Next Phase Newsletter

Juan Carlos Lopez

Andy Marshall

Although therapeutic antibodies represent a $160 billion-dollar annual market and comprise a third of all approved drugs, discovering new antibody molecules remains a labor-intensive process, requiring slow experimental approaches with low hit rates, such as animal immunizations and or the panning of phage- or yeast-displayed antibody libraries. The drug hunter’s dream would be to design an antibody to any target by simply entering information about that epitope into a computer. Now that dream is one step closer with a recent proof of principle peer-reviewed paper published in Nature on work disclosed last year from the team of 2024 Nobel Laureate David Baker. Baker and his colleagues at the University of Washington introduce the first generalizable machine-learning method for designing epitope-specific antibodies from scratch without relying on immunization, natural antibody repertoires, or knowledge of pre-existing binders.

Unlike small-molecule drug development, which has benefitted from an explosion of interest in the use of machine-learning models, in-silico design of antibody binders has lagged far behind. One reason for this is the paucity of high-resolution structures of human antibody–antigen pairs—currently only ~10,000 structures for 2,500 antibody-antigen pairs have been lodged in SAbDab (a subset of the RCSB Protein Data Bank). Most of these structures are soluble protein antigens, but there’s little data to model antibody binders to GPCRs, ion channels, multipass membrane proteins and glycan-rich targets, which are of most commercial interest. Overall, the antibody–antigen structural corpus is orders of magnitude smaller, noisier and narrower than that available for small molecules, lacking information on binding affinities and epitope competition maps via PDBBind/BindingDB/ChEMBL.

For these reasons, most companies have focused on machine learning prediction of developability properties—low aggregation, high thermostability, low non-specific binding, high solubility, low chemical liability/deamidation and low viscosity—for an antibody’s scaffold, rather than in-silico design of the six complementarity determining-regions (CDRs) on the end of an antibody’s two binding arms.

Even so, several recently founded startups have claimed to be using machine-learning models to predict/design antibody binders from scratch. These include Xaira Therapeutics, Nabla Bio, Chai Discovery and Aulos Bioscience.

Xaira debuted last year with >$1 billion in funding to advance models originating from the Baker lab. Nabla Bio also raised a $26 million series A in 2024, publishing preprints in 2024 and 2025 that describe its generative model (‘JAM’) for designing VHH antibodies with sub-nanomolar affinities against the G-protein coupled receptor (GPCR) chemokine CXC-motif receptor 7 (CXCR7), including several agonists. In August, Chai announced a $70 million series A financing based on its ‘Chai-2’ generative model disclosed in a preprint that details de novo antibodies/nanobodies against 52 protein targets, including platelet derived growth factor receptor (PDGFR), IL-7Rα, PD-L1, insulin receptor and tumor necrosis factor alpha, with “a 16% binding rate” and “at least one successful binder for 50% of targets”.

Finally, Aulos emerged with a $40 million series A in 2021 as a spinout from Biolojic Design. This program has generated computationally designed de novo CDR binders with picomolar affinities for epitopes on HER2, VEGF-A, and IL-2. The IL-2 antibody (imneskibart; AU-007)—designed to selectively bind the CD25-binding portion of IL-2, while still allowing IL-2 to bind the dimeric receptor on effector T cells and natural killer cells—reported positive phase 2 results in two types of cancer just last week. Absci, another more established company, has also been developing de novo antibodies, publishing a generative model for de novo antibody design of CDR3 loops against HER2, VEGF-A and SARS-CoV-2 S protein receptor binding domain.

Overall, though, computational efforts have largely optimized existing antibodies or proposed variants once a binder already exists. Recent generative approaches have often needed a starting binder, leaving de novo, epitope-specific antibody creation as an unmet goal. The Baker paper now provides a generalizable, open-source machine-learning approach that can find low nanomolar antibody binders to a wide range of targets.

To accomplish this task, the authors use RFdiffusion, a generative deep-learning framework for protein design, extending its capabilities by fine-tuning it specifically on antibody–antigen structures. Their goal was to enable the in-silico creation of heavy-chain variable domains (VHHs), single-chain variable fragments (scFvs), and full antibodies that target user-defined epitopes with atomic-level structural accuracy.

Their approach integrates three major components: backbone generation with a modified RFdiffusion model, CDR sequence design via the algorithm ProteinMPNN, and structural filtering using a fine-tuned RoseTTAFold2 predictor (the authors note that improved predictions can now be obtained by swapping out RoseTTAFold2 for AlphaFold3 developed last year by Google Deepmind and Isomorphic Labs). The refined RFdiffusion model can design new CDRs while preserving a fixed antibody framework and sampling diverse docking orientations around a target epitope. The resulting models generalize beyond training data, producing CDRs unlike any found in natural antibodies.

Baker and his colleagues created VHHs against several therapeutically relevant targets, including influenza H1 haemagglutinin, Clostridium difficile toxin B (TcdB), SARS-CoV-2 receptor-binding domain, and other viral or immune epitopes. High-throughput screening via yeast display or purified expression led to the identification of multiple binders, typically with initial low affinities in the tens to hundreds of nanomolar range. Cryo-EM confirmed near-perfect structural agreement between design models and experimental complexes, particularly for influenza haemagglutinin and TcdB, demonstrating atomic-level accuracy across the binding region and the designed CDR loops. To enhance affinity, the authors used OrthoRep, an in-vivo continuous evolution system, for the affinity maturation of selected VHHs. The affinity of the resulting VHHs improved by roughly two orders of magnitude while retaining the original binding orientation.

Baker and his team further challenged their method with the more difficult problem of de-novo scFv design, which requires simultaneous construction of six CDR loops across two amino acid chains. The team introduced a combinatorial assembly strategy in which heavy and light chains from structurally similar designs were mixed to overcome cases where a single imperfect CDR would compromise binding. This enabled the discovery of scFvs targeting the Frizzled epitope of TcdB and a PHOX2B peptide–MHC complex. Cryo-EM validation of two scFvs showed that all six CDR loops matched the design model with near-atomic precision.

Future work is needed to extend de novo antibody prediction via this method to tougher target classes, such as membrane proteins. Clearly, modeling across all six CDR loops and the heavy and light chains remains a hard problem; indeed, the paper’s marquee result was designing a single scFv where all six CDRs matched the designed pose at high resolution; more generally, scaling reliable heavy- and light-chain co-design beyond a few cases remains an open engineering challenge that future methods will need to solve. For the field to gather momentum, benchmarking efforts like the AIntibody challenge will be needed, together with public efforts to create datasets of negative binding data, akin to those described in a paper published earlier this year.

Overall, the Baker paper is seminal work that establishes a practical and accurate approach to designing epitope-specific antibodies from scratch. It represents a major advance in the development of therapeutic antibody discovery.

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Juan Carlos Lopez

Andy Marshall

This year’s Nobel Prize for Physiology or Medicine was awarded to Mary Brunkow, Fred Ramsdell and Shimon Sakaguchi for the discovery of regulatory T cells (Tregs)— white blood cells whose role it is to suppress overactivation of our immune system. The prize was unusual in that Brunkow made her discoveries while leading an industry R&D team at Darwin Molecular (now defunct). Ramsdell and Sakaguchi are also co-founders of two prominent biotech companies developing Treg therapies: Ramsdell’s Sonoma Biotherapeutics is developing autologous Treg therapies against arthritis and hidradenitis suppurativa, together with a LFA3-IgG1 fusion molecule for depleting CD2+ effector T cells; and Sakaguchi’s Coya Therapeutics is developing a low-dose interleukin 2 (IL-2)/CTLA-IgG1 fusion combination for amyotrophic lateral sclerosis and other neurodegenerative disorders; the Nobel prize likely helped boost Coya’s announcement in October to raise $20 million in follow-on funding on the public markets.

Tregs have long attracted the attention of drug developers interested in autoimmune conditions, diseases where the immune system is overactive. But progress in this field has been slow, and the first clinical results for T-reg cell therapies are only now beginning to emerge in liver transplantation and kidney transplantation. (Low-dose IL-2 treatments that promote Tregs have also begun to show promise in lupus and systemic sclerosis patients.)

The overarching idea behind Treg cell therapy has been to isolate these cells from a patient, introduce/upregulate expression of the FOXP3 transcription factor that marks them from other T cells, and expand them before giving them back to the patient.

Early attempts to develop this autologous therapy failed in part because Tregs are less numerous in the peripheral blood than effector CD4/CD8 T cells, difficult to isolate and problematic to expand. Moreover, the isolated Tregs are polyclonal, targeting multiple antigens. Approaches that expanded this unmodified polyclonal population of cells and put them back into patients resulted in a ‘diluted’, clinically insignificant, therapeutic effect.

To address this problem, companies are now turning to leverage advances in the chimeric antigen receptor (CAR)-T cell therapy field. A whole slew of Treg cell therapies is being engineered with CARs or T-cell receptors (TCRs), allowing targeting to specific antigens in specific organs.

As we mentioned above, the most advanced of these are in the organ-transplantation field, where chronic immunosuppression renders patients susceptible to infections that can be lethal. Sangamo Therapeutics’ TX200 and Quell Therapeutics’ QEL-001 are CAR-Treg therapies for renal- and liver-transplant rejection, respectively. These assets, which are in phase 1/2, both bind to human leukocyte antigen HLA-A2, which is exclusively expressed on the transplanted donor organ, ensuring that the Tregs travel exclusively to the place where they are needed. Elsewhere, Sonoma is also developing an autologous CAR-Treg therapy, SBT-77-7101, that targets citrullinated proteins abundant in rheumatoid arthritis (for which Sonoma recently announced positive interim phase 1 data) and the skin condition hidradenitis suppurativa.

A second focus for companies has been on TCR-engineered Tregs. The great theoretical advantages of TCRs over CARs are that 1) they have high sensitivity at low antigen density, 2) they focus exclusively on antigen-presenting cells which then reeducate/suppress effector T cells; 3) they don’t bind soluble antigen and 4) most autoimmune diseases are driven by intracellular proteins presented as processed peptides in the context of HLA. As yet, however, only a few companies are pursuing the approach. One example is GentiBio, which is developing GNTI-122 for type 1 diabetes. This Treg product expresses a TCR targeting a fragment (IGRP 305–324) of the pancreatic islet-specific antigen glucose-6-phosphatase catalytic subunit-related protein (IGRP). Another pioneer in this area, Abata Therapeutics, had also been developing a TCR-engineered Treg therapy (targeting myelin peptide/HLA-DRB1*15:01 for multiple sclerosis); however, the frosty financing environment in the first half of 2025 meant it ran out of cash and Abata closed its doors in August.

One challenge that all Treg cell therapies face is the plasticity of these cells and their tendency to shape shift into effector T cells, a phenotypic change that, in the therapeutic setting, could lower efficacy or even exacerbate pathology. One approach to address this problem has been to modify the cells by overexpressing the transcription factor FOXP3, the master regulator of Treg development. For example, as methylation of the FOXP3 promoter under inflammatory conditions can turn Tregs Into effector T cells, Quell’s Tregs are engineered with a methylation-resistant FOXP3 that compels the cells to remain in their suppressor phenotype. And to bring us back to where we started, Nobel laureate Sakaguchi turns out to be a serial entrepreneur, founding another company, Regcell, that recently relocated from Japan to the US on the back of a $45.8 million financing back in March. The company is using small-molecule CDK8/19 inhibitors that act as epigenetic modulators to lock in FOXP3+ Tregs that show a stable suppressive phenotype in vivo.

But Treg cell therapies still face stiff competition. Ironically, perhaps, from their antithesis: the effector CAR-T cell. Pioneering work by Georg Schett’s group at Friedrich Alexander University Erlangen-Nuremberg has galvanized numerous efforts to develop CAR-T depleters of pathogenic B-cell or plasma-cell subsets in autoimmune conditions. Evidence is growing for the clinical efficacy of this approach in diseases such as lupus or myasthenia gravis.

But the holy grail would be to dispense with cell therapy altogether and promote Treg activity in situ, without the need for purification and modification/expansion outside the body. By focusing on injectable biologics, many companies can bring products to market that are easily accommodated into current clinical practice, dispensing with the need for leukopheresis (an approach alien to most rheumatologists) and the complex logistics of ex vivo cell therapy.

​Nektar Therapeutics’ rezpegaldesleukin is a pegylated IL-2 given at low doses that acts on CD25, the high-affinity IL-2 receptor enriched in Tregs. The company recently reported positive phase 2 data in atopic dermatitis. Elsewhere, Egle Therapeutics and Mozart Therapeutics have discovery programs developing bispecific antibody Treg engagers for multiple autoimmune diseases. TrexBio has developed a peptide agonist of tumor necrosis factor receptor 2 (TNFR2), announcing in June the dosing of its first participant in a phase 1 trial for atopic dermatitis and other inflammatory diseases. Zag Bio is another T-cell engager play that recently came out of stealth,

The Treg field can rightly celebrate its Nobel recognition and the progress made towards bringing this cell type to patients. Although it will likely be several years before we gain a full picture of how Treg biology can be leveraged to fight autoimmune disease, the field eagerly awaits the readout from early efficacy trials of cell therapies and potentially an FDA-approved product for the biologics in later development.

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