Calvo, G. F. | Search bioRxiv

bioRxiv2025

Radiopharmaceutical therapy for metastatic prostate cancer: Insights from mechanistic modeling and in silico trials

Radiopharmaceutical therapy (RPT) has rapidly evolved into a key precision-oncology modality, with radioligands now approved or in late-stage development for multiple solid tumors, including neuroendocrine and prostate cancers. RPT with [177Lu]Lu-PSMA (Prostate-Specific Membrane Antigen) has recently been approved as a life-prolonging treatment for metastatic castration-resistant prostate cancer (mCRPC), but its clinical use still relies on non-personalized, empirically chosen fixed schedules. Here, we develop a mechanistic, patient-personalizable mathematical model simulating mCRPC response and organ-at-risk toxicity during [177Lu]Lu-PSMA RPT. The model integrates tumor growth dynamics, radiobiological response, and organ-resolved pharmacokinetics inferred from mass data and standardized uptake values obtained from positron emission tomography studies. Parameters were derived from the literature, although the framework allows personalization by fitting to patient-specific data such as imaging and prostate-specific antigen levels. Using virtual patient (VP) cohorts generated via stochastic parameter sampling, we conducted in silico trials and validated the model by comparing simulated outcomes with published dosimetry and survival data for [177Lu]Lu-PSMA trials. We then explored dosing and scheduling strategies to optimize efficacy-toxicity trade-offs. Consolidated regimens with fewer, higher-activity injections improved overall survival (OS) in silico but increased toxicity, especially in kidneys. Cycle length had a weaker influence on OS within a 2-9 week window, while it clearly affected toxicity, whereas excessive delays (> 12 weeks) markedly reduced efficacy. Global sensitivity analysis identified tumor growth, uptake, and radiosensitivity parameters as key drivers of interpatient variability, and convergence testing confirmed robustness with respect to VP cohort size. These methodological findings illustrate how mechanistic modeling and in silico trials can inform the design and personalization of RPT regimens. Author summaryStandard radiopharmaceutical therapy regimens for metastatic castration-resistant prostate cancer deliver the same doses at fixed intervals, without accounting for interpatient variability in tumor growth, drug uptake, or organ tolerance. Here, we focus on [177Lu]Lu-PSMA radiopharmaceutical therapy, which is now approved for these patients but still administered using one-size-fits-all protocols. We present a mechanistic mathematical model that simulates tumor and radiopharmaceutical dynamics in individual patients using a virtual patient framework. With appropriate patient-specific data, such as quantitative imaging and prostate-specific antigen levels, this model can be used to generate digital twins and evaluate personalized treatment strategies. By adjusting injection schedules and cycle timing in silico, we explored how standard treatment protocols could be optimized to improve survival while maintaining acceptable toxicity. We found that a 9 week treatment cycle achieved survival outcomes comparable to the standard 6 week protocol, with a significant reduction in toxicity, whereas longer cycle extensions led to loss of therapeutic efficacy. These results provide a quantitative basis for optimizing radiopharmaceutical therapy and highlight the potential of virtual patients and in silico trials to support patient-adapted treatments in modern oncology.

cancer biology↗

bioRxiv2020

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in-silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting. Author summaryComputer simulation based on mathematical models provides a way to improve the understanding of complex processes in oncology. In this paper we develop a stochastic mesoscopic simulation approach that incorporates key cellular processes while keeping computational costs to a minimum. Our methodology captures the development of tumor heterogeneity and the underlying evolutionary dynamics. The physical scale considered allows microscopic information to be included, tracking the spatio-temporal evolution of tumor heterogeneity and reconstructing clinically sized tumors from high-resolution medical imaging data, with a low computational cost. We illustrate the functionality of the modeling approach for the case of glioblastoma, an epitome of heterogeneity in tumors.

systems biology↗