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Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics

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Abstract

Mavoglurant (MVG) is an antagonist at the metabotropic glutamate receptor-5 currently under clinical development at Novartis Pharma AG for the treatment of central nervous system diseases. The aim of this study was to develop and optimise a population whole-body physiologically-based pharmacokinetic (WBPBPK) model for MVG, to predict the impact of drug–drug interaction (DDI) and age on its pharmacokinetics. In a first step, the model was fitted to intravenous (IV) data from a clinical study in adults using a Bayesian approach. In a second step, the optimised model was used together with a mechanistic absorption model for exploratory Monte Carlo simulations. The ability of the model to predict MVG pharmacokinetics when orally co-administered with ketoconazole in adults or administered alone in 3–11 year-old children was evaluated using data from three other clinical studies. The population model provided a good description of both the median trend and variability in MVG plasma pharmacokinetics following IV administration in adults. The Bayesian approach offered a continuous flow of information from pre-clinical to clinical studies. Prediction of the DDI with ketoconazole was consistent with the results of a non-compartmental analysis of the clinical data (threefold increase in systemic exposure). Scaling of the WBPBPK model allowed reasonable extrapolation of MVG pharmacokinetics from adults to children. The model can be used to predict plasma and brain (target site) concentration–time profiles following oral administration of various immediate-release formulations of MVG alone or when co-administered with other drugs, in adults as well as in children.

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References

  1. Wendling T, Ogungbenro K, Pigeolet E, Dumitras S, Woessner R, Aarons L (2015) Model-based evaluation of the impact of formulation and food intake on the complex oral absorption of mavoglurant in healthy subjects. Pharm Res 32(5):1764–1778. doi:10.1007/s11095-014-1574-1

    Article  CAS  PubMed  Google Scholar 

  2. Walles M, Wolf T, Jin Y, Ritzau M, Leuthold LA, Krauser J, Gschwind HP, Carcache D, Kittelmann M, Ocwieja M, Ufer M, Woessner R, Chakraborty A, Swart P (2013) Metabolism and disposition of the metabotropic glutamate receptor 5 antagonist (mGluR5) mavoglurant (AFQ056) in healthy subjects. Drug Metab Dispos 41(9):1626–1641. doi:10.1124/dmd.112.050716

    Article  CAS  PubMed  Google Scholar 

  3. Wendling T, Ogungbenro K, Pigeolet E, Dumitras S, Woessner R, Aarons L (2014) Model-based evaluation of the impact of formulation and food intake on the complex oral absorption of mavoglurant in healthy subjects. Pharm Res. doi:10.1007/s11095-014-1574-1

    PubMed  Google Scholar 

  4. Edginton AN, Theil FP, Schmitt W, Willmann S (2008) Whole body physiologically-based pharmacokinetic models: their use in clinical drug development. Expert Opin Drug Metab Toxicol 4(9):1143–1152. doi:10.1517/17425255.4.9.1143

    Article  CAS  PubMed  Google Scholar 

  5. Wakefield JC, Smith AFM (1994) Bayesian analysis of linear and non-linear population models by using the Gibbs sampler. Appl Stat 43:201–221

    Article  Google Scholar 

  6. Yates JW (2006) Structural identifiability of physiologically based pharmacokinetic models. J Pharmacokinet Pharmacodyn 33(4):421–439. doi:10.1007/s10928-006-9011-7

    Article  CAS  PubMed  Google Scholar 

  7. Tsamandouras N, Rostami-Hodjegan A, Aarons L (2015) Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol 79(1):48–55. doi:10.1111/bcp.12234

    Article  CAS  PubMed  Google Scholar 

  8. Sheiner LB (1984) The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev 15(1–2):153–171. doi:10.3109/03602538409015063

    Article  CAS  PubMed  Google Scholar 

  9. Wakefield JC (1996) The Bayesian analysis of population pharmacokinetic models. J Am Stat Assoc 91:61–76

    Google Scholar 

  10. Bois FY, Gelman A, Jiang J, Maszle DR, Zeise L, Alexeef G (1996) Population toxicokinetics of tetrachloroethylene. Arch Toxicol 70(6):347–355

    Article  CAS  PubMed  Google Scholar 

  11. Jonsson F, Johanson G (2001) Bayesian estimation of variability in adipose tissue blood flow in man by physiologically based pharmacokinetic modeling of inhalation exposure to toluene. Toxicology 157(3):177–193

    Article  CAS  PubMed  Google Scholar 

  12. Gueorguieva I, Aarons L, Rowland M (2006) Diazepam pharamacokinetics from preclinical to phase I using a Bayesian population physiologically based pharmacokinetic model with informative prior distributions in WinBUGS. J Pharmacokinet Pharmacodyn 33(5):571–594. doi:10.1007/s10928-006-9023-3

    Article  CAS  PubMed  Google Scholar 

  13. Nestorov I (2007) Whole-body physiologically based pharmacokinetic models. Expert Opin Drug Metab Toxicol 3(2):235–249. doi:10.1517/17425255.3.2.235

    Article  CAS  PubMed  Google Scholar 

  14. Willmann S, Hohn K, Edginton A, Sevestre M, Solodenko J, Weiss W, Lippert J, Schmitt W (2007) Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn 34(3):401–431. doi:10.1007/s10928-007-9053-5

    Article  PubMed  Google Scholar 

  15. Gomez-Mancilla B, Berry-Kravis E, Hagerman R, von Raison F, Apostol G, Ufer M, Gasparini F, Jacquemont S (2014) Development of mavoglurant and its potential for the treatment of fragile X syndrome. Expert Opin Investig Drugs 23(1):125–134. doi:10.1517/13543784.2014.857400

    Article  CAS  PubMed  Google Scholar 

  16. Jakab A, Winter S, Raccuglia M, Picard F, Dumitras S, Woessner R, Mistry S, Chudasama J, Guttikar S, Kretz O (2013) Validation of an LC-MS/MS method for the quantitative determination of mavoglurant (AFQ056) in human plasma. Anal Bioanal Chem 405(1):215–223. doi:10.1007/s00216-012-6456-y

    Article  CAS  PubMed  Google Scholar 

  17. Yu LX, Lipka E, Crison JR, Amidon GL (1996) Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Adv Drug Deliv Rev 19(3):359–376

    Article  CAS  PubMed  Google Scholar 

  18. Hintz RJ, Johnson KC (1989) The effect of particle size distribution on dissolution rate and oral absorption. Int J Pharm 51(9–17):9–17

    Article  CAS  Google Scholar 

  19. Takano R, Sugano K, Higashida A, Hayashi Y, Machida M, Aso Y, Yamashita S (2006) Oral absorption of poorly water-soluble drugs: computer simulation of fraction absorbed in humans from a miniscale dissolution test. Pharm Res 23(6):1144–1156. doi:10.1007/s11095-006-0162-4

    Article  CAS  PubMed  Google Scholar 

  20. Nestorov I (2001) Modelling and simulation of variability and uncertainty in toxicokinetics and pharmacokinetics. Toxicol Lett 120(1–3):411–420

    Article  CAS  PubMed  Google Scholar 

  21. Howgate EM, Rowland Yeo K, Proctor NJ, Tucker GT, Rostami-Hodjegan A (2006) Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica 36(6):473–497. doi:10.1080/00498250600683197

    Article  CAS  PubMed  Google Scholar 

  22. Fieller EC (1954) Some problems in interval estimation. J R Stat Soc B 16(2):175–185

    Google Scholar 

  23. Jansson R, Bredberg U, Ashton M (2008) Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity. J Pharm Sci 97(6):2324–2339. doi:10.1002/jps.21130

    Article  CAS  PubMed  Google Scholar 

  24. Boeckmann AJ, Sheiner LB, Beal SL (2013) NONMEM users guide—part VIII. ICON Development Solutions, Hanover

    Google Scholar 

  25. Martins JRRA, Sturdza P, Alonson JJ (2003) The complex-step derivative approximation. ACM Trans Math Softw 29:245–262

    Article  Google Scholar 

  26. Yetter RA, Dryer FL, Rabitz H (1985) Some interpretive aspects of elementary sensitivity gradients in combustion kinetics modeling. Combust Flame 59:107–133

    Article  CAS  Google Scholar 

  27. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472. doi:10.2307/2246093

    Article  Google Scholar 

  28. Best NG, Cowles MK, Vines SK (1995) CODA manual version 0.30. MRC Biostatistics Uni, Cambridge

    Google Scholar 

  29. Oberle RL, Chen TS, Lloyd C, Barnett JL, Owyang C, Meyer J, Amidon GL (1990) The influence of the interdigestive migrating myoelectric complex on the gastric emptying of liquids. Gastroenterology 99(5):1275–1282

    CAS  PubMed  Google Scholar 

  30. Yu LX, Amidon GL (1998) Saturable small intestinal drug absorption in humans: modeling and interpretation of cefatrizine data. Eur J Pharm Biopharm 45(2):199–203

    Article  CAS  PubMed  Google Scholar 

  31. Tsamandouras N, Wendling T, Rostami-Hodjegan A, Galetin A, Aarons L (2015) Incorporation of stochastic variability in mechanistic population pharmacokinetic models: handling the physiological constraints using normal transformations. J Pharmacokinet Pharmacodyn. doi:10.1007/s10928-015-9418-0

    PubMed  Google Scholar 

  32. Schiller C, Frohlich CP, Giessmann T, Siegmund W, Monnikes H, Hosten N, Weitschies W (2005) Intestinal fluid volumes and transit of dosage forms as assessed by magnetic resonance imaging. Aliment Pharmacol Ther 22(10):971–979. doi:10.1111/j.1365-2036.2005.02683.x

    Article  CAS  PubMed  Google Scholar 

  33. Lennernas H (2014) Human in vivo regional intestinal permeability: importance for pharmaceutical drug development. Mol Pharm 11(1):12–23. doi:10.1021/mp4003392

    Article  PubMed  Google Scholar 

  34. Valentin J (2002) Guide for the practical application of the ICRP Human Respiratory Tract Model. A report of ICRP supporting guidance 3: approved by ICRP committee 2 in October 2000. Ann ICRP 32(1–2):13–306

    Article  CAS  PubMed  Google Scholar 

  35. Matheson PJ, Wilson MA, Garrison RN (2000) Regulation of intestinal blood flow. J Surg Res 93(1):182–196. doi:10.1006/jsre.2000.5862

    Article  CAS  PubMed  Google Scholar 

  36. Paine MF, Khalighi M, Fisher JM, Shen DD, Kunze KL, Marsh CL, Perkins JD, Thummel KE (1997) Characterization of interintestinal and intraintestinal variations in human CYP3A-dependent metabolism. J Pharm Exp Ther 283(3):1552–1562

    CAS  Google Scholar 

  37. Sun D, Lennernas H, Welage LS, Barnett JL, Landowski CP, Foster D, Fleisher D, Lee KD, Amidon GL (2002) Comparison of human duodenum and Caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharm Res 19(10):1400–1416

    Article  CAS  PubMed  Google Scholar 

  38. Sjogren E, Abrahamsson B, Augustijns P, Becker D, Bolger MB, Brewster M, Brouwers J, Flanagan T, Harwood M, Heinen C, Holm R, Juretschke HP, Kubbinga M, Lindahl A, Lukacova V, Munster U, Neuhoff S, Nguyen MA, Peer A, Reppas C, Hodjegan AR, Tannergren C, Weitschies W, Wilson C, Zane P, Lennernas H, Langguth P (2014) In vivo methods for drug absorption - comparative physiologies, model selection, correlations with in vitro methods (IVIVC), and applications for formulation/API/excipient characterization including food effects. Eur J Pharm Sci 57:99–151. doi:10.1016/j.ejps.2014.02.010

    Article  CAS  PubMed  Google Scholar 

  39. 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(3):351–359. doi:10.1177/0091270008331196

    Article  CAS  PubMed  Google Scholar 

  40. Walsky RL, Gaman EA, Obach RS (2005) Examination of 209 drugs for inhibition of cytochrome P450 2C8. J Clin Pharmacol 45(1):68–78. doi:10.1177/0091270004270642

    Article  CAS  PubMed  Google Scholar 

  41. Stresser DM, Broudy MI, Ho T, Cargill CE, Blanchard AP, Sharma R, Dandeneau AA, Goodwin JJ, Turner SD, Erve JC, Patten CJ, Dehal SS, Crespi CL (2004) Highly selective inhibition of human CYP3Aa in vitro by azamulin and evidence that inhibition is irreversible. Drug Metab Dispos 32(1):105–112. doi:10.1124/dmd.32.1.105

    Article  CAS  PubMed  Google Scholar 

  42. Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A (2010) 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. Eur J Pharm Sci 39(5):298–309. doi:10.1016/j.ejps.2009.12.002

    Article  CAS  PubMed  Google Scholar 

  43. Johnson TN, Rostami-Hodjegan A, Tucker GT (2006) Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet 45(9):931–956. doi:10.2165/00003088-200645090-00005

    Article  CAS  PubMed  Google Scholar 

  44. Edginton AN, Schmitt W, Willmann S (2006) Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin Pharmacokinet 45(10):1013–1034. doi:10.2165/00003088-200645100-00005

    Article  CAS  PubMed  Google Scholar 

  45. Edginton AN, Schmitt W, Voith B, Willmann S (2006) A mechanistic approach for the scaling of clearance in children. Clin Pharmacokinet 45(7):683–704. doi:10.2165/00003088-200645070-00004

    Article  CAS  PubMed  Google Scholar 

  46. ICRP (1975) Report of the task group on reference man. ICRP Publication 23, Pergamon Press, Oxford

  47. Dokoumetzidis A, Aarons L (2009) A method for robust model order reduction in pharmacokinetics. J Pharmacokinet Pharmacodyn 36(6):613–628. doi:10.1007/s10928-009-9141-9

    Article  CAS  PubMed  Google Scholar 

  48. Jorgensen AL, FitzGerald RJ, Oyee J, Pirmohamed M, Williamson PR (2012) Influence of CYP2C9 and VKORC1 on patient response to warfarin: a systematic review and meta-analysis. PLoS One 7(8):e44064. doi:10.1371/journal.pone.0044064

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Wang J, Flanagan DR (1999) General solution for diffusion-controlled dissolution of spherical particles. 1. Theory. J Pharm Sci 88(7):731–738. doi:10.1021/js980236p

    Article  CAS  PubMed  Google Scholar 

  50. Wang J, Flanagan DR (2002) General solution for diffusion-controlled dissolution of spherical particles. 2. Evaluation of experimental data. J Pharm Sci 91(2):534–542

    Article  CAS  PubMed  Google Scholar 

  51. 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(7):1243–1250. doi:10.1002/mds.23616

    Article  PubMed  Google Scholar 

  52. Stocchi F, Rascol O, Destee A, Hattori N, Hauser RA, Lang AE, Poewe W, Stacy M, Tolosa E, Gao H, Nagel J, Merschhemke M, Graf A, Kenney C, Trenkwalder C (2013) AFQ056 in Parkinson patients with levodopa-induced dyskinesia: 13-week, randomized, dose-finding study. Mov Disord 28(13):1838–1846. doi:10.1002/mds.25561

    Article  CAS  PubMed  Google Scholar 

  53. Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP (1997) Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13(4):407–484

    Article  CAS  PubMed  Google Scholar 

  54. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A (2007) Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 8(7):676–684

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The authors would like to thank Nikolaos Tsamandouras and Andres Olivares-Morales (Manchester Pharmacy School, The University of Manchester, Manchester, United-Kingdom) for fruitful discussions.

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Authors and Affiliations

  1. Manchester Pharmacy School, The University of Manchester, Manchester, UK

    Thierry Wendling, Kayode Ogungbenro & Leon Aarons

  2. Drug Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research, Basel, Switzerland

    Thierry Wendling & Swati Dumitras

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  1. Thierry Wendling
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Correspondence to Leon Aarons.

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Thierry Wending is an employee of Novartis Pharma AG and a Ph.D. student at the University of Manchester.

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Wendling, T., Dumitras, S., Ogungbenro, K. et al. Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics. J Pharmacokinet Pharmacodyn 42, 639–657 (2015). https://doi.org/10.1007/s10928-015-9430-4

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