Skip to main content
SpringerLink
Log in

Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data

  • Pharmacokinetics and Disposition
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

Static and dynamic (PBPK) prediction models were applied to estimate the drug–drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared.

Methods

In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12 days. A cocktail of single doses of 100 mg caffeine (CYP1A2 probe), 500 mg tolbutamide (CYP2C9 probe), 20 mg omeprazole (CYP2C19 probe) and 7.5 mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150 mg) and 100 mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined.

Results

In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29 %) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform.

Conclusions

DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

AUC:

Area under the plasma time–concentration curve

AUCR :

AUC ratio

CL:

Clearance

CLform :

Formation clearance

fa :

Fraction absorbed

fu :

Fraction unbound

GLS:

Geometric least squares mean

IC50 :

Inhibitor concentration at 50 % inhibition

ka :

Absorption constant

K i :

Inhibition constant

lnTOL24h :

Concentration of tolbutamide at 24 h

mGluR5:

Metabotropic glutamate receptor subtype 5

MRAe :

Metabolic ratio in urine

NPMH:

Neuropathic pain with mechanical hypersensitivity

PBPK:

Physiologically based pharmacokinetic

R:

AUC Ratio with and without inhibitor

Vss :

Volume of distribution at steady state

References

  1. Food and Drug Administration (2012) Draft guidance for industry—drug interaction studies, study design, data analysis, implications for dosing, and labeling recommendations. Available at: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM292362.pdf. Accessed: 28 Aug 2012

  2. European Medicines Agency (2012) Guideline on the investigations of drug interactions. Available at: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf. Accessed 23 Aug 2012

  3. Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, Grimm S, Kao J, King SP, Miwa G, Ni L, Kumar G, McLeod J, Obach SR, Roberts S, Roe A, Shah A, Snikeris F, Sullivan JT, Tweedie D, Vega JM, Walsh J, Wrighton SA, Pharmaceutical Research and Manufacturers of America Drug Metabolism/Clinical Pharmacology Technical Working Groups (2003) The conduct of in vitro and in vivo drug–drug interaction studies: a PhRMA perspective. J Clin Pharmacol 43:443–469

    CAS  PubMed  Google Scholar 

  4. Guest EJ, Rowland-Yeo K, Rostami-Hodjegan A, Tucker GT, Houston JB, Galetin A (2011) Assessment of algorithms for predicting drug–drug interactions via inhibition mechanisms: comparison of dynamic and static models. Br J Clin Pharmacol 71:72–87

    Article  CAS  PubMed  Google Scholar 

  5. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73

    Article  CAS  PubMed  Google Scholar 

  6. Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, Song P, Brar SS, Madabushi R, Wu TC, Booth BP, Rahman NA, Reynolds KS, Gil Berglund E, Lesko LJ, Huang SM (2011) Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther 89:259–267

    Article  CAS  PubMed  Google Scholar 

  7. Berg D, Godau J, Trenkwalder C, Eggert K, Csoti I, Storch A, Huber H, Morelli-Canelo M, Stamelou M, Ries V, Wolz M, Schneider C, Di Paolo T, Gasparini F, Hariry S, Vandemeulebroecke M, Abi-Saab W, Cooke K, Johns D, Gomez-Mancilla B (2011) AFQ056 treatment of levodopa-induced dyskinesias: results of 2 randomized controlled trials. Mov Disord 26:1243–1250

    Article  PubMed  Google Scholar 

  8. Levenga J, Hayashi S, de Vrij FM, Koekkoek SK, van der Linde HC, Nieuwenhuizen I, Song C, Buijsen RA, Pop AS, Gomezmancilla B, Nelson DL, Willemsen R, Gasparini F, Oostra BA (2011) AFQ056, a new mGluR5 antagonist for treatment of fragile X syndrome. Neurobiol Dis 42:311–317

    Article  CAS  PubMed  Google Scholar 

  9. Rohof WO, Lei A, Hirsch DP, Ny L, Astrand M, Hansen MB, Boeckxstaens GE (2012) The effects of a novel metabotropic glutamate receptor 5 antagonist (AZD2066) on transient lower oesophageal sphincter relaxations and reflux episodes in healthy volunteers. Aliment Pharmacol Ther 35:1231–1242

    Article  CAS  PubMed  Google Scholar 

  10. Jonzon B, Butler S, Karlsten R, Malamut R, Ståhle L, Huizar K, Stacey B (2012) Efficacy and safety of the mGluR5 antagonist AZD2066 in peripheral neuropathic pain patients with mechanical hypersensitivity (NPMH): results of a phase IIa randomised, double-blind, placebo-controlled study PT 427, 14th World Congress of Pain, IAPS

  11. Karlsten R, Malamut R, Jonzon B, Ståhle L, Huizar K, Argoff C (2012) Efficacy and safety of the mGluR5 antagonist AZD2066 in painful diabetes neuropathy (PDN): results of a phase IIa randomised, double-blind, placebo-controlled study PT 429, 14th World Congress of Pain, IAPS

  12. Ståhle L, Karlsten R, Jonzon B, Eriksson B, Kågedal M, Dominicus A (2012) Safety evaluation of the mGluR5 antagonists AZD9272, AZD2066 and AZD2516 in healthy volunteers and patients with neuropathic pain or major depressive disorder PT 444, 14th World Congress of Pain, IAPS

  13. Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol 5:211–223

    Article  CAS  PubMed  Google Scholar 

  14. Blakey GE, Lockton JA, Perrett J, Norwood P, Russell M, Aherne Z, Plume J (2004) Pharmacokinetic and pharmacodynamic assessment of a five-probe metabolic cocktail for CYPs 1A2, 3A4, 2C9, 2D6 and 2E1. Br J Clin Pharmacol 57:162–169

    Article  CAS  PubMed  Google Scholar 

  15. Brandt RB, Laux JE, Yates SW (1987) Calculation of inhibitor Ki and inhibitor type from the concentration of inhibitor for 50% inhibition for Michaelis-Menten enzymes. Biochem Med Metab Biol 37:344–349

    Article  CAS  PubMed  Google Scholar 

  16. Ryu JY, Song IS, Sunwoo YE, Shon JH, Liu KH, Cha IJ, Shin JG (2007) Development of the "Inje cocktail" for high-throughput evaluation of five human cytochrome P450 isoforms in vivo. Clin Pharmacol Ther 82:531–540

    Article  CAS  PubMed  Google Scholar 

  17. Streetman DS, Bleakley JF, Kim JS, Nafziger AN, Leeder JS, Gaedigk A, Gotschall R, Kearns GL, Bertino JS Jr (2000) Combined phenotypic assessment of CYP1A2, CYP2C19, CYP2D6, CYP3A, N-acetyltransferase-2, and xanthine oxidase with the "Cooperstown cocktail". Clin Pharmacol Ther 68:375–383

    Article  CAS  PubMed  Google Scholar 

  18. Chainuvati S, Nafziger AN, Leeder JS, Gaedigk A, Kearns GL, Sellers E, Zhang Y, Kashuba AD, Rowland E, Bertino JS Jr (2003) Combined phenotypic assessment of cytochrome p450 1A2, 2C9, 2C19, 2D6, and 3A, N-acetyltransferase-2, and xanthine oxidase activities with the "Cooperstown 5+1 cocktail". Clin Pharmacol Ther 74:437–447

    Article  CAS  PubMed  Google Scholar 

  19. Christensen M, Andersson K, Dalen P, Mirghani RA, Muirhead GJ, Nordmark A, Tybring G, Wahlberg A, Yasar U, Bertilsson L (2003) The Karolinska cocktail for phenotyping of five human cytochrome P450 enzymes. Clin Pharmacol Ther 73:517–528

    Article  CAS  PubMed  Google Scholar 

  20. Rostami-Hodjegan A, Nurminen S, Jackson PR, Tucker GT (1996) Caffeine urinary metabolite ratios as markers of enzyme activity: a theoretical assessment. Pharmacogenetics 6:121–149

    Article  CAS  PubMed  Google Scholar 

  21. Fuhr U, Rost KL (1994) Simple and reliable CYP1A2 phenotyping by the paraxanthine/caffeine ratio in plasma and in saliva. Pharmacogenetics 4:109–116

    Article  CAS  PubMed  Google Scholar 

  22. Fuhr U, Rost KL, Engelhardt R, Sachs M, Liermann D, Belloc C, Beaune P, Janezic S, Grant D, Meyer UA, Staib AH (1996) Evaluation of caffeine as a test drug for CYP1A2, NAT2 and CYP2E1 phenotyping in man by in vivo versus in vitro correlations. Pharmacogenetics 6:159–176

    Article  CAS  PubMed  Google Scholar 

  23. Faucette SR, Hawke RL, Lecluyse EL, Shord SS, Yan B, Laethem RM, Lindley CM (2000) Validation of bupropion hydroxylation as a selective marker of human cytochrome P450 2B6 catalytic activity. Drug Metab Dispos 28:1222–1230

    CAS  PubMed  Google Scholar 

  24. Loboz KK, Gross AS, Williams KM, Liauw WS, Day RO, Blievernicht JK, Zanger UM, McLachlan AJ (2006) Cytochrome P450 2B6 activity as measured by bupropion hydroxylation: effect of induction by rifampin and ethnicity. Clin Pharmacol Ther 80:75–84

    Article  CAS  PubMed  Google Scholar 

  25. Palovaara S, Pelkonen O, Uusitalo J, Lundgren S, Laine K (2003) Inhibition of cytochrome P450 2B6 activity by hormone replacement therapy and oral contraceptive as measured by bupropion hydroxylation. Clin Pharmacol Ther 74:326–333

    Article  CAS  PubMed  Google Scholar 

  26. Turpeinen M, Tolonen A, Uusitalo J, Jalonen J, Pelkonen O, Laine K (2005) Effect of clopidogrel and ticlopidine on cytochrome P450 2B6 activity as measured by bupropion hydroxylation. Clin Pharmacol Ther 77:553–559

    Article  CAS  PubMed  Google Scholar 

  27. Kirchheiner J, Roots I, Goldammer M, Rosenkranz B, Brockmoller J (2005) Effect of genetic polymorphisms in cytochrome p450 (CYP) 2C9 and CYP2C8 on the pharmacokinetics of oral antidiabetic drugs: clinical relevance. Clin Pharmacokinet 44:1209–1225

    Article  CAS  PubMed  Google Scholar 

  28. Shon JH, Yoon YR, Kim KA, Lim YC, Lee KJ, Park JY, Cha IJ, Flockhart DA, Shin JG (2002) Effects of CYP2C19 and CYP2C9 genetic polymorphisms on the disposition of and blood glucose lowering response to tolbutamide in humans. Pharmacogenetics 12:111–119

    Article  CAS  PubMed  Google Scholar 

  29. Lee CR, Pieper JA, Frye RF, Hinderliter AL, Blaisdell JA, Goldstein JA (2003) Tolbutamide, flurbiprofen, and losartan as probes of CYP2C9 activity in humans. J Clin Pharmacol 43:84–91

    Article  CAS  PubMed  Google Scholar 

  30. Lee CR, Hawke RL, Pieper JA (2005) Twenty-four hour tolbutamide plasma concentration as a phenotypic measure of CYP2C9 activity. Eur J Clin Pharmacol 61:315–316

    Article  PubMed  Google Scholar 

  31. Veronese ME, Miners JO, Randles D, Gregov D, Birkett DJ (1990) Validation of the tolbutamide metabolic ratio for population screening with use of sulfaphenazole to produce model phenotypic poor metabolizers. Clin Pharmacol Ther 47:403–411

    Article  CAS  PubMed  Google Scholar 

  32. Jetter A, Kinzig-Schippers M, Skott A, Lazar A, Tomalik-Scharte D, Kirchheiner J, Walchner-Bonjean M, Hering U, Jakob V, Rodamer M, Jabrane W, Kasel D, Brockmoller J, Fuhr U, Sorgel F (2004) Cytochrome P450 2C9 phenotyping using low-dose tolbutamide. Eur J Clin Pharmacol 60:165–171

    Article  CAS  PubMed  Google Scholar 

  33. Lee CR, Pieper JA, Hinderliter AL, Blaisdell JA, Goldstein JA (2002) Evaluation of cytochrome P4502C9 metabolic activity with tolbutamide in CYP2C91 heterozygotes. Clin Pharmacol Ther 72:562–571

    Article  CAS  PubMed  Google Scholar 

  34. Chang M, Dahl ML, Tybring G, Gotharson E, Bertilsson L (1995) Use of omeprazole as a probe drug for CYP2C19 phenotype in Swedish Caucasians: comparison with S-mephenytoin hydroxylation phenotype and CYP2C19 genotype. Pharmacogenetics 5:358–363

    Article  CAS  PubMed  Google Scholar 

  35. Niioka T, Uno T, Sugimoto K, Sugawara K, Hayakari M, Tateishi T (2007) Estimation of CYP2C19 activity by the omeprazole hydroxylation index at a single point in time after intravenous and oral administration. Eur J Clin Pharmacol 63:1031–1038

    Article  CAS  PubMed  Google Scholar 

  36. Tamminga WJ, Wemer J, Oosterhuis B, Brakenhoff JP, Gerrits MG, de Zeeuw RA, de Leij LF, Jonkman JH (2001) An optimized methodology for combined phenotyping and genotyping on CYP2D6 and CYP2C19. Eur J Clin Pharmacol 57:143–146

    Article  CAS  PubMed  Google Scholar 

  37. Wandel C, Bocker RH, Bohrer H, deVries JX, Hofmann W, Walter K, Kleingeist B, Neff S, Ding R, Walter-Sack I, Martin E (1998) Relationship between hepatic cytochrome P450 3A content and activity and the disposition of midazolam administered orally. Drug Metab Dispos 26:110–114

    CAS  PubMed  Google Scholar 

  38. Gorski JC, Jones DR, Haehner-Daniels BD, Hamman MA, O'Mara EM Jr, Hall SD (1998) The contribution of intestinal and hepatic CYP3A to the interaction between midazolam and clarithromycin. Clin Pharmacol Ther 64:133–143

    Article  CAS  PubMed  Google Scholar 

  39. Gorski JC, Hall SD, Jones DR, VandenBranden M, Wrighton SA (1994) Regioselective biotransformation of midazolam by members of the human cytochrome P450 3A (CYP3A) subfamily. Biochem Pharmacol 47:1643–1653

    Article  CAS  PubMed  Google Scholar 

  40. Thummel KE, Shen DD, Podoll TD, Kunze KL, Trager WF, Hartwell PS, Raisys VA, Marsh CL, McVicar JP, Barr DM (1994) Use of midazolam as a human cytochrome P450 3A probe: I. In vitro-in vivo correlations in liver transplant patients. J Pharmacol Exp Ther 271:549–556

    CAS  PubMed  Google Scholar 

  41. Zhao P, Ragueneau-Majlessi I, Zhang L, Strong JM, Reynolds KS, Levy RH, Thummel KE, Huang SM (2009) Quantitative evaluation of pharmacokinetic inhibition of CYP3A substrates by ketoconazole: a simulation study. J Clin Pharmacol 49:351–359

    Article  CAS  PubMed  Google Scholar 

  42. Peters SA, Schroeder P, Giri N, Dolgos H (2012) Evaluation of the use of static and dynamic models to predict drug-drug interaction and its associated variability: impact on drug discovery and early development. Drug Metab Dispos 40(8):1495–1507

    Article  CAS  PubMed  Google Scholar 

  43. Yang Z, Vakkalagadda B, Shen G, Ahlers CM, Has T, Christopher LJ, Kurland JF, Roongta V, Masson E, Zhang S (2012) Inhibitory effect of ketoconazole on the pharmacokinetics of amMultireceptor tyrosine Kinase inhibitor BMS-690514 in healthy participants: assessing the mechanism of the interaction with physiologically-based pharmacokinetic simulations. J Clin Pharmacol. doi:10.1177/0091270011439208

Download references

Acknowledgements

The authors are grateful to Jenny Aasa and Elin Sohlberg for their assistance in the in vitro experiments and part of the SimCYP simulations. We also would like to thank Urban Fagerholm and all other AstraZeneca scientists involved for their assistance. The personnel at ICON Development Solution and PRA International are thanked for their involvement in this study.

Conflict of interest

This research was funded by AstraZeneca. All authors are or were employees at AstraZeneca.

Author information

Author notes

  1. Anna Nordmark

    Present address: Medical Products Agency, Uppsala, Sweden

Authors and Affiliations

  1. Clinical Pharmacology Science, AstraZeneca RD Södertälje, Södertälje, Sweden

    Anna Nordmark & Anita Andersson

  2. DMPK, AstraZeneca RD Södertälje, Södertälje, Sweden

    Pawel Baranczewski & Ewa Wanag

  3. Clinical Neuroscience, AstraZeneca RD Södertälje, Södertälje, Sweden

    Lars Ståhle

Authors
  1. Anna Nordmark
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anita Andersson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pawel Baranczewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ewa Wanag
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lars Ståhle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Nordmark.

Additional information

The views expressed in this article are those of the authors and do not reflect official views of the Medical Products Agency

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 212 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nordmark, A., Andersson, A., Baranczewski, P. et al. Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data. Eur J Clin Pharmacol 70, 167–178 (2014). https://doi.org/10.1007/s00228-013-1603-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00228-013-1603-8

Keywords

Search

Navigation