Serial diffusion MRI to monitor and model treatment response of the targeted nanotherapy CRLX101

摘要

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 (a cyclodextrin-based polymer particle containing the DNA topoisomerase I inhibitor camptothecin) 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 conducted following CRLX101 administration in a mouse lymphoma model. Apparent diffusion coefficients (ADCs) 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, terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling, Ki-67, and hematoxylin and eosin (H&E) were conducted on tumor samples for correlation with imaging results. Results: CRLX101-treated tumors at day 2, 4, and 7 posttreatment 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 noninvasive imaging and modeling to evaluate, monitor, and predict responses to targeted nanotherapeutics.