scholarly journals A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

Pharmaceutics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1074
Author(s):  
Denise Türk ◽  
Nina Hanke ◽  
Thorsten Lehr
Keyword(s):  
Open Systems ◽  
Hepatic Clearance ◽  
Gene Interaction ◽  
Tubular Secretion ◽  
Glycoprotein P ◽  
Time Profiles ◽  
Physiologically Based Pharmacokinetic ◽  
P Glycoprotein ◽  
Physiologically Based ◽  
Intestinal Efflux

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

scholarly journals Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network

Pharmaceutics ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 331
Author(s):  
Fatima Zahra Marok ◽  
Laura Maria Fuhr ◽  
Nina Hanke ◽  
Dominik Selzer ◽  
Thorsten Lehr
Keyword(s):  
Open Systems ◽  
Model Performance ◽  
Interaction Network ◽  
Gene Interaction ◽  
Whole Body ◽  
Gene Interaction Network ◽  
Physiologically Based Pharmacokinetic ◽  
Pharmacological Targets ◽  
Physiologically Based ◽  
The Impact

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug–drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug–gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

scholarly journals Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug–Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 270
Author(s):  
Laura Maria Fuhr ◽  
Fatima Zahra Marok ◽  
Nina Hanke ◽  
Dominik Selzer ◽  
Thorsten Lehr
Keyword(s):  
Drug Interaction ◽  
Glomerular Filtration ◽  
Open Systems ◽  
Term Therapy ◽  
Time Profiles ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction ◽  
Long Term Therapy

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent−metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug–drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration−time profiles (dosing range 50–800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

scholarly journals Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions

Pharmaceutics ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1200
Author(s):  
Simeon Rüdesheim ◽  
Jan-Georg Wojtyniak ◽  
Dominik Selzer ◽  
Nina Hanke ◽  
Felix Mahfoud ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Open Systems ◽  
Geometric Mean ◽  
Model Performance ◽  
Gene Interaction ◽  
Whole Body ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
The Usa ◽  
Dose Recommendations

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

scholarly journals Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions

2020 ◽  
Vol 37 (12) ◽  
Author(s):  
Hannah Britz ◽  
Nina Hanke ◽  
Mitchell E. Taub ◽  
Ting Wang ◽  
Bhagwat Prasad ◽  
...  
Keyword(s):  
Plasma Concentration ◽  
Organic Anion ◽  
Plasma Concentrations ◽  
Whole Body ◽  
Time Profiles ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Concentration Time ◽  
Anion Transporter ◽  
Physiologically Based

Abstract Purpose To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. Methods PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration–time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. Results The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. Conclusions Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

scholarly journals Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

2021 ◽  
Author(s):  
Nina Hanke ◽  
José David Gómez-Mantilla ◽  
Naoki Ishiguro ◽  
Peter Stopfer ◽  
Valerie Nock
Keyword(s):  
Drug Interactions ◽  
Pbpk Model ◽  
Open Systems ◽  
Whole Body ◽  
Good Prediction ◽  
Active Efflux ◽  
Physiologically Based Pharmacokinetic ◽  
Positron Emission ◽  
Physiologically Based

Abstract Purpose To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). Methods The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. Results The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. Conclusions A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

scholarly journals Development of a physiologically based pharmacokinetic (PBPK) model of psilocybin and psilocin from magic mushroom in rats and humans

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 209
Author(s):  
Prinya Musikaphongsakul ◽  
Kimheang Ya ◽  
Pakpoom Subsoontorn ◽  
Manupat Lohitnavy
Keyword(s):  
Oral Administration ◽  
Pbpk Model ◽  
Simulated Data ◽  
The Body ◽  
Time Profiles ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Intravenous And Oral Administration ◽  
The Brain ◽  
Good Agreement

Background: Psilocybin (PB) is a psychoactive compound commonly found in magic mushroom (Psilocybe cubensis). PB is quickly converted by the body to psilocin (PI), which has a psychedelic effect through the activation of the 5-HT2A receptor in the brain. The objective of this study is to develop a physiologically based pharmacokinetic (PBPK) model of PB and PI in rats and humans for predicting concentrations of the psychoactive substance in the brain. Methods: Following a search in PubMed, three studies were retrieved and information concerning concentration-time profiles of PI were extracted from the selected studies. In the study in rats, PI was orally administered with a dose of 10.1 mg/kg. There were two studies in humans following a single intravenous dose of PB (1 mg) and oral dose of PB (0.224 mg/kg and 0.3 mg/kg). Berkeley Madonna software was used for computer coding and simulations. The developed PBPK model consisted of seven organ compartments (i.e. lung, heart, brain, fat, muscle, kidney, and liver). Results: The simulations show a good agreement between observed and simulated data, although results for oral administration in rats and humans showed under-predictions and results for intravenous administration in humans showed over-predictions. Conclusions: A PBPK model of PB and PI in rats and humans was developed and could predict concentration-time profiles of PI in plasma, particularly in the brain, following intravenous and oral administration of PB. This model may be useful for a safer dosage regimen of PB for patients with some disorders.

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