are mere child’s play in the grand complexity of biology.

We still do not have good answers to the questions “when will I die?” and “how can I live longer?”

Our best minds are building B2B SaaS and consumer apps. Our best institutions are gate-kept by bureaucrats who believe that death is good. Our best researchers are focussed on niche, obscure, conservative projects backed by risk-averse government grants. 

This is deeply disturbing to me. We can do better.

Humanity’s most consequential problem is death. It is by far the most expensive and pressing consumer-facing problem. Emotional considerations aside, death deprives us of our most brilliant minds, and robs us of their economic output. We spend 17% of our GDP on healthcare precisely because we have not mastered biology.

It is more important than transitioning the planet to sustainable energy or becoming a multi-planetary species.

How can we call ourselves an advanced species when we die in the same way as a bear or a worm?

Yes there are problems with living forever. 

  1. Who decides who gets to live and who dies?

  2. Will the earth reach population carrying capacity?

  3. Who is allowed to have kids? 

  4. How does democracy work when the demographics of the electorate don’t change?

  5. How is power distributed when the old guard never gives way to young humans with new thoughts? 

  6. How is wealth distributed in a world where the rich never die?

  7. Do we become a stagnant society that collapses under its own bureaucracy? 

  8. Does social mobility decline to levels last seen during feudalism?

These are questions that need to be answered by a new constitution. And then a new bill of rights to fix the mistakes.

I’ll try my best to answer those questions too. But first, we must win the revolution. 

A couple weeks ago, I found myself hanging out with biotech founders and researchers in Lake Tahoe at the Longevity Biotech Fellowship.

Something is brewing in longevity biotech. 

Humanity needs to become an immortal and disease-free species. 

And you should be a part of it.

If you want to learn more about this space without doing an incredible amount of grunt work, then I am starting a newsletter called Frameshifts.

Every week, I'll be breaking down papers in genetic engineering, computational biology and lifespan extension research.

Stay tuned.

Frameshifts by Benjamin Arya is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

]]><![CDATA[Eldin Rostom: Biotech Entrepreneurship and Becoming a Dad | Insane Ambition Episode #14]]>https://frameshifts.bio/p/eldin-rostom-biotech-entrepreneurship-d0dhttps://frameshifts.bio/p/eldin-rostom-biotech-entrepreneurship-d0dThu, 18 Jul 2024 23:00:56 GMTForbes 30 Under 30 honoree, Eldin Rostom, CEO and Co-Founder of Diag-Nose.io, who is pioneering the way forward in the diagnosis of chronic nasal disorders. In this episode, Eldin recounts his journey from participating in the MedTech Actuator Accelerator to being one of the select Australian companies to enter the Creative Destruction Lab in Seattle. This episode explores how Diag-Nose.io employs AI to improve diagnostic accuracy for ENT specialists by analysing biological protein profiles, directly addressing the problem of antibiotic over-prescription.

Eldin also shares the personal motivations behind his professional pursuits, including his health challenges and how the impactful loss of loved ones plays a substantial role in shaping one's character and ambitions.

Join us as we dive into:

  • The challenges and triumphs of building a MedTech startup

  • The importance of community and networking in the Bay Area vs. Australia

  • Eldin’s vision for the future of medical technology

Tune in to Insane Ambition on Apple Podcasts, Spotify, and YouTube for this compelling episode and more conversations with experts in technology and innovation.

Subscribe via https://insaneambition.beehiiv.com/

#business #tech #innovation #leadership #technology #podcast #techshow #startups #insaneambition #podcast

A big thanks to Melbourne Connect Co-working, the heartbeat of Australia’s leading innovation precinct, for supporting the production of Insane Ambition and for fuelling the creative spirits that bring our stories to life!

Use our discount code "AMBITION" to reserve your co-working space now!

]]><![CDATA[Joe Foster: Founding Reebok & Building a Globally Recognised Brand | Insane Ambition Episode #13]]>https://frameshifts.bio/p/joe-foster-founding-reebok-and-building-b8ehttps://frameshifts.bio/p/joe-foster-founding-reebok-and-building-b8eThu, 04 Jul 2024 23:00:29 GMTMeet Joe Foster, founder of Reebok, who revolutionised the sportswear industry by building a multi-billion dollar shoe empire. In this episode, Joe recalls his earliest memories of war, shares stories of his grandfather the cobbler, and recounts founding Reebok as a competitor to his father's inherited shoe brand, eventually growing it into a billion-dollar empire.

This episode delves into Joe's philosophies and the "why" behind his success, exploring the strategies and mindset that drove Reebok's rise and providing valuable insights into the world of high-stakes business and brand building.

Don't miss this insightful episode on Insane Ambition!

Be part of our exclusive community on Apple Podcasts, Spotify, and YouTube, where you can access insightful conversations with industry leaders driving innovation in technology.

#reebok #sport #business #innovation #entrepreneur #success #motivation #insaneambition #podcast

A special thanks to Melbourne Connect Co-working, the pulse of Australia's leading innovation precinct, for supporting the production of Insane Ambition and fostering the creative spirits that bring our stories to life.

Use our discount code "AMBITION" to discover your ideal co-working space now!

]]><![CDATA[Ethan Donati: From Social Anxiety to hosting the World's Top Speakers | Insane Ambition Episode #12]]>https://frameshifts.bio/p/ethan-donati-from-social-anxiety-cc6https://frameshifts.bio/p/ethan-donati-from-social-anxiety-cc6Thu, 20 Jun 2024 23:01:04 GMTIn this episode, we sit down with Ethan Donati, a marketing expert who overcame social anxiety to host some of Australia's largest events. Ethan shares his journey from a shy individual to a prominent event organiser, detailing the challenges and strategies involved in bringing top speakers like Gary Vee and David Goggins to Australia.

Tune in as he discusses the nuances of event planning, the importance of personal branding, and key marketing principles that drive success.

Subscribe now on Apple Podcasts, Spotify, and YouTube for exclusive conversations with industry leaders driving innovation in business and events.

#promoter #businesssummit #events #success #entrepreneur #innovation #insaneambition #podcast

A special thanks to Melbourne Connect Co-working, the pulse of Australia’s leading innovation precinct, for supporting the production of Insane Ambition and fostering the creative spirits that bring our stories to life.

Use our discount code "AMBITION" to discover your ideal co-working space now!

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