Computational Prediction of Tissue Iron Dynamics in Iron Deficiency Anemia Following Intravenous Ferric Carboxymaltose Therapy.

Background: Iron deficiency anemia (IDA) is a global public health concern. Intravenous iron therapy, particularly ferric carboxymaltose (FCM), is a cornerstone therapy for IDA treatment. However, its application is hindered by limited understanding of long-term tissue iron distribution post-therapy and the lack of practical clinical methods to assess tissue iron. This study aims to investigate the tissue iron distribution following FCM and develop a computational model for predicting tissue iron levels in both rats and humans.

Methods: Using an IDA model in rats, we evaluated tissue distribution of iron and dynamic changes of serum iron biomarkers over time after a single dose of FCM. Then we developed a mathematical model to characterize tissue-specific iron kinetics. The model was further scaled to humans and validated using clinical data.

Results: The computational model accurately captured tissue-specific iron distribution and serum ferritin dynamics in IDA rats. Among the analyzed tissues, the liver and spleen exhibited the highest tissue-to-plasma partition coefficient (KP_t) values, estimated at 21.7 and 25.9, respectively. The bone marrow (BM) also demonstrated a notable KP_t value of 21.6, reflecting the prioritization of iron delivery to BM for erythropoiesis in IDA. Notably, the heart displayed a relatively high KP_t value of 18, underscoring its limited capacity to clear excess iron. Our model accurately predicted serum iron profiles in IDA patients. Correlation analysis revealed a strong correlation between model-predicted iron levels in the liver and spleen and magnetic resonance imaging (MRI)-derived relaxation time parameters (P < 0.001), highlighting the model's predictive capability for tissue iron levels in humans.

Conclusion: This study provides critical insights into the long-term tissue distribution of iron following single dose of FCM and highlights the clinical potential of the computational approach to predict tissue iron content, optimize dosing strategies, and ultimately enhance the safety and efficacy of iron therapy.