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2000
Volume 18, Issue 2
  • ISSN: 2949-6810
  • E-ISSN: 2949-6829

Abstract

Physiologically Based Pharmacokinetic (PBPK) modeling represents an advanced computational model that bridges the gap between theoretical pharmacology and clinical practice. These advanced mathematical frameworks integrate complex physiological parameters with absorption, distribution, metabolism, and excretion (ADME) processes to create dynamic simulations of drug behavior in biological systems. By providing mechanistic insights into drug disposition and interactions, PBPK models have become indispensable tools in modern drug development and clinical therapeutics. The evolution of PBPK modeling has particularly revolutionized pediatric pharmacology, where traditional dosing paradigms often fall short due to the unique physiological characteristics of developing organisms. These models excel in their ability to predict pharmacokinetic profiles across diverse age groups, offering crucial insights into the fundamental differences between adult and pediatric drug handling. Their capability to anticipate drug-drug interactions (DDIs) has proven especially valuable in pediatric settings, where complex medication regimens are increasingly common. The growing adoption of PBPK modeling by pharmaceutical companies, regulatory agencies, and clinical institutions underscores its pivotal role in contemporary drug development. These models demonstrate remarkable effectiveness in translating adult pharmacokinetic data to pediatric populations, integrating multiple evidence streams to elucidate age-specific differences in drug disposition. This translational capacity has become particularly crucial in optimizing pediatric drug development strategies and enhancing therapeutic decision-making. This article presents a comprehensive analysis of PBPK modeling, examining its foundational principles and recent advances in adult-to-pediatric pharmacokinetic translation. Special attention is devoted to the unique challenges and emerging solutions in pediatric PBPK (P-PBPK) modeling, particularly in the context of DDIs. Through detailed exploration of these aspects, we illuminate how PBPK modeling continues to advance our understanding of drug behavior in pediatric patients, ultimately contributing to more precise and safer therapeutic interventions for this vulnerable population.

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References

  1. SagerJ.E. YuJ. Ragueneau-MajlessiI. IsoherranenN. Physiologically based pharmacokinetic (PBPK) modeling and simulation Approaches: A systematic review of published models, applications, and model verification.Drug Metab. Dispos.201543111823183710.1124/dmd.115.065920 26296709
     [Google Scholar]
  2. JonesH.M. Rowland-YeoK. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development.CPT Pharmacometrics Syst. Pharmacol.20132811210.1038/psp.2013.41 23945604
     [Google Scholar]
  3. ZhuangX. LuC. PBPK modeling and simulation in drug research and development.Acta Pharm. Sin. B20166543044010.1016/j.apsb.2016.04.004 27909650
     [Google Scholar]
  4. ZhouX. DunJ. ChenX. XiangB. DangY. CaoD. Predicting the correct dose in children: Role of computational pediatric physiological‐based pharmacokinetics modeling tools.CPT Pharmacometrics Syst. Pharmacol.2023121132610.1002/psp4.12883 36330677
     [Google Scholar]
  5. WangK. YaoX. ZhangM. LiuD. GaoY. SahasranamanS. OuY.C. Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator.CPT Pharmacometrics Syst. Pharmacol.202110544145410.1002/psp4.12605 33687157
     [Google Scholar]
  6. OzbekO. GencD.E. O UlgenK. Advances in physiologically based pharmacokinetic (PBPK) modeling of nanomaterials.ACS Pharmacol. Transl. Sci.2024782251227910.1021/acsptsci.4c00250 39144562
     [Google Scholar]
  7. RiouxN. WatersN.J. Physiologically based pharmacokinetic modeling in pediatric oncology drug development.Drug Metab. Dispos.201644793494310.1124/dmd.115.068031 26936973
     [Google Scholar]
  8. GonzalezD. SinhaJ. Pediatric drug‐drug interaction evaluation: Drug, patient population, and methodological considerations.J. Clin. Pharmacol.202161S1S175S18710.1002/jcph.1881 34185913
     [Google Scholar]
  9. de WildtS.N. KearnsG.L. MurryD.J. KorenG. van den AnkerJ.N. Ontogeny of midazolam glucuronidation in preterm infants.Eur. J. Clin. Pharmacol.201066216517010.1007/s00228‑009‑0741‑5 19838691
     [Google Scholar]
  10. JohnsonT.N. BatchelorH.K. GoelenJ. HorniblowR.D. DinhJ. Combining data on the bioavailability of midazolam and physiologically‐based pharmacokinetic modeling to investigate intestinal CYP3A4 ontogeny.CPT Pharmacometrics Syst. Pharmacol.20241391570158110.1002/psp4.13192 38923249
     [Google Scholar]
  11. UpretiV.V. WahlstromJ.L. Meta‐analysis of hepatic cytochrome P450 ontogeny to underwrite the prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling.J. Clin. Pharmacol.201656326628310.1002/jcph.585 26139104
     [Google Scholar]
  12. DominguesC. JarakI. VeigaF. DouradoM. FigueirasA. Pediatric drug development: Reviewing challenges and opportunities by tracking innovative therapies.Pharmaceutics20231510243110.3390/pharmaceutics15102431 37896191
     [Google Scholar]
  13. KuepferL. NiederaltC. WendlT. SchlenderJ-F. WillmannS. LippertJ. BlockM. EissingT. TeutonicoD. Applied concepts in PBPK modeling: How to build a PBPK/PD model.CPT Pharmacometrics Syst. Pharmacol.201651051653110.1002/psp4.12134 27653238
     [Google Scholar]
  14. MaglalangP.D. WenJ. HornikC.P. GonzalezD. Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population.Clin. Transl. Sci.2024174e1379310.1111/cts.13793 38618871
     [Google Scholar]
  15. MaharajA.R. BarrettJ.S. EdgintonA.N. A workflow example of PBPK modeling to support pediatric research and development: Case study with lorazepam.AAPS J.201315245546410.1208/s12248‑013‑9451‑0 23344790
     [Google Scholar]
  16. VerscheijdenL.F.M. KoenderinkJ.B. JohnsonT.N. de WildtS.N. RusselF.G.M. Physiologically-based pharmacokinetic models for children: Starting to reach maturation?Pharmacol. Ther.202021110754110.1016/j.pharmthera.2020.107541 32246949
     [Google Scholar]
  17. RyuH. KangW. KimT. KimJ.K. ShinK.H. ChaeJ. YunH. A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases.Front. Pharmacol.20221396404910.3389/fphar.2022.964049 36034786
     [Google Scholar]
  18. SchoenfeldD.A. Hui Zheng FinkelsteinD.M. Bayesian design using adult data to augment pediatric trials.Clin. Trials20096429730410.1177/1740774509339238 19667026
     [Google Scholar]
  19. WoikeJ.K. HertwigR. GigerenzerG. Heterogeneity of rules in Bayesian reasoning: A toolbox analysis.Cognit. Psychol.202314310156410.1016/j.cogpsych.2023.10156437178617
     [Google Scholar]
  20. DinhJ. JohnsonT.N. GrimsteinM. LewisT. Physiologically based pharmacokinetics modeling in the neonatal population - Current advances, challenges, and opportunities.Pharmaceutics20231511257910.3390/pharmaceutics15112579 38004559
     [Google Scholar]
  21. BorkorR. SakyiA. Amoako-YirenkyiP. Investigation of fractional compartmental models with application to amiodarone drug diffusion in pharmacokinetics.arXiv2023Preprint10.48550/arXiv.2306.08015
     [Google Scholar]
  22. YangY. ChenY. WangL. XuS. FangG. GuoX. ChenZ. GuZ. PBPK modeling on organs-on-chips: An overview of recent advancements.Front. Bioeng. Biotechnol.20221090048110.3389/fbioe.2022.900481 35497341
     [Google Scholar]
  23. FührerF. GruberA. DiedamH. GöllerA.H. MenzS. SchneckenerS. A deep neural network: Mechanistic hybrid model to predict pharmacokinetics in rat.J. Comput. Aided Mol. Des.2024381710.1007/s10822‑023‑00547‑9 38294570
     [Google Scholar]
  24. WangW. OuyangD. Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery.Drug Discov. Today20222782100212010.1016/j.drudis.2022.04.01535452792
     [Google Scholar]
  25. HartmanshennC. ScherholzM. AndroulakisI.P. Physiologically-based pharmacokinetic models: Approaches for enabling personalized medicine.J. Pharmacokinet. Pharmacodyn.201643548150410.1007/s10928‑016‑9492‑y 27647273
     [Google Scholar]
  26. YurovY. VorsanovaS. IourovI. Ontogenetic variation of the human genome.Curr. Genomics201011642042510.2174/138920210793175958 21358986
     [Google Scholar]
  27. FigajiA.A. Anatomical and physiological differences between children and adults relevant to traumatic brain injury and the implications for clinical assessment and care.Front. Neurol.2017868510.3389/fneur.2017.00685 29312119
     [Google Scholar]
  28. ValentinJ. Basic anatomical and physiological data for use in radiological protection: Reference values. ICRP Publication 89.Ann. ICRP2002323-45265 14506981
     [Google Scholar]
  29. NicolasJ.M. BouzomF. HuguesC. UngellA.L. Oral drug absorption in pediatrics: the intestinal wall, its developmental changes and current tools for predictions.Biopharm. Drug Dispos.201738320923010.1002/bdd.2052 27976409
     [Google Scholar]
  30. MaharajA.R. GonzalezD. Cohen-WolkowiezM. HornikC.P. EdgintonA.N. Improving pediatric protein binding estimates: An evaluation of α1-acid glycoprotein maturation in healthy and infected subjects.Clin. Pharmacokinet.201857557758910.1007/s40262‑017‑0576‑7 28779462
     [Google Scholar]
  31. BrouwerK.L.R. AleksunesL.M. BrandysB. GiacoiaG.P. KnippG. LukacovaV. MeibohmB. NigamS.K. RiederM. de WildtS.N. Human ontogeny of drug transporters: Review and recommendations of the pediatric transporter working group.Clin. Pharmacol. Ther.201598326628710.1002/cpt.176 26088472
     [Google Scholar]
  32. GermovsekE. BarkerC.I.S. SharlandM. StandingJ.F. Scaling clearance in paediatric pharmacokinetics: All models are wrong, which are useful?Br. J. Clin. Pharmacol.201783477779010.1111/bcp.13160 27767204
     [Google Scholar]
  33. van GroenB.D. Pilla ReddyV. BadéeJ. Olivares-MoralesA. JohnsonT.N. NicolaïJ. AnnaertP. SmitsA. de WildtS.N. KnibbeC.A.J. de ZwartL. Pediatric pharmacokinetics and dose predictions: A report of a satellite meeting to the 10th juvenile toxicity symposium.Clin. Transl. Sci.2021141293510.1111/cts.12843 32702198
     [Google Scholar]
  34. BraterD.C. Measurement of renal function during drug development.Br. J. Clin. Pharmacol.2002541879510.1046/j.1365‑2125.2002.01625.x 12100232
     [Google Scholar]
  35. MahmoodI. Prediction of total and renal clearance of renally secreted drugs in neonates and infants (≤3 months of age).J. Clin. Transl. Res.202286445452 36452002
     [Google Scholar]
  36. HuntJ.P. DubinskyS. McKniteA.M. CheungK.W.K. van GroenB.D. GiacominiK.M. de WildtS.N. EdgintonA.N. WattK.M. Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling.CPT Pharmacometrics Syst. Pharmacol.202413457658810.1002/psp4.13102 38156758
     [Google Scholar]
  37. CalvierE.A.M. KrekelsE.H.J. JohnsonT.N. Rostami-HodjeganA. TibboelD. KnibbeC.A.J. Scaling drug clearance from adults to the young children for drugs undergoing hepatic metabolism: A simulation study to search for the simplest scaling method.AAPS J.20192133810.1208/s12248‑019‑0295‑0 30850923
     [Google Scholar]
  38. TuntlandT. EthellB. KosakaT. BlascoF. ZangR.X. JainM. GouldT. HoffmasterK. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research.Front. Pharmacol.2014517410.3389/fphar.2014.00174 25120485
     [Google Scholar]
  39. MusuambaF.T. ManolisE. HolfordN. CheungS.Y.A. FribergL.E. OgungbenroK. PoschM. YatesJ.W.T. BerryS. ThomasN. Corriol-RohouS. BornkampB. BretzF. HookerA.C. Van der GraafP.H. StandingJ.F. HayJ. ColeS. GiganteV. KarlssonK. DumortierT. BendaN. SeroneF. DasS. BrochotA. EhmannF. HemmingsR. RustenI.S. Advanced methods for dose and regimen finding during drug development: Summary of the EMA/EFPIA workshop on dose finding (London 4-5 December 2014).CPT Pharmacometrics Syst. Pharmacol.20176741842910.1002/psp4.12196 28722322
     [Google Scholar]
  40. CaiX. WuW. GuoG. ChenJ. XuJ. LinW. HuangP. LinC. LinR. Physiologically-based pharmacokinetic modeling to predict the exposure and provide dosage regimens of Ustekinumab in pediatric patients with inflammatory bowel disease.Eur. J. Pharm. Sci.202419910680710.1016/j.ejps.2024.106807 38797440
     [Google Scholar]
  41. BergB.A. Introduction to markov chain monte carlo simulations and their statistical analysis.arXiv2004[Preprint]
     [Google Scholar]
  42. LuH. RosenbaumS. Developmental pharmacokinetics in pediatric populations.J. Pediatr. Pharmacol. Ther.201419426227610.5863/1551‑6776‑19.4.262 25762871
     [Google Scholar]
  43. KernS.E. Challenges in conducting clinical trials in children: approaches for improving performance.Expert Rev. Clin. Pharmacol.20092660961710.1586/ecp.09.40 20228942
     [Google Scholar]
  44. ChristensenM.L. Best pharmaceuticals for children act and pediatric research equity act: Time for permanent status.J. Pediatr. Pharmacol. Ther.201217214014110.5863/1551‑6776‑17.2.140 23185144
     [Google Scholar]
  45. ThomsenM.D.T. Global pediatric drug development.Curr. Ther. Res. Clin. Exp.20199013514210.1016/j.curtheres.2019.02.001 31388369
     [Google Scholar]
  46. LepolaP. WangS. TöttermanA.M. GullbergN. HarboeK.M. KimlandE. Does the EU’s Paediatric Regulation work for new medicines for children in Denmark, Finland, Norway and Sweden? A cross-sectional study.BMJ Paediatr. Open202041e00088010.1136/bmjpo‑2020‑000880 33437879
     [Google Scholar]
  47. BiY. LiuJ. LiL. YuJ. BhattaramA. BewernitzM. LiR. LiuC. EarpJ. MaL. ZhuangL. YangY. ZhangX. ZhuH. WangY. Role of model‐informed drug development in pediatric drug development, regulatory evaluation, and labeling.J. Clin. Pharmacol.201959S1S104S11110.1002/jcph.1478 31502691
     [Google Scholar]
  48. MadabushiR. SeoP. ZhaoL. TegengeM. ZhuH. Review: role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making.Pharm. Res.20223981669168010.1007/s11095‑022‑03288‑w 35552984
     [Google Scholar]
  49. JainL. MehrotraN. WenningL. SinhaV. PDUFA VI: It is time to unleash the full potential of model‐informed drug development.CPT Pharmacometrics Syst. Pharmacol.2019815810.1002/psp4.12365 30370642
     [Google Scholar]
  50. GrimsteinM. YangY. ZhangX. GrilloJ. HuangS.M. ZinehI. WangY. Physiologically based pharmacokinetic modeling in regulatory science: An update from the U.S. food and drug administration’s office of clinical pharmacology.J. Pharm. Sci.20191081212510.1016/j.xphs.2018.10.033 30385284
     [Google Scholar]
  51. Advisory committee for pharmaceutical science and clinical pharmacology; Notice of meeting.(2012, July 16). Federal Register771364181841819Retrieved from https://www.federalregister.gov/documents/2012/07/16/2012-17193/advisory-committee-for-pharmaceutical-science-and-clinical-pharmacology-notice-of-meeting
    [Google Scholar]
  52. GiacominiK.M. HuangS.M. TweedieD.J. BenetL.Z. BrouwerK.L.R. ChuX. DahlinA. EversR. FischerV. HillgrenK.M. HoffmasterK.A. IshikawaT. KepplerD. KimR.B. LeeC.A. NiemiM. PolliJ.W. SugiyamaY. SwaanP.W. WareJ.A. WrightS.H. Wah YeeS. Zamek-GliszczynskiM.J. ZhangL. Membrane transporters in drug development.Nat. Rev. Drug Discov.20109321523610.1038/nrd3028 20190787
     [Google Scholar]
  53. ZhangL. LiuQ. HuangS.M. LionbergerR. Transporters in regulatory science: Notable contributions from Dr. Giacomini in the past two decades.Drug Metab. Dispos.20225091211121710.1124/dmd.121.000706 35768075
     [Google Scholar]
  54. ElsbyR. AtkinsonH. ButlerP. RileyR.J. Studying the right transporter at the right time: An in vitro strategy for assessing drug-drug interaction risk during drug discovery and development.Expert Opin. Drug Metab. Toxicol.2022181061965510.1080/17425255.2022.2132932 36205497
     [Google Scholar]
  55. AzmanM. SabriA.H. AnjaniQ.K. MustaffaM.F. HamidK.A. Intestinal absorption study: Challenges and absorption enhancement strategies in improving oral drug delivery.Pharmaceuticals (Basel)202215897510.3390/ph15080975 36015123
     [Google Scholar]
  56. ShugartsS. BenetL.Z. The role of transporters in the pharmacokinetics of orally administered drugs.Pharm. Res.20092692039205410.1007/s11095‑009‑9924‑0 19568696
     [Google Scholar]
  57. CvetkovicM. LeakeB. FrommM.F. WilkinsonG.R. KimR.B. OATP and P-glycoprotein transporters mediate the cellular uptake and excretion of fexofenadine.Drug Metab. Dispos.199927886687110.1016/S0090‑9556(24)15235‑X 10421612
     [Google Scholar]
  58. CheungK.W.K. van GroenB.D. BurckartG.J. ZhangL. de WildtS.N. HuangS.M. Incorporating ontogeny in physiologically based pharmacokinetic modeling to improve pediatric drug development: what we know about developmental changes in membrane transporters.J. Clin. Pharmacol.201959S1S56S6910.1002/jcph.1489 31502692
     [Google Scholar]
  59. KeiserM. KaltheunerL. WildbergC. MüllerJ. GrubeM. ParteckeL.I. HeideckeC.D. OswaldS. The organic anion-transporting peptide 2B1 is localized in the basolateral membrane of the human jejunum and CaCo2 monolayers.J. Pharm. Sci.201710692657266310.1016/j.xphs.2017.04.001 28408210
     [Google Scholar]
  60. MooijM.G. de KoningB.E.A. Lindenbergh-KortleveD.J. Simons-OosterhuisY. van GroenB.D. TibboelD. SamsomJ.N. de WildtS.N. Human intestinal PEPT1 transporter expression and localization in preterm and term infants.Drug Metab. Dispos.20164471041104610.1124/dmd.115.068809 27079248
     [Google Scholar]
  61. JiaX. HeX. HuangC. LiJ. DongZ. LiuK. Protein translation: biological processes and therapeutic strategies for human diseases.Signal Transduct. Target. Ther.2024914410.1038/s41392‑024‑01749‑9 38388452
     [Google Scholar]
  62. SinnH.P. SchneeweissA. KellerM. SchlombsK. LaibleM. SeitzJ. LakisS. VeltrupE. AltevogtP. EidtS. WirtzR.M. MarméF. Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer.BMC Cancer201717112410.1186/s12885‑017‑3111‑1 28193205
     [Google Scholar]
  63. BirhanuA.G. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories.Clin. Proteomics20232013210.1186/s12014‑023‑09424‑x 37633929
     [Google Scholar]
  64. NeaguA.N. JayathirthaM. BaxterE. DonnellyM. PetreB.A. DarieC.C. Applications of tandem mass spectrometry (MS/MS) in protein analysis for biomedical research.Molecules2022278241110.3390/molecules27082411 35458608
     [Google Scholar]
  65. KuL.C. SmithP.B. Dosing in neonates: special considerations in physiology and trial design.Pediatr. Res.20157712910.1038/pr.2014.143 25268145
     [Google Scholar]
  66. YinJ. WangJ. Renal drug transporters and their significance in drug-drug interactions.Acta Pharm. Sin. B20166536337310.1016/j.apsb.2016.07.013 27709005
     [Google Scholar]
  67. CheungK.W.K. van GroenB.D. SpaansE. van BorselenM.D. de BruijnA.C.J.M. Simons-OosterhuisY. TibboelD. SamsomJ.N. VerdijkR.M. SmeetsB. ZhangL. HuangS.M. GiacominiK.M. de WildtS.N. A comprehensive analysis of ontogeny of renal drug transporters: mRNA analyses, quantitative proteomics, and localization.Clin. Pharmacol. Ther.201910651083109210.1002/cpt.1516 31127606
     [Google Scholar]
  68. AhireD. KrugerL. SharmaS. MettuV.S. BasitA. PrasadB. Quantitative proteomics in translational absorption, distribution, metabolism, and excretion and precision medicine.Pharmacol. Rev.202274377179810.1124/pharmrev.121.000449 35738681
     [Google Scholar]
  69. DasS. VeraM. GandinV. SingerR.H. TutucciE. Intracellular mRNA transport and localized translation.Nat. Rev. Mol. Cell Biol.202122748350410.1038/s41580‑021‑00356‑8 33837370
     [Google Scholar]
  70. KimB.H. WooT.G. KangS.M. ParkS. ParkB.J. Splicing variants, protein-protein interactions, and drug targeting in hutchinson-gilford progeria syndrome and small cell lung cancer.Genes (Basel)202213216510.3390/genes13020165 35205210
     [Google Scholar]
  71. ShrimalS. CherepanovaN.A. GilmoreR. Cotranslational and posttranslocational N-glycosylation of proteins in the endoplasmic reticulum.Semin. Cell Dev. Biol.201541717810.1016/j.semcdb.2014.11.005 25460543
     [Google Scholar]
  72. JiangL. WangM. LinS. JianR. LiX. ChanJ. DongG. FangH. RobinsonA.E. SnyderM.P. AguetF. AnandS. ArdlieK.G. GabrielS. GetzG. GraubertA. HadleyK. HandsakerR.E. HuangK.H. KashinS. MacArthurD.G. MeierS.R. NedzelJ.L. NguyenD.Y. SegrèA.V. TodresE. BalliuB. BarbeiraA.N. BattleA. BonazzolaR. BrownA. BrownC.D. CastelS.E. ConradD. CotterD.J. CoxN. DasS. de GoedeO.M. DermitzakisE.T. EngelhardtB.E. EskinE. EulalioT.Y. FerraroN.M. FlynnE. FresardL. GamazonE.R. Garrido-MartínD. GayN.R. GuigóR. HamelA.R. HeY. HoffmanP.J. HormozdiariF. HouL. ImH.K. JoB. KaselaS. KellisM. Kim-HellmuthS. KwongA. LappalainenT. LiX. LiangY. MangulS. MohammadiP. MontgomeryS.B. Muñoz-AguirreM. NachunD.C. NobelA.B. OlivaM. ParkY. ParkY. ParsanaP. ReverterF. RouhanaJ.M. SabattiC. SahaA. SkolA.D. StephensM. StrangerB.E. StroberB.J. TeranN.A. ViñuelaA. WangG. WenX. WrightF. WucherV. ZouY. FerreiraP.G. LiG. MeléM. Yeger-LotemE. BarcusM.E. BradburyD. KrubitT. McLeanJ.A. QiL. RobinsonK. RocheN.V. SmithA.M. SobinL. TaborD.E. UndaleA. BridgeJ. BrighamL.E. FosterB.A. GillardB.M. HaszR. HunterM. JohnsC. JohnsonM. KarasikE. KopenG. LeinweberW.F. McDonaldA. MoserM.T. MyerK. RamseyK.D. RoeB. ShadS. ThomasJ.A. WaltersG. WashingtonM. WheelerJ. JewellS.D. RohrerD.C. ValleyD.R. DavisD.A. MashD.C. BrantonP.A. BarkerL.K. GardinerH.M. MosavelM. SiminoffL.A. FlicekP. HaeusslerM. JuettemannT. KentW.J. LeeC.M. PowellC.C. RosenbloomK.R. RuffierM. SheppardD. TaylorK. TrevanionS.J. ZerbinoD.R. AbellN.S. AkeyJ. ChenL. DemanelisK. DohertyJ.A. FeinbergA.P. HansenK.D. HickeyP.F. JasmineF. KaulR. KibriyaM.G. LiJ.B. LiQ. LinderS.E. PierceB.L. RizzardiL.F. SmithK.S. StamatoyannopoulosJ. TangH. CarithersL.J. GuanP. KoesterS.E. LittleA.R. MooreH.M. NierrasC.R. RaoA.K. VaughtJ.B. VolpiS. A Quantitative proteome map of the human body.Cell20201831269283.e1910.1016/j.cell.2020.08.03632916130
     [Google Scholar]
  73. MazaleuskayaL.L. SangkuhlK. ThornC.F. FitzGeraldG.A. AltmanR.B. KleinT.E. PharmGKB summary.Pharmacogenet. Genomics201525841642610.1097/FPC.0000000000000150 26049587
     [Google Scholar]
  74. FeghaliM. VenkataramananR. CaritisS. Pharmacokinetics of drugs in pregnancy.Semin. Perinatol.201539751251910.1053/j.semperi.2015.08.003 26452316
     [Google Scholar]
  75. PinheiroE.A. StikaC.S. Drugs in pregnancy: Pharmacologic and physiologic changes that affect clinical care.Semin. Perinatol.202044315122110.1016/j.semperi.2020.151221 32115202
     [Google Scholar]
  76. AlqudahM. Al-ShboulO. Al-DwairiA. Al-U’DatD.G. AlqudahA. Progesterone inhibitory role on gastrointestinal motility.Physiol. Res.202271219319810.33549/physiolres.934824 35344673
     [Google Scholar]
  77. RamuB. MohanP. RajasekaranM.S. JayanthiV. Prevalence and risk factors for gastroesophageal reflux in pregnancy.Indian J. Gastroenterol.201130314414710.1007/s12664‑010‑0067‑3 21125366
     [Google Scholar]
  78. VinarovZ. AbdallahM. AgundezJ.A.G. AllegaertK. BasitA.W. BraeckmansM. CeulemansJ. CorsettiM. GriffinB.T. GrimmM. KeszthelyiD. KoziolekM. MadlaC.M. MatthysC. McCoubreyL.E. MitraA. ReppasC. StappaertsJ. SteenackersN. TrevaskisN.L. VanuytselT. VertzoniM. WeitschiesW. WilsonC. AugustijnsP. Impact of gastrointestinal tract variability on oral drug absorption and pharmacokinetics: An UNGAP review.Eur. J. Pharm. Sci.202116210581210.1016/j.ejps.2021.105812 33753215
     [Google Scholar]
  79. MillerF. Nausea and vomiting in pregnancy: The problem of perception—is it really a disease?Am J. Obstet. Gynecol.20021865 Suppl UnderstandingS182S18310.1067/mob.2002.12259412011883
     [Google Scholar]
  80. EpsteinR.A. BoboW.V. MartinP.R. MorrowJ.A. WangW. ChandrasekharR. CooperW.O. Increasing pregnancy-related use of prescribed opioid analgesics.Ann. Epidemiol.201323849850310.1016/j.annepidem.2013.05.017 23889859
     [Google Scholar]
  81. CostantineM.M. Physiologic and pharmacokinetic changes in pregnancy.Front. Pharmacol.201456510.3389/fphar.2014.00065 24772083
     [Google Scholar]
  82. GeorgieffM.K. KrebsN.F. CusickS.E. The benefits and risks of iron supplementation in pregnancy and childhood.Annu. Rev. Nutr.201939112114610.1146/annurev‑nutr‑082018‑124213 31091416
     [Google Scholar]
  83. SobbrioG.A. GranataA. GraneseD. D’ArrigoF. PanaceaA. NicitaR. PullèC. TrimarchiF. Sex hormone binding globulin, cortisol binding globulin, thyroxine binding globulin, ceruloplasmin: changes in treatment with two oral contraceptives low in oestrogen.Clin. Exp. Obstet. Gynecol.19911814345 1829029
     [Google Scholar]
  84. McErleanS. KingC. Does an abnormally elevated maternal alkaline phosphatase pose problems for the fetus?BMJ Case Rep.2019124e22910910.1136/bcr‑2018‑229109 31040142
     [Google Scholar]
  85. MikolasevicI. Filipec-KanizajT. JakopcicI. MajurecI. Brncic-FischerA. SobocanN. HrsticI. StimacT. StimacD. MilicS. Liver disease during pregnancy: A challenging clinical issue.Med. Sci. Monit.2018244080409010.12659/MSM.907723 29905165
     [Google Scholar]
  86. JeongH. Altered drug metabolism during pregnancy: hormonal regulation of drug-metabolizing enzymes.Expert Opin. Drug Metab. Toxicol.20106668969910.1517/17425251003677755 20367533
     [Google Scholar]
  87. QuinneyS.K. MohamedA.N. HebertM.F. HaasD.M. ClarkS. UmansJ.G. CaritisS.N. LiL. A semi‐mechanistic metabolism model of CYP3A substrates in pregnancy: Predicting changes in midazolam and nifedipine pharmacokinetics.CPT Pharmacometrics Syst. Pharmacol.2012191910.1038/psp.2012.5 23835882
     [Google Scholar]
  88. ChenH. YangK. ChoiS. FischerJ.H. JeongH. Up-regulation of UDP-glucuronosyltransferase (UGT) 1A4 by 17beta-estradiol: A potential mechanism of increased lamotrigine elimination in pregnancy.Drug Metab. Dispos.20093791841184710.1124/dmd.109.026609 19546240
     [Google Scholar]
  89. Matuszkiewicz-RowińskaJ. MałyszkoJ. WieliczkoM. State of the art paper Urinary tract infections in pregnancy: Old and new unresolved diagnostic and therapeutic problems.Arch. Med. Sci.201511677710.5114/aoms.2013.39202 25861291
     [Google Scholar]
  90. CheungK.L. LafayetteR.A. Renal physiology of pregnancy.Adv. Chronic Kidney Dis.201320320921410.1053/j.ackd.2013.01.012 23928384
     [Google Scholar]
  91. DreisbachA.W. LertoraJ.J.L. The effect of chronic renal failure on drug metabolism and transport.Expert Opin. Drug Metab. Toxicol.2008481065107410.1517/17425255.4.8.1065 18680441
     [Google Scholar]
  92. PoelsE.M.P. BijmaH.H. GalballyM. BerginkV. Lithium during pregnancy and after delivery: A review.Int. J. Bipolar Disord.2018612610.1186/s40345‑018‑0135‑7 30506447
     [Google Scholar]
  93. ParienteG. LeibsonT. CarlsA. Adams-WebberT. ItoS. KorenG. Pregnancy-associated changes in pharmacokinetics: A systematic review.PLoS Med.20161311e100216010.1371/journal.pmed.1002160 27802281
     [Google Scholar]
  94. SongW. WangH. WuQ. Atrial natriuretic peptide in cardiovascular biology and disease (NPPA).Gene201556911610.1016/j.gene.2015.06.029 26074089
     [Google Scholar]
  95. ToneyG.M. VallonV. StockandJ.D. Intrinsic control of sodium excretion in the distal nephron by inhibitory purinergic regulation of the epithelial Na+ channel.Curr. Opin. Nephrol. Hypertens.2012211526010.1097/MNH.0b013e32834db4a0 22143248
     [Google Scholar]
  96. WestC.A. SasserJ.M. BaylisC. The enigma of continual plasma volume expansion in pregnancy: Critical role of the renin-angiotensin-aldosterone system.Am. J. Physiol. Renal Physiol.20163116F1125F113410.1152/ajprenal.00129.2016 27707703
     [Google Scholar]
  97. SchouM. AmdisenA. SteenstrupO.R. Lithium and pregnancy. II. Hazards to women given lithium during pregnancy and delivery.BMJ19732585913713810.1136/bmj.2.5859.137 4699591
     [Google Scholar]
  98. Soma-PillayP. Nelson-PiercyC. TolppanenH. MebazaaA. Physiological changes in pregnancy.Cardiovasc. J. Afr.2016272899410.5830/CVJA‑2016‑021 27213856
     [Google Scholar]
  99. GrajczykA. SobczykK. ZarzeckaJ. BarczE. DżamanK. Objective measurements of nasal obstruction and eustachian tube function in pregnant women.J. Clin. Med.2024139267110.3390/jcm13092671 38731199
     [Google Scholar]
  100. KimM.H. RheeC.K. ShimJ.S. ParkS.Y. YooK.H. KimB.Y. BaeH.W. SimY.S. ChangJ.H. ChoY.J. LeeJ.H. Inhaled corticosteroids in asthma and the risk of pneumonia.Allergy Asthma Immunol. Res.201911679580510.4168/aair.2019.11.6.795 31552715
     [Google Scholar]
  101. PleilJ.D. Ariel Geer WallaceM. DavisM.D. MattyC.M. The physics of human breathing: Flow, timing, volume, and pressure parameters for normal, on-demand, and ventilator respiration.J. Breath Res.202115404200210.1088/1752‑7163/ac2589 34507310
     [Google Scholar]
  102. LoMauroA. AlivertiA. Respiratory physiology of pregnancy.Breathe201511429730110.1183/20734735.008615 27066123
     [Google Scholar]
  103. BajwaS.J. BajwaS. Anaesthetic challenges and management during pregnancy: Strategies revisited.Anesth. Essays Res.20137216016710.4103/0259‑1162.118945 25885826
     [Google Scholar]
  104. Ortiz-PradoE. DunnJ.F. VasconezJ. CastilloD. ViscorG. Partial pressure of oxygen in the human body: A general review.Am. J. Blood Res.201991114 30899601
     [Google Scholar]
  105. Adeva-AndanyM.M. Fernández-FernándezC. Mouriño-BayoloD. Castro-QuintelaE. Domínguez-MonteroA. Sodium bicarbonate therapy in patients with metabolic acidosis.ScientificWorldJournal2014201462767310.1155/2014/627673
     [Google Scholar]
  106. LimmerM. de MaréesM. PlatenP. Alterations in acid-base balance and high-intensity exercise performance after short-term and long-term exposure to acute normobaric hypoxic conditions.Sci. Rep.20201011373210.1038/s41598‑020‑70762‑z 32792614
     [Google Scholar]
  107. ZhaoY. XiongW. LiC. ZhaoR. LuH. SongS. ZhouY. HuY. ShiB. GeJ. Hypoxia-induced signaling in the cardiovascular system: Pathogenesis and therapeutic targets.Signal. Transduct. Target. Ther.20238143110.1038/s41392‑023‑01652‑9 37981648
     [Google Scholar]
  108. DimitriadisE. RolnikD.L. ZhouW. Estrada-GutierrezG. KogaK. FranciscoR.P.V. WhiteheadC. HyettJ. da Silva CostaF. NicolaidesK. MenkhorstE. Pre-eclampsia.Nat. Rev. Dis. Primers202391810.1038/s41572‑023‑00417‑6 36797292
     [Google Scholar]
  109. NewtonE.R. MayL. Adaptation of maternal-fetal physiology to exercise in pregnancy: The basis of guidelines for physical activity in pregnancy.Clin. Med. Insights Womens. Health.2017101179562X1769322410.1177/1179562X1769322428579865
     [Google Scholar]
  110. CatovJ.M. NewmanA.B. Sutton-TyrrellK. HarrisT.B. TylavskyF. VisserM. AyonayonH.N. NessR.B. Parity and cardiovascular disease risk among older women: How do pregnancy complications mediate the association?Ann. Epidemiol.2008181287387910.1016/j.annepidem.2008.09.009 19041585
     [Google Scholar]
  111. HallM.E. GeorgeE.M. GrangerJ.P. The heart during pregnancy.Rev. Esp. Cardiol.201164111045105010.1016/j.recesp.2011.07.009 21962953
     [Google Scholar]
  112. MoogN.K. EntringerS. HeimC. WadhwaP.D. KathmannN. BussC. Influence of maternal thyroid hormones during gestation on fetal brain development.Neuroscience20173426810010.1016/j.neuroscience.2015.09.070 26434624
     [Google Scholar]
  113. HamidiO.P. BarbourL.A. Endocrine emergencies during pregnancy.Obstet. Gynecol. Clin. North Am.202249347348910.1016/j.ogc.2022.02.00336122980
     [Google Scholar]
  114. ZigmanJ.M. CohenS.E. GarberJ.R. Impact of thyroxine-binding globulin on thyroid hormone economy during pregnancy.Thyroid200313121169117510.1089/10507250360731587 14751039
     [Google Scholar]
  115. LiH. YuanX. LiuL. ZhouJ. LiC. YangP. BuL. ZhangM. QuS. Clinical evaluation of various thyroid hormones on thyroid function.Int. J. Endocrinol.2014201461857210.1155/2014/618572
     [Google Scholar]
  116. KoulouriO. MoranC. HalsallD. ChatterjeeK. GurnellM. Pitfalls in the measurement and interpretation of thyroid function tests.Best Pract. Res. Clin. Endocrinol. Metab.201327674576210.1016/j.beem.2013.10.003 24275187
     [Google Scholar]
  117. McNeilA.R. StanfordP.E. Reporting thyroid function tests in pregnancy.Clin. Biochem. Rev.2015364109126 26900190
     [Google Scholar]
  118. Klubo-GwiezdzinskaJ. BurmanK.D. Van NostrandD. WartofskyL. Levothyroxine treatment in pregnancy: Indications, efficacy, and therapeutic regimen.J. Thyroid Res.2011201184359110.4061/2011/843591 21876837
     [Google Scholar]
  119. CigniniP. CafàE.V. GiorlandinoC. CapriglioneS. SpataA. DugoN. Thyroid physiology and common diseases in pregnancy: Review of literature.J. Prenat. Med.2012646471 23272277
     [Google Scholar]
  120. TingiE. SyedA.A. KyriacouA. MastorakosG. KyriacouA. Benign thyroid disease in pregnancy: A state of the art review.J. Clin. Transl. Endocrinol.20166374910.1016/j.jcte.2016.11.001 29067240
     [Google Scholar]
  121. JoshiJ.S. ShanooA. PatelN. GuptaA. From conception to delivery: A comprehensive review of thyroid disorders and their far-reaching impact on feto-maternal health.Cureus2024162e5336210.7759/cureus.53362 38435202
     [Google Scholar]
  122. CroceL. ChiovatoL. TonaccheraM. PetrosinoE. TandaM.L. MoletiM. MagriF. OlivieriA. PearceE.N. RotondiM. Iodine status and supplementation in pregnancy: An overview of the evidence provided by meta-analyses.Rev. Endocr. Metab. Disord.202324224125010.1007/s11154‑022‑09760‑7 36227457
     [Google Scholar]
  123. BhoopalanS.V. HuangL.J-S. WeissM.J. Erythropoietin regulation of red blood cell production: From bench to bedside and back.F1000Res20209F1000 Faculty Rev-1153.10.12688/f1000research.26648.132983414
     [Google Scholar]
  124. AgureeS. GernandA.D. Plasma volume expansion across healthy pregnancy: A systematic review and meta-analysis of longitudinal studies.BMC Pregnancy Childbirth201919150810.1186/s12884‑019‑2619‑6 31856759
     [Google Scholar]
  125. PeetersL.L. VerkesteC.M. SaxenaP.R. WallenburgH.C.S. Relationship between maternal hemodynamics and hematocrit and hemodynamic effects of isovolemic hemodilution and hemoconcentration in the awake late-pregnant guinea pig.Pediatr. Res.198721658458910.1203/00006450‑198706000‑00016 3601476
     [Google Scholar]
  126. FisherA.L. NemethE. Iron homeostasis during pregnancy.Am. J. Clin. Nutr.2017106Suppl. 61567S1574S10.3945/ajcn.117.155812 29070542
     [Google Scholar]
  127. BrennerB. Haemostatic changes in pregnancy.Thromb. Res.20041145-640941410.1016/j.thromres.2004.08.004 15507271
     [Google Scholar]
  128. HvasC.L. LarsenJ.B. The fibrinolytic system and its measurement: History, current uses and future directions for diagnosis and treatment.Int. J. Mol. Sci.202324181417910.3390/ijms241814179 37762481
     [Google Scholar]
  129. GuimarãesM. VertzoniM. FotakiN. Performance evaluation of montelukast pediatric formulations: Part II — A PBPK modelling approach.AAPS J.20222412710.1208/s12248‑021‑00662‑1 35013803
     [Google Scholar]
  130. WuQ. PetersS.A. A retrospective evaluation of allometry, population pharmacokinetics, and physiologically‐based pharmacokinetics for pediatric dosing using clearance as a surrogate.CPT Pharmacometrics Syst. Pharmacol.20198422022910.1002/psp4.12385 30762304
     [Google Scholar]
  131. WangK. JiangK. WeiX. LiY. WangT. SongY. Physiologically based pharmacokinetic models are effective support for pediatric drug development.AAPS PharmSciTech202122620810.1208/s12249‑021‑02076‑w 34312742
     [Google Scholar]
  132. BatchelorH.K. MarriottJ.F. Formulations for children: Problems and solutions.Br. J. Clin. Pharmacol.201579340541810.1111/bcp.12268 25855822
     [Google Scholar]
  133. JohnsonT.N. BonnerJ.J. TuckerG.T. TurnerD.B. JameiM. Development and applications of a physiologically-based model of paediatric oral drug absorption.Eur. J. Pharm. Sci.2018115576710.1016/j.ejps.2018.01.009 29309876
     [Google Scholar]
  134. CristofolettiR. CharooN.A. DressmanJ.B. Exploratory investigation of the limiting steps of oral absorption of fluconazole and ketoconazole in children using an in silico pediatric absorption model.J. Pharm. Sci.201610592794280310.1016/j.xphs.2016.01.027 26987949
     [Google Scholar]
  135. KohlmannP. StillhartC. KuentzM. ParrottN. Investigating oral absorption of carbamazepine in pediatric populations.AAPS J.20171961864187710.1208/s12248‑017‑0149‑6 28971365
     [Google Scholar]
  136. JohnsonT.N. TannerM.S. TaylorC.J. TuckerG.T. Enterocytic CYP3A4 in a paediatric population: Developmental changes and the effect of coeliac disease and cystic fibrosis.Br. J. Clin. Pharmacol.200151545146010.1046/j.1365‑2125.2001.01370.x 11422003
     [Google Scholar]
  137. KissM. MbasuR. NicolaïJ. BarnouinK. KotianA. MooijM.G. KistN. WijnenR.M.H. UngellA.L. CutlerP. RusselF.G.M. de WildtS.N. Ontogeny of small intestinal drug transporters and metabolizing enzymes based on targeted quantitative proteomics.Drug Metab. Dispos.202149121038104610.1124/dmd.121.000559 34548392
     [Google Scholar]
  138. JonesH.M. ChenY. GibsonC. HeimbachT. ParrottN. PetersS.A. SnoeysJ. UpretiV.V. ZhengM. HallS.D. Physiologically based pharmacokinetic modeling in drug discovery and development: A pharmaceutical industry perspective.Clin. Pharmacol. Ther.201597324726210.1002/cpt.37 25670209
     [Google Scholar]
  139. GolhenK. BuettcherM. KostJ. HuwylerJ. PfisterM. Meeting challenges of pediatric drug delivery: The potential of orally fast disintegrating tablets for infants and children.Pharmaceutics2023154103310.3390/pharmaceutics15041033 37111519
     [Google Scholar]
  140. KenyonE.M. LipscombJ.C. PegramR.A. GeorgeB.J. HinesR.N. The impact of scaling factor variability on risk-relevant pharmacokinetic outcomes in children: A case study using bromodichloromethane (BDCM).Toxicol. Sci.2019167234735910.1093/toxsci/kfy236 30252107
     [Google Scholar]
  141. ResnikD.B. Intentional exposure studies of environmental agents on human subjects: Assessing benefits and risks.Account. Res.2007141355510.1080/08989620601122842 17847606
     [Google Scholar]
  142. AraujoJ.M. GomezA.C. PintoJ.A. RolfoC. RaezL.E. Profile of entrectinib in the treatment of ROS1-positive non-small cell lung cancer.Hematol. Oncol. Stem Cell Ther.202114319219810.1016/j.hemonc.2020.11.005 33290717
     [Google Scholar]
  143. StanzioneB. Del ConteA. BertoliE. De CarloE. RevelantA. SpinaM. BearzA. Therapeutical options in ROS1 - Rearranged advanced non small cell lung cancer.Int. J. Mol. Sci.202324141149510.3390/ijms241411495 37511255
     [Google Scholar]
  144. UmeharaK. ParrottN. SchindlerE. LegrasV. Meneses-LorenteG. PBPK modeling of entrectinib and its active metabolite to derive dose adjustments in pediatric populations co-administered with CYP3A4 inhibitors.Clin. Pharmacol. Ther.202411641130114010.1002/cpt.3386 39023380
     [Google Scholar]
  145. González-SalesM. DjebliN. Meneses-LorenteG. BuchheitV. BonnefoisG. TremblayP.O. FreyN. MercierF. Population pharmacokinetic analysis of entrectinib in pediatric and adult patients with advanced/metastatic solid tumors: Support of new drug application submission.Cancer Chemother. Pharmacol.2021886997100710.1007/s00280‑021‑04353‑8 34536094
     [Google Scholar]
  146. RasoolM.F. KhalilF. LäerS. A physiologically based pharmacokinetic drug-disease model to predict carvedilol exposure in adult and paediatric heart failure patients by incorporating pathophysiological changes in hepatic and renal blood flows.Clin. Pharmacokinet.201554994396210.1007/s40262‑015‑0253‑7 25773479
     [Google Scholar]
  147. SamantT.S. LukacovaV. SchmidtS. Development and qualification of physiologically based pharmacokinetic models for drugs with atypical distribution behavior: A desipramine case study.CPT Pharmacometrics Syst. Pharmacol.20176531532110.1002/psp4.12180 28398693
     [Google Scholar]
  148. ParrottN. DaviesB. HoffmannG. KoernerA. LaveT. PrinssenE. TheogarajE. SingerT. Development of a physiologically based model for oseltamivir and simulation of pharmacokinetics in neonates and infants.Clin. Pharmacokinet.201150961362310.2165/11592640‑000000000‑00000 21827216
     [Google Scholar]
  149. JohnsonT.N. ZhouD. BuiK.H. Development of physiologically based pharmacokinetic model to evaluate the relative systemic exposure to quetiapine after administration of IR and XR formulations to adults, children and adolescents.Biopharm. Drug Dispos.201435634135210.1002/bdd.1899 24797229
     [Google Scholar]
  150. WillmannS. ThelenK. KubitzaD. LensingA.W.A. FredeM. CoboekenK. StampfussJ. BurghausR. MückW. LippertJ. Pharmacokinetics of rivaroxaban in children using physiologically based and population pharmacokinetic modelling: An EINSTEIN-Jr phase I study.Thromb. J.20181613210.1186/s12959‑018‑0185‑1 30534008
     [Google Scholar]
  151. WillmannS. BeckerC. BurghausR. CoboekenK. EdgintonA. LippertJ. SiegmundH.U. ThelenK. MückW. Development of a paediatric population-based model of the pharmacokinetics of rivaroxaban.Clin. Pharmacokinet.20145318910210.1007/s40262‑013‑0090‑5 23912563
     [Google Scholar]
  152. KhalilF. LäerS. Physiologically based pharmacokinetic models in the prediction of oral drug exposure over the entire pediatric age range-sotalol as a model drug.AAPS J.201416222623910.1208/s12248‑013‑9555‑6 24399240
     [Google Scholar]
  153. VilligerA. StillhartC. ParrottN. KuentzM. Using Physiologically Based Pharmacokinetic (PBPK) modelling to gain insights into the effect of physiological factors on oral absorption in paediatric populations.AAPS J.201618493394710.1208/s12248‑016‑9896‑z 27060007
     [Google Scholar]
  154. MojD. BritzH. BurhenneJ. StewartC.F. EgererG. HaefeliW.E. LehrT. A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification.Cancer Chemother. Pharmacol.20178051013102610.1007/s00280‑017‑3447‑x 28988277
     [Google Scholar]
  155. HoltK. YeM. NagarS. KorzekwaK. Prediction of tissue-plasma partition coefficients using microsomal partitioning: Incorporation into physiologically based pharmacokinetic models and steady-state volume of distribution predictions.Drug Metab. Dispos.201947101050106010.1124/dmd.119.087973 31324699
     [Google Scholar]
  156. EmotoC. JohnsonT.N. NeuhoffS. HahnD. VinksA.A. FukudaT. PBPK model of morphine incorporating developmental changes in hepatic OCT1 and UGT2B7 proteins to explain the variability in clearances in neonates and small infants.CPT Pharmacometrics Syst. Pharmacol.20187746447310.1002/psp4.12306 29920988
     [Google Scholar]
  157. EmotoC. FukudaT. JohnsonT.N. NeuhoffS. SadhasivamS. VinksA.A. Characterization of contributing factors to variability in morphine clearance through PBPK modeling implemented with OCT1 transporter.CPT Pharmacometrics Syst. Pharmacol.20176211011910.1002/psp4.12144 27935268
     [Google Scholar]
  158. BhattD.K. MehrotraA. GaedigkA. ChapaR. BasitA. ZhangH. ChoudhariP. BobergM. PearceR.E. GaedigkR. BroeckelU. LeederJ.S. PrasadB. Age‐ and genotype‐dependent variability in the protein abundance and activity of six major uridine diphosphate‐glucuronosyltransferases in human liver.Clin. Pharmacol. Ther.2019105113114110.1002/cpt.1109 29737521
     [Google Scholar]
  159. ZhouW. JohnsonT.N. BuiK.H. CheungS.Y.A. LiJ. XuH. Al-HunitiN. ZhouD. Predictive performance of physiologically based pharmacokinetic (PBPK) modeling of drugs extensively metabolized by major cytochrome P450s in children.Clin. Pharmacol. Ther.2018104118820010.1002/cpt.905 29027194
     [Google Scholar]
  160. SalemF. JohnsonT.N. AbduljalilK. TuckerG.T. Rostami-HodjeganA. A re-evaluation and validation of ontogeny functions for cytochrome P450 1A2 and 3A4 based on in vivo data.Clin. Pharmacokinet.201453762563610.1007/s40262‑014‑0140‑7 24671884
     [Google Scholar]
  161. PalleriaC. Di PaoloA. GiofrèC. CagliotiC. LeuzziG. SiniscalchiA. De SarroG. GallelliL. Pharmacokinetic drug-drug interaction and their implication in clinical management.J. Res. Med. Sci.2013187601610 24516494
     [Google Scholar]
  162. SalernoS.N. EdgintonA. GerhartJ.G. LaughonM.M. AmbalavananN. SokolG.M. HornikC.D. StewartD. MillsM. MartzK. GonzalezD. Physiologically‐based pharmacokinetic modeling characterizes the CYP3A‐mediated drug‐drug interaction between fluconazole and sildenafil in infants.Clin. Pharmacol. Ther.2021109125326210.1002/cpt.1990 32691891
     [Google Scholar]
  163. ZhangW. ZhangQ. CaoZ. ZhengL. HuW. Physiologically based pharmacokinetic modeling in neonates: Current status and future perspectives.Pharmaceutics20231512276510.3390/pharmaceutics15122765 38140105
     [Google Scholar]
  164. SalernoS.N. CarreñoF.O. EdgintonA.N. Cohen-WolkowiezM. GonzalezD. Leveraging physiologically based pharmacokinetic modeling and experimental data to guide dosing modification of CYP3A-mediated drug-drug interactions in the pediatric population.Drug Metab. Dispos.202149984485510.1124/dmd.120.000318 34154994
     [Google Scholar]
  165. Vander SchaafM. LuthK. TownsendD.M. ChessmanK.H. MillsC.M. GarnerS.S. PetersonY.K. CYP3A4 drug metabolism considerations in pediatric pharmacotherapy.Med. Chem. Res.202433122221223510.1007/s00044‑024‑03360‑7
     [Google Scholar]
  166. van HasseltJ.G.C. van EijkelenburgN.K.A. BeijnenJ.H. SchellensJ.H.M. HuitemaA.D.R. Design of a drug‐drug interaction study of vincristine with azole antifungals in pediatric cancer patients using clinical trial simulation.Pediatr. Blood Cancer201461122223222910.1002/pbc.25198 25175364
     [Google Scholar]
  167. Pilla ReddyV. FretlandA.J. ZhouD. SharmaS. ChenB. VishwanathanK. McGinnityD.F. XuY. WareJ.A. Mechanistic physiology-based pharmacokinetic modeling to elucidate vincristine-induced peripheral neuropathy following treatment with novel kinase inhibitors.Cancer Chemother. Pharmacol.202188345146410.1007/s00280‑021‑04302‑5 34080039
     [Google Scholar]
  168. MoriyamaB. HenningS.A. LeungJ. Falade-NwuliaO. JarosinskiP. PenzakS.R. WalshT.J. Adverse interactions between antifungal azoles and vincristine: Review and analysis of cases.Mycoses201255429029710.1111/j.1439‑0507.2011.02158.x 22126626
     [Google Scholar]
  169. SabusA. MerrowM. BurkeE. KordasG. WilliamsM. LarsonM. EisenmanK. Incidence and severity of neuropathy with concurrent use of voriconazole and vincristine in pediatric patients with cancer.J. Hematol. Oncol. Pharm.2023132
     [Google Scholar]
  170. DuanP. FisherJ.W. YoshidaK. ZhangL. BurckartG.J. WangJ. Physiologically based pharmacokinetic prediction of linezolid and emtricitabine in neonates and infants.Clin. Pharmacokinet.201756438339410.1007/s40262‑016‑0445‑9 27596256
     [Google Scholar]
  171. RhodinM.M. AndersonB.J. PetersA.M. CoulthardM.G. WilkinsB. ColeM. ChatelutE. GrubbA. VealG.J. KeirM.J. HolfordN.H.G. Human renal function maturation: A quantitative description using weight and postmenstrual age.Pediatr. Nephrol.2009241677610.1007/s00467‑008‑0997‑5 18846389
     [Google Scholar]
  172. JohnsonT.N. Rostami-HodjeganA. TuckerG.T. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children.Clin. Pharmacokinet.200645993195610.2165/00003088‑200645090‑00005 16928154
     [Google Scholar]
  173. ZhouW. JohnsonT.N. XuH. CheungS.Y.A. BuiK.H. LiJ. Al-HunitiN. ZhouD. Predictive performance of physiologically based pharmacokinetic and population pharmacokinetic modeling of renally cleared drugs in children.CPT Pharmacometrics Syst. Pharmacol.20165947548310.1002/psp4.12101 27566992
     [Google Scholar]
  174. WalshC. BonnerJ.J. JohnsonT.N. NeuhoffS. GhazalyE.A. GribbenJ.G. BoddyA.V. VealG.J. Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer.Br. J. Clin. Pharmacol.201681598999810.1111/bcp.12878 26727248
     [Google Scholar]
  175. Balbas-MartinezV. MicheletR. EdgintonA.N. MeestersK. TrocónizI.F. VermeulenA. Physiologically-based pharmacokinetic model for ciprofloxacin in children with complicated urinary tract infection.Eur. J. Pharm. Sci.201912817117910.1016/j.ejps.2018.11.033 30503378
     [Google Scholar]
  176. LukacovaV. GoelzerP. ReddyM. GreigG. ReignerB. ParrottN. A physiologically based pharmacokinetic model for ganciclovir and its prodrug valganciclovir in adults and children.AAPS J.20161861453146310.1208/s12248‑016‑9956‑4 27450227
     [Google Scholar]
  177. BackH. LeeJ.B. HanN. GooS. JungE. KimJ. SongB. AnS.H. KimJ.T. RhieS.J. ReeY.S. ChaeJ. KimJ. YunH. Application of size and maturation functions to population pharmacokinetic modeling of pediatric patients.Pharmaceutics201911625910.3390/pharmaceutics11060259 31163633
     [Google Scholar]
  178. van GroenB.D. AllegaertK. TibboelD. de WildtS.N. Innovative approaches and recent advances in the study of ontogeny of drug metabolism and transport.Br. J. Clin. Pharmacol.202288104285429610.1111/bcp.14534 32851677
     [Google Scholar]
  179. KaizerA.M. BelliH.M. MaZ. NicklawskyA.G. RobertsS.C. WildJ. WoguA.F. XiaoM. SaboR.T. Recent innovations in adaptive trial designs: A review of design opportunities in translational research.J. Clin. Transl. Sci.202371e12510.1017/cts.2023.537 37313381
     [Google Scholar]
  180. XuJ. HilpertJ. WuK. van HeckeB. CollinsG. PatelA. MohindraR. DaviesM.J.B. XuY. ThompsonP. An adaptive design to investigate the effect of ketoconazole on pharmacokinetics of GSK239512 in healthy male volunteers.J. Clin. Pharmacol.201555550551110.1002/jcph.441 25470032
     [Google Scholar]
  181. SalernoS.N. BurckartG.J. HuangS.M. GonzalezD. Pediatric drug-drug interaction studies: Barriers and opportunities.Clin. Pharmacol. Ther.201910551067107010.1002/cpt.1234 30362111
     [Google Scholar]
  182. ThompsonE.J. FooteH.P. HillK.D. HornikC.P. A point-of-care pharmacokinetic/pharmacodynamic trial in critically ill children: Study design and feasibility.Contemp. Clin. Trials Commun.20233510118210.1016/j.conctc.2023.101182 37485397
     [Google Scholar]
  183. MaciasC.G. RemyK.E. BardaA.J. Utilizing big data from electronic health records in pediatric clinical care.Pediatr. Res.202393238238910.1038/s41390‑022‑02343‑x 36434202
     [Google Scholar]
  184. ŠímaM. BakhoucheH. HartingerJ. CikánkováT. SlanařO. Therapeutic drug monitoring of antibiotic agents: Evaluation of predictive performance.Eur. J. Hosp. Pharm. Sci. Pract.2019262858810.1136/ejhpharm‑2017‑001396 31157105
     [Google Scholar]
  185. MonteiroJ.F. HahnS.R. GonçalvesJ. FrescoP. Vancomycin therapeutic drug monitoring and population pharmacokinetic models in special patient subpopulations.Pharmacol. Res. Perspect.201864e0042010.1002/prp2.420 30156005
     [Google Scholar]
  186. LinD. YuL. ShangD. HuangL. WuL. LiaoX. ZhangY. ZiJ. ZhangJ. ZengY. WangX. YangL. Population pharmacokinetics of posaconazole in Chinese pediatric patients with acute leukaemia: Effect of food on bioavailability and dose optimization.Eur. J. Pharm. Sci.202217810628910.1016/j.ejps.2022.106289 36041707
     [Google Scholar]
  187. HoneyfordK. ExpertP. MendelsohnE.E. PostB. FaisalA.A. GlampsonB. MayerE.K. CostelloeC.E. Challenges and recommendations for high quality research using electronic health records.Front. Digit. Health2022494033010.3389/fdgth.2022.940330 36060540
     [Google Scholar]
  188. GillK.L. GardnerI. LiL. JameiM. A bottom-up whole-body physiologically based pharmacokinetic model to mechanistically predict tissue distribution and the rate of subcutaneous absorption of therapeutic proteins.AAPS J.201618115617010.1208/s12248‑015‑9819‑4 26408308
     [Google Scholar]
  189. MalikP. EdgintonA. Pediatric physiology in relation to the pharmacokinetics of monoclonal antibodies.Expert Opin. Drug Metab. Toxicol.201814658559910.1080/17425255.2018.1482278 29806953
     [Google Scholar]
  190. EdlundH. MelinJ. Parra-GuillenZ.P. KloftC. Pharmacokinetics and pharmacokinetic-pharmacodynamic relationships of monoclonal antibodies in children.Clin. Pharmacokinet.2015541358010.1007/s40262‑014‑0208‑4 25516414
     [Google Scholar]
  191. HardiansyahD. NgC.M. Effects of the FcRn developmental pharmacology on the pharmacokinetics of therapeutic monoclonal IgG antibody in pediatric subjects using minimal physiologically-based pharmacokinetic modelling.MAbs201810711310.1080/19420862.2018.1494479 29969360
     [Google Scholar]
  192. AnQ. ZhengY. ZhaoY. LiuT. GuoH. ZhangD. QianW. WangH. GuoY. HouS. LiJ. Physicochemical characterization and phase I study of CMAB008, an infliximab biosimilar produced by a different expression system.Drug Des. Devel. Ther.20191379180510.2147/DDDT.S170913 30880912
     [Google Scholar]
  193. BasuS. LienY.T.K. VozmedianoV. SchlenderJ.F. EissingT. SchmidtS. NiederaltC. Physiologically based pharmacokinetic modeling of monoclonal antibodies in pediatric populations using PK-Sim.Front. Pharmacol.20201186810.3389/fphar.2020.00868 32595502
     [Google Scholar]
  194. ZhangZ. ImperialM.Z. Patilea-VranaG.I. WedagederaJ. GaohuaL. UnadkatJ.D. Development of a novel maternal-fetal physiologically based pharmacokinetic model I: Insights into factors that determine fetal drug exposure through simulations and sensitivity analyses.Drug Metab. Dispos.201745892093810.1124/dmd.117.075192 28588050
     [Google Scholar]
  195. KeA.B. GreupinkR. AbduljalilK. Drug dosing in pregnant women: Challenges and opportunities in using physiologically based pharmacokinetic modeling and simulations.CPT Pharmacometrics Syst. Pharmacol.20187210311010.1002/psp4.12274 29349870
     [Google Scholar]
  196. PemathilakaR.L. ReynoldsD.E. HashemiN.N. Drug transport across the human placenta: Review of placenta-on-a-chip and previous approaches.Interface Focus2019952019003110.1098/rsfs.2019.0031 31485316
     [Google Scholar]
  197. DallmannA. LiuX.I. BurckartG.J. van den AnkerJ. Drug transporters expressed in the human placenta and models for studying maternal‐fetal drug transfer.J. Clin. Pharmacol.201959S1S70S8110.1002/jcph.1491 31502693
     [Google Scholar]
  198. LiuX. WangG. HuangH. LvX. SiY. BaiL. WangG. LiQ. YangW. Exploring maternal-fetal interface with in vitro placental and trophoblastic models.Front. Cell Dev. Biol.202311127922710.3389/fcell.2023.1279227 38033854
     [Google Scholar]
  199. MorinA.M. GatevE. McEwenL.M. MacIsaacJ.L. LinD.T.S. KoenN. CzamaraD. RäikkönenK. ZarH.J. KoenenK. SteinD.J. KoborM.S. JonesM.J. Maternal blood contamination of collected cord blood can be identified using DNA methylation at three CpGs.Clin. Epigenetics2017917510.1186/s13148‑017‑0370‑2 28770015
     [Google Scholar]
  200. TylutkiZ. PolakS. A four-compartment PBPK heart model accounting for cardiac metabolism - model development and application.Sci. Rep.2017713949410.1038/srep39494 28051093
     [Google Scholar]
  201. SalehM.A.A. LooC.F. Elassaiss-SchaapJ. De LangeE.C.M. Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: Insights from LeiCNS-PK3.0 simulations.J. Pharmacokinet. Pharmacodyn.202148572574110.1007/s10928‑021‑09768‑7 34142308
     [Google Scholar]
  202. YamamotoY. VälitaloP.A. WongY.C. HuntjensD.R. ProostJ.H. VermeulenA. KrauwinkelW. BeukersM.W. KokkiH. KokkiM. DanhofM. van HasseltJ.G.C. de LangeE.C.M. Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach.Eur. J. Pharm. Sci.201811216817910.1016/j.ejps.2017.11.011 29133240
     [Google Scholar]
  203. PesentiG. FoppoliM. MancaD. A minimal physiologically based pharmacokinetic model for high-dose methotrexate.Cancer Chemother. Pharmacol.202188459560610.1007/s00280‑021‑04305‑2 34120234
     [Google Scholar]
  204. GermovsekE. BarkerC.I.S. SharlandM. StandingJ.F. Pharmacokinetic-pharmacodynamic modeling in pediatric drug development, and the importance of standardized scaling of clearance.Clin. Pharmacokinet.2019581395210.1007/s40262‑018‑0659‑0 29675639
     [Google Scholar]
  205. DeanL. KaneM. Codeine therapy and CYP2D6 genotype.Medical Genetics Summaries.Bethesda, MDNational Center for Biotechnology Information2012[Internet]
     [Google Scholar]
  206. MandaV. AvulaB. DaleO. ChittiboyinaA. KhanI. WalkerL. KhanS. Studies on pharmacokinetic drug interaction potential of vinpocetine.Medicines2015229310510.3390/medicines2020093 28930203
     [Google Scholar]
  207. PainterM.J. MinnighM.B. GausL. ScherM. BrozanskiB. AlvinJ. Neonatal phenobarbital and phenytoin binding profiles.J. Clin. Pharmacol.199434431231710.1002/j.1552‑4604.1994.tb01999.x 8006198
     [Google Scholar]
  208. GuanY. LiuX. HuangK. WangY. QiuK. WangX. HuangM. ZhouD. YuX. ZhongG. Physiologically-based pharmacokinetic modelling to investigate the effect of CYP3A4/3A5 maturation on tacrolimus pharmacokinetics in paediatric HSCT patients.Eur. J. Pharm. Sci.202420110683910.1016/j.ejps.2024.106839 38906231
     [Google Scholar]
  209. HankeN. KunzC. ThiemannM. FrickeH. LehrT. Translational PBPK modeling of the protein therapeutic and cd95l inhibitor asunercept to develop dose recommendations for its first use in pediatric glioblastoma patients.Pharmaceutics201911415210.3390/pharmaceutics11040152 30939793
     [Google Scholar]
  210. CepedaC. LevinsonS. YazonV.W. BarryJ. MathernG.W. FallahA. VintersH.V. LevineM.S. WuJ.Y. Cellular antiseizure mechanisms of everolimus in pediatric tuberous sclerosis complex, cortical dysplasia, and non-mTOR‐mediated etiologies.Epilepsia Open20183S218019010.1002/epi4.12253 30564777
     [Google Scholar]
  211. LiA. YeoK. WeltyD. RongH. Development of guanfacine extended-release dosing strategies in children and adolescents with ADHD using a physiologically based pharmacokinetic model to predict drug-drug interactions with moderate CYP3A4 inhibitors or inducers.Paediatr. Drugs201820218119410.1007/s40272‑017‑0270‑0 29098603
     [Google Scholar]
  212. CristeaS. KrekelsE.H.J. Rostami-HodjeganA. AllegaertK. KnibbeC.A.J. The influence of drug properties and ontogeny of transporters on pediatric renal clearance through glomerular filtration and active secretion: A simulation-based study.AAPS J.20202248710.1208/s12248‑020‑00468‑7 32566984
     [Google Scholar]
  213. Caleffi-MarchesiniE.R. HerlingA.A. MacenteJ. BonanR.H. de Freitas LimaP. MorenoR. AlexandreV. CharbeN.B. Borghi-PangoniF.B. CristofolettiR. DinizA. Adult and pediatric physiologically‐based biopharmaceutics modeling to explain lamotrigine immediate release absorption process.CPT Pharmacometrics Syst. Pharmacol.202413220822110.1002/psp4.13071 37916262
     [Google Scholar]
  214. EmotoC. JohnsonT.N. HahnD. ChristiansU. AllowayR.R. VinksA.A. FukudaT. A theoretical physiologically‐based pharmacokinetic approach to ascertain covariates explaining the large interpatient variability in tacrolimus disposition.CPT Pharmacometrics Syst. Pharmacol.20198527328410.1002/psp4.12392 30843669
     [Google Scholar]
  215. ZazoH. LagarejosE. Prado-VelascoM. Sánchez-HerreroS. SernaJ. Rueda-FerreiroA. Martín-SuárezA. CalvoM.V. Pérez-BlancoJ.S. LanaoJ.M. Physiologically-based pharmacokinetic modelling and dosing evaluation of gentamicin in neonates using PhysPK.Front. Pharmacol.20221397737210.3389/fphar.2022.977372 36249803
     [Google Scholar]
  216. GalileyaL.T. WasmannR.E. ChabalaC. RabieH. LeeJ. Njahira MukuiI. HesselingA. ZarH. AarnoutseR. TurkovaA. GibbD. CottonM.F. McIlleronH. DentiP. Evaluating pediatric tuberculosis dosing guidelines: A model-based individual data pooled analysis.PLoS Med.20232011e100430310.1371/journal.pmed.1004303 37988391
     [Google Scholar]
  217. PepinX.J.H. Johansson Soares MedeirosJ. Deris PradoL. Suarez SharpS. The development of an age-appropriate fixed dose combination for tuberculosis using Physiologically-Based Pharmacokinetic Modeling (PBBM) and risk assessment.Pharmaceutics20241612158710.3390/pharmaceutics16121587 39771565
     [Google Scholar]
  218. RamachandranA. GadgilC.J. A physiologically‐based pharmacokinetic model for tuberculosis drug disposition at extrapulmonary sites.CPT Pharmacometrics Syst. Pharmacol.20231291274128410.1002/psp4.13008 37431175
     [Google Scholar]
  219. DentiP. WasmannR.E. van RieA. WincklerJ. BekkerA. RabieH. HesselingA.C. van der LaanL.E. Gonzalez-MartinezC. ZarH.J. DaviesG. WiesnerL. SvenssonE.M. McIlleronH.M. Optimizing dosing and fixed-dose combinations of rifampicin, isoniazid, and pyrazinamide in pediatric patients with tuberculosis: A prospective population pharmacokinetic study.Clin. Infect. Dis.202275114115110.1093/cid/ciab908 34665866
     [Google Scholar]
  220. NgoH.X. XuA.Y. VelásquezG.E. ZhangN. ChangV.K. KurbatovaE.V. WhitworthW.C. SizemoreE. BryantK. CarrW. WeinerM. DooleyK.E. EngleM. DormanS.E. NahidP. SwindellsS. ChaissonR.E. NsubugaP. LourensM. DawsonR. SavicR.M. Pharmacokinetic-pharmacodynamic evidence from a phase 3 trial to support flat-dosing of rifampicin for tuberculosis.Clin. Infect. Dis.20247861680168910.1093/cid/ciae119 38462673
     [Google Scholar]
  221. HumphriesH. AlmondL. BergA. GardnerI. HatleyO. PanX. SmallB. ZhangM. JameiM. RomeroK. Development of physiologically‐based pharmacokinetic models for standard of care and newer tuberculosis drugs.CPT Pharmacometrics Syst. Pharmacol.202110111382139510.1002/psp4.12707 34623770
     [Google Scholar]
  222. GangulyS. EdgintonA.N. GerhartJ.G. Cohen-WolkowiezM. GreenbergR.G. GonzalezD. BenjaminD.K. HornikC. ZimmermanK. KennelP. BeciR. HornikC.D. KearnsG.L. LaughonM. PaulI.M. SullivanJ. WadeK. DelmoreP. Taylor-ZapataP. LeeJ. AnandR. SharmaG. SimoneG. KaneshigeK. TaylorL. GreenT. KantakA. OhlingerJ. HorganM. BoyntonS. EichenwaldE.C. JonesK. DurandD.J. AsselinJ. ArrietaA. SheaK. WadeK. MorrisonT. BrozanskiB.S. BakerR. WeitkampJ-H. NannieM. SanchezP. MontanyeS. van den AnkerJ. WilliamsE. SmithP.B. Cohen-WolkowiezM. BidegainM. BenjaminD.K. GrimesS. MacKendrickW. WolfS. PoindexterB. WilsonL.D. CastroL.M. HarrisA. BalaramanV. MorseR. RasmussenM. ArnellK. ValenciaG. HiggersonS. WalshM. ZadellA. RoaneC.M. FinerN. CapparelliE.V. RichW. BurchfieldD. MillerC. SullivanJ.E. PierceG. Bhatt-MehtaV. DechertR. WardR.M. NarusJ.A. BizzaroM. KonstantinoM. Physiologically based pharmacokinetic modeling of meropenem in preterm and term infants.Clin. Pharmacokinet.202160121591160410.1007/s40262‑021‑01046‑6 34155614
     [Google Scholar]
  223. ZhouW. JohnsonT. BuiK. CheungA. LiJ. XuH. Al-HunitiN. ZhouD. Application of physiologically based pharmacokinetic (Pbpk) Modeling to predict ondansetron pharmacokinetics in children.Clin. Pharmacol. Ther.2017101S94S95
     [Google Scholar]
  224. AlqahtaniF. AlruwailiA.H. AlasmariM.S. AlmazroaS.A. AlsuhaibaniK.S. RasoolM.F. AlruwailiA.F. AlsaneaS. A physiologically based pharmacokinetic model to predict systemic ondansetron concentration in liver cirrhosis patients.Pharmaceuticals20231612169310.3390/ph16121693 38139819
     [Google Scholar]
  225. LimA. SharmaP. StepanovO. ReddyV.P. Application of modelling and simulation approaches to predict pharmacokinetics of therapeutic monoclonal antibodies in pediatric population.Pharmaceutics2023155155210.3390/pharmaceutics15051552 37242793
     [Google Scholar]
  226. ChangH.P. ShakhnovichV. FrymoyerA. FunkR.S. BeckerM.L. ParkK.T. ShahD.K. A population physiologically‐based pharmacokinetic model to characterize antibody disposition in pediatrics and evaluation of the model using infliximab.Br. J. Clin. Pharmacol.202288129030210.1111/bcp.14963 34189743
     [Google Scholar]
  227. EastmanR.T. RothJ.S. BrimacombeK.R. SimeonovA. ShenM. PatnaikS. HallM.D. Remdesivir: A review of its discovery and development leading to emergency use authorization for treatment of COVID-19.ACS Cent. Sci.20206567268310.1021/acscentsci.0c00489 32483554
     [Google Scholar]
  228. EmotoC. JohnsonT.N. McPhailB.T. VinksA.A. FukudaT. Using a vancomycin PBPK model in special populations to elucidate case‐based clinical PK observations.CPT Pharmacometrics Syst. Pharmacol.20187423725010.1002/psp4.12279 29446256
     [Google Scholar]
  229. Summary on compassionate use.2020Available from: https://www.ema.europa.eu/en/documents/other/summary-compassionate-use-remdesivir-gilead_en.pdf
  230. DonnerT. Insulin - Pharmacology, therapeutic regimens and principles of intensive insulin therapy.Endotext; MDText.com, Inc: South Dartmouth (MA)2000
     [Google Scholar]
  231. SuryavanshiS.V. WangS. HajducekD.M. HamadehA. YeungC.H.T. MaglalangP.D. ItoS. AutmizguineJ. GonzalezD. EdgintonA.N. Coupling pre- and postnatal infant exposures with physiologically based pharmacokinetic modeling to predict cumulative maternal levetiracetam exposure during breastfeeding.Clin. Pharmacokinet.202463121735174810.1007/s40262‑024‑01447‑3 39586935
     [Google Scholar]
  232. DeepikaD. KumarV. The role of “physiologically based pharmacokinetic model (PBPK)” new approach methodology (NAM) in pharmaceuticals and environmental chemical risk assessment.Int. J. Environ. Res. Public Health2023204347310.3390/ijerph2004347336834167
     [Google Scholar]
  233. MooijM.G. SchwarzU.I. de KoningB.A.E. LeederJ.S. GaedigkR. SamsomJ.N. SpaansE. van GoudoeverJ.B. TibboelD. KimR.B. de WildtS.N. Ontogeny of human hepatic and intestinal transporter gene expression during childhood: Age matters.Drug Metab. Dispos.20144281268127410.1124/dmd.114.056929 24829289
     [Google Scholar]
  234. SimeoliR. CairoliS. DecembrinoN. CampiF. Dionisi ViciC. CoronaA. GoffredoB.M. Use of antibiotics in preterm newborns.Antibiotics2022119114210.3390/antibiotics11091142 36139921
     [Google Scholar]
  235. JunaidS.B. ImamA.A. BalogunA.O. De SilvaL.C. SurakatY.A. KumarG. AbdulkarimM. ShuaibuA.N. GarbaA. SahaluY. MohammedA. MohammedT.Y. AbdulkadirB.A. AbbaA.A. KakumiN.A.I. MahamadS. Recent advancements in emerging technologies for healthcare management systems: A survey.Healthcare20221010194010.3390/healthcare10101940 36292387
     [Google Scholar]
  236. HsiehN.H. ReisfeldB. BoisF.Y. ChiuW.A. Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic modeling.Front. Pharmacol.2018958810.3389/fphar.2018.00588 29937730
     [Google Scholar]
  237. WenZ. HeJ. TaoH. HuangS.Y. PepBDB: A comprehensive structural database of biological peptide-protein interactions.Bioinformatics201935117517710.1093/bioinformatics/bty579 29982280
     [Google Scholar]
  238. LuanW. LuL. LiX. MaC. Integrating extended fourier amplitude sensitivity test and set pair analysis for sustainable development evaluation from the view of uncertainty analysis.Sustainability2018107243510.3390/su10072435
     [Google Scholar]
  239. KraussM. BurghausR. LippertJ. NiemiM. NeuvonenP. SchuppertA. WillmannS. KuepferL. GörlitzL. Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification.In Silico Pharmacol.201311610.1186/2193‑9616‑1‑6 25505651
     [Google Scholar]
  240. FrechenS. Rostami-HodjeganA. Quality assurance of pbpk modeling platforms and guidance on building, evaluating, verifying and applying pbpk models prudently under the umbrella of qualification: Why, when, what, how and by whom?Pharm. Res.20223981733174810.1007/s11095‑022‑03250‑w 35445350
     [Google Scholar]
  241. van BorselenM.D. SluijtermanL.A.Æ. GreupinkR. de WildtS.N. Towards more robust evaluation of the predictive performance of physiologically based pharmacokinetic models: Using confidence intervals to support use of model-informed dosing in clinical care.Clin. Pharmacokinet.202463334335510.1007/s40262‑023‑01326‑3 38361163
     [Google Scholar]
  242. RajputA.J. AldibaniH.K.A. Rostami-HodjeganA. In‐depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: Part I - Applications and rationale behind the use of open source‐code software.Biopharm. Drug Dispos.202344327428510.1002/bdd.2357 37083200
     [Google Scholar]
  243. WuF. Challenges and opportunities when using oral PBPK to support risk assessment and biowaiver in regulatory submissions.2022Available from: https://www.fda.gov/media/166598/download
  244. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), & Center for Biologics Evaluation and Research (CBER). (2018, December). General Clinical Pharmacology Considerations for Pediatric Studies of Drugs, Including Biological Products (Guidance for Industry). Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/general-clinical-pharmacology-considerations-pediatric-studies-drugs-including-biological-products
  245. XieF. Van BocxlaerJ. VermeulenA. Physiologically based pharmacokinetic modelling of lisinopril in children: A case story of angiotensin converting enzyme inhibitors.Br. J. Clin. Pharmacol.20218731203121410.1111/bcp.1449232693432
     [Google Scholar]
  246. PaulP. ColinP.J. Musuamba TshinanuF. VersantvoortC. ManolisE. BlakeK. Current use of physiologically based pharmacokinetic modeling in new medicinal product approvals at EMA.Clin. Pharmacol. Ther.2025117380881710.1002/cpt.3525
     [Google Scholar]
  247. ICH Guideline M12 on drug interaction studies.2022Available from: https://www.ema.europa.eu/en/documents/scientificguideline/draft-ich-guideline-m12-drug-interaction-studies-step-2b_en.pdf
  248. Unlocking the power of words: An introduction to the LENA program.Available from: (Accessed on: February 9, 2025) https://starting-point.org/unlocking-the-power-of-words-an-introduction-to-the-lena-program/
  249. Cystatin C with estimated Glomerular Filtration Rate (eGFR), serum.Available from: (Accessed on: February 9, 2025) https://pediatric.testcatalog.org/show/CSTCE?utm_source=chatgpt.com
  250. YlinenE.A. Ala-HouhalaM. HarmoinenA.P.T. KnipM. Cystatin C as a marker for glomerular filtration rate in pediatric patients.Pediatr. Nephrol.199913650650910.1007/s004670050647 10452279
     [Google Scholar]
/content/journals/dmbl/10.2174/0118723128367217250602073115
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Advancing Pediatric Drug Safety: The Potential of Physiologically Based Pharmacokinetic Modeling
Drug Metab. Bioanal. Lett. 18, 122 (2025); https://doi.org/10.2174/0118723128367217250602073115
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  • Article Type:     Review Article
Keyword(s): clinical; computational approach; drug-drug interactions; pediatric; Physiologically based pharmacokinetic; preclinical

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