Beyond Radiochemistry: An Integrated Translational Blueprint for the Next Generation of Radionuclide Ligand Conjugates.

Authors

Chen Fu, Xiaoyan Li

Published in

Journal of nuclear medicine : official publication, Society of Nuclear Medicine. Aug 06, 2026. Epub Aug 06, 2026.

Abstract

Radionuclide ligand conjugates (RLCs) have emerged as a powerful therapeutic and theranostic platform in oncology, with clinical validation achieved in prostate-specific membrane antigen- and somatostatin receptor-directed settings. Yet the field now faces a pivotal translational challenge: future expansion will not be determined by radionuclide availability alone, but by whether RLC development can move beyond modular radiochemistry toward an integrated biologic design framework. Current pipelines still tend to optimize targeting ligand, chelator, and radionuclide as separable components, whereas clinical performance is ultimately governed by their interaction with target density, intratumoral distribution, internalization behavior, normal-organ exposure, and adaptive resistance. Here, we assert that the next generation of RLCs should be developed through a translational logic that aligns target biology, ligand pharmacology, isotope physics, and resistance mechanisms from the outset. We first examine why the conventional ligand-chelator-payload model, although foundational, is insufficient to support broad clinical generalization beyond a small number of validated targets. We then highlight 3 underrecognized barriers to expansion: inadequate biologic stratification in target selection, incomplete matching between radionuclide properties and disease architecture, and limited integration of resistance-informed combination strategies. Finally, we propose a practical blueprint for next-generation RLC development centered on biologically prioritized target discovery, disease-contextual isotope selection, and biomarker-guided combination therapy. Reframing RLC development in this way may help move the field from isolated successes to a scalable precision oncology platform.

PMID:
42562611
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.