10 research outputs found
Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its DrugâDrug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach
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) parentâmetabolite model of carbamazepine and its metabolite
carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drugâdrug
interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma
concentrationâtime profiles (dosing range 50â800 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
Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7âDihydroxybergamottin to Describe CYP3A4 Mediated GrapefruitâDrug Interactions
Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The
inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal
CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24hours after grapefruit
juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK)
grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate
and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs.
The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4
substrates that were publicly available and already evaluated for CYP3A4-mediated drugâdrug interactions. Overall,
43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin
(DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed
by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive
glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4
victim drugs, simulating the effect of the CYP3A4 inactivation on the victimsâ pharmacokinetics as well as their main
metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well
as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations
Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network
The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6
and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drugâdrug 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 drugâgene interaction (DGI) and DDI data. The model was built in PK-SimÂŽ
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
A Physiologically Based Pharmacokinetic and Pharmacodynamic Model of the CYP3A4 Substrate Felodipine for DrugâDrug Interaction Modeling
The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type,
and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive
substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral
bioavailability and is susceptible to drugâdrug interactions (DDIs) with CYP3A4 perpetrators. This
study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD)
parentâmetabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The
model was developed in PK-SimÂŽ and MoBiÂŽ using 49 clinical studies (94 plasma concentrationâtime
profiles in total) that investigated different doses (1â40 mg) of the intravenous and oral adminis tration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine
by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of
dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included,
using an Emax function to describe the felodipine concentrationâeffect relationship. The model was
tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4
perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values.
The model will be freely available in the Open Systems Pharmacology model repository and can be
applied in DDI predictions as a CYP3A4 victim drug
A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as DrugâDrug Interaction Perpetrators
The antifungal ketoconazole, which is mainly used for dermal infections and treatment of
Cushingâs syndrome, is prone to drugâfood interactions (DFIs) and is well known for its strong drugâ
drug interaction (DDI) potential. Some of ketoconazoleâs potent inhibitory activity can be attributed
to its metabolites that predominantly accumulate in the liver. This work aimed to develop a wholebody physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites
for fasted and fed states and to investigate the impact of ketoconazoleâs metabolites on its DDI
potential. The parentâmetabolites model was developed with PK-SimÂŽ and MoBiÂŽ using 53 plasma
concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold
of observed ratios, the developed model demonstrated good predictive performance under fasted
and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were
simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and
digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp
performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed
ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27
DDI AUClast and 18/21 DDI Cmax ratios were within the success limits
A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators
The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing’s syndrome, is prone to drug–food interactions (DFIs) and is well known for its strong drug–drug interaction (DDI) potential. Some of ketoconazole’s potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole’s metabolites on its DDI potential. The parent–metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits
Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its DrugâDrug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach
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) parentâmetabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drugâdrug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentrationâtime profiles (dosing range 50â800 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
A physiologicallyâbased pharmacokinetic precision dosing approach to manage dasatinib drugâdrug interactions
Abstract Dasatinib, a secondâgeneration tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pHâsensitive solubility, dasatinib is susceptible to enzymeâmediated drugâdrug interactions (DDIs) with CYP3A4 perpetrators and pHâdependent DDIs with acidâreducing agents. This work aimed to develop a wholeâbody physiologicallyâbased pharmacokinetic (PBPK) model of dasatinib to describe and predict enzymeâmediated and pHâdependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentrationâtime curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%â80% for strong and 0%â70% for moderate CYP3A4 inhibitors and a 2.3â3.1âfold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patientârelated factors, such as elevated gastric pH
Physiologicallyâbased pharmacokinetic modeling of quinidine to establish a CYP3A4, Pâgp, and CYP2D6 drugâdrugâgene interaction network
Abstract The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and Pâglycoprotein (Pâgp) and is therefore recommended for use in clinical drugâdrug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and Pâgp, it is susceptible to DDIs involving these proteins. Physiologicallyâbased pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drugâdrug(âgene) interaction (DD(G)I) network with a diverse set of CYP3A4 and Pâgp perpetrators as well as CYP2D6 and Pâgp victims. The quinidine parentâmetabolite model including 3âhydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1â600âmg). The model covers efflux transport via Pâgp and metabolic transformation to either 3âhydroxyquinidine or unspecified metabolites via CYP3A4. The 3âhydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within twoâfold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within twoâfold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is