Imaging of cyclosporine inhibition of P-glycoprotein activity using 11C-verapamil in the brain: studies of healthy humans

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.

Methods: 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)).

Results: 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.

Conclusion: (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.

FIGURE 1

(A) P-gp, which acts on a wide range of xenobiotic agents, is an adenosine triphosphate–dependent efflux pump involved in multiple-drug resistance. P-gp enables secretory excretion from the BBB, acting on substrates such as ¹¹C-verapamil. (B) In our experiment, P-gp inhibitor CsA binds to P-gp and inhibits both drug efflux activity and verapamil binding. PET of ¹¹C-verapamil before and during CsA administration allows estimation of inhibition of P-gp by CsA directly in humans by determining ¹¹C-verapamil transport in brain. (C) PET timeline for 5-injection protocol to assess ¹¹C-verapamil uptake in human brain before and after administration of P-gp modifier CsA.

FIGURE 2

(A) Verapamil in blood declined rapidly to an average of 37% on average at 45 min after injection. In this subject example, exponential washout function (—) fit to verapamil measurements (●) provided fraction of verapamil as a function of time. Metabolites of verapamil in plasma, D617 fraction (▲) and other, polar metabolites (■) continually rose during imaging study, reaching a combined total metabolite fraction of over 60%. (B) Verapamil model input function, C_p-Ver, is a combination of total blood activity, C_B, and the fraction of verapamil determined from plasma metabolite analysis. Similar curves were obtained for all 12 subjects. Mean verapamil fraction at 45 min was 40% (range, 66%–24%), D617 fraction was 30% (range, 49%–17%), and other metabolites were 30% (range, 44%–16%).

FIGURE 3

Compartmental models of verapamil uptake for assessing P-gp activity at the BBB. (A) 2C model accounts for verapamil transport (K₁) and overall retention in brain and is kinetically described by 2 differential equations expressing the quantity of verapamil in exchangeable compartment (Q_e) and in retained compartment (Q_r): dQ_e/dt = K₁C_p-Ver − k₂Q_e − k₃Q_e + k₄Q_r and dQ_r/dt = k₃Q_e − k₄Q_r. Total tissue uptake (C_t) is then C_t = (Q_e + Q_r + VbC_B) ρ, where ρ is tissue density in grams per milliliter, and Vb is measured fractional blood volume in milliliters per gram. (B) 1C model using 10 min of data can closely approximate transport parameter, K₁ of 2C model using 45 min of data, and can be formulized as C_t = (Q_e + VbC_B) ρ, where dQ_e/dt = K₁C_p-Ver − k₂Q_e.

FIGURE 4

(A) Brain time–activity curves for ¹¹C-verapamil before and after CsA treatment illustrate differences in uptake after administration of CsA. (B) A representative brain time–activity curve (C_t) was fitted using a simple 1C model with 45 min of data (1C₄₅), the initial 10 min of data (1C₁₀), and the 2C model. (C) Logan plot analysis is the brain time–activity curve normalized for blood activity, where the slope (Vd_Logan) is the ratio of integral tissue activity over integral blood activity similar to AUCR analysis (tissue AUC/blood AUC).

FIGURE 5

T1-weighted MR image (A) from representative subject and corresponding T2-weighted MR image (B) provide anatomic reference. (C) ¹¹C-verapamil uptake image (SUV) before CsA treatment was acquired between 5 and 25 min after injection. (D) ¹¹C-verapamil uptake image after 1 h of CsA infusion shows general increase in verapamil uptake in all areas of brain after inhibition of P-gp by CsA. Color scale reflects SUV as shown by thermometer.