Physiologically based mechanistic modelling to predict complex drug–drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—The effect of diltiazem on the time-course of exposure to triazolam

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Abstract

Aim

To predict the magnitude of metabolic drug–drug interaction (mDDI) between triazolam and diltiazem and its primary metabolite N-desmethyldiltiazem (MA).

Methods

Relevant in vitro metabolic and inhibitory data were incorporated into a mechanistic physiologically based pharmacokinetic model within Simcyp (Version 9.1) to simulate the time-course of changes in active CYP3A4 content in gut and liver and plasma concentrations of diltiazem, MA and triazolam in a virtual population with characteristics related to in vivo studies.

Results

The predicted median increases in AUC(0,∞) of triazolam, which ranged from 3.9 to 9.5 for 20 simulated trials (median 5.9), were within 1.5-fold of the observed median value (4.4) in 14 of the trials. Considering the effects of diltiazem only and not those of MA, and ignoring auto-inhibition of MA metabolism and inhibition of its metabolism by diltiazem, resulted in lower increases in triazolam exposure (AUC ratios of 1.5–2.0 (median 1.7) and 2.7–5.3 (median 3.4), respectively).

Conclusion

Prediction of mDDIs involving diltiazem requires consideration of both competitive and time-dependent inhibition in gut and liver by both diltiazem and MA, as well as the complex interplay between the two moieties with respect to mutual inhibition of parent compound and its metabolite.

Introduction

The calcium channel antagonist diltiazem undergoes extensive metabolism through multiple pathways including deacetylation by esterases and cytochrome P450 (CYP)-mediated N- and O-demethylation. N-demethylation to desmethyldiltiazem (MA) appears to be the major pathway of elimination in humans and is mediated primarily by CYP3A with minor contributions from CYP2C8 and CYP2C9 (Pichard et al., 1990, Sutton et al., 1997). MA is further N-demethylated, mainly by CYP3A, to N,N-didesmethyl diltiazem (MD) (Zhao et al., 2007). CYP2D6 is involved in the O-demethylation of diltiazem (Molden et al., 2002a) and the metabolism of desacetyl-diltiazem (Molden et al., 2002b).

Diltiazem causes clinically significant drug–drug interactions with compounds that are metabolised by CYP3A, including midazolam, triazolam, quinidine and simvastatin (Backman et al., 1994, Laganiere et al., 1996, Varhe et al., 1996, Mousa et al., 2000). Thus, inhibition of CYP3A has been attributed to the parent compound and its metabolites, consistent with the accumulation of MA and desacetyl-diltiazem after 2 weeks of administration (Montamat and Abernethy, 1987, Hoglund and Nilsson, 1989). This was supported by in vitro studies with human liver microsomes showing that MA and MD are potent competitive inhibitors of CYP3A (Ki values of 2 and 0.1 μM, respectively, for testosterone 6β-hydroxylation) (Sutton et al., 1997). Subsequently it was shown that both diltiazem and MA, but not MD, cause time-dependent inhibition through metabolite intermediate complex formation, with MA having a 4-fold greater kinact value than diltiazem (Jones et al., 1999, Mayhew et al., 2000; Rowland-Yeo and Yeo, 2001; Zhao et al., 2007, Zhang et al., 2009).

The application of a physiologically based pharmacokinetic model (PBPK) to predict in vivo drug–drug interactions involving mechanism (time)-based CYP3A inhibition from in vitro data has been described with respect to the coadministration of erythromycin and benzodiazepines (Ito et al., 2003) and the time-based auto-inhibition of CYP2D6 by methylenedioxymethamphetamine (MDMA, ‘ecstasy’) (Yang et al., 2006). However, only inhibition in the liver was considered in these simulations. More recently, a semi-PBPK model incorporating competitive and time-dependent inhibition at the gut wall for the interaction between diltiazem and midazolam was reported (Zhang et al., 2009). The inhibitory effect of the primary metabolite MA was also considered but only in the liver, and inhibition of the sequential CYP3A-mediated metabolism of MA by itself and by diltiazem was not incorporated. We now present the development and validation of a mechanistic PBPK model that considers both competitive and time-dependent inhibition in both gut and liver by both diltiazem and MA, as well as the complex interplay between the two moieties with respect to mutual inhibition of parent compound and its metabolite, using the interaction between diltiazem and triazolam as an example.

Section snippets

‘Victim’ drug kinetics

The kinetics of triazolam after oral administration were described by the model shown in Fig. 1A. This is essentially the same as that used by Ito et al., 1998, Ito et al., 2003, but with the incorporation of gut wall metabolism and a more physiological representation of the blood supply to the liver (Yang et al., 2003). The gut and the liver are represented as separate compartments and the other organs are lumped into a single systemic compartment. The following additional assumptions were

Concentration–time profiles following a single dose of diltiazem

Predicted and observed mean plasma concentration–time profiles of diltiazem and its inhibitory metabolite MA after a single oral dose of 60 mg diltiazem in solution are compared for 20 virtual trials in Fig. 2A. Mean predicted AUC(0,∞) values of diltiazem ranged from 0.39 to 0.58 mg/L h for the 20 simulated trials (median 0.50); the observed mean value was 0.44 mg/L h. Mean predicted AUC(0,∞) values of MA ranged from 0.19 to 0.34 mg/L h for the 20 simulated trials (median 0.25); the observed value

Discussion

N-desmethyl diltiazem (MA) is a much more potent competitive inhibitor and time-dependent inactivator of CYP3A than diltiazem (Zhao et al., 2007). Given that this metabolite is present in the systemic circulation at concentrations approaching half those of parent drug after diltiazem administration (Hoglund and Nilsson, 1989), predictions of drug–drug interactions based solely on inhibition by diltiazem should result in significant underestimation. Accordingly, we have incorporated competitive

References (65)

  • M.F. Fromm et al.

    Differential induction of prehepatic and hepatic metabolism of verapamil by rifampin

    Hepatology

    (1996)
  • P. Maurel

    The use of adult human hepatocytes in primary culture and other in vitro systems to investigate drug metabolism in man

    Adv. Drug Deliv. Rev.

    (1996)
  • J. Yang et al.

    The effects of dose staggering on metabolic drug–drug interactions

    Eur. J. Pharm. Sci.

    (2003)
  • R.P. Austin et al.

    The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties

    Drug Metab. Dispos.

    (2002)
  • J.T. Backman et al.

    Dose of midazolam should be reduced during diltiazem and verapamil treatments

    Br. J. Clin. Pharmacol.

    (1994)
  • R.H. Barbhaiya et al.

    Coadministration of nefazodone and benzodiazepines. II. A pharmacokinetic interaction study with triazolam

    J. Clin. Psychopharmacol.

    (1995)
  • Z.E. Barter et al.

    Covariation of human microsomal protein per gram of liver with age: absence of influence of operator and sample storage may justify interlaboratory data pooling

    Drug Metab. Dispos.

    (2008)
  • R.A. Boyd et al.

    The pharmacokinetics and pharmacodynamics of diltiazem and its metabolites in healthy adults after a single oral dose

    Clin. Pharmacol. Ther.

    (1989)
  • C.L. Derry et al.

    Pharmacokinetics and pharmacodynamics of triazolam after two intermittent doses in obese and normal-weight men

    J. Clin. Psychopharmacol.

    (1995)
  • F.S. Eberts et al.

    Triazolam disposition

    Clin. Pharmacol. Ther.

    (1981)
  • A.D. Fraser

    Urinary screening for alprazolam, triazolam, and their metabolites with the EMIT d.a.u. benzodiazepine metabolite assay

    J. Anal. Toxicol.

    (1987)
  • H. Friedman et al.

    Population study of triazolam pharmacokinetics

    Br. J. Clin. Pharmacol.

    (1986)
  • A. Galetin et al.

    Utility of recombinant enzyme kinetics in prediction of human clearance: impact of variability, CYP3A5, and CYP2C19 on CYP3A probe substrates

    Drug Metab. Dispos.

    (2004)
  • A. Galetin et al.

    CYP3A substrate selection and substitution in the prediction of potential drug–drug interactions

    J. Pharmacol. Exp. Ther.

    (2005)
  • D.J. Greenblatt et al.

    Reduced clearance of triazolam in old age: relation to antipyrine oxidizing capacity

    Br. J. Clin. Pharmacol.

    (1983)
  • D.J. Greenblatt et al.

    Inhibition of triazolam clearance by macrolide antimicrobial agents: in vitro correlates and dynamic consequences

    Clin. Pharmacol. Ther.

    (1998)
  • D.J. Greenblatt et al.

    Time course of recovery of cytochrome p450 3A function after single doses of grapefruit juice

    Clin. Pharmacol. Ther.

    (2003)
  • P. Hermann et al.

    Pharmacokinetics of diltiazem after intravenous and oral administration

    Eur. J. Clin. Pharmacol.

    (1983)
  • P. Hoglund et al.

    Pharmacokinetics of diltiazem and its metabolites after single and multiple dosing in healthy volunteers

    Ther. Drug Monit.

    (1989)
  • E.M. Howgate et al.

    Prediction of in vivo drug clearance from in vitro data. I. Impact of inter-individual variability

    Xenobiotica

    (2006)
  • A. Hsu et al.

    Multiple-dose pharmacokinetics of ritonavir in human immunodeficiency virus-infected subjects

    Antimicrob. Agents Chemother.

    (1997)
  • K. Ito et al.

    Prediction of pharmacokinetic alterations caused by drug–drug interactions: metabolic interaction in the liver

    Pharmacol. Rev.

    (1998)
  • K. Ito et al.

    Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes

    Drug Metab. Dispos.

    (2003)
  • M. Jamei et al.

    The Simcyp population based ADME simulator

    Exp. Opin. Drug Metab. Toxicol.

    (2009)
  • D.R. Jones et al.

    Diltiazem inhibition of cytochrome P450, 3A activity is due to metabolite-intermediate complex formation

    J. Pharmacol. Exp. Ther.

    (1999)
  • T.N. Johnson et al.

    Changes in liver volume from birth to adulthood: a meta-analysis

    Liver Transpl.

    (2005)
  • E.U. Kolle et al.

    Pharmacokinetic model of diltiazem

    Arzneimittelforschung

    (1983)
  • P.D. Kroboth et al.

    Triazolam pharmacokinetics after intravenous, oral, and sublingual administration

    J. Clin. Psychopharmacol.

    (1995)
  • S. Laganiere et al.

    Pharmacokinetic and pharmacodynamic interactions between diltiazem and quinidine

    Clin. Pharmacol. Ther.

    (1996)
  • A.A. Lai et al.

    Time-course of interaction between carbamazepine and clonazepam in normal man

    Clin. Pharmacol. Ther.

    (1978)
  • R.H. Levy et al.

    Pharmacokinetic implications of chronic drug treatment in epilepsy: carbamazepine

  • J.J. Lilja et al.

    Effect of grapefruit juice dose on grapefruit juice-triazolam interaction: repeated consumption prolongs triazolam half-life

    Eur. J. Clin. Pharmacol.

    (2000)
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