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Physiologically Based Pharmacokinetic (PBPK) Modelling

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Computer Aided Pharmaceutics and Drug Delivery

Abstract

Since its introduction in 1937 by Teorell, physiologically based pharmacokinetic (PBPK) modelling has come to a point, that apart from becoming an integral part of drug discovery and development process, it has also gained worldwide acceptance by drug regulatory bodies. PBPK models correlate drug properties with information of the physiology and biology of a species, in order to achieve a mechanistic representation of the drug in biological systems. The prediction involves consideration of different physiological organs of the body (referred to as compartments) that are interconnected via blood circulation. The characteristic volume and blood flow for each compartment is considered, and further, mass balance differential equations are used to describe the fate of the drug in different compartments. The model generates data in the form of concentration-time curves, which are then utilized to generate PK parameters (e.g. clearance, half-life, area under the curve, bioavailability, etc.) for the drug. The results of simulation can be then implemented for the intended purpose. This book chapter delves into the history, regulatory aspects and various components of PBPK, along with general workflow and approaches for model development, along with variety of salient applications of PBPK modelling.

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References

  1. Jones HM, Gardner IB, Watson KJ (2009) Modelling and PBPK simulation in drug discovery. AAPS J 11(1):155–166. https://doi.org/10.1208/s12248-009-9088-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Shardlow CE, Generaux GT, Patel AH, Tai G, Tran T, Bloomer JC (2013) Impact of physiologically based pharmacokinetic modeling and simulation in drug development. Drug Metab Dispos 41(12):1994–2003. https://doi.org/10.1124/dmd.113.052803

    Article  CAS  PubMed  Google Scholar 

  3. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos 43(11):1823–1837. https://doi.org/10.1124/dmd.115.065920

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Obach RS, Baxter JG, Liston TE, Silber BM, Jones BC, Macintyre F et al (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp 283(1):46–58. PMID: 9336307

    CAS  Google Scholar 

  5. Fahmi OA, Maurer TS, Kish M, Cardenas E, Boldt S, Nettleton D (2008) A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro. Drug Metab Dispos 36(8):1698–1708. https://doi.org/10.1124/dmd.107.018663

    Article  CAS  PubMed  Google Scholar 

  6. Varma MV, Lin J, Bi YA, Rotter CJ, Fahmi OA, Lam JL et al (2013) Quantitative prediction of repaglinide-rifampicin complex drug interactions using dynamic and static mechanistic models: delineating differential CYP3A4 induction and OATP1B1 inhibition potential of rifampicin. Drug Metab Dispos 41(5):966–974. https://doi.org/10.1124/dmd.112.050583

    Article  CAS  PubMed  Google Scholar 

  7. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73. https://doi.org/10.1146/annurev-pharmtox-010510-100540

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  9. Jones HM, Rowland-Yeo K (2013) Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol 2(8):1–2. https://doi.org/10.1038/psp.2013.41

    Article  CAS  Google Scholar 

  10. Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J et al (2016) Applied concepts in PBPK modeling: how to build a PBPK/PD model. CPT Pharmacometrics Syst Pharmacol 5(10):516–531. https://doi.org/10.1002/psp4.12134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rostami-Hodjegan A, Tucker GT (2007) Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov 6(2):140–148. https://doi.org/10.1038/nrd2173

    Article  CAS  PubMed  Google Scholar 

  12. Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV et al (2013) A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab Dispos 41(12):1975–1993. https://doi.org/10.1124/dmd.113.054031

    Article  CAS  PubMed  Google Scholar 

  13. Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan A et al (2009) Population-based mechanistic prediction of oral drug absorption. AAPS J 11(2):225–237. https://doi.org/10.1208/s12248-009-9099-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chow ECY, Sandy Pang K (2013) Why we need proper PBPK models to examine intestine and liver oral drug absorption. Curr Drug Metab 14(1):57–79. PMID: 22935069

    Article  CAS  Google Scholar 

  15. Jamei M, Dickinson GL, Rostami-Hodjegan A (2009) A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab Pharmacokinet 24(1):53–75. https://doi.org/10.2133/dmpk.24.53

    Article  CAS  PubMed  Google Scholar 

  16. Huang SM, Abernethy DR, Wang Y, Zhao P, Zineh I (2013) The utility of modeling and simulation in drug development and regulatory review. J Pharm Sci 102(9):2912–2923. https://doi.org/10.1002/jps.23570

    Article  CAS  PubMed  Google Scholar 

  17. Committee for Medicinal Products for Human Use (CHMP) (2005) Guideline on the evaluation of the pharmacokinetics of medicinal products in patients with impaired hepatic function. CPMP/EWP/2339/02, European Medicines Agency, London

    Google Scholar 

  18. Center for Drug Evaluation and Research (CDER) (2014) General clinical pharmacology considerations for pediatric studies for drugs and biological products. U.S. Food and Drug Administration, Silver Spring

    Google Scholar 

  19. Committee for Medicinal Products for Human Use (CHMP) (2012) Guideline on the investigation of drug-drug interactions. European Medicines Agency, London

    Google Scholar 

  20. Center for Drug Evaluation and Research (CDER) (2012) Guidance for industry: drug interaction studies study design, data analysis, implications for dosing, and labeling recommendations. U.S. Food and Drug Administration, Silver Spring

    Google Scholar 

  21. Ministry of Health, Labour and Welfare Research Group (2014) Drug interaction guideline for drug development and labeling recommendations. Japanese Ministry of Health, Labour and Welfare, Tokyo

    Google Scholar 

  22. Committee for Medicinal Products for Human Use (CHMP) (2011) Guideline on the use of pharmacogenetic methodologies in the pharmacokinetic evaluation of medicinal products. EMA/CHMP/37646/2009, European Medicines Agency, London

    Google Scholar 

  23. Center for Drug Evaluation and Research (CDER) (2013) Guidance for industry: clinical pharmacogenomics: premarket evaluation in early-phase clinical studies and recommendations for labeling. U.S. Food and Drug Administration, Silver Spring

    Google Scholar 

  24. Teorell T (1937) Kinetics of distribution of substances administered to the body, I: the extravascular modes of administration. Arch Int Pharmacodyn 57:205–225

    CAS  Google Scholar 

  25. Bellman R, Jacquez JA, Kalaba R (1960) Some mathematical aspects of chemotherapy: I. One-organ models. Bull Math Biol 22(2):181–198. https://doi.org/10.1007/BF02478005

    Article  Google Scholar 

  26. Bischoff KB (1967) Applications of a mathematical model for drug distribution in mammals. In: Hershey D (ed) Chemical engineering in medicine and biology. Springer, Boston. https://doi.org/10.1007/978-1-4757-4748-5_13

    Chapter  Google Scholar 

  27. Andersen ME, Yang RS, Clewell HJ III, Reddy MB (2005) Introduction: a historical perspective of the development and applications of PBPK models. In: Anderson ME (ed) Physiologically based pharmacokinetic modeling: science and applications, vol 10. Wiley, Hoboken, pp 1–8. https://doi.org/10.1002/0471478768.ch1

    Chapter  Google Scholar 

  28. Clewell HJ III, Reddy MB, Lave T, Andersen ME (2010) Physiologically based pharmacokinetic modeling. In: Clewell HJ (ed) Pharmaceutical sciences encyclopaedia: drug discovery, development, and manufacturing, vol 15. Wiley, Hoboken, pp 1–62. https://doi.org/10.1002/9780470571224.pse065

    Chapter  Google Scholar 

  29. Clewell HJ III, Andersen ME, Wills RJ, Latriano L (1997) A physiologically based pharmacokinetic model for retinoic acid and its metabolites. J Am Acad Dermatol 36(3):S77–S85. https://doi.org/10.1016/s0190-9622(97)70063-x

    Article  PubMed  Google Scholar 

  30. Zhao P, Rowland M, Huang SM (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92(1):17–20. https://doi.org/10.1038/clpt.2012.68

    Article  CAS  PubMed  Google Scholar 

  31. Leong R, Vieira ML, Zhao P, Mulugeta Y, Lee CS, Huang SM et al (2012) Regulatory experience with physiologically based pharmacokinetic modeling for pediatric drug trials. Clin Pharmacol Ther 91(5):926–931. https://doi.org/10.1038/clpt.2012.19

    Article  CAS  PubMed  Google Scholar 

  32. Huang SM, Rowland M (2012) The role of physiologically based pharmacokinetic modeling in regulatory review. Clin Pharmacol Ther 91(3):542–549. https://doi.org/10.1038/clpt.2011.320

    Article  CAS  PubMed  Google Scholar 

  33. Pan Y, Grillo J, Hsu V, Zhang L, Sinha V, Huang SM et al (2014) Application of the FDA PBPK knowledgebase in evaluating model predictability for drug-drug interactions. Clin Pharmacol Ther 95(1)

    Google Scholar 

  34. Sinha V, Zhao P, Huang SM, Zineh I (2014) Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy. Clin Pharmacol Ther 95(5):478–480. https://doi.org/10.1038/clpt.2014.46

    Article  CAS  PubMed  Google Scholar 

  35. Grimstein M, Yang Y, Zhang X, Grillo J, Huang SM, Zineh I et al (2019) Physiologically based pharmacokinetic modeling in regulatory science: an update from the US Food and Drug Administration’s Office of Clinical Pharmacology. J Pharm Sci 108(1):21–25. https://doi.org/10.1016/j.xphs.2018.10.033

    Article  CAS  PubMed  Google Scholar 

  36. US Food and Drug Administration (2019) Impact story: supporting drug development through physiologically based pharmacokinetic modeling. https://www.fda.gov/drugs/regulatory-science-action/impact-story-supporting-drug-development-through-physiologically-based-pharmacokinetic-modeling. Accessed 5 Apr 2020

  37. Center for Drug Evaluation and Research (CDER) (2018) Guidance for industry: physiologically based pharmacokinetic analyses—format and content. U.S. Food and Drug Administration, Silver Spring

    Google Scholar 

  38. Committee for Medicinal Products for Human Use (CHMP) (2018) Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. EMA/CHMP/458101/2016. European Medicines Agency, London

    Google Scholar 

  39. Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel A et al (2018) Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: a consortium perspective. Clin Pharmacol Ther 104(1):88–110. https://doi.org/10.1002/cpt.1013

    Article  PubMed  PubMed Central  Google Scholar 

  40. Peters SA, Ungell AL, Dolgos H (2009) Physiologically based pharmacokinetic (PBPK) modeling and simulation: applications in lead optimization. Curr Opin Drug Discov Devel 12(4):509–518. PMID: 19562647

    CAS  PubMed  Google Scholar 

  41. Parrott N, Jones H, Paquereau N, Lavé T (2005) Application of full physiological models for pharmaceutical drug candidate selection and extrapolation of pharmacokinetics to man. Basic Clin Pharmacol Toxicol 96(3):193–199. https://doi.org/10.1111/j.1742-7843.2005.pto960308.x

    Article  CAS  PubMed  Google Scholar 

  42. Peters SA (2008) Evaluation of a generic physiologically based pharmacokinetic model for line-shape analysis. Clin Pharmacokinet 47(4):261–275. https://doi.org/10.2165/00003088-200847040-00004

    Article  CAS  PubMed  Google Scholar 

  43. Thiel C, Schneckener S, Krauss M, Ghallab A, Hofmann U, Kanacher T et al (2015) A systematic evaluation of the use of physiologically based pharmacokinetic modeling for cross-species extrapolation. J Pharm Sci 104(1):191–206. https://doi.org/10.1002/jps.24214

    Article  CAS  PubMed  Google Scholar 

  44. Miller NA, Reddy MB, Heikkinen AT, Lukacova V, Parrott N (2019) Physiologically based pharmacokinetic modelling for first-in-human predictions: an updated model building strategy illustrated with challenging industry case studies. Clin Pharmacokinet 58(6):727–746. https://doi.org/10.1007/s40262-019-00741-9

    Article  CAS  PubMed  Google Scholar 

  45. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A (2007) Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 8(7):676–684. https://doi.org/10.2174/138920007782109733

    Article  CAS  PubMed  Google Scholar 

  46. Sugano K (2009) Introduction to computational oral absorption simulation. Expert Opin Drug Metab Toxicol 5(3):259–293. https://doi.org/10.1517/17425250902835506

    Article  CAS  PubMed  Google Scholar 

  47. Yeo KR, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A (2010) Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci 39(5):298–309. https://doi.org/10.1016/j.ejps.2009.12.002

    Article  CAS  Google Scholar 

  48. Chien JY, Mohutsky MA, Wrighton SA (2003) Physiological approaches to the prediction of drug-drug interactions in study populations. Curr Drug Metab 4(5):347–356. https://doi.org/10.2174/1389200033489307

    Article  CAS  PubMed  Google Scholar 

  49. Rostami-Hodjegan A, Tucker G (2004) ‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug-drug interactions. Drug Discov Today Technol 1(4):441–448. https://doi.org/10.1016/j.ddtec.2004.10.002

    Article  CAS  PubMed  Google Scholar 

  50. Anderson BJ, Holford NH (2013) Understanding dosing: children are small adults, neonates are immature children. Arch Dis Child 98(9):737–744. https://doi.org/10.1136/archdischild-2013-303720

    Article  PubMed  Google Scholar 

  51. Jong GT (2014) Pediatric development: physiology. Enzymes, drug metabolism, pharmacokinetics and pharmacodynamics. In: Shalom DB (ed) Pediatric formulations: a roadmap. Springer, Boston, pp 9–23. https://doi.org/10.1007/978-1-4899-8011-3

    Chapter  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  53. Johnson TN, Tucker GT, Tanner MS, Rostami-Hodjegan A (2005) Changes in liver volume from birth to adulthood: a meta-analysis. Liver Transpl 11(12):1481–1493. https://doi.org/10.2165/00003088-200645090-00005

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  55. Butler JM, Begg EJ (2008) Free drug metabolic clearance in elderly people. Clin Pharmacokinet 47(5):297–321. https://doi.org/10.2165/00003088-200847050-00002

    Article  CAS  PubMed  Google Scholar 

  56. Kusama M, Maeda K, Chiba K, Aoyama A, Sugiyama Y (2009) Prediction of the effects of genetic polymorphism on the pharmacokinetics of CYP2C9 substrates from in vitro data. Pharm Res 26(4):822. https://doi.org/10.1007/s11095-008-9781-2

    Article  CAS  PubMed  Google Scholar 

  57. Jornil J, Jensen KG, Larsen F, Linnet K (2010) Identification of cytochrome P450 isoforms involved in the metabolism of paroxetine and estimation of their importance for human paroxetine metabolism using a population-based simulator. Drug Metab Dispos 38(3):376–385. https://doi.org/10.1124/dmd.109.030551

    Article  CAS  PubMed  Google Scholar 

  58. Edginton AN, Willmann S (2008) Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet 47(11):743–752. https://doi.org/10.2165/00003088-200847110-00005

    Article  PubMed  Google Scholar 

  59. Johnson TN, Boussery K, Rowland-Yeo K, Tucker GT, Rostami-Hodjegan A (2010) A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance. Clin Pharmacokinet 49(3):189–206. https://doi.org/10.2165/11318160-000000000-00000

    Article  CAS  PubMed  Google Scholar 

  60. Nolin TD, Naud J, Leblond FA, Pichette V (2008) Emerging evidence of the impact of kidney disease on drug metabolism and transport. Clin Pharmacol Ther 83(6):898–903. https://doi.org/10.1038/clpt.2008.59

    Article  CAS  PubMed  Google Scholar 

  61. Schmith VD, Foss JF (2010) Inflammation: planning for a source of pharmacokinetic/pharmacodynamic variability in translational studies. Clin Pharmacol Ther 87(4):488–491. https://doi.org/10.1038/clpt.2009.258

    Article  CAS  PubMed  Google Scholar 

  62. Hanley MJ, Abernethy DR, Greenblatt DJ (2010) Effect of obesity on the pharmacokinetics of drugs in humans. Clin Pharmacokinet 49(2):71–87. https://doi.org/10.2165/11318100-000000000-00000

    Article  CAS  PubMed  Google Scholar 

  63. Rasool MF, Khalid S, Majeed A, Saeed H, Imran I, Mohany M, Al-Rejaie SS et al (2019) Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting rifampicin exposure in tuberculosis and cirrhosis populations. Pharmaceutics 11(11):578. https://doi.org/10.3390/pharmaceutics11110578

    Article  CAS  PubMed Central  Google Scholar 

  64. You X, Wu W, Xu J, Jiao Z, Ke M, Huang P, Lin C (2020) Development of a physiologically based pharmacokinetic model for prediction of pramipexole pharmacokinetics in Parkinson’s disease patients with renal impairment. J Clin Pharmacol 60(8):999–1010. https://doi.org/10.1002/jcph.1593

    Article  CAS  PubMed  Google Scholar 

  65. Center for Drug Evaluation and Research (CDER) (2002) Guidance for industry: food-effect bioavailability and fed bioequivalence studies. U.S. Food and Drug Administration, Silver Spring

    Google Scholar 

  66. Tistaert C, Heimbach T, Xia B, Parrott N, Samant TS, Kesisoglou F (2019) Food effect projections via physiologically based pharmacokinetic modeling: predictive case studies. J Pharm Sci 108(1):592–602. https://doi.org/10.1016/j.xphs.2018.05.024

    Article  CAS  PubMed  Google Scholar 

  67. Li M, Zhao P, Pan Y, Wagner C (2018) Predictive performance of physiologically based pharmacokinetic models for the effect of food on oral drug absorption: current status. CPT Pharmacometrics Syst Pharmacol 7(2):82–89. https://doi.org/10.1002/psp4.12260

    Article  CAS  PubMed  Google Scholar 

  68. Abduljalil K, Jamei M, Rostami-Hodjegan A, Johnson TN (2014) Changes in individual drug-independent system parameters during virtual paediatric pharmacokinetic trials: introducing time-varying physiology into a paediatric PBPK model. AAPS J 16(3):568–576. https://doi.org/10.1208/s12248-014-9592-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Gaohua L, Wedagedera J, Small BG, Almond L, Romero K, Hermann D, Hanna D et al (2015) Development of a multi-compartment permeability-limited lung PBPK model and its application in predicting pulmonary pharmacokinetics of anti-tuberculosis drugs. CPT Pharmacometrics Syst Pharmacol 4(10):605–613. https://doi.org/10.1002/psp4.12034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Verscheijden LF, Koenderink JB, de Wildt SN, Russel FG (2019) Development of a physiologically-based pharmacokinetic pediatric brain model for prediction of cerebrospinal fluid drug concentrations and the influence of meningitis. PLoS Comput Biol 15(6):e1007117. https://doi.org/10.1371/journal.pcbi.1007117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhou L, Tong X, Sharma P, Xu H, Al-Huniti N, Zhou D (2019) Physiologically based pharmacokinetic modelling to predict exposure differences in healthy volunteers and subjects with renal impairment: ceftazidime case study. Basic Clin Pharmacol Toxicol 125(2):100–107. https://doi.org/10.1111/bcpt.13209

    Article  CAS  PubMed  Google Scholar 

  72. Loisios-Konstantinidis I, Cristofoletti R, Fotaki N, Turner DB, Dressman J (2020) Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. Case example: naproxen. Eur J Pharm Sci 143:105170. https://doi.org/10.1016/j.ejps.2019.105170

    Article  CAS  PubMed  Google Scholar 

  73. Khot A, Tibbitts J, Rock D, Shah DK (2017) Development of a translational physiologically based pharmacokinetic model for antibody-drug conjugates: a case study with T-DM1. AAPS J 19(6):1715–1734. https://doi.org/10.1208/s12248-017-0131-3

    Article  CAS  PubMed  Google Scholar 

  74. Nestorov IA, Aarons LJ, Arundel PA, Rowland M (1998) Lumping of whole-body physiologically based pharmacokinetic models. J Pharmacokinet Biopharm 26(1):21–46. https://doi.org/10.1023/a:1023272707390

    Article  CAS  PubMed  Google Scholar 

  75. Jones HM, Parrott N, Jorga K, Lavé T (2006) A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 45(5):511–542. https://doi.org/10.2165/00003088-200645050-00006

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  77. Nestorov I (2003) Whole body pharmacokinetic models. Clin Pharmacokinet 42(10):883–908. https://doi.org/10.2165/00003088-200342100-00002

    Article  CAS  PubMed  Google Scholar 

  78. Harwood MD, Neuhoff S, Carlson GL, Warhurst G, Rostami-Hodjegan A (2013) Absolute abundance and function of intestinal drug transporters: a prerequisite for fully mechanistic in vitro-in vivo extrapolation of oral drug absorption. Biopharm Drug Dispos 34(1):2–8. https://doi.org/10.1002/bdd.1810

    Article  CAS  PubMed  Google Scholar 

  79. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA et al (2015) Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther 97(3):247–262. https://doi.org/10.1002/cpt.37

    Article  CAS  PubMed  Google Scholar 

  80. Edginton AN, Joshi G (2011) Have physiologically-based pharmacokinetic models delivered? Expert Opin Drug Metab Toxicol 7(8):929–934. https://doi.org/10.1517/17425255.2011.585968

    Article  CAS  PubMed  Google Scholar 

  81. Varma MV, Lai Y, Feng B, Litchfield J, Goosen TC, Bergman A (2012) Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions. Pharm Res 29(10):2860–2873. https://doi.org/10.1007/s11095-012-0792-7

    Article  CAS  PubMed  Google Scholar 

  82. Jones HM, Barton HA, Lai Y, Bi YA, Kimoto E, Kempshall S (2012) Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos 40(5):1007–1017. https://doi.org/10.1124/dmd.111.042994

    Article  CAS  PubMed  Google Scholar 

  83. Chu X, Korzekwa K, Elsby R, Fenner K, Galetin A, Lai Y (2013) Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver. Clin Pharmacol Ther 94(1):126–141. https://doi.org/10.1038/clpt.2013.78

    Article  CAS  PubMed  Google Scholar 

  84. Chetty M, Li L, Rose R, Machavaram K, Jamei M, Rostami-Hodjegan A (2015) Prediction of the pharmacokinetics, pharmacodynamics, and efficacy of a monoclonal antibody, using a physiologically based pharmacokinetic FcRn model. Front Immunol 5:670. https://doi.org/10.3389/fimmu.2014.00670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Rostami-Hodjegan A, Tamai I, Pang KS (2012) Physiologically based pharmacokinetic (PBPK) modeling: it is here to stay! Biopharm Drug Dispos 33(2):47–50. https://doi.org/10.1002/bdd.1776

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab, India

    Ankit Balhara, Sumeet Kale & Saranjit Singh

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  1. Department of Pharmaceutics, School of Pharmaceutical Sciences and Technology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India

    Vikas Anand Saharan

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Balhara, A., Kale, S., Singh, S. (2022). Physiologically Based Pharmacokinetic (PBPK) Modelling. In: Saharan, V.A. (eds) Computer Aided Pharmaceutics and Drug Delivery. Springer, Singapore. https://doi.org/10.1007/978-981-16-5180-9_9

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