While agents like AbbVie’s Humira have transformed the treatment landscape, not all patients benefit equally from the drug. Better biomarker analysis and more investment in mechanistic trials can inform the development of more effective therapies with broader clinical value.
Anti‑TNF therapies such as AbbVie’s blockbuster Humira, along with advances targeting the IL‑23 and JAK pathways, have helped millions of patients with immune-mediated diseases, providing deep, lasting remission and enhanced quality of life. As we build on these first generation drugs, the next era of discovery will be defined by how we learn from every trial and every patient.
As leaders at AbbVie Immunology, we are advancing biomarker-driven approaches to better match therapies to the patients most likely to benefit. By investing early in the drug development process, sophisticated data collection and mechanistic clinical trials, we believe that our and other R&D teams can accelerate learning, sharpen patient selection and deliver longer-lasting benefits to more patients.
Innovation needed for nonresponders
A quarter-century ago, the immunology field witnessed a breakthrough with the introduction of monoclonal antibodies (mAbs) such as Humira, the first fully human anti-TNF-alpha mAb. This first wave of immune therapies was transformative: these treatments were designed to recognize and bind to a specific protein, neutralize its activity and thereby block inflammation. This approach improved outcomes for many people living with immune-mediated diseases. However, not all people experienced deep or lasting remission, and many who initially benefited eventually reached a plateau.
This plateau, or “efficacy ceiling,” and nonresponse among some patients are a result of the complex biology of immune-mediated diseases, which involve networks of overlapping immunoinflammatory pathways that vary significantly between patients and can evolve over time. In inflammatory bowel disease, for example, sustained nonresponse to anti-TNF therapy has been linked to ongoing JAK-STAT signaling, type 1 interferon signatures and fibroblast-driven stromal remodeling—pathways that operate largely independent of TNF blockade and that standard population-level efficacy readouts cannot easily disentangle. This variability is why a universal solution falls short and why precision medicine may offer a solution to break through nonresponse and the efficacy ceiling.
While current treatments are guided by clinical and preclinical insights into mechanisms of action, two questions remain insufficiently explored: Why does therapy work for some but not others, and why can response decrease over time? The answers may be hidden in clinical data from responders and nonresponders.
Biomarkers, tissue and the path to precision
Reverse translation—re-examining clinical patient data with innovative approaches—has already uncovered important new insights into disease. Clinical research is increasingly designed to generate more data points to better enable reverse translation and link those observations back to discovery and study design. This strategy demands we raise the bar on what we measure and where we measure it.
In many immune-mediated diseases, blood-based measurements offer an incomplete view of the biological interactions shaping patient response. Blood can provide a readout of inflammation or change over time, but it may not sufficiently explain why a therapy is not working for a specific individual. Hence, there is a growing emphasis on obtaining tissue at the area of inflammation to better understand disease biology and nonresponse.
Of course, tissue collection is not simple. Feasibility and patient burden vary by disease and tissue type, and inflammation itself can be patchy or heterogeneous, making sampling and interpretation challenging. To help address these realities, the field is developing less-invasive technologies that can extract richer insight from smaller samples, reducing burden while capturing information closer to the disease activity than a standard blood draw.
Advances in single-cell RNA sequencing, spatial transcriptomics and high-sensitivity proteomics are enhancing insights from minimally invasive biopsy samples, enabling researchers to resolve distinct immune cell states and stromal microenvironments at the site of disease with unprecedented resolution and depth. A 2020 rheumatoid arthritis study, for example, showed how integrative multi-omics and machine learning approaches can help forecast patient response to treatment with a TNF-alpha inhibitor.
The need for mechanistic clinical trials
As biomarker ambitions rise, a structural reality remains: pivotal trials are primarily designed to demonstrate safety and efficacy for regulatory decision-making. Samples are collected at baseline and later endpoints, typically weeks after treatment begins, making it difficult to capture early mechanistic changes that can reveal how or whether a therapy is working in tissue.
The upshot is that some of the most important biological questions, particularly those related to nonresponse, may only be addressed after major development decisions have been made. Because they do not systemically examine nonresponder biology, either retrospectively or prospectively, these studies cannot generate the insights that can help unify signals, identify novel targets and strengthen efficacy.
This can help answer a more fundamental question before late-stage commitments: is the drug hitting the right target in the right tissue, at the right time?
Smaller, more targeted clinical trials can be designed to address that challenge by prioritizing biological understanding over broad efficacy signals. With a focus on defined patient subgroups informed by molecular profiling, these studies can reveal whether a therapy is producing the expected mechanistic effect, how pathways are changing at the site of inflammation and what distinguishes patients who respond from those who do not. This type of research plays a crucial role in elucidating the biological mechanisms that underpin effective combination therapies.
In contrast to traditional approaches, mechanistic trials incorporate target engagement biomarkers and tissue-based pharmacodynamic endpoints, rather than relying only on blood-based measures such as drug concentration or broader systemic activity. This can help answer a more fundamental question before late-stage commitments: Is the drug hitting the right target in the right tissue, at the right time?
When a therapy shows limited benefit, teams can determine which disease pathways remain active and identify alternative targets or combinations. Such insights can inform development decisions, helping teams reprioritize pipelines, reduce cycle time and focus investment on programs with the greatest potential to deliver meaningful advances for patients.
Together, these approaches signal an evolution in immunology research to prioritizing early human data, deeper mechanistic understanding and more intentional learning from nonresponse. To meet the needs of underserved patients, the industry needs precision medicine that pairs therapies with the measurement and monitoring of molecular biomarkers and tissue-informed signals to clarify what’s driving disease.
At AbbVie, we’re pursuing these approaches, embedding mechanistic cohorts across early-phase immunology programs and building multi-omic tissue profiling capabilities to ensure development decisions are grounded in the biology of the patients we aim to serve. The efficacy ceiling is not an endpoint. It’s a starting point for the next era of discovery.
