Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Current Tools, Techniques, and Uncharted Territories

Abstract

Radiopharmaceutical therapy (RPT), with its targeted delivery of cytotoxic ionizing radiation, demonstrates significant potential for treating a wide spectrum of malignancies, with particularly unique benefits for metastatic disease. There is an opportunity to optimize RPTs and enhance the precision of theranostics by moving beyond a one-size-fits-all approach and using patient-specific image-based dosimetry for personalized treatment planning. Such an approach, however, requires accurate methods and tools for the mathematic modeling and prediction of dose and clinical outcome. To this end, the SNMMI AI-Dosimetry Working Group is promoting the paradigm of computational nuclear oncology: mathematic models and computational tools describing the hierarchy of etiologic mechanisms involved in RPT dose response. This includes radiopharmacokinetics for image-based internal dosimetry and radiobiology for the mapping of dose response to clinical endpoints. The former area originates in pharmacotherapy, whereas the latter originates in radiotherapy. Accordingly, models and methods developed in these predecessor disciplines serve as a foundation on which to develop a repurposed set of tools more appropriate to RPT. Over the long term, this computational nuclear oncology framework also promises to facilitate widespread cross-fertilization of ideas between nuclear medicine and the greater mathematic and computational oncology communities.

Keywords: artificial intelligence; computational nuclear oncology; dosimetry; radiobiology; radionuclide therapy; theranostic digital twin.

FIGURE 1.

Diagram comparing present one-size-fits-all RPT approach (A) with vision of precision RPTs (B). Central to such a future is development of robust models that translate therapeutic inputs to clinical outputs, from which one can prescribe optimal protocols in cycle 1 and refine the process in later cycles to adaptively optimize treatment.

FIGURE 2.

RPT is hybridization of 2 therapeutic paradigms, pharmacotherapy and radiation therapy, but with additional element of physiologically mediated delivery that can be imaged through nuclear medicine. Accordingly, CNO may be considered analogous hybrid of pharmacokinetic modeling and radiobiology-calibrated dosimetry, linked via intermediary of image-based internal dosimetry. PD = pharmacodynamics.

FIGURE 3.

PBPK modeling and simulation from drug discovery and development communities can be applied to enable RPT dosimetry via theranostic principle, which enables estimation of patient-specific pharmacokinetics with pretherapy PET imaging, and use these models to help prospectively predict patient-specific therapeutic doses that can be verified with real-time SPECT imaging. PI = after injection. (PBPK figure adapted from (118), theranostics schematic adapted from (119), diagnostic PET and therapeutic SPECT cartoons adapted from (120), model-parameterized PBRPK network adapted from (8), and time-integrated activity–to–AD diagram adapted from (121).)

FIGURE 4.

Concepts, tools, and models of external-beam radiobiology, from which much of modern EBRT treatment planning is based, must be modified for RPT because of physiologically mediated small-scale biodistribution and dosimetry, which modify radiation response via series of complex and incompletely understood mechanisms. RPT-adapted radiobiologic modeling must ultimately be paired with suitably estimated multiscale extrapolation to enable robust end-to-end prediction of clinical outcome, illustrated here for the example of acute kidney injury. AKD = acute kidney disease; AKI = acute kidney injury; CKD = chronic kidney disease; CVD = cardiovascular disease; IM = inner medulla; ISOM = inner strip of outer medulla; OSOM = outer strip of outer medulla. (Multiscale anatomy diagrams adapted from (122), acute injury dynamic function schematic adapted from (123), PET/CT adapted from (124), and multiscale dosimetry figure adapted from (125).)