Purpose: To compare the value of enhancement and pharmacokinetic parameters measured at dynamic gadoxetate-enhanced magnetic resonance (MR) imaging in determining hepatic organic anion transporter expression in control rats and rats with advanced liver fibrosis.
Materials And Methods: Institutional animal review board approval was received before the study began. Advanced liver fibrosis was created in rats by means of carbon tetrachloride injections over an 8-week period. In 17 rats with liver fibrosis and eight control rats, dynamic gadoxetate-enhanced MR images of the liver were obtained during 1 hour after injection of 0.025 mmol gadoxetate per kilogram of body weight. Enhancement parameters (maximum enhancement [Emax], time to peak [Tmax], and elimination half-life) were measured on enhancement-versus-time curves, and pharmacokinetic parameters (hepatic extraction fraction [HEF] and mean residence time [MRT]) were obtained by means of deconvolution analysis of the concentration-versus-time curves in the liver and the portal vein. The parameters were correlated at simple and multiple regression analysis with the expression of the hepatic anion uptake transporter organic anion-transporting polypeptide 1A1 (Oatp1a1), the hepatobiliary transporter multidrug resistance-associated protein 2 (Mrp2), and the backflux transporter Mrp4, as determined with reverse transcription polymerase chain reaction.
Results: In rats with advanced liver fibrosis, the Emax, Tmax, HEF, and MRT decreased significantly relative to those in control rats, whereas the elimination half-life increased significantly. The enhancement and pharmacokinetic parameters correlated significantly with the expression of the transporters at simple regression analysis. At multiple regression analysis, HEF was the only parameter that was significantly associated with the expression of Oatp1a1 and Mrp2 (P < .001, r = 0.74 and P < .001, r = 0.70, respectively).
Conclusion: The pharmacokinetic parameter HEF at dynamic gadoxetate-enhanced MR imaging is independently correlated with hepatic organic anion transporter expression.
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http://dx.doi.org/10.1148/radiol.14140313 | DOI Listing |
J Antimicrob Chemother
January 2025
URP 7328 Federation for Research into Innovative Explorations and Therapeutics in Utero, University of Paris-Cité, Paris, France.
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Curr Drug Discov Technol
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Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences and Research University, PushpViharSector-3, M-B Road, New Delhi, 110017, India.
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Objective: This study aims to entice researchers by investigating the benefits of Computer-Aided Drug and Design (CADD) and its fundamental principles.
Clin Pharmacokinet
January 2025
College of Pharmacy, Kyungsung University, 309, Suyeong-ro, Nam-gu, Busan, 48434, Republic of Korea.
Background And Objective: Telmisartan exhibits significant pharmacokinetic (PK) variability, but it remains unclear whether its PK profile is altered in hypertensive patients. This study aimed to characterize telmisartan PKs by conducting a meta-analysis and developing a pooled population PK model based on data from healthy subjects and hypertensive patients.
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AAPS J
January 2025
Clinical Pharmacology Modeling and Simulation, Amgen, One Amgen Center Drive, Thousand Oaks, CA, 91320-0777, USA.
Sotorasib is a novel KRAS inhibitor that has shown robust efficacy, safety, and tolerability in patients with KRAS mutation. The objectives of the population pharmacokinetic (PK) analysis were to characterize sotorasib population PK in healthy subjects and patients with advanced solid tumors with KRAS mutation from 6 clinical studies, evaluate the effects of intrinsic and extrinsic factors on PK parameters, and perform simulations to further assess the impact of identified covariates on sotorasib exposures. A two-compartment disposition model with three transit compartments for absorption and time-dependent clearance and bioavailability well described sotorasib PK.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiation Oncology, Henry Ford Hospital, Detroit, USA.
Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace.
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