Prediction of isoprene diepoxide levels in vivo in mouse, rat and man using enzyme kinetic data in vitro and physiologically-based pharmacokinetic modelling

2001 ◽  
Vol 138 (3) ◽  
pp. 247-265 ◽  
Author(s):  
Jan J.P Bogaards ◽  
Andreas P Freidig ◽  
Peter J van Bladeren
Keyword(s):  
Kinetic Data ◽  
Enzyme Kinetic ◽  
Pharmacokinetic Modelling ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based Pharmacokinetic Modelling ◽  
Physiologically Based

scholarly journals Physiologically Based Pharmacokinetic Modelling with Dynamic PET Data to Study the In Vivo Effects of Transporter Inhibition on Hepatobiliary Clearance in Mice

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Marco F. Taddio ◽  
Linjing Mu ◽  
Claudia Keller ◽  
Roger Schibli ◽  
Stefanie D. Krämer
Keyword(s):  
Whole Body ◽  
Laboratory Animals ◽  
Pharmacokinetic Parameters ◽  
Pharmacokinetic Modelling ◽  
Pbpk Modelling ◽  
Time Activity ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based Pharmacokinetic Modelling ◽  
Physiologically Based

Physiologically based pharmacokinetic modelling (PBPK) is a powerful tool to predict in vivo pharmacokinetics based on physiological parameters and data from in vivo studies and in vitro assays. In vivo PBPK modelling in laboratory animals by noninvasive imaging could help to improve the in vivo-in vivo translation towards human pharmacokinetics modelling. We evaluated the feasibility of PBPK modelling with PET data from mice. We used data from two of our PET tracers under development, [11C]AM7 and [11C]MT107. PET images suggested hepatobiliary excretion which was reduced after cyclosporine administration. We fitted the time-activity curves of blood, liver, gallbladder/intestine, kidney, and peripheral tissue to a compartment model and compared the resulting pharmacokinetic parameters under control conditions ([11C]AM7 n=2; [11C]MT107, n=4) and after administration of cyclosporine ([11C]MT107, n=4). The modelling revealed a significant reduction in [11C]MT107 hepatobiliary clearance from 35.2±10.9 to 17.1±5.6 μl/min after cyclosporine administration. The excretion profile of [11C]MT107 was shifted from predominantly hepatobiliary (CLH/CLR = 3.8±3.0) to equal hepatobiliary and renal clearance (CLH/CLR = 0.9±0.2). Our results show the potential of PBPK modelling for characterizing the in vivo effects of transporter inhibition on whole-body and organ-specific pharmacokinetics.

scholarly journals Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 813
Author(s):  
Yoo-Seong Jeong ◽  
Min-Soo Kim ◽  
Nora Lee ◽  
Areum Lee ◽  
Yoon-Jee Chae ◽  
...  
Keyword(s):  
Gastric Acid ◽  
Pbpk Model ◽  
Pbpk Modeling ◽  
Drug Candidate ◽  
Metabolite Formation ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (VSS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The Cmax, AUClast, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

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