Curing Age-Related Diseases w Epigenetic Medicines | Lada Nuzhna, General Control

Explanation of how epigenetics works as the 'operating system' of cells, controlling which genes express in different cell types. Unlike traditional drugs that require daily dosing, epigenetic editors can permanently alter cell states, potentially offering one-time cures instead of chronic treatments. Discusses Yamanaka factors and cellular age reset mechanisms.

• •Every cell has identical DNA; epigenome determines which genes express and cell identity
• •Epigenetic changes persist through cell division, enabling permanent therapeutic effects vs daily pills
• •Yamanaka factors can reset cellular age by reprogramming epigenetic state
• •Example: permanent cholesterol suppression vs lifelong statins
• •Nature already uses epigenetic reset - babies aren't born old despite coming from aged cells

Before starting General Control, Lada ran Impetus Grants distributing $15M+ to aging researchers. The program addressed broken academic funding where 60% of National Institute of Aging budget goes to Alzheimer's alone. Two-page applications with two-week response times versus typical 70-page grants taking 7-12 months, with no reporting requirements to maximize researcher productivity.

• •Distributed $15M+ in first round with 2-page applications and 2-week turnaround vs 70-page/7-12 month government grants
• •60% of National Institute of Aging funding goes to Alzheimer's alone, starving other aging research
• •No progress reports required - trusted ambitious researchers to execute
• •Key challenge: finding reviewers who are both rigorous and ambitious, not just protecting their own research territory
• •Academics spend 70% of time writing grants instead of doing science

Detailed analysis of why China can develop drugs 40x cheaper than the US. Example: Eso Biotech went from idea to human data in under 4 years with <$50M, then sold to AstraZeneca for $1B. Monkey safety studies in the US cost more than running human clinical trials in China. The goal is reaching human data as fast as possible since that's the key inflection point for funding and exits.

• •Eso Biotech: idea to human data in <4 years, raised <$50M, sold for $1B to AstraZeneca
• •Monkey studies in US cost more than human trials in China ($1.5-2M vs <$2M for 10-person trial)
• •Human safety data is the key inflection point - can exit at $1B valuation after Phase 1
• •China has 300+ GLP-1 companies - massive competition on known targets
• •US investors now funding Chinese biotech teams due to 996 work culture and execution speed

Critique of using mouse models for age-related disease research. Mice don't naturally develop Alzheimer's, heart disease, or most human aging conditions. Researchers create artificial models by overexpressing toxic proteins, which doesn't replicate the multifactorial nature of human aging diseases. This is identified as the biggest bottleneck slowing the aging field.

• •Mice don't develop Alzheimer's - researchers artificially induce it by overexpressing human proteins
• •Mice primarily die of cancer, not cardiovascular disease like humans
• •Age-related diseases are multifactorial - single protein models are fundamentally flawed
• •Testing in wrong models is the #1 pitfall slowing drug development
• •Organoids are only slightly better than cell culture, not actual validation

Analysis of why the US healthcare system discourages one-time cures. Multi-payer insurance system means no single insurer benefits from preventing diseases that occur after age 65 (when Medicare takes over). Patients rotate insurance every few years, so insurers won't pay for permanent treatments when competitors reap the benefits. Direct-to-consumer models emerging as potential solution.

• •No insurer incentivized to prevent disease after 65 since Medicare pays then
• •Patients change insurance every few years - insurers won't pay for permanent cures benefiting competitors
• •Statins now generic and cheap, but gene editing for permanent cholesterol fix faces payment challenges
• •Eli Lilly bought gene editor for permanent cholesterol reduction - testing new payment models
• •Direct-to-consumer (like HIMS for GLP-1s) may be necessary for aging drugs

Framework for startup risk management in biotech. Scientific risk (unknown if targeting a protein cures disease) should be avoided - only work on genetically validated targets with existing evidence. Engineering risk (can you make the molecule work as a drug) is acceptable since it's been solved before. Companies that raised $200M and spent 6 years without reaching clinic are cautionary tales.

• •Scientific risk = unknown unknowns (will this target work?) - avoid this
• •Engineering risk = known challenges (can we make this molecule?) - acceptable
• •Best-selling drugs (Humira, Keytruda) targeted well-studied proteins, weren't first to target them
• •Companies raised $200M, spent 6 years on science, never pushed drug to clinic
• •Genetic validation from human studies is critical - find people who never develop disease due to mutations

Day-to-day reality of running a biotech startup focused on speed. Biology has inherent bottlenecks (cells divide at fixed rates), but US biotech has become complacent. Constant negotiation with vendors to reduce costs (100k to 10k studies) and timelines (next week to this week). CRISPR companies spent too long optimizing technology and chasing better IP instead of getting drugs to patients.

• •Biology has hard limits - can't make cells grow faster than their natural division rate
• •Constant vendor negotiations: reducing $100k studies to $10k, accelerating timelines
• •US biotech became complacent - not rushing to develop drugs for patients
• •CRISPR companies obsessed with slightly better IP from Stanford vs MIT instead of patient outcomes
• •Developing technology is easy; taking it to patients is the hard part

Discussion of best and worst case scenarios for lifespan extension. Worst case: linear progress solving cardiovascular disease adds few years. Best case: acceleration from unknown breakthroughs like epigenetic reprogramming. Cardiovascular disease likely solved in current generation through GLP-1s and gene editing. Societal concerns include dictators living thousands of years, but also unprecedented ambition from longer time horizons.

• •Worst case: solve cardiovascular disease, extend lifespan by few years (US 72 to Japan's 78-80)
• •Best case: stumble upon unknown unknowns that multiply drug discovery speed
• •Current generation likely won't die of heart disease - GLP-1s and gene editing solving it
• •Moral issue: dictators living thousands of years, no natural system cleansing
• •Longer lifespans enable more ambitious projects - 15-20 year PhDs become viable

Personal story of moving from war-torn Eastern Ukraine at age 14, living alone, learning English through SAT prep, and getting into US college (9 rejections, 1 acceptance). Philosophy of optimizing for interesting outcomes, treating life like a video game, and taking risks that consistently pay off. Parents initially critical of every decision (coming to US, dropping out) but each risk led to better outcomes.

• •Lived alone from age 14 after school shut down in Eastern Ukraine war zone (2014)
• •Hometown now doesn't exist - taken over by Russia in 2024
• •Applied to 10 colleges, rejected by 9, almost gave up before final acceptance
• •Learned English solely through SAT prep, recorded college lectures first year to understand later
• •Philosophy: optimize for most interesting outcome, risk tolerance from consistent positive outcomes
• •Parents worried about every decision but each risk paid off