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Fundamental & clinical pharmacology
Jun, 2024;38 (3): 402-409.

Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini-review on availability and limitations.

Monika Berezowska, Pradeep Sharma, Venkatesh Pilla Reddy, Paola Coppola
Author Information
  1. Monika Berezowska: Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK.
  2. Pradeep Sharma: Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK. ORCID
  3. Venkatesh Pilla Reddy: Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK.
  4. Paola Coppola: Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK. ORCID
PMID: 37968879 DOI: 10.1111/fcp.12967

Abstract

Physiologically based pharmacokinetic (PBPK) modelling in pregnancy is a relatively new approach that is increasingly being used to assess drug systemic exposure in pregnant women to potentially inform dosing adjustments. Physiological changes throughout pregnancy are incorporated into mathematical models to simulate drug disposition in the maternal and fetal compartments as well as the transfer of drugs across the placenta. This mini-review gathers currently available pregnancy PBPK models for drugs commonly used during pregnancy. In addition, information about the main PBPK modelling platforms used, metabolism pathways, drug transporters, data availability and drug labels were collected. The aim of this mini-review is to provide a concise overview, demonstrate trends in the field, highlight understudied areas and identify current gaps of PBPK modelling in pregnancy. Possible future applications of this PBPK approach are discussed from a clinical, regulatory and industry perspective.

Keywords

ADME PBPK clinical pharmacology pharmacokinetics pregnancy

References

  1. Lupattelli A, Spigset O, Twigg MJ, et al. Medication use in pregnancy: a cross‐sectional, multinational web‐based study. BMJ Open. 2014;4(2):e004365. doi:10.1136/bmjopen‐2013‐004365
  2. Lauren A. Borda MN, David W. Boulton R, Venkataramanan PC. A systematic review of pregnancy‐related clinical intervention of drug regimens (under submission). 2022.
  3. Coppola P, Kerwash E, Nooney J, Omran A, Cole S. Pharmacokinetic data in pregnancy: a review of available literature data and important considerations in collecting clinical data. Front Med (Lausanne). 2022;9:940644. doi:10.3389/fmed.2022.940644
  4. Tasnif Y, Morado J, Hebert MF. Pregnancy‐related pharmacokinetic changes. Clin Pharmacol Ther. 2016;100(1):53‐62. doi:10.1002/cpt.382
  5. Feghali M, Venkataramanan R, Caritis S. Pharmacokinetics of drugs in pregnancy. Semin Perinatol. 2015;39(7):512‐519. doi:10.1053/j.semperi.2015.08.003
  6. Pariente G, Leibson T, Carls A, Adams‐Webber T, Ito S, Koren G. Pregnancy‐associated changes in pharmacokinetics: a systematic review. PLoS Med. 2016;13(11):e1002160. doi:10.1371/journal.pmed.1002160
  7. Dallmann A, Pfister M, van den Anker J, Eissing T. Physiologically based pharmacokinetic modeling in pregnancy: a systematic review of published models. Clin Pharmacol Ther. 2018;104(6):1110‐1124. doi:10.1002/cpt.1084
  8. Ke AB, Greupink R, Abduljalil K. Drug dosing in pregnant women: challenges and opportunities in using physiologically based pharmacokinetic modeling and simulations. CPT Pharmacometrics Syst Pharmacol. 2018;7(2):103‐110. doi:10.1002/psp4.12274
  9. Abduljalil K, Badhan RKS. Drug dosing during pregnancy‐opportunities for physiologically based pharmacokinetic models. J Pharmacokinet Pharmacodyn. 2020;47(4):319‐340. doi:10.1007/s10928‐020‐09698‐w
  10. Drug interactions database. Seattle: University of Washington School of Pharmacy; 2005‐2023.
  11. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074‐D1082. doi:10.1093/nar/gkx1037
  12. Liu XI, Momper JD, Rakhmanina N, et al. Physiologically based pharmacokinetic models to predict maternal pharmacokinetics and fetal exposure to emtricitabine and acyclovir. J Clin Pharmacol. 2020;60(2):240‐255. doi:10.1002/jcph.1515
  13. Liu XI, Green DJ, van den Anker JN, et al. Mechanistic modeling of placental drug transfer in humans: how do differences in maternal/fetal fraction of unbound drug and placental influx/efflux transfer rates affect fetal pharmacokinetics? Front Pediatr. 2021;9:723006. doi:10.3389/fped.2021.723006
  14. Abduljalil K, Pansari A, Ning J, Jamei M. Prediction of maternal and fetal acyclovir, emtricitabine, lamivudine, and metformin concentrations during pregnancy using a physiologically based pharmacokinetic modeling approach. Clin Pharmacokinet. 2022;61(5):725‐748. doi:10.1007/s40262‐021‐01103‐0
  15. Dallmann A, Himstedt A, Solodenko J, Ince I, Hempel G, Eissing T. Integration of physiological changes during the postpartum period into a PBPK framework and prediction of amoxicillin disposition before and shortly after delivery. J Pharmacokinet Pharmacodyn. 2020;47(4):341‐359. doi:10.1007/s10928‐020‐09706‐z
  16. Abduljalil K, Ning J, Pansari A, Pan X, Jamei M. Prediction of maternal and fetoplacental concentrations of cefazolin, cefuroxime, and amoxicillin during pregnancy using bottom‐up physiologically based pharmacokinetic models. Drug Metab Dispos. 2022;50(4):386‐400. doi:10.1124/dmd.121.000711
  17. Coppola P, Kerwash E, Cole S. The use of pregnancy physiologically based pharmacokinetic modeling for renally cleared drugs. J Clin Pharmacol. 2022;62(Suppl 1):S129‐s39. doi:10.1002/jcph.2110
  18. Li S, Xie F. Foetal and neonatal exposure prediction and dosing evaluation for ampicillin using a physiologically‐based pharmacokinetic modelling approach. Br J Clin Pharmacol. 2022;89(4):1402‐1412. doi:10.1111/bcp.15589
  19. Zheng L, Tang S, Tang R, Xu M, Jiang X, Wang L. Dose adjustment of quetiapine and aripiprazole for pregnant women using physiologically based pharmacokinetic modeling and simulation. Clin Pharmacokinet. 2021;60(5):623‐635. doi:10.1007/s40262‐020‐00962‐3
  20. Song L, Yu Z, Xu Y, et al. Preliminary physiologically based pharmacokinetic modeling of renally cleared drugs in Chinese pregnant women. Biopharm Drug Dispos. 2020;41(6):248‐267. doi:10.1002/bdd.2243
  21. Anoshchenko O, Milad MA, Unadkat JD. Estimating fetal exposure to the P‐gp substrates, corticosteroids, by PBPK modeling to inform prevention of neonatal respiratory distress syndrome. CPT Pharmacometrics Syst Pharmacol. 2021;10(9):1057‐1070. doi:10.1002/psp4.12674
  22. Silva LL, Silvola RM, Haas DM, Quinney SK. Physiologically based pharmacokinetic modelling in pregnancy: model reproducibility and external validation. Br J Clin Pharmacol. 2022;88(4):1441‐1451. doi:10.1111/bcp.15018
  23. Anoshchenko O, Storelli F, Unadkat JD. Successful prediction of human fetal exposure to P‐glycoprotein substrate drugs using the proteomics‐informed relative expression factor approach and PBPK modeling and simulation. Drug Metab Dispos. 2021;49(10):919‐928. doi:10.1124/dmd.121.000538
  24. Abduljalil K, Pansari A, Jamei M. Prediction of maternal pharmacokinetics using physiologically based pharmacokinetic models: assessing the impact of the longitudinal changes in the activity of CYP1A2, CYP2D6 and CYP3A4 enzymes during pregnancy. J Pharmacokinet Pharmacodyn. 2020;47(4):361‐383. doi:10.1007/s10928‐020‐09711‐2
  25. Szeto KX, Le Merdy M, Dupont B, Bolger MB, Lukacova V. PBPK modeling approach to predict the behavior of drugs cleared by kidney in pregnant subjects and fetus. AAPS j. 2021;23(4):89. doi:10.1208/s12248‐021‐00603‐y
  26. Alsmadi MM. The investigation of the complex population‐drug‐drug interaction between ritonavir‐boosted lopinavir and chloroquine or ivermectin using physiologically‐based pharmacokinetic modeling. Drug Metab Pers Ther. 2022;38(1):87‐105.
  27. Badaoui S, Hopkins AM, Rodrigues AD, Miners JO, Sorich MJ, Rowland A. Application of model informed precision dosing to address the impact of pregnancy stage and CYP2D6 phenotype on foetal morphine exposure. AAPS j. 2021;23(1):15. doi:10.1208/s12248‐020‐00541‐1
  28. Roelofsen D, van Hove H, Bukkems V, Russel F, Eliesen G, Greupink R. Predicting fetal exposure of crizotinib during pregnancy: combining human ex vivo placenta perfusion data with physiologically‐based pharmacokinetic modeling. Toxicol in Vitro. 2022;85:105471. doi:10.1016/j.tiv.2022.105471
  29. Freriksen JJM, Schalkwijk S, Colbers AP, et al. Assessment of maternal and fetal dolutegravir exposure by integrating ex vivo placental perfusion data and physiologically‐based pharmacokinetic modeling. Clin Pharmacol Ther. 2020;107(6):1352‐1361. doi:10.1002/cpt.1748
  30. Liu XI, Momper JD, Rakhmanina NY, et al. Prediction of maternal and fetal pharmacokinetics of dolutegravir and raltegravir using physiologically based pharmacokinetic modeling. Clin Pharmacokinet. 2020;59(11):1433‐1450. doi:10.1007/s40262‐020‐00897‐9
  31. Liu XI, Momper JD, Rakhmanina NY, et al. Physiologically based pharmacokinetic modeling framework to predict neonatal pharmacokinetics of transplacentally acquired emtricitabine, dolutegravir, and raltegravir. Clin Pharmacokinet. 2021;60(6):795‐809. doi:10.1007/s40262‐020‐00977‐w
  32. Shenkoya B, Atoyebi S, Eniayewu I, Akinloye A, Olagunju A. Mechanistic modeling of maternal lymphoid and fetal plasma antiretroviral exposure during the third trimester. Front Pediatr. 2021;9:734122. doi:10.3389/fped.2021.734122
  33. Bukkems VE, van Hove H, Roelofsen D, et al. Prediction of maternal and fetal doravirine exposure by integrating physiologically based pharmacokinetic modeling and human placenta perfusion experiments. Clin Pharmacokinet. 2022;61(8):1129‐1141. doi:10.1007/s40262‐022‐01127‐0
  34. Peng J, Ladumor MK, Unadkat JD. Estimation of fetal‐to‐maternal unbound steady‐state plasma concentration ratio of P‐glycoprotein and/or breast cancer resistance protein substrate drugs using a maternal‐fetal physiologically based pharmacokinetic model. Drug Metab Dispos. 2022;50(5):613‐623. doi:10.1124/dmd.121.000733
  35. Shum S, Shen DD, Isoherranen N. Predicting maternal‐fetal disposition of fentanyl following intravenous and epidural administration using physiologically based pharmacokinetic modeling. Drug Metab Dispos. 2021;49(11):1003‐1015. doi:10.1124/dmd.121.000612
  36. Pillai VC, Shah M, Rytting E, et al. Prediction of maternal and fetal pharmacokinetics of indomethacin in pregnancy. Br J Clin Pharmacol. 2022;88(1):271‐281. doi:10.1111/bcp.14960
  37. Fairman KLM, Lumen A. Pregnancy PBPK modeling of UGT substrate labetalol: an application of parameter Contribution analysis to guide predictive performance of life‐stage models. FDA SCIENCE FORUM; 05/26/2021; National Center for Toxicological Research; 2021.
  38. Ho H, Zhang S, Kurosawa K, Chiba K. In silico modeling for ex vivo placental transfer of morphine. J Clin Pharmacol. 2022;62(S1):140‐146. doi:10.1002/jcph.2105
  39. Zheng L, Yang H, Dallmann A, Jiang X, Wang L, Hu W. Physiologically based pharmacokinetic modeling in pregnant women suggests minor decrease in maternal exposure to olanzapine. Front Pharmacol. 2021;12:793346. doi:10.3389/fphar.2021.793346
  40. He L, Ke M, Wu W, et al. Application of physiologically based pharmacokinetic modeling to predict maternal pharmacokinetics and fetal exposure to oxcarbazepine. Pharmaceutics. 2022;14(11):2367. doi:10.3390/pharmaceutics14112367
  41. Mian P, van den Anker JN, van Calsteren K, et al. Physiologically based pharmacokinetic modeling to characterize acetaminophen pharmacokinetics and N‐acetyl‐p‐benzoquinone imine (NAPQI) formation in non‐pregnant and pregnant women. Clin Pharmacokinet. 2020;59(1):97‐110. doi:10.1007/s40262‐019‐00799‐5
  42. Mian P, Allegaert K, Conings S, et al. Integration of placental transfer in a fetal‐maternal physiologically based pharmacokinetic model to characterize acetaminophen exposure and metabolic clearance in the fetus. Clin Pharmacokinet. 2020;59(7):911‐925. doi:10.1007/s40262‐020‐00861‐7
  43. Almurjan A, Macfarlane H, Badhan RKS. Precision dosing‐based optimisation of paroxetine during pregnancy for poor and ultrarapid CYP2D6 metabolisers: a virtual clinical trial pharmacokinetics study. J Pharm Pharmacol. 2020;72(8):1049‐1060. doi:10.1111/jphp.13281
  44. Vazquez B, Tomson T, Dobrinsky C, Schuck E, O'Brien TJ. Perampanel and pregnancy. Epilepsia. 2021;62(3):698‐708. doi:10.1111/epi.16821
  45. Badhan RKS, Macfarlane H. Quetiapine dose optimisation during gestation: a pharmacokinetic modelling study. J Pharm Pharmacol. 2020;72(5):670‐681. doi:10.1111/jphp.13236
  46. George B, Lumen A, Nguyen C, et al. Application of physiologically based pharmacokinetic modeling for sertraline dosing recommendations in pregnancy. NPJ Syst Biol Appl. 2020;6(1):36. doi:10.1038/s41540‐020‐00157‐3
  47. Abduljalil K, Gardner I, Jamei M. Application of a physiologically based pharmacokinetic approach to predict theophylline pharmacokinetics using virtual non‐pregnant, pregnant, fetal, breast‐feeding, and neonatal populations. Front Pediatr. 2022;10:840710. doi:10.3389/fped.2022.840710
  48. Wu X, Zhang X, Xu R, Shaik IH, Venkataramanan R. Physiologically based pharmacokinetic modelling of treprostinil after intravenous injection and extended‐release oral tablet administration in healthy volunteers: an extrapolation to other patient populations including patients with hepatic impairment. Br J Clin Pharmacol. 2022;88(2):587‐599. doi:10.1111/bcp.14966
  49. EMA. Workshop on benefit‐risk of medicines used during pregnancy and breastfeeding. 2020; 22.09.2020. https://www.ema.europa.eu/en/documents/report/report-workshop-benefit-risk-medicines-used-during-pregnancy-breastfeeding_en.pdf
  50. FDA. FDA/M‐CERSI workshop: pharmacokinetic evaluation in pregnancy. 2022; 16‐17.05.2022. https://www.fda.gov/drugs/news-events-human-drugs/pharmacokinetic-evaluation-pregnancy-virtual-public-workshop-05162022
  51. European Medicines Agency. EMA guideline on the exposure to medicinal products during pregnancy: need for post‐authorisation data. EMEA/CHMP/313666/2005. London: European Medicines Agency; 2005.
  52. US FDA. Pregnant women: scientific and ethical considerations for inclusion in clinical trials. Guidance for Industry (Silver Spring, MD); 2018.
  53. Coppola P, Kerwash E, Cole S. Physiologically based pharmacokinetics model in pregnancy: a regulatory perspective on model evaluation. Front Pediatr. 2021;9:687978. doi:10.3389/fped.2021.687978

MeSH Term

Female
Humans
Pregnancy
Maternal-Fetal Exchange
Models, Biological
Pharmaceutical Preparations
Pharmacokinetics
Placenta

Chemicals

Pharmaceutical Preparations
Journal Article Review
Article has an altmetric score of 1

Word Cloud

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