Abstract

The multiple-drug resistance (MDR) transporter P-glycoprotein (P-gp) is highly expressed at the human blood-brain barrier (BBB). P-gp actively effluxes a wide variety of drugs from the central nervous system, including anticancer drugs. We have previously demonstrated P-gp activity at the human BBB using PET of (11)C-verapamil distribution into the brain in the absence and presence of the P-gp inhibitor cyclosporine-A (CsA). Here we extend the initial noncompartmental analysis of these data and apply compartmental modeling to these human verapamil imaging studies. Healthy volunteers were injected with (15)O-water to assess blood flow, followed by (11)C-verapamil to assess BBB P-gp activity. Arterial blood samples and PET images were obtained at frequent intervals for 5 and 45 min, respectively, after injection. After a 60-min infusion of CsA (intravenously, 2.5 mg/kg/h) to inhibit P-gp, a second set of water and verapamil PET studies was conducted, followed by (11)C-CO imaging to measure regional blood volume. Blood flow was estimated using dynamic (15)O-water data and a flow-dispersion model. Dynamic (11)C-verapamil data were assessed by a 2-tissue-compartment (2C) model of delivery and retention and a 1-tissue-compartment model using the first 10 min of data (1C(10)). The 2C model was able to fit the full dataset both before and during P-pg inhibition. CsA modulation of P-gp increased blood-brain transfer (K(1)) of verapamil into the brain by 73% (range, 30%-118%; n = 12). This increase was significantly greater than changes in blood flow (13%; range, 12%-49%; n = 12, P < 0.001). Estimates of K(1) from the 1C(10) model correlated to estimates from the 2C model (r = 0.99, n = 12), indicating that a short study could effectively estimate P-gp activity. (11)C-verapamil and compartmental analysis can estimate P-gp activity at the BBB by imaging before and during P-gp inhibition by CsA, indicated by a change in verapamil transport (K(1)). Inhibition of P-gp unmasks verapamil trapping in brain tissue that requires a 2C model for long imaging times; however, transport can be effectively measured using a short scan time with a 1C(10) model, avoiding complications with labeled metabolites and tracer retention.

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