Lada Nuzhna, founder and CEO of General Control, is building a biotech company that engineers epigenetic medicines to treat age-related diseases. She previously founded Impetus Grants, a funding initiative that distributed ~$50 million to early-stage aging research. The conversation covers her path from Ukraine to the US, the science of epigenetic reprogramming, why she targets disease rather than aging directly, how China is reshaping biotech competitiveness, and her philosophy on risk, longevity, and ambition.
Epigenetic reprogramming as a new therapeutic modality
Every cell in the body has the same DNA sequence, but the epigenome determines which genes are expressed and what cell type emerges (e.g., neuron vs. liver cell).
The epigenome also encodes cellular aging. Nature already resets epigenetic age during embryogenesis—babies born to 30-year-old parents are not born “old,” despite originating from aged gametes.
Shinya Yamanaka discovered that expressing four transcription factors (now called Yamanaka factors) can reset a cell’s epigenetic age, creating induced pluripotent stem cells younger than the cells they were derived from.
This inspired the field of epigenetic reprogramming: the idea that you can rewrite the “operating system” of a cell rather than just tweaking downstream proteins.
Most existing drugs (small molecules, antibodies) work transiently—you must take them daily. Epigenetic editing can permanently alter cell state, moving closer to a cure than a chronic treatment.
General Control’s approach is targeted: rather than broadly reprogramming cells with Yamanaka factors, they aim to rewrite gene expression of specific genes driving disease. For example, permanently suppressing the gene that degrades LDL receptors could replace a lifetime of statins with a single treatment.
Targeting disease vs. targeting aging directly
General Control focuses on age-related diseases rather than aging itself, because:
You can run clinical trials for specific diseases; there is no agreed-upon way to run a trial for “aging.”
Aging trials would require measuring lifespan, which takes decades.
Drugs for healthy people (aging prevention) must have extremely high safety bars, limiting effect size.
The company selects disease indications based on genetic validation: studying people with natural mutations that protect against certain diseases (e.g., those who never develop high cholesterol regardless of diet). This provides human proof-of-concept before any drug is built.
The goal is to pick indications where the effect size will be dramatic, making it easiest to validate the technology platform.
Why biotech must move faster: the China factor
A company called IOBiotech went from idea to human data in under four years by running studies in China, then sold to AstraZeneca for ~$1 billion. They raised less than $15 million total.
China’s advantages:
Two waves of regulatory deregulation over the past decade have shortened IND (Investigational New Drug) approval timelines.
Investigator-initiated trials (IITs) allow a doctor at a hospital to co-author a small first-in-human study without full regulatory overhead.
A monkey study in the US costs $1.5–2 million; a 10-person human trial in China can cost less than $2 million.
The key inflection point is human data: before that, a biotech startup has essentially no value because most drugs fail at efficacy (Phase II). After Phase I safety data, companies can partner with pharma or exit at billion-dollar valuations.
US biotech has become complacent: spending years optimizing technology, chasing slightly better IP, and running efficacy studies in mice—even when mice don’t naturally get the disease being studied (e.g., mice don’t develop Alzheimer’s).
Chinese teams are no longer just fast executors; they are producing original science that hasn’t been published in Western journals.
Lada keeps General Control deliberately small and agile to remain competitive with Chinese teams operating on a fraction of US capital.
Impetus Grants: funding aging research before starting a company
Before founding General Control, Lada started Impetus Grants to fund early-stage aging research that traditional grant mechanisms ignore.
The National Institute on Aging directs ~60% of its funding to Alzheimer’s alone, creating broken incentives: if you want funding, you must work on a single disease, not on aging itself.
Impetus Grants raised ~$50 million (initially from crypto investors, including Juan Benet) and distributed it through a streamlined process: two-page applications, two-week response times, no reporting requirements.
This contrasted with typical academic grants: 70 pages, 7–12 months for a response, plus ongoing reporting.
The program taught Lada which scientific approaches were gaining traction and helped her identify the path for General Company.
Scientific risk vs. engineering risk
Lada distinguishes between two types of risk in biotech:
Scientific risk: “If I target this protein, will it cure this disease?”—this is the unknown unknown that costs billions to resolve.
Engineering risk: “Can I get my molecule to have the right properties to be a drug?”—this is solvable with known methods like protein engineering and high-throughput screening.
She deliberately avoids scientific risk in the early stage. Best-selling drugs of all time (e.g., Humira, Keytruda) targeted proteins with years of published research behind them, not new biology.
With a small startup, you need the first thing to work. Only after reaching scale and having infinite runway should you take on more scientific risk.
Company roadmap and next steps
General Control has existed for almost two years.
Year 1: Technology development and optimization, mostly in cell lines, then formulating for mouse studies.
Now: Testing drugs in mice for efficacy.
Next: Final specificity and safety optimization, then monkey studies (IND-enabling), followed by an IND submission to the FDA.
After IND: First-in-human trials, likely in China for cost and speed.
The company may run monkey studies in China as well, since even primate studies are cheaper there.
The primary goal of monkey studies is safety (does the monkey die?), not efficacy.
Why mouse studies are often misleading for human disease
Mice don’t naturally develop most human age-related diseases:
Mice don’t get Alzheimer’s. Researchers create artificial models by overexpressing toxic human proteins, but this doesn’t capture the multifactorial nature of the disease.
Mice largely die of cancer, not cardiovascular disease.
Curing cancer in mice extends lifespan dramatically, but cancer is only one cause of death in humans.
Organoids (3D cell cultures) are only marginally better than standard cell culture—they’re not actual organs.
Testing drugs in the wrong model creates an illusion of progress: you can “cure Alzheimer’s in mice” without solving anything real.
Longevity escape velocity and the future
Longevity escape velocity is the idea that if you can extend life faster than you age, you could theoretically live indefinitely.
Worst case: Linear progress. Cardiovascular disease gets solved (it’s close already—GLP-1s, gene editors that permanently lower cholesterol). US lifespan rises from ~72 to maybe ~80, similar to Japan today.
Best case: Acceleration through unknown unknowns—breakthroughs like partial reprogramming or GLP-1s that multiply the speed of drug discovery and testing.
Lada believes people living 200–300 years would be more ambitious, not less, because they’d have time to pursue increasingly complex projects (e.g., a 20-year PhD in a new field).
She personally optimizes for interesting outcomes rather than conventional success, and takes big risks because every major risk she’s taken has paid off.
The broken incentive structure for curative therapies
In the US, a one-and-done curative drug faces a misaligned payment problem:
Before 65: people rotate insurance every few years, so no insurer wants to pay for a cure whose benefits they won’t capture.
After 65: Medicare pays, but the multi-payer system still disincentivizes prevention.
Statins are no longer highly profitable (they’re generic), but the systemic problem remains for new curatives.
Possible solutions include direct-to-consumer models (e.g., Lilly Direct selling GLP-1s directly), but this is unsolved at scale.
Personal background: from Ukraine to the US
Lada grew up in eastern Ukraine, where war started in 2014 (not 2022). Her school was shelled that year; her hometown is now occupied by Russia.
She was living alone at 14 because there were no functioning schools in her town and her parents didn’t want to move.
She went to Poland at 16 on a foreign exchange, didn’t know Polish, and spent months in classes she didn’t understand.
She applied to 10 US colleges, was rejected by 9, and got into one in Romania before eventually making it to the US.
She learned English by studying for the SAT, arrived in the US having never been to an English-speaking country, and recorded all her first-year college lectures on audio to re-listen later.
She dropped out of college, which she considers a benefit—biology education is tailored to pre-med memorization (organelles, protein names) rather than practical drug development knowledge.
She is self-taught in biology: reading key papers, collaborating with scientists by cold-emailing them, and learning by doing rather than accumulating credentials.
Her parents remain skeptical of each new risky decision (dropping out, starting a company), but she operates on the principle that most risks are less bad than they seem and that serendipity favors action.
Role models and influences
Bob Swanson, co-founder of Genentech: a VC with no PhD who read a paper on recombinant DNA cloning, cold-called the scientist (Herbert Boyer), and started the first biotech company. He faced criticism for commercializing science but executed at extraordinary speed.
John Maraganore, founder of Alnylam: similarly built a company around a new therapeutic modality (RNAi).
Both figures exemplify Lada’s philosophy: identify a transformative technology, pick a clear direction early, and execute fast rather than optimizing endlessly.
Daily reality of building a biotech startup
Biology is inherently slow—cells divide at a fixed rate, and you cannot accelerate experiments.
Much of Lada’s day-to-day involves arguing with vendors to reduce costs (e.g., $100K → $10K per study) and compress timelines (next week → this week).
The cumulative effect of shaving days off every interaction and reducing every cost is what separates fast-moving companies from slow ones.
She spends significant time on vendor management and procurement—the unglamorous but critical work of keeping experiments moving.
