Antibody-drug conjugates (ADCs) continue to generate momentum in oncology, offering the promise of targeted cytotoxic delivery with greater specificity and reduced systemic toxicity. However, the complexity of these molecules, from antigen binding and internalization to payload release and clearance, makes them notoriously difficult to optimize. Many ADC programs stall not because the payload is ineffective, but because developers lack insight into whether the antibody is reaching and engaging the target in vivo.
Radiotracers offer a unique opportunity to bridge this knowledge gap. By radiolabeling antibodies, , or ADC-like constructs with radioisotopes, researchers can evaluate in vivo biodistribution, tumor targeting, and off-target accumulation long before clinical trials. These tools provide a non-invasive, high-resolution view of compound behavior that traditional pharmacokinetic assays or histology cannot capture alone.
In this article, we explore how radiotracers techniques, when combined with clinically relevant preclinical models, are helping ADC developers de-risk critical decisions earlier in the development cycle. From model selection to target validation to biodistribution profiling, radiotracers are becoming an indispensable part of the ADC toolkit.
Understanding Risk in ADC Development
The appeal of ADCs lies in their elegant concept: deliver a cytotoxic payload directly to cancer cells via a highly specific antibody, sparing healthy tissue. In practice, however, ADC development is fraught with failure points. Many candidates show limited efficacy or unacceptable toxicity — not due to poor payload design, but because of incomplete understanding of how the construct behaves in vivo.
Key risks include:
These risks are difficult to detect with traditional in vitro methods alone. Even when early pharmacology data appear favorable, translational failures often emerge when the compound enters more physiologically complex systems or encounters unexpected biological variability.
What ADC developers need is a translational lens into antibody behavior — one that captures how the full construct distributes across tumor and normal tissue, how long it persists at the target site, and how that behavior varies across models. That’s where radiotracers come in.
Radiolabeling ADCs with radioisotopes enables developers to evaluate compound behavior with far greater granularity than traditional methods. By tagging the antibody or the full ADC construct with an isotope such as Zirconium-89 (Zr-89), Lutetium-177 (Lu-177) or Indium-111 (In-111), researchers can track distribution, uptake, and retention across tissues and tumor models in real time.
This technique serves multiple functions:
Radiolabeled ADC constructs can be studied across multiple timepoints to assess dynamic distribution and clearance profiles. When paired with well-characterized preclinical models—particularly those that reflect human heterogeneity in target expression, this approach allows for an evidence-based refinement of lead selection and study design.
In addition to guiding compound optimization, radiolabeled compounds generate data that supports IND-enabling work by illustrating tumor specificity and helping predict potential toxicity risks related to off-target delivery.
While radiolabeling provides a powerful tool for visualizing distribution, the choice of model system ultimately determines how meaningful those insights will be. Cell line–derived xenografts (CDX), though commonly used, often overexpress the target antigen in a uniform and artificial manner. This can mask important limitations in targeting specificity and distribution, leading to false confidence in a compound’s performance.
In contrast, patient-derived xenograft (PDX) models preserve the inter- and intra-tumoral heterogeneity found in human cancers. Differences in antigen density, vascularization, stromal composition, and tumor architecture all impact how an ADC or radiolabeled construct will behave in vivo. Testing compounds across a panel of PDX models allows developers to assess performance across a range of real-world tumor phenotypes, gaining visibility into variability that may influence clinical response.
Champions Oncology’s Lumin platform includes hundreds of PDX models annotated with genomic, phenotypic, and treatment-response data. These models can be screened in advance using tissue microarrays (TMAs) to identify tumors with varying levels of antigen expression, enabling strategic model selection and rational study design.
In the context of radiopharmaceuticals or radiolabeled ADCs, this means developers can:
By integrating radiolabeling with clinically annotated, heterogenous models, developers gain a more complete picture of how a compound is likely to perform across the clinical population —and avoid late-stage surprises.
The high cost and complexity of ADC development demand early, informed decision-making. Traditional pharmacology and histology provide critical insights, but they don’t tell the whole story, especially when it comes to understanding how a biologic behaves in real biological systems over time.
Radiotracers tools fill that gap by enabling non-invasive, temporal, and quantitative evaluation of ADC behavior across diverse, clinically relevant models. When used early in development, radio labeling and biodistribution studies can help developers:
By integrating these insights into the design phase — not as a retrospective check — developers can refine their therapeutic strategy while reducing attrition risk later in the pipeline. Radiotracers not only illuminate compound behavior but also serve as a translational bridge that connects target biology, delivery, and clinical feasibility.
In a field where timelines are long, investment is high, and failure is costly, this approach offers a pragmatic and data-rich path forward: study smarter, screen earlier, and develop with confidence.