Alex Federation

AI x Bio teams like Origin have coding agents, scaling laws, and a wave of big biotech deals all at their backs. This is barely touched territory. Crazy what this small team can do now.

New study: for years Yamanaka factors have driven longevity optimism. They factory reset cellular age, also erase the cellular identity. The drawback is potential cancer. Transcription Factor Perturbations is a new scalpel approach targeting specific rejuvenation levers while keeping the cell's identity. Example breakthrough EZH2: mouse liver age reversal by 8 human-year equivalent by reducing liver fibrosis and fat by 50% and significantly improving glucose tolerance. Study details Transcriptional factors, particularly the Yamanaka factors (OSKM), are key to understanding and potentially reversing aging. OSKM can perform a cellular "factory reset," erasing epigenetic memory and inducing a stem cell-like state. Partial reprogramming with OSKM offers a path to rejuvenation, but its clinical use requires precise temporal and dosage control to prevent dedifferentiation and tumor formation. A recent study established a Transcriptional Rejuvenation Discovery Platform (TRDP) to identify novel transcription factor perturbations capable of driving cellular rejuvenation and reversing aspects of replicative aging in human fibroblasts. The platform was trained on transcriptional shifts between early- and late-passage human cells in culture to identify gene expression and transcription factor changes associated with aging. These changes were ranked computationally to prioritize transcription factors relevant to rejuvenation. The top 200 candidates were screened by parallel overexpression (CRISPRa) or inhibition (CRISPRi), followed by single-cell RNA sequencing to evaluate transcriptional consequences. Rejuvenating factors were identified by their ability to reverse aging-associated gene expression, quantified by a negative correlation score (R₍rej₎). Four were selected for further study: inhibition of STAT3 and ZFX, and activation of EZH2 and E2F3. Findings in human cells (fibroblasts in cell culture) In high-passage cells, all four perturbations induced rejuvenation-associated phenotypes, including increased proliferation (KI67), improved proteasome activity, reduced lysosomal staining, p21 downregulation, and improved mitochondrial function (strongest with EZH2). These effects mirrored in vitro OSKM reprogramming for the measured hallmarks, but without changing cellular identity. DNA methylation clocks remained stable, consistent with the decoupling of senescence and epigenetic aging. Findings in aging mouse livers For in vivo validation, the aging mouse liver was chosen. EZH2 was selected due to its age-associated decline, favorable safety profile over E2F3, and lack of STAT3-like disease-specific liver involvement. Three weeks of liver-specific EZH2 overexpression via AAV8 delivery reversed aging-associated gene expression and phenotypes by an equivalent of roughly eight months of mouse aging, including reductions in steatosis and fibrosis and improvements in glucose tolerance. EZH2 overexpression produced stronger rejuvenation-associated transcriptional changes in vivo than those observed in vitro, particularly affecting inflammatory pathways and age-related loss of cellular identity, including inappropriate activation of muscle and cardiac gene programs in aged liver tissue. In 20-month-old mice, fibrosis and glucose intolerance improved by approximately 50% relative to young mice. Importantly, cellular identity was preserved, no liver damage or histological abnormalities were observed, and comparisons with multiple mouse liver cancer models showed no overlap with cancer-associated transcriptional signatures over the short treatment window. Significance This work introduces a systematic framework for identifying transcriptional factors as potential levers for longevity and rejuvenation. EZH2 emerges as a promising target for further exploration via gene therapy or targeted modulators, based on rejuvenation-associated signatures observed in human fibroblasts and functional rejuvenation of the aging mouse liver without overt damage or cancer-like signals. While the OSKM reprogramming strategy demonstrate the reversibility of aging through global epigenetic resetting, it carries intrinsic risks related to identity destabilization. In contrast, targeted transcription factor perturbations enable the reversal of multiple aging-associated hallmarks without engaging in full or partial reprogramming, suggesting a more precise and potentially safer route to rejuvenation. These distinct approaches collectively indicate that rejuvenation operates across various biological layers, from broad epigenetic resets to targeted transcriptional network recalibration. Limitations The current screening strategy and computational model are biased towards transcription factors effective in replicative aging models of passaged neonatal human dermal fibroblasts, which differ substantially from organismal aging in vivo. While many hallmarks overlap, this model does not fully capture aging in post-mitotic cells or complex tissue and organ environments. The platform uses cellular proliferation and survival in culture as proxies for rejuvenation, whereas in vivo aging is influenced by additional factors such as differentiation state, immune interactions, and intercellular communication. Reliance on proliferative capacity also carries inherent oncogenic risk. Although cancer-associated transcriptional signatures were not observed in this study, longer-term effects cannot be excluded. Finally, liver rejuvenation was demonstrated in a single organ over a short treatment period in mice. The absence of damage or oncogenic signatures cannot be considered a definitive safety signal, and long-term studies, including large-animal and non-human primate models, will be required to establish safety, durability, and systemic relevance.

The Humanity Project People always ask me: if we want to cure all diseases with AI, shouldn't we be building a huge robotic warehouse to run closed loop experiments on mice, human cells, and so on? From a naive outside perspective, this may seem like the obvious thing to do, analogous to what Periodic Labs is doing for materials and what the robotics companies like Physical Intelligence are doing for laundry and so on. The problem is, for most diseases, experiments in cultured cells, mice, and other models are poorly predictive of success in actual humans. If you synthesize a material in the lab and find it has a certain conductivity, then that’s the conductivity; by contrast, if something works in a mouse, you still have to test it in a human, and mouse biology is so poorly predictive of human biology that for some diseases success in mice actually anticorrelates with success in humans. Closed loop reinforcement learning in mice might be a great way to learn how to cure mouse diseases, but the only way to cure human diseases is to run more experiments in humans. To cure all diseases, in other words, what we actually need is a massive, centralized effort to scale up clinical trials testing new AI-generated therapeutic hypotheses. Such an effort would serve several goals: it would identify novel mechanisms for treating diseases; it would train AI systems to understand human biology; and it would build the infrastructure needed to streamline and accelerate clinical trials going forward. Centralization would allow us to minimize duplication, maximize data availability for training, and ensure the resulting infrastructure is shared. An effort of this scale -- which would dwarf the genome project and consume resources similar to the entire NIH budget -- is not just our best chance to cure all diseases. It is also our best chance to remain ahead in biotechnology, where the US is rapidly losing ground to China. I call it the Humanity Project, and I describe it in a new essay, linked below. The first emphasis in the Humanity Project is on novelty. Medicine moves forward when we identify new therapeutic mechanisms -- think GLP-1s, CAR-T cells, and checkpoint inhibitors. Within a few years, AI agents will be as good as or better than humans at proposing novel therapeutic mechanisms. However, testing novel therapeutic mechanisms is risky and rarely pays off, so most biopharmas largely avoid it. Instead, 96% of clinical trials today are aimed at obtaining approval for new drugs that use existing therapeutic mechanisms, rather than testing new ones. If we want to cure all diseases, we need to massively expand the number of clinical trials we run that examine novel hypotheses, both as a way of identifying novel therapeutic mechanisms and as a way of training AI models to better understand human biology. This brings us to the second emphasis of the Humanity Project, which is on scale. The Humanity Project would be a centralized project to run 2000 drug programs testing novel, AI-generated therapeutic hypotheses through clinical proof of concept over 5 years. The Project would cost between $50B and $200B -- equivalent to the amount allocated for the California High Speed Rail, or the 5-year budget for the NIH --, would increase the number of first-in-class drug trials by 400% to 800% over that period, and would benefit from various advantages of AI in proposing and prosecuting clinical trials. In the process, the Project would lay down the infrastructure needed to accelerate and scale future trials run by the private sector, accelerating US biotechnology as a whole. The logistical challenges associated with such a problem are intense. We need better infrastructure to recruit and identify patients; we need to streamline regulation to make it easier to get drugs into patients faster while ensuring safety; we need to scale up and accelerate manufacturing; and we need to plug all of this infrastructure into AI systems that can efficiently integrate information, generate hypotheses and plan experiments, and learn from outcomes. The Humanity Project would work on developing these, and would make the resulting infrastructure available subsequently to accelerate US biotech as a whole. And, importantly, if we are serious about curing all diseases in the coming decades and maintaining US leadership in biotechnology and medicine, there is no alternative. With all the wealth and value set to be created by AI, finding the resources to accelerate medicine in this way should be straightforward. We should begin now. Read the full proposal below.

Difficulty Levels of Startups: Insane: Robotics Hard: Hardware, consumer social Medium: Consumer Software, marketplaces Easy: B2B SaaS What did i miss?