Evolution designed us to die fast; we can change that - Jacob Kimmel

Kimmel further delves into how these pressures manifest as a limited gradient signal during evolution. Since few individuals survived long enough to benefit materially from longevity beyond reproductive years, natural selection had little incentive to strongly optimize for it. This interplay results in the biological phenomenon that the evolutionary "optimizer" could only take small steps constrained by mutation rates and population size, directing most of its efforts toward traits that improve early-life fitness, such as intelligence needed for survival, rather than indefinite youthfulness.

The Role of Intelligence and Developmental Trade-offs

The conversation smoothly transitions into an analysis of human intelligence as an evolutionarily optimized trait. Kimmel observes that while bigger brains and longer adolescence set humans apart from other primates, there are strong selective forces limiting how long these developmental phases can extend. A prolonged adolescence, for example, is disadvantageous in environments with high mortality risks since one must reach reproductive maturity swiftly to ensure gene propagation. Therefore, the optimization of intelligence likely involves a trade-off, balancing increased cognitive capacity with survival imperatives.

He points out an intriguing consequence of this trade-off: many scientific and intellectual achievements happen early in life, frequently before the age of 30. This peak aligns with the evolutionary period during which fluid intelligence would have been maximally selected for. Kimmel suggests that these developmental and life-history traits reflect deeply ingrained evolutionary patterns rather than solely societal or cultural factors.

The Complex Constraints of Evolutionary Optimization

Exploring deeper, Kimmel frames the genome and natural selection through the lens of optimization theory and machine learning analogies. Evolution acts like a parameter optimizer but is limited by mutation rates and population sizes, imposing strict constraints on the rate and nature of adaptations. For instance, mutation rates that are too high cause cancers and other deleterious effects, while rates too low impede adaptation. Moreover, evolutionary "step sizes" are limited, meaning large beneficial changes—such as drastically extending lifespan—are unlikely to evolve within the constraints of these processes.

Another critical pressure shaping evolutionary trajectories is infectious disease resistance because pathogens have historically imposed a dominant selective burden. This focus on immune defense can divert resources away from traits like longevity or cognitive enhancements, making certain evolutionary shortcuts unfeasible. Hence, even if selection for longer life existed, the genomic "loss function" might be weighted such that fighting infections takes precedence, leaving aging as an unoptimized residual.

Kin Selection and Aging as a Population 'Regularizer'

Kimmel explains how the concept of kin selection complicates the straightforward evolutionary argument for longevity. From a gene-centered perspective, there can be negative pressure against living longer because aging and death promote generational turnover. If an old individual persists but declines in fitness or resource contribution, it could lower the overall genome propagation compared to the scenario where they die and are replaced by more reproductively capable younger individuals.

He describes this as aging acting like a "length regularizer" analogous to constraints in machine learning that prevent overfitting. In this case, it restricts organism lifespan to an optimal range for gene dissemination and population renewal. This principle can explain why anti-aging solutions might face complex pleiotropic effects that could inadvertently reduce fitness or be selected against over evolutionary timescales. Therefore, evolutionary design does not "favor" indefinite youthfulness, but rather a balance shaped by reproductive efficiency and population dynamics.

Evolutionary Arms Races and the Origin of Antibiotics

Pivoting to another fascinating topic, Kimmel addresses why humans—or mammals broadly—have not evolved their own antibiotics despite the potent selective advantages such defenses would confer. He emphasizes the Red Queen hypothesis to explain the evolutionary arms race characteristic of microbial life, where high mutation rates and enormous population sizes of bacteria and fungi allow rapid co-evolution of resistance and countermeasures.

Unlike metazoans, microbes can tolerate high mutation loads without the catastrophic consequences seen in multicellular organisms. This difference facilitates the development of specialized metabolic pathways producing antibiotics in microorganisms, but also rapid microbial resistance. Mammals do not partake directly in this microbial warfare on a genetic/chemical level, but instead rely largely on immune system strategies. Kimmel discusses examples of co-evolutionary "arms races" in mammals, including genes like TRIM5alpha that protect against viral relatives of HIV but have evolved in a complex context of past infections and genomic changes, illustrating evolutionary gains and losses over time.

Epigenetic Reprogramming in Aging

Kimmel presents epigenetic reprogramming as a promising avenue for intervening in aging. Transcription factors (TFs), which act as genomic "orchestra conductors," regulate which genes are turned on or off and thus dictate cell state and function. He emphasizes that while all cells share the same DNA, their divergent functions result from epigenomic modifications controlled largely by these TFs. With aging, epigenetic marks degrade, leading to impaired gene regulation and cellular functionality.

The approach NewLimit pursues involves identifying and delivering combinations of transcription factors to "rewind" the epigenome to a more youthful state, restoring cell function across multiple tissues. However, this process is delicate; broad genome remodeling risks pushing cells toward improper states, potentially causing pathological effects like tumor formation. Therefore, they employ high-resolution molecular and functional assays, including single-cell genomics, to characterize the precise effects of candidate TF combinations and ensure both efficacy and safety.

AI Identifying Effective TF Combinations

Why AI? Kimmel underscores the scale and complexity of searching combinatorial spaces of TFs as a challenge far beyond what is tractable with traditional trial-and-error approaches. Unlike classic work such as Yamanaka's discovery of four TFs sufficient for induced pluripotency—which benefited from easy binary success criteria and cell proliferation amplifying rare successes—aging reversal involves subtler gradations in cell state that require nuanced measurements.

With around 1,000 to 2,000 transcription factors and possible combinations numbering in the quadrillions, exhaustive experimental screening is impossible. AI models trained on extensive single-cell perturbation data allow prediction of TF combinations that yield desired youthful states while avoiding deleterious outcomes. This paradigm enables prioritizing the most promising combinations for experimental validation and accelerates the development of effective, specific reprogramming therapies.

Why Targeting Transcription Factors Has Been Difficult

The conversation reveals why most existing drugs do not target transcription factors directly, despite TFs' central role in regulating cell states. Unlike surface receptors or secreted proteins, TFs operate inside the nucleus, binding large DNA interfaces that small molecule drugs struggle to disrupt or activate effectively. Protein therapeutics like antibodies are too large to penetrate cells and access nuclear targets, and small molecules lack the necessary size and specificity for TF interactions.

Recent advances in nucleic acid delivery and genetic medicines, such as mRNA therapeutics and viral vectors, now offer novel ways to directly modulate TFs by delivering their coding RNAs into cells. These innovations represent a shift away from indirect "bank shot" approaches, laying a foundation for potent and precise interventions at the level of gene regulation itself, overcoming longstanding delivery and targeting barriers.

The Promise of Cellular Therapeutics

Addressing the formidable challenge of delivering therapies broadly and specifically across diverse cell types, Kimmel acknowledges that while lipid nanoparticles and viral vectors offer promising solutions, each has limitations in tissue targeting and immunogenicity. The promise lies in engineering living cells—immune cells such as T cells—that can patrol the body, sense complex environmental signals, and deliver therapeutic payloads precisely where needed, acting as autonomous biological drug delivery systems.

This approach, inspired by the body's own surveillance mechanisms, could overcome current physical constraints by harnessing complex cellular circuits encoded in large genomes. As living therapeutics, these engineered cells could persist and dynamically respond over years, offering a more adaptive, durable, and comprehensive method for administering gene or TF-based therapies. CAR-T therapies represent an early clinical example of this paradigm, though broader applications require further technological maturation.

Systemic Benefits of Localized Reprogramming

Kimmel calmly tempers expectations about achieving whole-body rejuvenation in one swift treatment, especially given current delivery constraints. Even if only a subset of cells or tissues—such as hepatocytes of the liver—can be targeted initially, substantial systemic health benefits may still emerge due to the interconnected nature of organ systems. Organs like the liver and hematopoietic stem cells secrete signals that influence the function of distant tissues, yielding positive ripple effects.

Clinical transplantation evidence supports this concept: recipients of younger livers show broader improvements beyond just hepatic health. This suggests that partial reprogramming or rejuvenation in specific tissues could translate into widespread systemic aging mitigation, reducing the burden of age-related diseases and thus offering meaningful extensions to healthspan well before comprehensive cellular delivery becomes ubiquitous.

Virtual Cells and Scaling Drug Discovery

Jacob Kimmel elucidates the ambition to create "virtual cells" — computational models that predict how cellular systems respond to genetic and epigenetic perturbations. By measuring transcriptomic changes induced by various gene edits or factor combinations using advanced single-cell genomics methods like Perturb-seq, researchers can train AI systems to map interventions to cellular states. These models provide a platform for rapid in silico exploration, narrowing experimental demands in the vast combinatorial space.

Kimmel acknowledges that while the data and technology are still emerging, such approaches hold the key to overcoming the industry's notorious "Eroom's Law," where drug discovery costs rise despite technological advances. By translating sparse biological data into robust predictive frameworks, the possibility of compounding returns in drug discovery arises, enabling broader addressable markets and accelerating the development of therapies with transformational impact on human health.

The Changing Landscape of Pharma

Finally, Kimmel provides insight into how traditional pharmaceutical companies engage with disruptive technologies. While major pharmas invest in AI and innovation, much of today's pioneering drug discovery, especially for novel and complex interventions like epigenetic reprogramming, is performed by nimble biotechnology startups. These companies generate proprietary data and develop specialized expertise, which then attracts collaborations or acquisitions by larger firms for late-stage clinical development and commercialization.

This ecosystem, shaped in part by regulatory and market dynamics, encourages specialization and distributed innovation rather than monolithic undertakings. Kimmel characterizes this model as akin to venture capital dynamics, where startups push frontiers and pharmas leverage scale and resources to bring treatments to market. As the field advances, integrating AI-driven discovery, cellular therapies, and innovative delivery will progressively transform both the science and business of medicine.