A pregnancy physiologically based pharmacokinetic (p-PBPK) model for disposition of drugs metabolized by CYP1A2, CYP2D6 and CYP3A4.
Journal: 2013/March - British Journal of Clinical Pharmacology
ISSN: 1365-2125
Abstract:
OBJECTIVE
Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies.
METHODS
A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations.
RESULTS
The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women.
CONCLUSIONS
In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study.
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Br J Clin Pharmacol 74(5): 873-885

A pregnancy physiologically based pharmacokinetic (p-PBPK) model for disposition of drugs metabolized by CYP1A2, CYP2D6 and CYP3A4

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Simcyp Limited, Sheffield, UK
School of Pharmacy and Pharmaceutical Sciences, Manchester University, Manchester, UK
Professor Amin Rostami-Hodjegan, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, UK. Tel.: +44 161 3060634 Fax: +44 114 2922333 E-mail: ku.ca.retsehcnam@imatsor.nima
The first two authors contributed equally to this work.
Received 2012 Mar 16; Accepted 2012 Jun 11.

Abstract

Aims

Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies.

Methods

A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations.

Results

The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women.

Conclusions

In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study.

Keywords: caffeine, metoprolol, midazolam, physiologically based pharmacokinetic model, pregnancy
Abstract

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

  • Changes in physiology and biology during pregnancy lead to variation of the kinetics of drugs; therefore, a similar dose of a drug may not be associated with similar drug exposure to that found in nonpregnant women.

WHAT THIS STUDY ADDS

  • This study introduces a novel physiologically based model, which integrates the knowledge of changes in various elements related to kinetics, particularly those of enzyme activity for three different cytochrome P450 enzymes, and shows the validity of assumptions on three cases related to caffeine, metoprolol and midazolam.

AUC, area under the concentration-time profile; CL, clearance; Cmax, the maximum concentration; n, number of women; Tmax, the time at which Cmax occurs.

Acknowledgments

This project was partly funded by an FSA (Food Standards Agency) grant (T01065) of the UK Government. We thank Dr Hora Soltani for consultation on pregnancy-related physiology and Mr James Kay for assistance with collecting the references and preparation of the manuscript.

Acknowledgments

References

  • 1. Anderson GDPregnancy-induced changes in pharmacokinetics: a mechanistic-based approach. Clin Pharmacokinet. 2005;44:989–1008.[PubMed][Google Scholar]
  • 2. Hodge LS, Tracy TSAlterations in drug disposition during pregnancy: implications for drug therapy. Expert Opin Drug Metab Toxicol. 2007;3:557–571.[PubMed][Google Scholar]
  • 3. Abduljalil K, Furness P, Johnson TN, Rostami-Hodjegan A, Soltani HPhysiological, anatomical and metabolic changes with gestational age during normal pregnancy: creating a database and analysing trends for parameters required in physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2012;51:365–396.[PubMed][Google Scholar]
  • 4. Rodger MA, Makropoulos D, Walker M, Keely E, Karovitch A, Wells PSParticipation of pregnant women in clinical trials: will they participate and why? Am J Perinatol. 2003;20:69–76.[PubMed][Google Scholar]
  • 5. McCullough LB, Coverdale JH, Chervenak FAA comprehensive ethical framework for responsibly designing and conducting pharmacologic research that involves pregnant women. Am J Obstet Gynecol. 2005;193:901–907.[PubMed][Google Scholar]
  • 6. Chambers CD, Polifka JE, Friedman JMDrug safety in pregnant women and their babies: ignorance not bliss. Clin Pharmacol Ther. 2008;83:181–183.[PubMed][Google Scholar]
  • 7. Herring C, McManus A, Weeks AOff-label prescribing during pregnancy in the UK: an analysis of 18,000 prescriptions in Liverpool Women's Hospital. Int J Pharm Pract. 2010;18:226–229.[PubMed][Google Scholar]
  • 8. Rayburn WF, Turnbull GLOff-label drug prescribing on a state university obstetric service. J Reprod Med. 1995;40:186–188.[PubMed][Google Scholar]
  • 9. Mattison D, Zajicek AGaps in knowledge in treating pregnant women. Gend Med. 2006;3:169–182.[PubMed][Google Scholar]
  • 10. Freeman MP, Nolan PE, Jr, Davis MF, Anthony M, Fried K, Fankhauser M, Woosley RL, Moreno FPharmacokinetics of sertraline across pregnancy and postpartum. J Clin Psychopharmacol. 2008;28:646–653.[PubMed][Google Scholar]
  • 11. Hostetter A, Stowe ZN, Strader JR, Jr, McLaughlin E, Llewellyn ADose of selective serotonin uptake inhibitors across pregnancy: clinical implications. Depress Anxiety. 2000;11:51–57.[PubMed][Google Scholar]
  • 12. Sit DK, Perel JM, Helsel JC, Wisner KLChanges in antidepressant metabolism and dosing across pregnancy and early postpartum. J Clin Psychiatry. 2008;69:652–658.[Google Scholar]
  • 13. Center for Drug Evaluation Research. Guidance for Industry: Establishing Pregnancy Exposure Registries. Rockville: Food and Drug Administration, US Department of Health and Human Services; 2002. pp. 1–24. Available at (last accessed March 2012) [PubMed]
  • 14. Center for Drug Evaluation Research. Guidance for Industry, Pharmacokinetics in Pregnancy – Study Design, Data Analysis, and Impact on Dosing and Labeling. Rockville: Food and Drug Administration, US Department of Health and Human Services; 2004. pp. 1–14. Available at (last accessed March 2012) [PubMed]
  • 15. Committee for Medicinal Products for Human Use. Guideline on the Exposure to Medicinal Products during Pregnancy: Need for Post-Authorisation Data. London: European Medicines Agency; 2005. pp. 1–21. Available at (last accessed March 2012) [PubMed]
  • 16. Gaohua L, Abduljalil K, Jamei M, Johnson TN, Soltani H, Rostami-Hodjegan APhysiologically-based pharmacokinetic (PBPK) models for assessing the kinetics of xenobiotics during pregnancy: achievements and shortcomings. Curr Drug Metab. 2012;13:695–720.[PubMed][Google Scholar]
  • 17. Luecke RH, Wosilait WD, Pearce BA, Young JFA computer model and program for xenobiotic disposition during pregnancy. Comput Methods Programs Biomed. 1997;53:201–224.[PubMed][Google Scholar]
  • 18. Clewell HJ, Gearhart JM, Gentry PR, Covington TR, VanLandingham CB, Crump KS, Shipp AMEvaluation of the uncertainty in an oral reference dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Anal. 1999;19:547–558.[PubMed][Google Scholar]
  • 19. Corley RA, Mast TJ, Carney EW, Rogers JM, Daston GPEvaluation of physiologically based models of pregnancy and lactation for their application in children's health risk assessments. Crit Rev Toxicol. 2003;33:137–211.[PubMed][Google Scholar]
  • 20. Andrew MA, Hebert MF, Vicini PPhysiologically based pharmacokinetic model of midazolam disposition during pregnancy. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5454–5457.[PubMed][Google Scholar]
  • 21. Pilari S, Preusse C, Huisinga WGestational influences on the pharmacokinetics of gestagenic drugs: a combined in silico, in vitro and in vivo analysis. Eur J Pharm Sci. 2011;42:318–331.[PubMed][Google Scholar]
  • 22. Jamei M, Marciniak S, Feng K, Barnett A, Tucker GT, Rostami-Hodjegan AThe Simcyp® Population-Based ADME Simulator. Expert Opin Drug Metab Toxicol. 2009;5:211–223.[PubMed][Google Scholar]
  • 23. Luecke RH, Wosilait WD, Young JFMathematical modeling of human embryonic and fetal growth rates. Growth Dev Aging. 1999;63:49–59.[PubMed][Google Scholar]
  • 24. Brazier JL, Ritter J, Berland M, Khenfer D, Faucon GPharmacokinetics of caffeine during and after pregnancy. Dev Pharmacol Ther. 1983;6:315–322.[PubMed][Google Scholar]
  • 25. Aldridge A, Bailey J, Neims AHThe disposition of caffeine during and after pregnancy. Semin Perinatol. 1981;5:310–314.[PubMed][Google Scholar]
  • 26. Knutti R, Rothweiler H, Schlatter CEffect of pregnancy on the pharmacokinetics of caffeine. Eur J Clin Pharmacol. 1981;21:121–126.[PubMed][Google Scholar]
  • 27. Parsons WD, Pelletier JGDelayed elimination of caffeine by women in the last 2 weeks of pregnancy. Can Med Assoc J. 1982;127:377–380.[Google Scholar]
  • 28. Hogstedt S, Lindberg B, Peng DR, Regardh CG, Rane APregnancy-induced increase in metoprolol metabolism. Clin Pharmacol Ther. 1985;37:688–692.[PubMed][Google Scholar]
  • 29. Hebert MF, Easterling TR, Kirby B, Carr DB, Buchanan ML, Rutherford T, Thummel KE, Fishbein DP, Unadkat JDEffects of pregnancy on CYP3A and P-glycoprotein activities as measured by disposition of midazolam and digoxin: a University of Washington specialized center of research study. Clin Pharmacol Ther. 2008;84:248–253.[PubMed][Google Scholar]
  • 30. Rostami-Hodjegan A, Tucker GTSimulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 2007;6:140–148.[PubMed][Google Scholar]
  • 31. Rodgers T, Leahy D, Rowland MPhysiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94:1259–1276.[PubMed][Google Scholar]
  • 32. Rodgers T, Rowland MPhysiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95:1238–1257.[PubMed][Google Scholar]
  • 33. Luecke RH, Wosilait WD, Pearce BA, Young JFA physiologically based pharmacokinetic computer model for human pregnancy. Teratology. 1994;49:90–103.[PubMed][Google Scholar]
  • 34. Luecke RH, Wosilait WD, Young JFMathematical analysis for teratogenic sensitivity. Teratology. 1997;55:373–380.[PubMed][Google Scholar]
  • 35. Young JF, Branham WS, Sheehan DM, Baker ME, Wosilait WD, Luecke RHPhysiological ‘constants’ for PBPK models for pregnancy. J Toxicol Environ Health. 1997;52:385–401.[PubMed][Google Scholar]
  • 36. Young JFPhysiologically-based pharmacokinetic model for pregnancy as a tool for investigation of developmental mechanisms. Comput Biol Med. 1998;28:359–364.[PubMed][Google Scholar]
  • 37. Andrew MA, Hebert MF, Vicini P. Physiologically Based Pharmacokinetic (PBPK) modeling of midazolam disposition in pregnant and postpartum women. Abstract 1760. Berlin: Population Approach Group in Europe, Nineteenth Meeting, 8-11 June 2010. Available at (last accessed March 2012)[PubMed]
  • 38. Tsutsumi K, Kotegawa T, Matsuki S, Tanaka Y, Ishii Y, Kodama Y, Kuranari M, Miyakawa I, Nakano SThe effect of pregnancy on cytochrome P4501A2, xanthine oxidase, and N-acetyltransferase activities in humans. Clin Pharmacol Ther. 2001;70:121–125.[PubMed][Google Scholar]
  • 39. Tracy TS, Venkataramanan R, Glover DD, Caritis SNTemporal changes in drug metabolism (CYP1A2, CYP2D6 and CYP3A Activity) during pregnancy. Am J Obstet Gynecol. 2005;192:633–639.[PubMed][Google Scholar]
  • 40. Wadelius M, Darj E, Frenne G, Rane AInduction of CYP2D6 in pregnancy. Clin Pharmacol Ther. 1997;62:400–407.[PubMed][Google Scholar]
  • 41. Laurent-Kenesi MA, Funck-Brentano C, Poirier JM, Decolin D, Jaillon PInfluence of CYP2D6-dependent metabolism on the steady-state pharmacokinetics and pharmacodynamics of metoprolol and nicardipine, alone and in combination. Br J Clin Pharmacol. 1993;36:531–538.[Google Scholar]
  • 42. Kaila N, Straka RJ, Brundage RCMixture models and subpopulation classification: a pharmacokinetic simulation study and application to metoprolol CYP2D6 phenotype. J Pharmacokinet Pharmacodyn. 2007;34:141–156.[PubMed][Google Scholar]
  • 43. Syme MR, Paxton JW, Keelan JADrug transfer and metabolism by the human placenta. Clin Pharmacokinet. 2004;43:487–514.[PubMed][Google Scholar]
  • 44. Myllynen P, Immonen E, Kummu M, Vahakangas KDevelopmental expression of drug metabolizing enzymes and transporter proteins in human placenta and fetal tissues. Expert Opin Drug Metab Toxicol. 2009;5:1483–1499.[PubMed][Google Scholar]
  • 45. Vahakangas K, Myllynen PDrug transporters in the human blood-placental barrier. Br J Pharmacol. 2009;158:665–678.[Google Scholar]
  • 46. Prat A, Biernacki K, Wosik K, Antel JPGlial cell influence on the human blood-brain barrier. Glia. 2001;36:145–155.[PubMed][Google Scholar]
  • 47. de Boer AG, van der Sandt IC, Gaillard PJThe role of drug transporters at the blood-brain barrier. Annu Rev Pharmacol Toxicol. 2003;43:629–656.[PubMed][Google Scholar]
  • 48. Virgintino D, Errede M, Girolamo F, Capobianco C, Robertson D, Vimercati A, Serio G, Di Benedetto A, Yonekawa Y, Frei K, Roncali LFetal blood-brain barrier P-glycoprotein contributes to brain protection during human development. J Neuropathol Exp Neurol. 2008;67:50–61.[PubMed][Google Scholar]
  • 49. Dutheil F, Dauchy S, Diry M, Sazdovitch V, Cloarec O, Mellottee L, Bieche I, Ingelman-Sundberg M, Flinois JP, de Waziers I, Beaune P, Decleves X, Duyckaerts C, Loriot MAXenobiotic-metabolizing enzymes and transporters in the normal human brain: regional and cellular mapping as a basis for putative roles in cerebral function. Drug Metab Dispos. 2009;37:1528–1538.[PubMed][Google Scholar]
  • 50. Ambrosi G, Virgintino D, Benagiano V, Maiorano E, Bertossi M, Roncali LGlial cells and blood-brain barrier in the human cerebral cortex. Ital J Anat Embryol. 1995;100(Suppl. 1):177–184.[PubMed][Google Scholar]
  • 51. Riquelme GReview: placental syncytiotrophoblast membranes–domains, subdomains and microdomains. Placenta. 2011;32(Suppl. 2):S196–202.[PubMed][Google Scholar]
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