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Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

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Abstract

Purpose

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration–time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

Methods

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

Results

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug–drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

Conclusion

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

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References

  1. 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. https://doi.org/10.1124/dmd.113.052803

    Article  CAS  Google Scholar 

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

    Google Scholar 

  3. Rowland M, Lesko LJ, Rostami-Hodjegan A (2015) Physiologically based pharmacokinetics is impacting drug development and regulatory decision making. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1002/psp4.52

    CAS  Google Scholar 

  4. Theil FP, Guentert TW, Haddad S, Poulin P (2003) Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection. Toxicol Lett. https://doi.org/10.1016/S0378-4274(02)00374-0

    Article  CAS  Google Scholar 

  5. Poggesi I, Snoeys J, Van Peer A (2014) The successes and failures of physiologically based pharmacokinetic modeling: there is room for improvement. Expert Opin Drug Metab Toxicol. https://doi.org/10.1517/17425255.2014.888058

    Article  CAS  Google Scholar 

  6. Abuqayyas L, Balthasar JP (2012) Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer. J Pharmacokinet Pharmacodyn. https://doi.org/10.1007/s10928-012-9279-8

    Article  CAS  PubMed Central  Google Scholar 

  7. Brandhonneur N, Noury F, Bruyère A, Saint-Jalmes H, Le Corre P (2016) PBPK model of methotrexate in cerebrospinal fluid ventricles using a combined microdialysis and MRI acquisition. Eur J Pharm Biopharm. https://doi.org/10.1016/j.ejpb.2016.04.012

    Article  CAS  Google Scholar 

  8. Gustafson DL, Thamm DH (2010) Pharmacokinetic modeling of doxorubicin pharmacokinetics in dogs deficient in ABCB1 drug transporters. J Vet Intern Med. https://doi.org/10.1111/j.1939-1676.2010.0496.x

    Article  CAS  Google Scholar 

  9. Bradshaw-Pierce EL, Steinhauer CA, Raben D, Gustafson DL (2008) Pharmacokinetic-directed dosing of vandetanib and docetaxel in a mouse model of human squamous cell carcinoma. Mol Cancer Ther. https://doi.org/10.1158/1535-7163.MCT-08-0370

    Article  CAS  PubMed Central  Google Scholar 

  10. Bradshaw-Pierce EL, Eckhardt SG, Gustafson DL (2007) A physiologically based pharmacokinetic model of docetaxel disposition: from mouse to man. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-06-2362

    Article  CAS  Google Scholar 

  11. Shimizu M, Suemizu H, Mitsui M, Shibata N, Guengerich FP, Yamazaki H (2017) Metabolic profiles of pomalidomide in human plasma simulated with pharmacokinetic data in control and humanized-liver mice. Xenobiotica. https://doi.org/10.1080/00498254.2016.1247218

    Article  PubMed Central  Google Scholar 

  12. Chen Y, Zhao K, Liu F, Xie Q, Zhong X, Miao X, Liu X, Liu L (2016) Prediction of deoxypodophyllotoxin disposition in mouse, rat, monkey, and dog by physiologically based pharmacokinetic model and the extrapolation to human. Front Pharmacol. https://doi.org/10.3389/fphar.2016.00488

  13. Takahashi RH, Choo EF, Ma S, Wong S, Halladay J, Deng Y, Rooney I, Gates M, Hop CE, Khojasteh SC, Dresser MJ, Musib L (2016) Absorption, metabolism, excretion, and the contribution of intestinal metabolism to the oral disposition of [14C]cobimetinib, a MEK inhibitor, in humans. Drug Metab Dispos. https://doi.org/10.1124/dmd.115.066282

    Article  Google Scholar 

  14. Bi Y, Deng J, Murry DJ, An G A (2016) whole-body physiologically based pharmacokinetic model of gefitinib in mice and scale-up to humans. AAPS J. https://doi.org/10.1208/s12248-015-9836-3

    Article  PubMed Central  Google Scholar 

  15. Hu ZY, Lu J, Zhao Y (2014) A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans. Br J Pharmacol. https://doi.org/10.1111/bph.12609

    Article  CAS  PubMed Central  Google Scholar 

  16. Hudachek SF, Gustafson DL (2011) Customized in silico population mimics actual population in docetaxel population pharmacokinetic analysis. J Pharm Sci. https://doi.org/10.1002/jps.22322

    Article  CAS  Google Scholar 

  17. Kletting P, Kull T, Bunjes D, Mahren B, Luster M, Reske SN, Glatting G (2010) Radioimmunotherapy with anti-CD66 antibody: improving the biodistribution using a physiologically based pharmacokinetic model. J Nucl Med. https://doi.org/10.2967/jnumed.109.067546

    Article  CAS  Google Scholar 

  18. Hardiansyah D, Maass C, Attarwala AA, Müller B, Kletting P, Mottaghy FM, Glatting G (2016) The role of patient-based treatment planning in peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-015-3248-6

    Article  Google Scholar 

  19. Xia B, Heimbach T, Lin TH, He H, Wang Y, Tan E (2012) Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions. Cancer Chemother Pharmacol. https://doi.org/10.1007/s00280-012-1863-5

    Article  CAS  Google Scholar 

  20. Diestelhorst C, Boos J, McCune JS, Russell J, Kangarloo SB, Hempel G (2013) Physiologically based pharmacokinetic modelling of busulfan: a new approach to describe and predict the pharmacokinetics in adults. Cancer Chemother Pharmacol. https://doi.org/10.1007/s00280-013-2275-x

    Article  CAS  Google Scholar 

  21. Lu XF, Bi K, Chen X (2016) Physiologically based pharmacokinetic model of docetaxel and interspecies scaling: comparison of simple injection with folate receptor-targeting amphiphilic copolymer-modified liposomes. Xenobiotica. https://doi.org/10.3109/00498254.2016.1155128

    Article  CAS  Google Scholar 

  22. Glassman PM, Balthasar JP (2017) Physiologically-based modeling to predict the clinical behavior of monoclonal antibodies directed against lymphocyte antigens. MAbs. https://doi.org/10.1080/19420862.2016.1261775

    Article  PubMed Central  Google Scholar 

  23. Glassman PM, Balthasar JP (2016) Physiologically-based pharmacokinetic modeling to predict the clinical pharmacokinetics of monoclonal antibodies. J Pharmacokinet Pharmacodyn. https://doi.org/10.1007/s10928-016-9482-0

    Article  Google Scholar 

  24. Tan W, Yamazaki S, Johnson TR, Wang R, O’Gorman MT, Kirkovsky L, Boutros T, Brega NM, Bello A (2017) Effects of renal function on crizotinib pharmacokinetics: dose recommendations for patients with ALK-positive non-small cell lung cancer. Clin Drug Investig. https://doi.org/10.1007/s40261-016-0490-z

    Article  Google Scholar 

  25. Lu C, Suri A, Shyu WC, Prakash S (2014) Assessment of cytochrome P450-mediated drug–drug interaction potential of orteronel and exposure changes in patients with renal impairment using physiologically based pharmacokinetic modeling and simulation. Biopharm Drug Dispos. https://doi.org/10.1002/bdd.1919

    Article  CAS  Google Scholar 

  26. Hudachek SF, Gustafson DL (2013) Physiologically based pharmacokinetic model of lapatinib developed in mice and scaled to humans. J Pharmacokinet Pharmacodyn. https://doi.org/10.1007/s10928-012-9295-8

    Article  CAS  PubMed Central  Google Scholar 

  27. Ogungbenro K, Aarons L, CRESim & Epi-CRESim Project Groups (2015) Physiologically based pharmacokinetic model for 6-mercpatopurine: exploring the role of genetic polymorphism in TPMT enzyme activity. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.12588

    Article  CAS  PubMed Central  Google Scholar 

  28. Walsh C, Bonner JJ, Johnson TN, Neuhoff S, Ghazaly EA, Gribben JG, Boddy AV, Veal GJ (2016) Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.12878

    Article  CAS  PubMed Central  Google Scholar 

  29. Thai HT, Mazuir F, Cartot-Cotton S, Veyrat-Follet C (2015) Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.12702

    Article  CAS  PubMed Central  Google Scholar 

  30. Diestelhorst C, Boos J, McCune JS, Russell J, Kangarloo SB, Hempel G (2014) Predictive performance of a physiologically based pharmacokinetic model of busulfan in children. Pediatr Hematol Oncol. https://doi.org/10.3109/08880018.2014.927945

    Article  CAS  Google Scholar 

  31. Narayanan R, Hoffmann M, Kumar G, Surapaneni S (2016) Application of a “fit for purpose” PBPK model to investigate the CYP3A4 induction potential of enzalutamide. Drug Metab Lett 10(3):172–179

    Article  CAS  Google Scholar 

  32. Filppula AM, Neuvonen M, Laitila J, Neuvonen PJ, Backman JT (2013) Autoinhibition of CYP3A4 leads to important role of CYP2C8 in imatinib metabolism: variability in CYP2C8 activity may alter plasma concentrations and response. Drug Metab Dispos. https://doi.org/10.1124/dmd.112.048017

    Article  Google Scholar 

  33. Mao J, Johnson TR, Shen Z, Yamazaki S (2013) Prediction of crizotinib-midazolam interaction using the Simcyp population-based simulator: comparison of CYP3A time-dependent inhibition between human liver microsomes versus hepatocytes. Drug Metab Dispos. https://doi.org/10.1124/dmd.112.049114

    Article  Google Scholar 

  34. Posada MM, Bacon JA, Schneck KB, Tirona RG, Kim RB, Higgins JW, Pak YA, Hall SD, Hillgren KM (2015) Prediction of renal transporter mediated drug-drug interactions for pemetrexed using physiologically based pharmacokinetic modeling. Drug Metab Dispos. https://doi.org/10.1124/dmd.114.059618

    Article  Google Scholar 

  35. Freise KJ, Shebley M, Salem AH (2017) Quantitative prediction of the effect of CYP3A inhibitors and inducers on venetoclax pharmacokinetics using a physiologically based pharmacokinetic model. J Clin Pharmacol. https://doi.org/10.1002/jcph.858

    Article  CAS  Google Scholar 

  36. de Zwart L, Snoeys J, De Jong J, Sukbuntherng J, Mannaert E, Monshouwer M (2016) Ibrutinib dosing strategies based on interaction potential of CYP3A4 perpetrators using physiologically based pharmacokinetic modeling. Clin Pharmacol Ther. https://doi.org/10.1002/cpt.419

    Article  CAS  Google Scholar 

  37. Xu Y, Hijazi Y, Wolf A, Wu B, Sun YN, Zhu M (2015) Physiologically based pharmacokinetic model to assess the influence of blinatumomab-mediated cytokine elevations on cytochrome P450 enzyme activity. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1002/psp4.12003

    CAS  Google Scholar 

  38. Chen Y, Samineni D, Mukadam S, Wong H, Shen BQ, Lu D, Girish S, Hop C, Jin JY, Li C (2015) Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody–drug conjugates. Clin Pharmacokinet. https://doi.org/10.1007/s40262-014-0182-x

    Article  CAS  Google Scholar 

  39. Yu Y, Loi CM, Hoffman J, Wang D (2017) Physiologically based pharmacokinetic modeling of palbociclib. J Clin Pharmacol. https://doi.org/10.1002/jcph.792

    Article  Google Scholar 

  40. Dickschen K, Willmann S, Thelen K, Lippert J, Hempel G, Eissing T (2012) Physiologically based pharmacokinetic modeling of tamoxifen and its metabolites in women of different CYP2D6 phenotypes provides new insight into the tamoxifen mass balance. Front Pharmacol. https://doi.org/10.3389/fphar.2012.00092

  41. Shi JG, Fraczkiewicz G, Williams WV, Yeleswaram S (2015) Predicting drug-drug interactions involving multiple mechanisms using physiologically based pharmacokinetic modeling: a case study with ruxolitinib. Clin Pharmacol Ther. https://doi.org/10.1002/cpt.30

    Article  Google Scholar 

  42. Yamazaki S, Johnson TR, Smith BJ (2015) Prediction of drug-drug interactions with crizotinib as the CYP3A substrate using a physiologically based pharmacokinetic model. Drug Metab Dispos. https://doi.org/10.1124/dmd.115.064618

    Article  CAS  Google Scholar 

  43. Gufford BT, Barr JT, González-Pérez V, Layton ME, White JR Jr, Oberlies NH, Paine MF (2015) Quantitative prediction and clinical evaluation of an unexplored herb-drug interaction mechanism in healthy volunteers. CPT Pharmacometr Syst Pharmacol. https://doi.org/10.1002/psp4.12047

    CAS  Google Scholar 

  44. Dickschen K, Eissing T, Mürdter T, Schwab M, Willmann S, Hempel G (2014) Concomitant use of tamoxifen and endoxifen in postmenopausal early breast cancer: prediction of plasma levels by physiologically-based pharmacokinetic modeling. Springerplus. https://doi.org/10.1186/2193-1801-3-285

    Article  PubMed Central  Google Scholar 

  45. Budha NR, Ji T, Musib L, Eppler S, Dresser M, Chen Y, Jin JY (2016) Evaluation of cytochrome P450 3A4-mediated drug-drug interaction potential for cobimetinib using physiologically based pharmacokinetic modeling and simulation. Clin Pharmacokinet. https://doi.org/10.1007/s40262-016-0412-5

    Article  CAS  Google Scholar 

  46. Hartmanshenn C, Scherholz M, Androulakis IP (2016) Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. J Pharmacokinet Pharmacodyn. https://doi.org/10.1007/s10928-016-9492-y

    Article  CAS  PubMed Central  Google Scholar 

  47. Yoshida K, Budha N, Jin JY (2017) Impact of physiologically based pharmacokinetic models on regulatory reviews and product labels: frequent utilization in the field of oncology. Clin Pharmacol Ther. https://doi.org/10.1002/cpt.622

    Article  CAS  Google Scholar 

  48. U.S. Food and Drug Administration (FDA) (2016) Physiologically based pharmacokinetic analyses-format and content guidance for industry. Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research at the Food and Drug Administration, Silver Spring. https://www.fda.gov/downloads/Drugs/GuidanceCOmplianceRegulatoryInformation/Guidances/UCM531207.pdf. Accessed 30 Dec 2016

  49. European Medicine Agency (EMA) (2016) Guidance for quantification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation (draft guidance). The committee for medicinal products for human use (CHMP). http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/clinical_pharmacology_pharmacokinetics/general_content_001729.jsp&mid=WC0b01ac0580032ec5. Accessed 30 Dec 2016

  50. Chen HG, Gross JF (1979) Physiologically based pharmacokinetic models for anticancer drugs. Cancer Chemother Pharmacol 2(2):85–94

    Article  CAS  Google Scholar 

  51. Rioux N, Waters NJ (2016) Physiologically based pharmacokinetic modeling in pediatric oncology drug development. Drug Metab Dispos. https://doi.org/10.1124/dmd.115.068031

    Article  CAS  Google Scholar 

  52. Block M (2015) Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps. Expert Opin Drug Metab Toxicol. https://doi.org/10.1517/17425255.2015.1037276

    Article  CAS  Google Scholar 

  53. Isoherranen N (2018) How should a PBPK model be evaluated for intended use? Drug Metab Pharmacokinetics. https://doi.org/10.1016/j.dmpk.2017.11.040

    Article  Google Scholar 

  54. 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. https://doi.org/10.1124/dmd.115.065920

    Article  CAS  PubMed Central  Google Scholar 

  55. Urva SR, Yang VC, Balthasar JP (2010) Physiologically based pharmacokinetic model for T84.66: a monoclonal anti-CEA antibody. J Pharm Sci. https://doi.org/10.1002/jps.21918

    Article  CAS  Google Scholar 

  56. Poulin P, Jones RD, Jones HM, Gibson CR, Rowland M, Chien JY, Ring BJ, Adkison KK, Ku MS, He H, Vuppugalla R, Marathe P, Fischer V, Dutta S, Sinha VK, Björnsson T, Lavé T, Yates JW (2011) PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J Pharm Sci. https://doi.org/10.1002/jps.22550

    Article  CAS  Google Scholar 

  57. Zou P, Yu Y, Zheng N, Yang Y, Paholak HJ, Yu LX, Sun D (2012) Applications of human pharmacokinetic prediction in first-in-human dose estimation. AAPS J. https://doi.org/10.1208/s12248-012-9332-y

    Article  PubMed Central  Google Scholar 

  58. Wong H, Chow TW (2017) Physiologically based pharmacokinetic modeling of therapeutic proteins. J Pharm Sci. https://doi.org/10.1016/j.xphs.2017.03.038

    Article  CAS  Google Scholar 

  59. Tsukamoto Y, Kato Y, Ura M, Horii I, Ishitsuka H, Kusuhara H, Sugiyama Y (2001) A physiologically based pharmacokinetic analysis of capecitabine, a triple prodrug of 5-FU, in humans: the mechanism for tumor-selective accumulation of 5-FU. Pharm Res 18(8):1190–1202

    Article  CAS  Google Scholar 

  60. Huh Y, Smith DE, Feng MR (2011) Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs. Xenobiotica. https://doi.org/10.3109/00498254.2011.598582

    Article  CAS  PubMed Central  Google Scholar 

  61. Deng R, Iyer S, Theil FP, Mortensen DL, Fielder PJ, Prabhu S (2011) Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: what have we learned? MAbs. https://doi.org/10.4161/mabs.3.1.13799

    Article  PubMed Central  Google Scholar 

  62. Mangoni AA, Jackson SH (2004) Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol. https://doi.org/10.1046/j.1365-2125.2003.02007.x

    Article  Google Scholar 

  63. Herrington JD, Tran HT, Riggs MW (2006) Prospective evaluation of carboplatin AUC dosing in patients with a BMI> or = 27 or cachexia. Cancer Chemother Pharmacol. https://doi.org/10.1007/s00280-005-0012-9

    Article  Google Scholar 

  64. Fearon KC, Preston T (1990) Body composition in cancer cachexia. Infusiontherapie 17(3):63–66

    Google Scholar 

  65. Cova D, Lorusso V, Silvestris N (2006) The pharmacokinetics and pharmacodynamics of drugs in elderly cachectic (Cancer) patients. In: Mantovani G, Anker SD, Inui A, Morley JE, Rossi Fanelli F, Scevola D, Schuster MW, Yeh S-S (eds) Cachexia and wasting: a modern approach. Springer, Milan, pp 377–382

  66. Skirvin JA, Lichman SM (2002) Pharmacokinetic considerations of oral chemotherapy in elderly patients with cancer. Drugs Aging 19(1):25–42

    Article  CAS  Google Scholar 

  67. European Medicine Agency (EMA), 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. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003122.pdf. Accessed 17 Mar 2017

  68. European Medicine Agency (EMA), Committee for Medicinal Products for Human Use (CHMP) (2014) Guideline on the evaluation of the pharmacokinetics of medicinal products in patients with decreased renal function. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/02/WC500162133.pdf. Accessed 17 Mar 2017

  69. Launay-Vacher V, Oudard S, Janus N, Gligorov J, Pourrat X, Morere JF RO, Beuzeboc P, Deray G, Renal Insufficiency and Cancer Medications (IRMA) Study Group (2007) Prevalence of renal insufficiency in cancer patients and implications for anticancer drug management: the renal insufficiency and anticancer medications (IRMA) study. Cancer. https://doi.org/10.1002/cncr.22904

    Article  CAS  Google Scholar 

  70. Dahab AA, El-Hag D, Moutamed GM, Dahab SA, Abuknesha R, Smith NW (2016) Pharmacokinetic variations in cancer patients with liver dysfunction: applications and challenges of pharmacometabolomics. Cancer Chemother Pharmacol. https://doi.org/10.1007/s00280-016-3028-4

    Article  CAS  Google Scholar 

  71. Xiao JJ, Chen JS, Lum BL, Graham RA (2017) A survey of renal impairment pharmacokinetic studies for new oncology drug approvals in the USA from 2010 to early 2015: a focus on development strategies and future directions. Anti-Cancer Drugs. https://doi.org/10.1097/CAD.0000000000000513

    Article  CAS  PubMed Central  Google Scholar 

  72. Humphreys BD, Soiffer RJ, Magee CC (2005) Renal failure associated with cancer and its treatment: an update. J Am Soc Nephrol. https://doi.org/10.1681/ASN.2004100843

    Article  Google Scholar 

  73. Verbeeck RK (2008) Pharmacokinetics and dosage adjustment in patients with hepatic dysfunction. Eur J Clin Pharmacol. https://doi.org/10.1007/s00228-008-0553-z

    Article  CAS  Google Scholar 

  74. Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, Sinha V (2015) Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: report of an FDA public workshop on PBPK. CPT Pharmaco Syst Pharmacol. https://doi.org/10.1002/psp4.33

    CAS  Google Scholar 

  75. Shepard T, Scott G, Cole S, Nordmark S, Bouzom F (2015) Physiologically based models in regulatory submissions: output from the ABPI/MHRA forum on physiologically based modeling and simulation. CPT Pharmaco Syst Pharmacol. https://doi.org/10.1002/psp4.30

    CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Clinical Product Development, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan

    Teerachat Saeheng & Juntra Karbwang

  2. Leading Program, Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan

    Teerachat Saeheng

  3. Chulabhorn International College of Medicine, Thammasat University, Pathumthani, 12121, Thailand

    Kesara Na-Bangchang

  4. Center of Excellence in Pharmacology and Molecular Biology of Malaria and Cholangiocarcinoma, Chulabhorn International College of Medicine, Thammasat University, Pathumthani, 12121, Thailand

    Kesara Na-Bangchang

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  1. Teerachat Saeheng
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TS was responsible for data collection, selection of the published articles, data analysis and data interpretation and prepared the draft manuscript. KN and JK were responsible for the selection of the published articles, concept of data analysis, data interpretation and revision of the manuscript.

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Correspondence to Juntra Karbwang.

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Teerachat Saeheng, Kesara Na-Bangchang and Juntra Karbwang have no conflicts of interest in this review.

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Saeheng, T., Na-Bangchang, K. & Karbwang, J. Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review. Eur J Clin Pharmacol 74, 1365–1376 (2018). https://doi.org/10.1007/s00228-018-2513-6

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