Use of Three-Compartment Physiologically Based Pharmacokinetic Modeling to Predict Hepatic Blood Levels of Fluvoxamine Relevant for Drug-Drug Interactions

Iga, Kanji

doi:10.1002/jps.24320

Cited by 9 publications

(15 citation statements)

References 17 publications

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“…Nevertheless, according to a previous report, if a drug such as FLV and TAC (victim drug in the present study) exhibits preferential hepatic distribution, CL tot and F h should be determined using f d (≤1) (net dose fraction delivered directly to the extra‐hepatic drug pool after i.v. administration):

\begin{matrix} true \\ right {CL}_{tot} = \frac{D_{i . normalv .}}{AUC {(C_{b})}_{normali . v .}} = \frac{Q_{h} ((), 1 - F)}{((), 1 - A_{e}) f_{d}} \end{matrix}

\begin{matrix} true \\ right F_{h} = \frac{F \times f_{d}}{1 - F + F \times f_{d}} \end{matrix}

In this case, vCL′ int should be calculated from Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Based on the tube model, hepatic availability ( F h ), hepatic clearance (CL h ), and CL tot as a function of apparent hepatic intrinsic clearance (CL′ int = α × CL int ; α , ratio of hepatic cellular unbound‐drug level to hepatic blood unbound‐drug level) for almost all the drugs eliminated by the liver are determined as:

\begin{matrix} true \\ right F_{h} = prefixexp ((), - \frac{{fu}_{b} {CL}^{'}_{int}}{Q_{normalh}}) \end{matrix}

\begin{matrix} true \\ right {CL}^{'}_{int} = - \frac{Q_{h} \ln ([], F_{normalh})}{{fu}_{normalb}} \end{matrix}

\begin{matrix} true \\ right {CL}_{h} = Q_{h} (1 - F_{normalh}) \end{matrix}

\begin{matrix} true \\ right {CL}_{tot} = \frac{{CL}_{normalh}}{1 - A_{e}} = \frac{Q_{h} ((), 1 - F_{h})}{1 - A_{e}} \end{matrix}

where A e (≤1) represents urinary excretion ratio of the unchanged drug.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, determination of hepatic blood perpetrator levels (p C hb ) is essential for DDI prediction. Most recently, Iga has predicted p C hb ( t ) [i.e., time‐dependent p C hb ; time‐dependent parameters are indexed by “( t )”] of FLV using a three‐compartment physiologically based pharmacokinetic (PBPK) method assuming hepatic‐drug‐pool (hdp) based tube modeling (a model better describing E h kinetic), where the maximal p C hb was 2.3 times the maximal p C b . However, even with the use of this maximal p C hb,u for I u , the gap between in vivo and in vitro K i s will not be decreased.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, a simpler and more predictive method can be derived from hybridizing a common two‐compartment (2‐Comp) model with an hdp‐based tube model as used in the previous 3‐Comp PBPK model . Thus, the objectives of the present study were: (i) to derive a 2‐Comp hdp tube‐based dynamic method that allows simulation (SM) or prediction of DDI by inputting common PK data, and tuning or imputing K i ; (ii) to simulate the reported FLV‐perpetrated DDIs of eleven victim drugs and estimate in vivo K i for each DDI; (iii) to compare the in vivo K i s among the victims with the reported in vitro K i s estimated in some victim‐FLV interactions; and (iv) to examine the possibility of DDI prediction by both tube‐based dynamic and static DDI methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants

Iga

2015

Journal of Pharmaceutical Sciences

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\begin{matrix} true \\ right {CL}_{tot} = \frac{D_{i . normalv .}}{AUC {(C_{b})}_{normali . v .}} = \frac{Q_{h} ((), 1 - F)}{((), 1 - A_{e}) f_{d}} \end{matrix}

\begin{matrix} true \\ right F_{h} = \frac{F \times f_{d}}{1 - F + F \times f_{d}} \end{matrix}

In this case, vCL′ int should be calculated from Eq.…”

Section: Methodsmentioning

confidence: 99%

\begin{matrix} true \\ right F_{h} = prefixexp ((), - \frac{{fu}_{b} {CL}^{'}_{int}}{Q_{normalh}}) \end{matrix}

\begin{matrix} true \\ right {CL}^{'}_{int} = - \frac{Q_{h} \ln ([], F_{normalh})}{{fu}_{normalb}} \end{matrix}

\begin{matrix} true \\ right {CL}_{h} = Q_{h} (1 - F_{normalh}) \end{matrix}

\begin{matrix} true \\ right {CL}_{tot} = \frac{{CL}_{normalh}}{1 - A_{e}} = \frac{Q_{h} ((), 1 - F_{h})}{1 - A_{e}} \end{matrix}

where A e (≤1) represents urinary excretion ratio of the unchanged drug.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants

Iga

2015

Journal of Pharmaceutical Sciences

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“…[12][13][14][15] Thus, the present review focuses on our achievements in DDI prediction by use of this method.…”

Section: Introductionmentioning

confidence: 99%

Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug–Drug Interaction

Iga

Kiriyama

2017

Biological & Pharmaceutical Bulletin

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Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

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Physiologically‐Based Pharmacokinetic Models for CYP1A2 Drug–Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

Britz

Hanke

Volz

et al. 2019

CPT Pharmacom & Syst Pharma

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This study provides whole‐body physiologically‐based pharmacokinetic models of the strong index cytochrome P450 ( CYP )1A2 inhibitor and moderate CYP 3A4 inhibitor fluvoxamine and of the sensitive CYP 1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug–drug interaction ( DDI ) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP 1A2 substrate), rifampicin (moderate CYP 1A2 inducer), and midazolam (sensitive index CYP 3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve ( AUC ) ratios ( AUC during DDI / AUC control) and seven of seven of the predicted DDI peak plasma concentration (C max ) ratios (C max during DDI /C max control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community.

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Use of Three-Compartment Physiologically Based Pharmacokinetic Modeling to Predict Hepatic Blood Levels of Fluvoxamine Relevant for Drug-Drug Interactions

Cited by 9 publications

References 17 publications

Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants

Simulation of Metabolic Drug-Drug Interactions Perpetrated by Fluvoxamine Using Hybridized Two-Compartment Hepatic Drug-Pool-Based Tube Modeling and Estimation of In Vivo Inhibition Constants

Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug–Drug Interaction

Physiologically‐Based Pharmacokinetic Models for CYP1A2 Drug–Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

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