Lada Nuzhna and A New Bid To Control The Genome

RapAdmin · November 18, 2025, 4:18am

Lada Nuzhna is impatient.

At 21, she co-founded Impetus Grants. This non-profit, backed by wealthy pro-science philanthropists like James Fickel, Jed McCaleb, Juan Benet and Vitalik Buterin, sought to speed up longevity research. Starting in 2021, it awarded grants to high-risk, high-potential research with the promise of cutting checks quickly and little bureaucratic overhead. About 150 efforts were fired up with the money going to new tools, new science and clinical trials at a time when neither the U.S. government nor typical investors were backing much in the way of aging technology. “We raised money to back crazy science, knowing that most of the projects would fail,” Nuzhna says. “But we also knew the ones that succeeded would be a big deal.”

Now, at 26, Nuzhna has her next act. It’s a biotech start-up called General Control. The company, backed by age1 and Fifty Years to the tune of $5.5 million, has been operating in secret since its founding in 2024, and it arrives with some lofty aspirations. General Control seeks to develop compounds that can alter numerous genes in the human body to create lasting effects such as increased muscle mass or better liver function. If the company can do what it hopes, General Control would produce aging therapies far more potent than anything available today.

The work General Control is chasing falls into the category of epigenetic editing, where scientists try to adjust the cellular instructions that tell our genes how active to be. The basic idea is to find compounds that can dial a gene’s activity up or down, depending on what the body needs. If, for example, the genes involved in building and repairing muscle get quieter as we age, a targeted epigenetic edit might turn them back up.

Read the full story: Lada Nuzhna and A New Bid To Control The Genome (Core Memory)

Company Website: https://generalcontrol.inc/

The Company Vision: Vision

RapAdmin · November 18, 2025, 4:23am

RapAdmin · November 18, 2025, 4:40am

The Brutal Truth About Biotech: Why $2B Per Drug Is Killing Innovation

CGPT Summary:

A. Executive Summary (≈200–230 words)

The conversation is a 2025 “state of biotech” diagnosis: scientifically, the field is booming; economically and structurally, it’s jammed. The guests argue we’re living e-room’s law in practice: the cost per approved drug now exceeds $2B, per-patient trial costs have exploded from ~$10k in the early Regeneron days to ~$500k, and regulation plus ossified clinical-research infrastructure are the main bottlenecks, not physics.

Biotech public markets are still working through the post-COVID bubble: many EV-negative public companies, months with no IPOs, and milestone bars reset upward. At the same time, platform tech, AI-enabled design, synthetic biology, and “virtual cell” models make this arguably the most exciting time scientifically, especially for new modalities and complex biologics that standard discovery tools can’t reach.

China’s deregulation and speed/cost advantages in trials (especially investigator-initiated trials for cell/gene therapy) are pulling clinical development offshore and compressing the “half-life” of US inventions, pushing founders toward secrecy (fewer patents, less publishing) versus open science.

GLP-1s are held up as a generational blockbuster that restored biotech’s confidence in going after huge indications and possibly aging itself, but incentives and payers (Medicare, multi-payer churn) still don’t reward true prevention or “aging drugs.” Aging science is framed as ahead of regulation: we can extend lifespan in animals, but lack accepted human biomarkers and trial endpoints.

Their forward bet: the next iconic biotechs will come from new modalities and new infrastructure (AI + sequencing + delivery) that can tackle multifactorial diseases like aging and massively lower the cost and complexity of making and testing drugs.

B. Bullet Summary (12–20 standalone insights)

Biotech markets are in a downturn: many public biotechs trade at or below cash and IPO activity froze for months.
Despite that, early-stage science and tools (AI, virtual cells, synthetic biology) are progressing faster than ever.
e-room’s law dominates: the average cost per approved drug is now >$2B and rising.
Per-patient clinical trial cost has inflated from ~$10k (early Regeneron era) to ~$500k+, with no physical necessity for this.
Regulation has ratcheted only in one direction since thalidomide; safety and efficacy bars continuously increased.
FDA modernization alone isn’t enough; a handful of mega-CROs with misaligned incentives slow adoption of cheaper, tech-enabled trial models.
US startups routinely run first-in-human studies in Australia/Asia because it’s faster and cheaper than the US.
China executed several waves of deregulation (e.g., implied IND approval, parallel review, fast investigator-initiated trials), creating huge speed and cost advantages.
Chinese groups are no longer just “cheap printers” of US ideas; they’re leading in CAR-T, gene therapy, and rapidly copying or leapfrogging new mechanisms.
This dynamic is shortening the effective monopoly window for US inventions and pushing founders toward secrecy (less publishing, fewer conference disclosures).
AI will be ubiquitous in biotech within five years but currently focuses too much on preclinical modeling and not enough on solving phase-2 efficacy failure and human data.
Biggest AI upside: enabling “impossible” medicines (complex polyspecifics, better TPPs) and generative platforms where the platform itself is the product.
GLP-1s show how a consensus biology target plus a contrarian market thesis (obesity is real and injectable) can yield a massive blockbuster.
Payer incentives and Medicare’s structure still strongly disfavor true aging/ prevention drugs, especially one-and-done interventions.
Aging science: we can extend lifespan in animals, but lack agreed human biomarkers and regulatory pathways to approve “aging” indications.
Orphan drug incentives have massively shifted approvals toward rare diseases (~half of recent approvals), misaligned with population disease burden.
The guests argue for an “orphan-like” incentive regime for common, age-related diseases to push industry into harder, higher-impact indications.
Next wave of iconic companies will likely be new modality players and infrastructure/AI platforms that enable net-new types of drugs, not just better single targets.

D. Claims & Evidence Table

Claim	Evidence given in video	Assessment
Average cost per approved drug now exceeds $2B	Referred to as “e-room’s law” and “we now spend more than $2B per approved drug”	Strong (directionally consistent with industry analyses; exact figure depends on methodology)
Per-patient trial cost rose from ~$10k (Regeneron early days) to ~$500k	Historical anecdote about Regeneron vs. current estimates; no formal data cited	Speculative (plausible orders of magnitude but not rigorously backed in discussion)
Biotech public markets had 7–8 straight months with no IPOs	Stated as observed market condition	Strong (easily checkable market stat; consistent with recent drought)
Many public biotechs trade at or below cash (EV-negative)	Presenter cites fraction of public biotechs below cash	Strong (commonly reported in sector commentary; numbers fluctuate but pattern is real)
China’s regulatory reforms made trials 5–6× faster via investigator-initiated trials	Describes specific reforms: implied IND approval, parallel review, and IIT timelines	Moderate (qualitatively credible; exact “5–6×” factor is not evidenced)
China now leads in CAR-T, gene editing, and gene therapy	Cites volume and speed of IITs and first-in-human programs	Moderate (China is highly competitive; “leading” depends on metric—patients treated vs. first approvals vs. innovation)
Most of drug-development spend is in clinical validation and commercialization, not preclinical	States that majority of cost is in demonstrating safety/efficacy and post-phase-3 commercialization	Strong (matches standard cost breakdowns: clinical and commercial dominate budgets)
Phase-2 is the highest-failure clinical stage	Claimed as fact about current failure patterns	Strong (consistent with industry data: efficacy failures dominate in phase 2)
Nearly everyone in biotech will use AI within 5 years	Framed as a forward-looking belief	Speculative (trend likely, but “everyone” and specific timeframe are predictions)
AI will solve many big biology problems before we can reliably predict human drug efficacy	Opinion: virtual cells and mechanistic models will mature faster than clinical prediction	Speculative (no hard evidence, but reasonable forecast)
~50% of new drugs approved in 2024 are orphan drugs	States this as a recent statistic	Moderate (directionally right; recent years ~40–50% of approvals are orphan; exact year/percentage not evidenced in talk)
Orphan-drug incentives have pulled industry disproportionately toward rare diseases	Uses approval share vs. population affected to argue misalignment	Strong (the incentive structure clearly favors rare diseases; “disproportionate” is judgment but well supported)
GLP-1s may function as aging drugs if Alzheimer’s trial is positive	Uses upcoming semaglutide Alzheimer’s data as litmus test for “aging drug” status	Speculative (depends on pending trial; mechanistically plausible but unproven)
We could prevent most heart-attack deaths with existing lipid-lowering options	Asserts “our generation shouldn’t die of heart attacks” given PCSK9s, statins, siRNA, etc.	Moderate (biologically plausible if widely adopted; constrained by adherence, access, and side-effects)
Aging drugs will be approved before we fully understand aging or how to measure it	Claim based on analogy to animal lifespan work and regulatory lag	Speculative (reasonable scenario, but contingent on future FDA behavior)
Next trillion-dollar-scale biotechs will come from new modalities and/or AI-based infrastructure	Synthesizes historic pattern (recombinant DNA, mAbs, mRNA) with current tech trends	Speculative but plausible (good strategic thesis, not empirical fact)

E. Actionable Insights (for operators, investors, and policy people)

If you’re building a biotech company, optimize for trial geography and regulatory arbitrage from day one(Australia, New Zealand, select Asian or Chinese IIT structures) rather than assuming US first-in-human is default.
Design your company around human data as early as possible. AI/ML and fancy preclinical platforms only matter if they are aggressively tied to human endpoints and real-world clinical datasets.
Don’t over-index on “novel biology” if the target is consensus but the modality or product profile can be meaningfully differentiated (e.g., GLP-1s and Humira both won by execution and bet-selection, not exotic targets).
Expect shorter defensibility windows. Plan IP, trade secrets, and publication strategy assuming fast-follower pressure from China and others; default to deeper secrecy around mechanisms you plan to commercialize.
If you’re in AI-for-bio, focus on making “impossible medicines” rather than incremental screening tools—polyspecifics, highly tuned immune modulators, or patient-specific products where the platform is inseparable from the drug.
Structure pipelines toward large, age-related indications where contrarian theses exist (e.g., sarcopenia, muscle preservation, early cancer interception), but pair them with realistic payer and Medicare strategies.
For regulators and policymakers, treat cost-per-patient in trials as a first-class metric. Any policy that doesn’t move this down meaningfully is cosmetic.
Push for an “orphan-like” incentive framework for common, age-related diseases, or you’ll continue to see a skew toward tiny populations while the bulk of morbidity is under-served.
For individual “longevity-maximizers,” current practical stack is boring but real: aggressive lipids management, metabolic control (possibly GLP-1s where appropriate), lifestyle interventions, and early cancer detection—not speculative gene editing.
If you’re founding in this space, lean heavily into new modalities or new infrastructure bundles (sequencing + delivery + modeling) rather than “one more target-screening startup.”

H. Technical Deep-Dive (mechanisms & industry structure)

e-room’s law and cost structure The discussion frames e-room’s law as the inverse of Moore’s law: despite better tools (HTS, better antibodies, recombinant DNA, NGS), the effective cost of bringing a drug to market has increased. Mechanistically, this is driven by: tighter safety/efficacy standards post-thalidomide, longer and more complex trial designs, and a clinical-research ecosystem dominated by a few mega-CROs with little incentive to compress cost or timelines.
Regulation vs. infrastructure Even when FDA allows modernized approaches (e-source, decentralized trials, adaptive designs), the bottleneck becomes operational: entrenched CRO workflows, risk-averse sponsors, and fragmented sites. So the “regulatory problem” is really a two-layer system: formal rules and the embedded practices of CROs and sponsors.
China’s deregulation as an engineering move China implemented implied IND approval, parallelized CMC/clinical review, and extremely fast investigator-initiated trial mechanisms for high-risk modalities (CAR-T, gene therapy). That changed the “engineering boundary conditions”: same science, different regulatory constants, resulting in 5–6× faster cycles and earlier first-in-human starts.
Three horsemen of e-room’s law in R&D:
1. Time & cost of clinical development – large, multi-arm trials, chronic dosing, complex endpoints.
2. Biology ignorance – phase-2 failures reveal we still can’t reliably predict efficacy from preclinical models.
3. Design constraints – existing discovery platforms struggle with highly complex, polyspecific, or context-dependent molecules.
Where AI fits mechanically AI can: (a) learn richer sequence–structure–function relationships for proteins/antibodies/small molecules; (b) help build “virtual cell” or cell-state manifold models that predict how perturbations move cells between phenotypic states; and (c) generate patient-specific therapeutics (e.g., neoantigen mRNA vaccines). Its immediate leverage is in design space expansion and hit/lead quality, not magically fixing clinical economics.
Aging biology vs. regulation We have multiple interventions that extend lifespan in mice (e.g., caloric restriction analogues, metabolic modulators) and some in primates; but no accepted human biomarker stack that regulators will treat as a surrogate endpoint for “aging.” Hence the workaround: test across multiple age-related indications (e.g., obesity, diabetes, NASH, maybe Alzheimer’s) and infer aging impact ex-post.