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This article is from EJNMMI Research , volume 1 . Abstract Purpose: The objective of this study is the implementation of a kinetic model for 11C-desmethylloperamide (11C-dLop) and the determination of a typical parameter for P-glycoprotein (P-gp) functionality in mice. Since arterial blood sampling in mice is difficult, an alternative method to obtain the arterial plasma input curve used in the kinetic model is proposed. Methods: Wild-type (WT) mice (pre-injected with saline or cyclosporine) and P-gp knock-out (KO) mice were injected with 20 MBq of 11C-dLop, and a dynamic μPET scan was initiated. Afterwards, 18.5 MBq of 18F-FDG was injected, and a static μPET scan was started. An arterial input and brain tissue curve was obtained by delineation of an ROI on the left heart ventricle and the brain, respectively based on the 18F-FDG scan. Results: A comparison between the arterial input curves obtained by the alternative and the blood sampling method showed an acceptable agreement. The one-tissue compartment model gives the best results for the brain. In WT mice, the K1/k2 ratio was 0.4 ± 0.1, while in KO mice and cyclosporine-pretreated mice the ratio was much higher (2.0 ± 0.4 and 1.9 ± 0.2, respectively). K1 can be considered as a pseudo value K1, representing a combination of passive influx of 11C-desmethylloperamide and a rapid washout by P-glycoprotein, while k2 corresponds to slow passive efflux out of the brain. Conclusions: An easy to implement kinetic modeling for imaging P-glycoprotein function is presented in mice without arterial blood sampling. The ratio of K1/k2 obtained from a one-tissue compartment model can be considered as a good value for P-glycoprotein functionality.

This article is from EJNMMI Research , volume 1 . Abstract Purpose: The objective of this study is the implementation of a kinetic model for 11C-desmethylloperamide (11C-dLop) and the determination of a typical parameter for P-glycoprotein (P-gp) functionality in mice. Since arterial blood sampling in mice is difficult, an alternative method to obtain the arterial plasma input curve used in the kinetic model is proposed. Methods: Wild-type (WT) mice (pre-injected with saline or cyclosporine) and P-gp knock-out (KO) mice were injected with 20 MBq of 11C-dLop, and a dynamic μPET scan was initiated. Afterwards, 18.5 MBq of 18F-FDG was injected, and a static μPET scan was started. An arterial input and brain tissue curve was obtained by delineation of an ROI on the left heart ventricle and the brain, respectively based on the 18F-FDG scan. Results: A comparison between the arterial input curves obtained by the alternative and the blood sampling method showed an acceptable agreement. The one-tissue compartment model gives the best results for the brain. In WT mice, the K1/k2 ratio was 0.4 ± 0.1, while in KO mice and cyclosporine-pretreated mice the ratio was much higher (2.0 ± 0.4 and 1.9 ± 0.2, respectively). K1 can be considered as a pseudo value K1, representing a combination of passive influx of 11C-desmethylloperamide and a rapid washout by P-glycoprotein, while k2 corresponds to slow passive efflux out of the brain. Conclusions: An easy to implement kinetic modeling for imaging P-glycoprotein function is presented in mice without arterial blood sampling. The ratio of K1/k2 obtained from a one-tissue compartment model can be considered as a good value for P-glycoprotein functionality.

The instrumentation purchased with funds from this Durip grant was part of a total upgrade of the computing facility of the newly formed Princeton Neuroscience Institute (PNI, formerly the Center for the Study of Brain, Mind and Behavior). This proposal to the Air Force Office of Sponsored Research's (AFOSR) Durip program was intended to complement a parallel proposal for a file server upgrade that was submitted to the National Institute of Health (NIH). Both proposals were funded, and AFOSR permitted the PNI to submit a revised budget that redistributed the Durip funds, reducing the amount spent on the file server (as that was primarily covered in the NIH grant) and increasing the amount spent on the computational cluster. The result was that the PNI was able to procure a new computational cluster and large mirrored file server, resulting in a total facility upgrade that is now sufficient to support the ever growing needs of the PNI for the next three to five years.

The instrumentation purchased with funds from this Durip grant was part of a total upgrade of the computing facility of the newly formed Princeton Neuroscience Institute (PNI, formerly the Center for the Study of Brain, Mind and Behavior). This proposal to the Air Force Office of Sponsored Research's (AFOSR) Durip program was intended to complement a parallel proposal for a file server upgrade that was submitted to the National Institute of Health (NIH). Both proposals were funded, and AFOSR permitted the PNI to submit a revised budget that redistributed the Durip funds, reducing the amount spent on the file server (as that was primarily covered in the NIH grant) and increasing the amount spent on the computational cluster. The result was that the PNI was able to procure a new computational cluster and large mirrored file server, resulting in a total facility upgrade that is now sufficient to support the ever growing needs of the PNI for the next three to five years.

This article is from EJNMMI Research , volume 2 . Abstract Background: This study investigated the influence of P-glycoprotein (P-gp) inhibitor tariquidar on the pharmacokinetics of P-gp substrate radiotracer (R)-[11C]verapamil in plasma and brain of rats and humans by means of positron emission tomography (PET). Methods: Data obtained from a preclinical and clinical study, in which paired (R)-[11C]verapamil PET scans were performed before, during, and after tariquidar administration, were analyzed using nonlinear mixed effects (NLME) modeling. Administration of tariquidar was included as a covariate on the influx and efflux parameters (Qin and Qout) in order to investigate if tariquidar increased influx or decreased outflux of radiotracer across the blood–brain barrier (BBB). Additionally, the influence of pilocarpine-induced status epilepticus (SE) was tested on all model parameters, and the brain-to-plasma partition coefficient (VT-NLME) was calculated. Results: Our model indicated that tariquidar enhances brain uptake of (R)-[11C]verapamil by decreasing Qout. The reduction in Qout in rats during and immediately after tariquidar administration (sevenfold) was more pronounced than in the second PET scan acquired 2 h after tariquidar administration (fivefold). The effect of tariquidar on Qout in humans was apparent during and immediately after tariquidar administration (twofold reduction in Qout) but was negligible in the second PET scan. SE was found to influence the pharmacological volume of distribution of the central brain compartment Vbr1. Tariquidar treatment lead to an increase in VT-NLME, and pilocarpine-induced SE lead to increased (R)-[11C]verapamil distribution to the peripheral brain compartment. Conclusions: Using NLME modeling, we were able to provide mechanistic insight into the effects of tariquidar and SE on (R)-[11C]verapamil transport across the BBB in control and 48 h post SE rats as well as in humans.

This article is from EJNMMI Research , volume 2 . Abstract Background: This study investigated the influence of P-glycoprotein (P-gp) inhibitor tariquidar on the pharmacokinetics of P-gp substrate radiotracer (R)-[11C]verapamil in plasma and brain of rats and humans by means of positron emission tomography (PET). Methods: Data obtained from a preclinical and clinical study, in which paired (R)-[11C]verapamil PET scans were performed before, during, and after tariquidar administration, were analyzed using nonlinear mixed effects (NLME) modeling. Administration of tariquidar was included as a covariate on the influx and efflux parameters (Qin and Qout) in order to investigate if tariquidar increased influx or decreased outflux of radiotracer across the blood–brain barrier (BBB). Additionally, the influence of pilocarpine-induced status epilepticus (SE) was tested on all model parameters, and the brain-to-plasma partition coefficient (VT-NLME) was calculated. Results: Our model indicated that tariquidar enhances brain uptake of (R)-[11C]verapamil by decreasing Qout. The reduction in Qout in rats during and immediately after tariquidar administration (sevenfold) was more pronounced than in the second PET scan acquired 2 h after tariquidar administration (fivefold). The effect of tariquidar on Qout in humans was apparent during and immediately after tariquidar administration (twofold reduction in Qout) but was negligible in the second PET scan. SE was found to influence the pharmacological volume of distribution of the central brain compartment Vbr1. Tariquidar treatment lead to an increase in VT-NLME, and pilocarpine-induced SE lead to increased (R)-[11C]verapamil distribution to the peripheral brain compartment. Conclusions: Using NLME modeling, we were able to provide mechanistic insight into the effects of tariquidar and SE on (R)-[11C]verapamil transport across the BBB in control and 48 h post SE rats as well as in humans.