Engineering Smarter Immunotherapies: A Conversation with Coding Bio CEO Simon Bornschein

Earlier this year, the Myeloma Investment Fund® (MIF), a wholly owned subsidiary of the Multiple Myeloma Research Foundation® (MMRF), made a new investment in Coding Bio, an Oxford, U.K.–based biotechnology company.

Coding Bio’s platform could accelerate the development of best-in-class immunotherapies for multiple myeloma and other cancers. By combining machine learning with advanced cellular screening technology, Coding Bio can rapidly test millions of protein candidates to pinpoint the designs that are most capable of triggering powerful immune responses against cancer. The company’s modular approach allows multiple cancer targets to be addressed by a single therapy, potentially overcoming one of the most persistent challenges in myeloma treatment: drug resistance. 

“Coding Bio’s work exemplifies the kind of bold, rigorous science that we believe will move the needle for myeloma patients and reflects the MIF and MMRF’s commitment to fostering bold innovation,” said Managing Director Stephanie Oestreich, PhD, MPA. 

We spoke with Coding Bio’s CEO Simon Bornschein about the promise of the company’s platform and how it could speed up the drug development process, why the MIF’s funding and support is so crucial, and more. 

Tell us about Coding Bio and how the company got started. 

I have a PhD in cancer immunology and have spent much of my career working on immunotherapies. One thing I noticed over the years was that the field has traditionally focused on generating high-affinity antibodies—essentially measuring how strongly a therapy binds to cancer cells. But what really matters is how well the immune system becomes activated and mounts a response against those cells. That insight became the foundation for what we built at Coding Bio. 

When my co-founder, Yulia Lampi, and I started the company four years ago, we built a platform that can test millions of potential therapeutics and measure what candidates actually trigger the most powerful immune response against cancer directly in immune cells. We can now test more than 70 million different therapeutics and use that dataset to train machine learning models that simulate immune response. That lets us generate and refine new immunotherapy candidates far more efficiently than traditional methods. 

How is machine learning central to what Coding Bio does? 

Three years ago, we made the decision to build machine learning models around our growing dataset. The idea is that once you’ve measured the immune response for tens of millions of therapeutics, you can train a model to simulate that response—and then use the model to design better candidates without having to run every experiment from scratch. The advances that have driven large language models like Claude and ChatGPT are the same advances we’re tapping into for drug discovery. Instead of predicting the next word in a sentence, we’re predicting the next amino acid in a protein sequence. 

Why is a modular approach to immunotherapy so innovative, especially for myeloma? 

One of the big limitations with immunotherapies today is that they tend to target only a single antigen on cancer cells. In myeloma, most available therapies go after the same targets, which means when patients develop resistance—as often happens—there aren’t many options left to try. 

Our approach is different. Our lead program, CB101, targets two different antigens on myeloma cells simultaneously. The therapy can kill cancer cells if target A is present, or if target B is present, or both. If the cancer loses one of those targets, the other can still do the job. We’re essentially trying to preemptively outsmart resistance. 

And that modularity is designed to scale. The next iteration could go after three targets, or it could target the tumor microenvironment. Our platform lets us quickly swap new binding domains [the parts of the therapy that physically latch onto a target] as we discover them, which means we can build increasingly sophisticated therapies without starting from zero each time. 

What problem is Coding Bio ultimately trying to solve for patients? 

We’re trying to develop a therapy that can match the remarkable efficacy of CAR T-cell therapy but be available off the shelf for any patient who needs it. CAR T requires manufacturing individualized cells from each patient’s own blood, which is time-intensive and expensive. Few myeloma patients receive CAR T simply because they don’t live near a major academic medical center that can administer it. 

With an antibody-based therapy like CB101, we can produce hundreds of thousands of liters of it to freeze, and have it ready to inject when a patient needs it. The goal is that the next generation of therapies, like ours, can approach the same effectiveness as CAR T but be accessible to far more patients. 

How does Coding Bio’s platform speed up drug development? 

Drug development is inherently slow, especially when it comes to clinical development. But we can make a real difference in the discovery and early development phase. Once we identified a target for CB101, it took us six months to go from that insight to having a candidate ready to test in animals. With traditional approaches, that process can take two to five years. We’re hoping to move into a phase 1 clinical trial in early 2027, less than a year and a half after identifying the target. That’s the kind of acceleration that this technology can enable. 

How will early funding from the MIF advance Coding Bio’s work? 

This funding is critical for getting us through the most capital-intensive phase of development: manufacturing the molecule at a scale suitable for clinical use and for pushing it toward a first-in-human trial. You can build a lot of promising molecules in the lab, but at some point, you have to start manufacturing. That costs money. The MIF’s investment is what’s getting us across that threshold. In addition, MIF has a vast investor network and pharma contacts for business development discussions. 

Beyond capital, what makes the MIF’s investment different from traditional venture capital funding? 

The validation alone is significant. Having one of the world’s largest nonprofit focused solely on myeloma research back your myeloma program is a powerful signal to external investors, to the scientific community, to everyone watching the space. It says the people who know this disease best believe this therapy could make a difference for patients. 

But beyond that, the MMRF brings something venture capital can’t: deep, direct connections to the clinical community and expertise in clinical trial design. As we move toward a phase 1 trial, we need guidance on where to run it, which investigators to work with, and how to design the trial to protect patients while generating meaningful data. Having access to leading myeloma physicians and a network of 20-plus clinical centers through the MMRF’s clinical research subsidiary is invaluable. That network is something you simply can’t buy.