Muzi Mark, Mankoff David A, Link Jeanne M, Shoner Steve, Collier Ann C, Sasongko Lucy, Unadkat Jashvant D
Department of Radiology, University of Washington, Seattle, Washington 98195-6004, USA.
J Nucl Med. 2009 Aug;50(8):1267-75. doi: 10.2967/jnumed.108.059162. Epub 2009 Jul 17.
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.
多药耐药（MDR）转运蛋白P-糖蛋白（P-gp）在人血脑屏障（BBB）处高度表达。P-gp可将多种药物从中枢神经系统主动外排，包括抗癌药物。我们之前利用正电子发射断层扫描（PET）技术，在有无P-gp抑制剂环孢素A（CsA）的情况下，研究了（11）C-维拉帕米在脑中的分布，从而证明了人血脑屏障处的P-gp活性。在此，我们扩展了对这些数据的初始非房室分析，并将房室模型应用于这些人体维拉帕米成像研究。
向健康志愿者注射（15）O-水以评估血流量，随后注射（11）C-维拉帕米以评估血脑屏障P-gp活性。注射后，分别在5分钟和45分钟内频繁采集动脉血样和PET图像。在静脉输注CsA（2.5 mg/kg/h，持续60分钟）以抑制P-gp后，进行第二组水和维拉帕米PET研究，随后进行（11）C-CO成像以测量局部血容量。利用动态（15）O-水数据和流扩散模型估算血流量。通过给药和滞留的双组织房室（2C）模型以及使用前10分钟数据的单组织房室模型（1C（10））评估动态（11）C-维拉帕米数据。
2C模型能够拟合P-pg抑制前后的完整数据集。CsA对P-gp的调节使维拉帕米向脑内的血脑转运（K（1））增加了73%（范围为30%-118%；n = 12）。这种增加显著大于血流量的变化（13%；范围为12%-49%；n = 12，P < 0.001）。1C（10）模型的K（1）估计值与2C模型的估计值相关（r = 0.99，n = 12），表明短期研究可以有效地估计P-gp活性。
（11）C-维拉帕米和房室分析可通过在CsA抑制P-gp之前和期间进行成像，根据维拉帕米转运（K（1））的变化来估计血脑屏障处的P-gp活性。P-gp的抑制揭示了维拉帕米在脑组织中的滞留，对于长时间成像需要2C模型；然而，使用1C（10）模型在短扫描时间内即可有效测量转运，避免了标记代谢物和示踪剂滞留的并发症。
