Blood-Brain Barrier Penetration of Zolmitriptan—Modelling of Positron Emission Tomography Data

Positron emissiontomography (PET) with the drug radiolabelled allows a direct measurement of brain or other organ kinetics, information which can be essential in drug development. Usually, however, a PET-tracer is administered intravenously (i.v.), whereas the therapeutic drug is mostly given orally or by a different route to the PET-tracer. In such cases, a recalculation is needed to make the PET data representative for the alternative administration route. To investigate the blood-brain barrier penetration of a drug (zolmitriptan) using dynamic PET and by PK modelling quantify the brain concentration of the drug after the nasal administration of a therapeutic dose. [11C]Zolmitriptan at tracer dose was administered as a short i.v. infusion and the brain tissue and venous blood kinetics of [11C]zolmitriptan was measured by PET in 7 healthy volunteers. One PET study was performed before and one 30min after the administration of 5mg zolmitriptan as nasal spray. At each of the instances, the brain radioactivity concentration after subtraction of the vascular component was determined up to 90min after administration and compared to venous plasma radioactivity concentration after correction for radiolabelled metabolites. Convolution methods were used to describe the relationship between arterial and venous tracer concentrations, respectively between brain and arterial tracer concentration. Finally, the impulse response functions derived from the PET studies were applied on plasma PK data to estimate the brain zolmitriptan concentration after a nasal administration of a therapeutic dose. The studies shows that the PET data on brain kinetics could well be described as the convolution of venous tracer kinetics with an impulse response including terms for arterial-to-venous plasma and arterial-to-brain impulse responses. Application of the PET derived impulse responses on the plasma PK from nasal administration demonstrated that brain PK of zolmitriptan increased with time, achieving about 0.5mg/ml at 30min and close to a maximum of 1.5mg/ml after 2hr. A significant brain concentration was observed already after 5min. The data support the notation of a rapid brain availability of zolmitriptan after nasal administratio

Autoradiographic Mapping of 5-HT1B/1D Binding Sites in the Rhesus Monkey Brain Using [carbonyl-11C]zolmitriptan

Zolmitriptan is a serotonin 5-HT1B/1D receptor agonist that is an effective and well-tolerated drug for migraine treatment. In a human positron emission tomography study, [11C]zolmitriptan crossed the blood-brain barrier but no clear pattern of regional uptake was discernable. The objective of this study was to map the binding of [11C]zolmitriptan in Rhesus monkey brain using whole hemisphere in vitro autoradiography with [11C]zolmitriptan as a radioligand. In saturation studies, [11C]zolmitriptan showed specific (90%) binding to a population of high-affinity binding sites (Kd 0.95–5.06 nM). There was regional distribution of binding sites with the highest density in the ventral pallidum, followed by the external globus pallidus, substantia nigra, visual cortex, and nucleus accumbens. In competitive binding studies with 5-HT1 receptor antagonists, [11C]zolmitriptan binding was blocked by selective 5-HT1B and 5-HT1D ligands in all target areas. There was no appreciable change in binding with the addition of a 5-HT1A receptor antagonist

Nonlinear Mixed Effects Methods for Improved Estimation of Receptor Occupancy in PET Studies

Receptor occupancy assessed by Positron Emission Tomography (PET) can provide important translational information to help bridge information from one drug to another or from animal to man. The aim of this thesis was to develop nonlinear mixed effects methods for estimation of the relationship between drug exposure and receptor occupancy for the two mGluR5 antagonists AZD9272 and AZD2066 and for the 5HT1B receptor antagonist AZD3783. Also the optimal design for improved estimation of the relationship between drug exposure and receptor occupancy as well as for improved dose finding in neuropathic pain treatment, was investigated. Different modeling approaches were applied. For AZD9272, the radioligand kinetics and receptor occupancy was simultaneously estimated using arterial concentrations as input function and including two brain regions of interest. For AZD2066, a model was developed where brain/plasma partition coefficients from ten different brain regions were included simultaneously as observations. For AZD3783, the simplified reference tissue model was extended to allow different non-specific binding in the reference region and brain regions of interest and the possibility of using white matter as reference was also evaluated. The optimal dose-selection for improved precision of receptor occupancy as well as for improved precision of the minimum effective dose of a neuropathic pain treatment was assessed, using the D-optimal as well as the Ds-optimal criteria. Simultaneous modelling of radioligand and occupancy provided a means to avoid simplifications or approximations and provided the possibility to tests or to relax assumptions. Inclusion of several brain regions of different receptor density simultaneously in the analysis, markedly improved the precision of the affinity parameter. Higher precision was achieved in relevant parameters with designs based on the Ds compared to the D-optimal criterion. The optimal design for improved precision of the relationship between dose and receptor occupancy depended on the number of brain regions and the receptor density of these regions. In conclusion, this thesis presents novel non-linear mixed effects models estimating the relationship between drug exposure and receptor occupancy, providing useful translational information, allowing for a better informed drug-development

A disease model predicting placebo response and remission status of patients with ulcerative colitis using modified Mayo score

Abstract The Mayo Clinical Score is used in clinical trials to describe the clinical status of patients with ulcerative colitis (UC). It comprises four subscores: rectal bleeding (RB), stool frequency (SF), physician's global assessment, and endoscopy (ENDO). According to recent US Food and Drug Administration guidelines (Ulcerative colitis: developing drugs for treatment, Guidance Document, https://www.fda.gov/regulatory‐information/s. 2022), clinical response and remission should be based on modified Mayo Score (mMS) relying on RB, SF, and ENDO. Typically, ENDO is performed at the beginning and end of each phase, whereas RB and SF are more frequently available. Item response theory (IRT) models allow the shared information to be used for prediction of all subscores at each observation time; therefore, it leverages information from RB and SF to predict ENDO. A UC disease IRT model was developed based on four etrolizumab phase III studies to describe the longitudinal mMS subscores, placebo response, and remission at the end of induction and maintenance. For each subscore, a bounded integer model was developed. The placebo response was characterized by a mono‐exponential function acting on all mMS subscores similarly. The final model reliably predicted longitudinal mMS data. In addition, remission was well‐predicted by the model, with only 5% overprediction at the end of induction and 3% underprediction at the end of maintenance. External evaluation of the final model using placebo arms from five different studies indicated adequate performance for both longitudinal mMS subscores and remission status. These results suggest utility of the current disease model for informed decision making in UC clinical development, such as assisting future clinical trial designs and evaluations

gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve

Abstract GPKPDviz is a Shiny application (app) dedicated to real‐time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app‐generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built‐in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open‐source application in GitHub under the terms of GNU General Public License