Whole-body Imaging of Cell Death Provides a Systemic, Minimally Invasive, Dynamic, and Near-real Time Indicator for...

Response to toxicity in chemotherapies varies considerably from tissue to tissue and from patient to patient. An ability to monitor the tissue damage done by chemotherapy may have a profound impact on treatment and prognosis allowing for a proactive management in understanding and mitigating such events. For the first time, we investigated the feasibility of using whole-body imaging to map chemotherapeutic drug-induced toxicity on an individual basis.

In a preclinical proof-of-concept, rats were treated with a single clinical dose of cyclophosphamide, methotrexate, or cisplatin. In vivo whole-body imaging data were acquired using ^99mTc-duramycin, which identifies dead and dying cells as an unambiguous marker for tissue injury in susceptible organs. Imaging results were cross-validated using quantitative ex vivo measurements and histopathology and compared with standard blood and serum panels for toxicology.

The in vivo whole-body imaging data detected widespread changes, where spatially heterogeneous toxic effects were identified across different tissues, within substructures of organs, as well as among different individuals. The signal changes were consistent with established toxicity profiles of these chemotherapeutic drugs. Apart from generating a map of susceptible tissues, this in vivo imaging approach was more sensitive compared with conventional blood and serum markers used in toxicology. Also, repeated imaging during the acute period after drug treatment captured different kinetics of tissue injury among susceptible organs in males and females.

This novel and highly translational imaging approach shows promise in optimizing therapeutic decisions by detecting and managing drug toxicity on a personalized basis.Toxicity to normal tissues is a significant limitation in chemotherapies. This work demonstrated an in vivo imaging-based approach for characterizing toxicity-induced tissue injury in a systemic, dynamic, and near-real time fashion. This novel approach shows promise in optimizing therapeutic decisions by monitoring drug toxicity on a personalized basis.