Radiopharmaceuticals represent a rapidly advancing class of targeted oncology therapeutics, leveraging radionuclide-labeled molecules to deliver ionizing radiation directly to tumor cells. Despite the promising clinical potential of alpha- and beta-emitting radiopharmaceuticals, achieving translational success remains challenging. Robust, well-characterized preclinical models are essential to increase confidence in compound performance before entering the clinic.
Where biodistribution, receptor heterogeneity, and tumor penetration critically influence therapeutic index and patient selection strategies, traditional preclinical models often fall short. Cell line–derived xenografts (CDX), in particular, offer limited predictive value due to their clonal homogeneity, uniform tumor architecture, and lack of biological diversity, factors that can lead to inaccurate assessments of targeting performance, distribution, and treatment efficacy.
Patient-derived xenograft (PDX) models offer a superior alternative, retaining the histological architecture, molecular diversity, and intra, and inter-tumoral heterogeneity of the donor patient’s tumor. These attributes enable more physiologically relevant assessment of targeting efficacy, radiotracer distribution, and therapeutic response—key metrics in determining compound viability prior to clinical translation.
In this article, we examine the limitations of traditional models, the biological advantages of PDX platforms, and the specific ways in which PDX enhances radiopharmaceutical study design. We also highlight how access to large, clinically annotated model libraries—such as Champions Oncology’s Lumin platform can support more informed, data-driven decisions during preclinical development.
The Limits of Traditional Preclinical Models
Despite their ubiquity in oncology research, traditional preclinical models—particularly cell line–derived xenografts (CDX)—present significant limitations for targeted drug development, including radiopharmaceuticals.
CDX models are generated by implanting immortalized cancer cell lines into immunodeficient mice. While they offer logistical advantages such as rapid tumor growth and reproducibility, these models are inherently reductionist. Their clonal architecture lacks the genomic and phenotypic heterogeneity observed in primary tumors, which can lead to misleading conclusions regarding target accessibility, tumor penetration, and intratumoral uptake of radiolabeled compounds.
Moreover, CDX models typically fail to recapitulate the complex tumor microenvironment (TME), including stromal interactions, vasculature, and immune contexture—all of which are known to influence radiopharmaceutical distribution and efficacy. In addition, receptor expression in cell lines is often artificially uniform or overexpressed, providing an inaccurate representation of clinical target variability.
For radiopharmaceuticals—where therapeutic performance depends heavily on fine, tuned targeting, localized retention, and clearance kinetics, these simplifications are not benign. Data generated from CDX models may overestimate therapeutic potential or fail to predict safety liabilities, contributing to a translational gap between preclinical validation and clinical outcomes.
What Makes PDX Models Different
Patient-derived xenograft (PDX) models are established by implanting primary tumor tissue directly from oncology patients into immunodeficient mice, preserving the cellular heterogeneity, stromal components, and histopathological architecture of the original tumor. Unlike CDX systems, PDX models retain critical aspects of human tumor biology across multiple passages.
This biological fidelity translates into substantial advantages for radiopharmaceutical development. First, PDX models capture both intratumoral and intertumoral heterogeneity, a key determinant of response variability in radiolabeled therapies. Differences in antigen density, receptor expression, vascularization, and stromal composition can significantly affect radiotracer uptake and therapeutic distribution—elements that are often uniform or absent in traditional systems.
Second, because PDX tumors grow in vivo without prior dissociation or in vitro manipulation, their tumor microenvironments more accurately reflect the spatial and structural complexity of human malignancies. This includes irregular vasculature, hypoxic regions, and heterogeneous interstitial pressure—factors that influence compound diffusion, radiation deposition, and biological effects.
PDX models have demonstrated greater predictive validity than CDX systems across multiple drug classes, with treatment responses that more closely reflect clinical outcomes. This makes them particularly valuable for de-risking radiopharmaceutical assets in the early stages of development, providing insight into variability in target engagement and therapeutic effect. While tumor-specific uptake can be assessed in a human-relevant context, off-target distribution in preclinical models may not fully reflect human cross-reactivity due to interspecies differences in antigen expression.
In short, PDX models offer a translational bridge between mechanistic discovery and clinical decision making, one that is especially important when developing complex, spatially dependent therapies like radiopharmaceuticals.
How PDX Enhances Radiopharmaceutical Testing
The evaluation of radiopharmaceuticals requires more than evidence of cytotoxicity; it demands a nuanced understanding of how a radiolabeled compound distributes within and interacts with—a tumor and its microenvironment. PDX models provide the translational resolution needed to interrogate these complex dynamics.
One of the primary advantages of using PDX models in this context is the ability to model inter-patient variability in target expression. Radiopharmaceuticals often rely on the presence of specific cell surface antigens or receptors for tumor localization. In a clinical setting, these markers are rarely expressed uniformly across patient populations. By leveraging a library of diverse PDX models—each with distinct molecular and phenotypic profiles—researchers can assess how differences in target expression influence uptake, specificity, and efficacy.
Additionally, PDX models enable realistic biodistribution analysis in tumors that replicate human heterogeneity in vascular density, stromal content, and perfusion. These factors play a significant role in modulating the intratumoral deposition of radiolabeled compounds, Preclinical studies in PDX therefore allow developers to anticipate challenges related to tracer penetration, off, target accumulation, and clearance kinetics, challenges that CDX models routinely obscure.
Efficacy evaluation is another area where PDX models offer substantial value. Because these tumors respond to treatment in ways that reflect clinical patterns, including partial response, acquired resistance, and heterogeneous regression, they offer a more realistic basis for determining therapeutic window, optimal dosing, and potential biomarkers of response.
When used systematically, PDX models allow radiopharmaceutical developers to move beyond binary efficacy readouts and instead generate layered, clinically relevant insights into compound behavior—insights that inform both development decisions and regulatory discussions.
The Lumin Advantage
While the value of PDX models in radiopharmaceutical development is clear, the ability to scale these insights depends on access to a diverse, well-characterized model library. Champions Oncology’s PDX platform is the most deeply annotated and clinically relevant PDX collections available globally, enabling sponsors to tailor studies with unprecedented precision.
The library encompasses thousands of PDX models derived from a wide range of solid tumors, each backed by comprehensive clinical, histological, and molecular data. This includes mutational profiles, gene expression signatures, prior treatment history, and, critically for radiopharmaceutical programs, data on target expression heterogeneity across tumor types.
To accelerate the design of rational studies, complementary tissue microarrays (TMAs) prepared from the PDX collection are also available. These arrays allow researchers to screen panels of models for antigen expression or biomarker prevalence prior to initiating in vivo work, enabling efficient model selection, improving study design, and reducing downstream variability.
In radiopharmaceutical testing, where variability in receptor density or antigen availability can dramatically influence tracer uptake and therapeutic effect, this level of pre-screening and data integration is a strategic advantage. It allows developers to assess compound performance across diverse biological backgrounds and identify model subsets most likely to inform clinical translation.
Combined with Champions’ in-house imaging, conjugation, and radiolabeling capabilities, Lumin platform offers a comprehensive ecosystem for generating radiopharmaceutical data that’s not only robust—but truly relevant to human disease.