January // February 2025 Progress Update
Therapeutic beginnings
Our new year at NewLimit has already been filled with discoveries. We’re now firmly in a regime where each month reveals more promising starting points for reprogramming medicines than the last. For many of us, it feels like we’ve arrived at the beginning of our therapeutic story.
A few highlights from the first months of winter:
+3 TF sets with pre-clinical efficacy in animal models of liver disease
+3 TF sets that restore youthful function in aged T cells
+10 TF sets that make old T cells look young based on gene expression
20X increase in humanized liver screening bandwidth
2X better performance for in silico reprogramming than the best baseline
People
We welcomed two new team members at the beginning of the year:
Kelly Chu joined as an Operations Specialist to upgrade our laboratory & office operations.
Julianne Obiniana joined as an Operations Specialist to support our G&A functions.
Restoring youthful function in hepatocytes
At the end of 2024, we completed our first reprogramming screens in humanized livers and discovered transcription factor (TF) sets that made old hepatocytes both look and act young. We formulated the top hit into a prototype LNP-mRNA medicine and demonstrated that it restored youthful function in a pre-clinical model of liver disease.
In the first months of 2025, we’ve repeated that process and discovered 3 more TF sets that restore youthful function when delivered with prototype LNP-mRNA medicines. We refer to these prototype medicines as leads.
Our first lead rescued the ability of old hepatocytes to regenerate the liver after damage. Youthful hepatocytes are not only more regenerative once damage has occurred, but they’re also more resilient and less likely to die in the face of damage in the first place. This month, we built prototype medicines based on 2 unique TF sets that make old hepatocytes more resilient, mimicking the performance of younger cells.
We demonstrated improved resilience in an ethanol injury mouse model. This model mimics the damage caused by alcohol in human liver disease. In this context, we can measure the amount of damage that hepatocytes experience using biomarkers of hepatocyte death in the blood.
After treatment with our prototype reprogramming medicines, old livers show a near youthful level of resilience to injury. To our knowledge, this is the first time that a prototype partial reprogramming medicine has been shown to treat an alcohol-related liver disease model.
We took a gamble when we tested one of these successful TF sets. We were excited because it made old hepatocytes look young based on gene expression profiles in our pooled screens, but we didn’t have much evidence that it could improve youthful function. This payload hadn’t been studied much at all outside NewLimit. These functional results suggest that making old hepatocytes look young is a reasonable way to discover reprogramming payloads that make old hepatocytes act young. It’s hard to imagine better motivation to scale our screens!
We have now demonstrated 4 separate prototype reprogramming medicines that treat disease in pre-clinical models. Prior to our initial results in late 2024, this had never been shown before.
Restoring youthful function in T cells
T cells are key orchestrators of the immune system. In one of their many roles, CD8 T cells kill “target” cells that present foreign antigens like virally-infected cells or cancer cells. We’ve found that young CD8 T cells are much better at killing target cells than their old counterparts. This result suggests that old T cells may be less effective at clearing infections and cancers.
Last month, we discovered 3 TF sets that restored youthful killing activity in old CD8 T cells. We believe this is the first demonstration that partial reprogramming can rescue human CD8 T cell function. Similar to our functional experiments in hepatocytes, we performed these validations with a prototype mRNA medicine, mimicking a realistic therapeutic modality.
The raw data from this experiment is shown in the timelapse microscopy videos below. Here, the target cells are engineered to express a red fluorescent protein. If the T cells are functioning well, the red cells don’t grow and there is less red in the frame.
On the left, a culture without T cells allows for rapid target cell growth. Old T cells treated with a negative control mRNA in the center can modestly prevent target cell growth. By contrast, old T cells reprogrammed with our top lead I0001 on the right are able to restrict growth similar to young T cells (less red in the frame).
We’re excited to carry these results forward into pre-clinical models.
High-bandwidth humanized liver screens
We completed the world’s first reprogramming screen in humanized livers at the end of 2024. This system is powerful because it allows us to simultaneously discover TF sets that make old human hepatocytes look young based on gene expression and act young based on regenerative potential.
In January and February, we doubled down on our investment in these tools and improved the technology to increase screening bandwidth by 20X. This means that for every humanized liver we build, we can now test 20X as many TF sets.
There was no singular trick that unlocked this improvement. Rather, a combination of improved molecular engineering, animal model development, and genomics workflows came together to yield an order of magnitude increase in performance. We’ve already completed our first screens and launched several more using this improved technology.
Performant in silico reprogramming
No matter how many TF sets we test in the laboratory, we’ll never be able to exhaustively search the space of all possible reprogramming payloads. The number of possible TF sets we might target is roughly 1016, or about 10,000X the number of stars in the Milky Way galaxy. Experimental efficiency is critical but insufficient to explore this space. We also need methods to help us prioritize which experiments to run in the first place.
To expand our search space, we’ve built AI models that can perform reprogramming experiments in silico. These models take in a representation of an old cell state and a set of TFs and predict the effect of partial reprogramming on cell age and type. We use these predictions in the world of bits to prioritize our work in the world of atoms.
This month, we demonstrated that our models are superior to the best baseline method in the broader field of genetic perturbation prediction. Our models can now explain more than half of the variation in cell age effects for unseen TF sets.
Our model’s strong performance is enabled by two main factors: (1) our proprietary data corpus and (2) recent advances in foundational models of biology. We’ve observed a data scaling law in our in silico reprogramming models where performance increases as a function of our training data scale, similar to results in natural language and computer vision. As our data set grows, our models get better, and as our models get better, the marginal data point we collect increases in value.
Foundational models of biology allow our in silico reprogramming models to start from rich representations of TFs that emerge from their DNA and protein sequences, instead of learning all of TF biology de novo. This means that our models can make reasonable predictions for new TF sets we’ve never tested before based on the TF sets that we have already tested.
We’ve already incorporated these models into our production screening process, and we look forward to sharing the results of our active learning campaigns soon.
Our team is growing
We are always recruiting talented scientists, engineers, and operators. If you’re excited by our mission to add healthy, happy years to every human life, please reach out about our open roles.