Moving beyond intracellular targets with antibody-based TPD

TPD represents a valuable therapeutic strategy that exploits cellular degradation pathways rather than conventional inhibition of protein function. Initially introduced through PROteolysis TArgeting Chimeras (PROTACs), TPD leverages the cell’s endogenous ubiquitin-proteasome system to eliminate disease-specific intracellular proteins. Traditional PROTACs consist of bifunctional small molecules that simultaneously bind a protein of interest (POI) and an E3 ubiquitin ligase, promoting ubiquitination and subsequent proteasomal degradation of the target protein. More recently, the scope of TPD has been expanded beyond the intracellular proteome to include extracellular and membrane proteins. These expanded approaches utilize bispecific antibodies (bsAbs), antibody-ligand conjugates, or small molecules to direct cell-surface or secreted proteins to the lysosome for degradation, giving rise to extracellular TPD strategies.

Antibody-based PROTACs

A significant advancement in this field is the development of antibody-based PROTACs (AbTACs), which are fully recombinant bispecific IgG antibodies engineered to target cell-surface proteins for lysosomal degradation. One arm of the bsAb binds a transmembrane E3 ligase while the other arm targets a membrane POI. This dual binding facilitates internalization and trafficking to the lysosome, leading to degradation of the POI. This concept has been successfully demonstrated with AbTACs targeting programmed death-ligand 1 (PD-L1) through recruitment of the transmembrane E3 ligase RNF43, effectively inducing lysosomal degradation.¹ By expanding TPD capabilities to the cell surface, AbTACs offer a highly specific modality for difficult-to-treat diseases.

Targeting proteolytic neoepitopes

A complementary strategy capitalizes on the unique molecular signatures revealed by proteolytic processing. Antibodies can be generated that selectively bind to proteolytic neoepitopes—novel peptide sequences exposed by extracellular proteolysis, often arising from the cleavage of membrane or secreted proteins. These neoepitopes are typically absent in healthy tissue, making them attractive targets for tumor-selective therapies. For instance, protease-cleaved forms of CUB domain-containing protein 1 (CDCP1) in RAS-driven cancers expose distinct neoepitopes, enabling highly selective targeting. Lim et al. leveraged this feature to generate recombinant antibodies that specifically bind the cleaved form of CDCP1, while sparing the full-length protein.² These antibodies demonstrated selective targeting of CDCP1-expressing cancer cells and were further developed into antibody-drug conjugates (ADCs), antibody radionuclide conjugates, and bispecific T-cell engagers. In preclinical models of pancreatic cancer, these cleaved-specific antibodies showed tumor-specific localization and antitumor efficacy, while demonstrating a superior safety profile compared to pan-CDCP1 targeting approaches.

Epitope-directed selection

To further refine specificity, epitope-directed selection (EDS) uses bioinformatics algorithms to engineer antibodies that bind to specific functional sites on target proteins, particularly those critical to disease progression. By engineering a decoy version of the protein with mutations in the target epitope then performing alternating rounds of positive and negative selection to eliminate antibodies that bind outside the target epitope, this method can identify antibodies with high specificity for the unaltered epitope. Using this approach, Zhou et al. identified antibodies that block proteolytic cleavage sites on a range of disease-relevant proteins.³ For instance, they developed antibodies that recognize the junction between the pro- and catalytic domains of matrix metalloproteases MMP1, MMP3, and MMP9, blocking their activation and impairing cell migration.

Advances in ADC technology

ADCs represent another major advancement in targeted therapy, combining the targeting specificity of monoclonal antibodies (mAbs) with the potency of chemotherapeutic agents. Typically, ADCs are composed of three components: a mAb that recognizes a specific tumor-associated antigen (TAA), a cytotoxic payload, and a chemical linker. To exert their effect, they bind to a specific antigen on the surface of cancer cells. Once bound, the ADC is internalized by receptor-mediated endocytosis and trafficked to lysosomes where the linker is cleaved, releasing the cytotoxic payload to induce apoptosis. While the development of ADCs has achieved notable success, with over 200 in clinical trials and 11 approved by the FDA, issues such as poor internalization, off-target effects, and drug resistance present significant challenges, highlighting the need for next-generation ADC strategies.

Bispecific ADCs

One such innovation is the development of bispecific ADCs (bsADCs), which leverage the strengths of both bsAbs and ADCs. bsADCs can bind two distinct antigens or epitopes on tumor cells, enhancing their specificity and internalization efficiency while minimizing the risk of off-target toxicity. Several bsADCs are currently in clinical development, targeting a range of cancers with different bsAb designs, linkers, and payloads. For example, IDP-001 is a proximity-based bsADC designed to target both EGFR and a novel tumor-associated proximity antigen (TAPA) referred to as TAPA-E1. IDP-001 has shown potent activity in preclinical studies, demonstrating significant cytotoxic activity in multiple models with an IND filing planned for early 2026.⁴ A key innovation in the development of IDP-001 is the use of proximity mapping. This method identifies optimal target pairs that are naturally co-expressed on tumor cell surfaces. By identifying tumor-associated proximity antigens that are adjacent to known TAAs, bsADCs can be developed with enhanced selectivity for cancer cells over normal tissue.

Next-generation bispecific and multispecific antibodies

Building upon the bsADC approaches discussed earlier, broader innovations in bispecific and multispecific antibody engineering are transforming therapeutic development across multiple disease areas. bsAbs engage two different targets simultaneously, potentially overcoming the limitations of traditional mAbs by targeting multiple immune pathways at once.

Bispecific immune synapse engagers

Advanced formats such as bispecific immune synapse engagers (BiCEs) are engineered to facilitate critical immune cell interactions that would otherwise be suppressed or absent in the tumor microenvironment (TME) by simultaneously binding to receptors on different immune cell types, such as T cells and dendritic cells. In a notable example, Itai et al. developed a novel BiCE specifically designed to facilitate physical interactions between PD-1+ T cells and conventional type I dendritic cells (cDC1).⁵ This BiCE strategy directly addresses limitations of traditional anti-PD-1 (aPD-1) therapies, which block inhibitory signals but do not actively promote productive immune cell interactions. In preclinical models, BiCE treatment was shown to promote the formation of active DC/T cell interactions in both tumors and tumor-draining lymph nodes. Furthermore, in vivo studies using single-cell and physical interacting cell analyses revealed that BiCE therapy induced distinct and superior immune reprogramming compared to conventional aPD-1 treatment, offering a potentially more effective strategy for boosting anti-tumor immunity.

Immune-stimulatory bsAbs

bsAbs can also directly enhance T-cell activation through co-stimulatory pathways. Immune-stimulatory bsAbs are designed to target TAAs while also delivering co-stimulatory signals to T cells through receptors such as CD28, addressing challenges like T-cell exhaustion and enhancing cytotoxic responses within the TME. A key demonstration of this approach is RNDO-564, a CD28 x Nectin-4 bsAb. RNDO-564 is engineered to provide CD28-mediated co-stimulation specifically in the presence of Nectin-4, a TAA overexpressed in several cancers. Preclinical data show that RNDO-564 triggers robust Nectin-4 and signal-1 dependent T-cell mediated killing of Nectin-4-expressing tumor cells.⁶ In vivo, RNDO-564 not only eliminates established tumors as a monotherapy but also enhanced efficacy when combined with checkpoint inhibitors. Notably, RNDO-564 demonstrated cytotoxic activity against bladder cancer cells that are resistant to ADCs, highlighting its potential in overcoming therapeutic resistance in both immune-primed tumors such as bladder cancer and immune-cold tumors like ovarian cancer.

2+1 antibody formats

The 2+1 multispecific format represents a significant advancement beyond conventional 1+1 bsAbs. This architecture features two binding domains for one target antigen and a single domain for a second target, enhancing tumor selectivity, minimizing off-target effects, and preventing TAA escape resistance. The bivalent binding to TAAs also provides higher avidity for tumor cells overexpressing the target compared to normal tissues with lower expression levels.

In T-cell engager (TCE) applications, the 2+1 strategy strategically positions the CD3-binding domain to remain inactive until the antibody is anchored to tumor cells by its dual-binding domains. This conditional activation mechanism significantly reduces the risk of systemic T-cell activation, addressing a key safety limitation of traditional bispecific designs. An example of this approach is ISB 2001, a 2+1 multispecific antibody designed to enhance cytotoxicity against multiple myeloma.⁷ By simultaneously engaging two TAAs, it demonstrates superior cytotoxic potency across various BCMA and CD38 tumor expression profiles. This dual targeting strategy confers improved resistance to competing soluble factors and enhances activity in both patient-derived samples and mouse models. Notably, despite the broad expression of CD38 in normal tissues, ISB 2001 shows reduced T-cell activation in the absence of tumor cells compared to CD38-only TCEs. A first-in-human, Phase 1, open label clinical trial is currently underway to evaluate safety and anti-myeloma activity of ISB 2001 in participants with relapsed/refractory multiple myeloma (NCT05862012).

Accelerating discovery with AI and machine learning

Artificial intelligence (AI) and machine learning are playing an increasingly central role in antibody discovery by synergizing experimental data with computational modeling. These technologies enable researchers to predict antibody structures, optimize binding affinities, and identify promising candidates with greater speed and accuracy. By analyzing vast libraries of sequences and structural information, AI algorithms can suggest targeted modifications that enhance specificity and reduce immunogenicity, accelerating the traditional discovery pipeline while reducing development costs.

Recent advances have demonstrated how AI-driven design can be used to aid the development of next-generation antibodies for challenging targets such as the central nervous system (CNS). For example, Ogden et al. developed a high-throughput, multiplexed screening platform that enables the simultaneous in vivo evaluation of hundreds of protein variants within a single experiment.⁸ Central to their approach was an innovative protein barcoding strategy, where unique molecular codes (mCodes) are incorporated into each protein, allowing for multiplexed tracking of their pharmacokinetics, biodistribution, and ability to cross the blood-brain barrier (BBB). AI-driven design tools were used to guide the optimization of brain shuttle proteins, identifying functional variants with enhanced BBB penetration capabilities. These AI-guided designs were iteratively refined, with in vivo screening data informing each optimization cycle and providing key insights into the structural elements that make an effective shuttle. This iterative cycle of design, screening, and optimization yielded novel shuttle proteins with enhanced brain uptake and extended residence time, demonstrating the power of AI-driven discovery approaches in addressing previously challenging therapeutic targets.

Deep learning models have further revolutionized antibody design and are increasingly being used to accelerate processes and improve the success rates of antibody discovery and optimization. These models can be used to predict 3D structures, improve binding affinities through targeted mutations, and generate virtual antibody libraries to accelerate discovery. In clinical development, AI algorithms are being used to predict immunogenicity risks, optimize formulations, and identify patient populations that are most likely to respond to specific antibody therapeutics.

Future outlook

As innovative strategies continue to reshape the landscape of antibody therapeutics, advances in TPD, proteolytic neoepitope targeting, and multispecific modalities are expanding the boundaries of what is druggable. Furthermore, the integration of AI and machine learning into antibody discovery and development is helping accelerate ideas into innovations, allowing for the development of next-generation biologics. These technologies, along with others beyond the scope of this article, are set to overcome many of the challenges faced by traditional antibodies, enabling the development of next-generation biologics with improved precision and the potential to treat diseases that have remained challenging to target.

References

• Cotton, A. D., Nguyen, D. P., Gramespacher, J. A., Seiple, I. B., & Wells, J. A. (2021). Development of Antibody-Based PROTACs for the degradation of the Cell-Surface immune checkpoint protein PD-L1. Journal of the American Chemical Society, 143(2), 593–598. https://doi.org/10.1021/jacs.0c10008
• Lim, S. A., Zhou, J., Martinko, A. J., Wang, Y., Filippova, E. V., Steri, V., Wang, D., Remesh, S. G., Liu, J., Hann, B., Kossiakoff, A. A., Evans, M. J., Leung, K. K., & Wells, J. A. (2022). Targeting a proteolytic neoepitope on CUB domain containing protein 1 (CDCP1) for RAS-driven cancers. Journal of Clinical Investigation, 132(4). https://doi.org/10.1172/jci154604
• Zhou, J., Le, C. Q., Zhang, Y., & Wells, J. A. (2024). A general approach for selection of epitope-directed binders to proteins. Proceedings of the National Academy of Sciences, 121(19). https://doi.org/10.1073/pnas.2317307121
• Pipeline - InduPro. (2025, February 26). InduPro. https://www.induprotx.com/pipeline/
• Itai, Y. S., Barboy, O., Salomon, R., Bercovich, A., Xie, K., Winter, E., Shami, T., Porat, Z., Erez, N., Tanay, A., Amit, I., & Dahan, R. (2024). Bispecific dendritic-T cell engager potentiates anti-tumor immunity. Cell, 187(2), 375-389.e18. https://doi.org/10.1016/j.cell.2023.12.011
• Kaur, M., Rüger, K., Chen, E. C., Rangaswamy, U. S., Davison, L. M., Arteaga, S. M., Smith, I., Chu, R., Chattopadhyay, S., Rickert, M., Aldred, S. F., Harris, K. E., Trinklein, N. D., & Clarke, S. C. (2025). Potency-optimized CD28-activating bispecific antibody for the targeted treatment of Nectin-4 positive cancers. Journal for ImmunoTherapy of Cancer, 13(4), e011323. https://doi.org/10.1136/jitc-2024-011323
• Carretero-Iglesia, L., Hall, O. J., Berret, J., Pais, D., Estoppey, C., Chimen, M., Monney, T., Loyau, J., Dreyfus, C., Macoin, J., Perez, C., Menon, V., Gruber, I., Laurendon, A., Caro, L. N., Gudi, G. S., Matsuura, T., Van Der Graaf, P. H., Blein, S., . . . Pihlgren, M. (2024). ISB 2001 trispecific T cell engager shows strong tumor cytotoxicity and overcomes immune escape mechanisms of multiple myeloma cells. Nature Cancer. https://doi.org/10.1038/s43018-024-00821-1
• Ogden, P., Garcia, A., Alioto, S., Nichols, M., Nudel, K., Carroll, S., Avera, J., Gerew, A., Anand, A., Ho-Rath, Y., Lofgren, S., Reis, A., Malekoltojari, A., & Stoica, A. I. (2024). Inverting the Drug Discovery Funnel: In Vivo First Screening for mAb Blood-brain Barrier Penetration Yields Molecules with Novel Uptake Properties (P8-4.007). Neurology, 102(17_supplement_1). https://doi.org/10.1212/wnl.0000000000206390