Serial diffusion MRI to monitor and model treatment response of the targeted nanotherapy CRLX101
Menée à l'aide d'un modèle murin de lymphome, cette étude évalue l'intérêt de réaliser des séries d'images par IRM à diffusion pondérée pour suivre la réponse à une nanothérapie ciblée appelée CRLX101
Purpose Targeted nanotherapies are being developed to improve tumor drug delivery and enhance therapeutic response. Techniques that can predict response will facilitate clinical translation and may help define optimal treatment strategies. We evaluated the efficacy of diffusion-weighted magnetic resonance imaging to monitor early response to CRLX101 nanotherapy (formerly IT-101), and explored its potential as a therapeutic response predictor using a mechanistic model of tumor cell-proliferation.
Experimental Design Diffusion MRI was serially performed following CRLX101 administration in a mouse lymphoma model. Apparent diffusion coefficients (ADC) extracted from the data were used as treatment response biomarkers. Animals treated with irinotecan (CPT-11) and saline were imaged for comparison. ADC data were also input into a mathematical model of tumor growth. Histological analysis using cleaved-caspase 3, TUNEL, Ki-67 and H&E were conducted on tumor samples for correlation with imaging results.
Results CRLX101 treated tumors at day 2, 4, 7 post-treatment exhibited changes in mean ADC=16±9%, 24±10% 49±17% and size (TV)=-5±3%, -30±4% and -45±13% respectively. Both parameters were statistically greater than controls (p(ADC)≤0.02, and p(TV)≤0.01 at day 4 and 7), and noticeably greater than CPT-11 treated tumors (ADC=5±5%, 14±7% and 18±6%, TV=-15±5%, -22±13% and -26±8%). Model-derived parameters for cell-proliferation obtained using ADC data distinguished CRLX101 treated tumors from controls (p=0.02).
Conclusions Temporal changes in ADC specified early CRLX101 treatment response and could be used to model image-derived cell-proliferation rates following treatment. Comparisons of targeted and non-targeted treatments highlight the utility of non-invasive imaging and modeling to evaluate, monitor and predict responses to targeted nanotherapeutics.
Clinical Cancer Research , résumé, 2013