scholarly journals Improved Prediction of the Drug-Drug Interactions of Pemafibrate Caused by Cyclosporine A and Rifampicin via PBPK Modeling: Consideration of the Albumin-Mediated Hepatic Uptake of Pemafibrate and Inhibition Constants With Preincubation Against OATP1B

2021 ◽  
Vol 110 (1) ◽  
pp. 517-528
Author(s):  
Ji Eun Park ◽  
Yoshihisa Shitara ◽  
Wooin Lee ◽  
Shigemichi Morita ◽  
Jasminder Sahi ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Cyclosporine A ◽  
Hepatic Uptake ◽  
Pbpk Modeling ◽  
Inhibition Constants

scholarly journals Preclinical Evaluation of [18F]LCATD as a PET Tracer to Study Drug-Drug Interactions Caused by Inhibition of Hepatic Transporters

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Andrea Testa ◽  
Sergio Dall’Angelo ◽  
Marco Mingarelli ◽  
Andrea Augello ◽  
Lutz Schweiger ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Lithocholic Acid ◽  
Hepatic Uptake ◽  
Drug Induced ◽  
Liver Uptake ◽  
Rifamycin Sv ◽  
Pet Tracer ◽  
Hepatic Transporters ◽  
Uptake Transporters

The bile acid analogue [18F]LCATD (LithoCholic Acid Triazole Derivative) is transported in vitro by hepatic uptake transporters such as OATP1B1 and NTCP and efflux transporter BSEP. In this in vivo “proof of principle” study, we tested if [18F]LCATD may be used to evaluate drug-drug interactions (DDIs) caused by inhibition of liver transporters. Hepatic clearance of [18F]LCATD in rats was significantly modified upon coadministration of rifamycin SV or sodium fusidate, which are known to inhibit clinically relevant uptake transporters (OATP1B1, NTCP) and canalicular hepatic transporters (BSEP) in humans. Treatment with rifamycin SV (total dose 62.5 mg·Kg−1) reduced the maximum radioactivity of [18F]LCATD recorded in the liver from 14.2 ± 0.8% to 10.2 ± 0.9% and delayed t_max by 90 seconds relative to control rats. AUCliver 0–5 min, AUCbile 0–10 min and hepatic uptake clearance CLuptake,in vivo of rifamycin SV treated rats were significantly reduced, whereas AUCliver 0–30 min was higher than in control rats. Administration of sodium fusidate (30 mg·Kg−1) inhibited the liver uptake of [18F]LCATD, although to a lesser extent, reducing the maximum radioactivity in the liver to 11.5 ± 0.3%. These preliminary results indicate that [18F]LCATD may be a good candidate for future applications as an investigational tracer to evaluate altered hepatobiliary excretion as a result of drug-induced inhibition of hepatic transporters.

scholarly journals Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

2021 ◽  
Vol 12 ◽  
Author(s):  
Kenza Abouir ◽  
Caroline F Samer ◽  
Yvonne Gloor ◽  
Jules A Desmeules ◽  
Youssef Daali
Keyword(s):  
Plasma Concentration ◽  
Drug Interactions ◽  
Pbpk Model ◽  
Model Development ◽  
Pbpk Modeling ◽  
Maximum Plasma ◽  
Extrinsic Factors ◽  
Pbpk Models ◽  
Emerging Trends ◽  
Clearance Prediction

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

scholarly journals Raltegravir Has a Low Propensity To Cause Clinical Drug Interactions through Inhibition of Major Drug Transporters: anIn VitroEvaluation

2013 ◽  
Vol 58 (3) ◽  
pp. 1294-1301 ◽  
Author(s):  
Matthew L. Rizk ◽  
Robert Houle ◽  
Grace Hoyee Chan ◽  
Mike Hafey ◽  
Elizabeth G. Rhee ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Organic Cation ◽  
Drug Transporters ◽  
Organic Anion ◽  
Organic Cation Transporter ◽  
Hepatic Uptake ◽  
Efflux Transporters ◽  
Renal Uptake ◽  
No Inhibition ◽  
Uptake Transporters

ABSTRACTRaltegravir (RAL) is a human immunodeficiency virus type 1 (HIV-1) integrase inhibitor approved to treat HIV infection in adults in combination with other antiretrovirals. The potential of RAL to cause transporter-related drug-drug interactions (DDIs) as an inhibitor has not been well described to date. In this study, a series ofin vitroexperiments were conducted to assess the inhibitory effects of RAL on major human drug transporters known to be involved in clinically relevant drug interactions, including hepatic and renal uptake transporters and efflux transporters. For hepatic uptake transporters, RAL showed no inhibition of organic anion-transporting polypeptide 1B1 (OATP1B1), weak inhibition of OATP1B3 (40% inhibition at 100 μM), and no inhibition of organic cation transporter 1 (OCT1). Studies of renal uptake transporters showed that RAL inhibited organic anion transporters 1 and 3 (OAT1 and OAT3) with 50% inhibitory concentrations (IC50s) (108 μM and 18.8 μM, respectively) well above the maximum concentration of drug in plasma (Cmax) at the clinical 400-mg dose and did not inhibit organic cation transporter 2 (OCT2). As for efflux transporters, RAL did not inhibit breast cancer resistance protein (BCRP) and showed weak inhibition of multidrug and toxin extrusion protein 1 (MATE1) (52% inhibition at 100 μM) and MATE2-K (29% inhibition at 100 μM). These studies indicate that at clinically relevant exposures, RAL does not inhibit or only weakly inhibits hepatic uptake transporters OATP1B1, OATP1B3, and OCT1, renal uptake transporters OCT2, OAT1, and OAT3, as well as efflux transporters BCRP, MATE1, and MATE2-K. The propensity for RAL to cause DDIs via inhibition of these transporters is therefore considered low.

In Vitro Characterization of Axitinib Interactions with Human Efflux and Hepatic Uptake Transporters: Implications for Disposition and Drug Interactions

2013 ◽  
Vol 41 (8) ◽  
pp. 1575-1583 ◽  
Author(s):  
Eric L. Reyner ◽  
Samantha Sevidal ◽  
Mark A. West ◽  
Andrea Clouser-Roche ◽  
Sascha Freiwald ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Hepatic Uptake ◽  
In Vitro Characterization ◽  
Uptake Transporters

PBPK modeling of irbesartan: incorporation of hepatic uptake

2015 ◽  
Vol 36 (8) ◽  
pp. 491-506 ◽  
Author(s):  
Helene Chapy ◽  
Sylvie Klieber ◽  
Priscilla Brun ◽  
Sabine Gerbal-Chaloin ◽  
Xavier Boulenc ◽  
...  
Keyword(s):  
Hepatic Uptake ◽  
Pbpk Modeling

scholarly journals Simulation of Remdesivir Pharmacokinetics and Its Drug Interactions

2021 ◽  
Vol 24 ◽  
pp. 277-291
Author(s):  
Subrata Deb ◽  
Anthony Allen Reeves
Keyword(s):  
Renal Function ◽  
Drug Interaction ◽  
Liver Function ◽  
Drug Interactions ◽  
Spatial Data ◽  
Water Solubility ◽  
Pbpk Modeling ◽  
Strong Interactions ◽  
Hydrophilic Lipophilic Balance ◽  
Drug Regimens

Purpose: Remdesivir, a drug originally developed against Ebola virus, is currently recommended for patients hospitalized with coronavirus disease of 2019 (COVID-19). In spite of United States Food and Drug Administration’s recent assent of remdesivir as the only approved agent for COVID-19, there is limited information available about the physicochemical, metabolism, transport, pharmacokinetic (PK), and drug-drug interaction (DDI) properties of this drug. The objective of this in silico simulation work was to simulate the biopharmaceutical and DDI behavior of remdesivir and characterize remdesivir PK properties in special populations which are highly affected by COVID-19. Methods: The Spatial Data File format structures of remdesivir prodrug (GS-5734) and nucleoside core (GS-441524) were obtained from the PubChem database to upload into the GastroPlus software 9.8 version (Simulations Plus Inc., USA). The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules of GastroPlus were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug Interaction module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for comorbidities. Results: Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas agents used for COVID-19 treatment such as chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. Conclusions: GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potentially be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities. Methods: The Spatial Data File format structures of remdesivir prodrug and nucleoside core were obtained from the PubChem database to upload into the GastroPlus software 9.8 version. The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) PredictorTM and PKPlusTM modules were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug InteractionTM module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for commodities. Results: Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. Conclusions: GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potential be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities.

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