Skip to main content
SpringerLink
Log in

Drug Exposure to Establish Pharmacokinetic–Response Relationships in Oncology

  • Review Article
  • Published:
Clinical Pharmacokinetics Aims and scope Submit manuscript

Abstract

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as ‘drivers’ for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients’ favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient’s exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1151–210.

    Article  Google Scholar 

  2. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62:10–29.

    Article  PubMed  Google Scholar 

  3. Kummar S, Gutierrez M, Doroshow JH, Murgo AJ. Drug development in oncology: classical cytotoxics and molecularly targeted agents. Br J Clin Pharmacol. 2006;62:15–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Standing JF. Understanding and applying pharmacometric modelling and simulation in clinical practice and research. Br J Clin Pharmacol. 2017;83(2):247–54.

    Article  PubMed  Google Scholar 

  5. Sheiner LB. Learning versus confirming in clinical drug development. Clin Pharmacol Ther. 1997;61:275–91.

    Article  CAS  PubMed  Google Scholar 

  6. Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, et al. impact of pharmacometric reviews on new drug approval and labeling decisions: a survey of 31 new drug applications submitted between 2005 and 2006. Clin Pharmacol Ther. 2007;81:213–21.

    Article  CAS  PubMed  Google Scholar 

  7. Lesko LJ. Paving the critical path: how can clinical pharmacology help achieve the vision? Clin Pharmacol Ther. 2007;81(2):170–7.

    Article  CAS  PubMed  Google Scholar 

  8. Barret JS, Labbé L, Pfister M. Application and impact of population pharmacokinetics in the assessment of antiretroviral pharmacotherapy. Clin Pharmacokinet. 2005;44:591–625.

    Article  Google Scholar 

  9. EFPIA MID3 Workgroup, Marshall SF, Burghaus R, Cosson V, Cheing SY, Chenel M, et al. Good practices in model-informed drug discovery and development: practice, application, and documentation. CPT Pharmacometrics Syst Pharmacol. 2016;5(3):93–122.

    Article  PubMed Central  CAS  Google Scholar 

  10. Bender BC, Schindler E, Friberg LE. Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response. Br J Clin Pharmacol. 2015;79(1):56–71.

    Article  CAS  PubMed  Google Scholar 

  11. Hryniuk W, Levine MN. Analysis of dose intensity for adjuvant chemotherapy trials in stage II breast cancer. J Clin Oncol. 1986;4:1162–70.

    Article  CAS  PubMed  Google Scholar 

  12. Hryniuk WM, Bush H. The importance of dose intensity in chemotherapy of metastatic breast cancer. J Clin Oncol. 1984;2:1281–7.

    Article  CAS  PubMed  Google Scholar 

  13. Gibialdi M, Levy G. Dose-dependent decline of pharmacologic effects of drugs with linear pharmacokinetics characteristics. J Pharm Sci. 1972;36:567–9.

    Article  Google Scholar 

  14. Verotta D, Sheiner LB. Semiparametric analysis of non-steady state pharmacodynamics. J Pharmacokinet Biopharm. 1991;19:691–712.

    Article  CAS  PubMed  Google Scholar 

  15. Gabrielsson J, Jusko WJ, Alari L. Modeling of dose-response-time data: four examples of estimating the turnover parameters and generating kinetic functions from response profiles. Biopharm Drug Dispos. 2000;21:41–52.

    Article  CAS  PubMed  Google Scholar 

  16. Jacqmin P, Snoeck E, van Schaick EA, Gieschke R, Pillai P, Steimer JL, et al. Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model. J Pharmacokinet Pharmacodyn. 2007;34(11):57–85.

    Article  CAS  PubMed  Google Scholar 

  17. Meljem M, Delor I, Pérez-Ruixo JJ, Harrold J, Chow A, Wu L, et al. Pharmacokinetic–pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia. Br J Clin Pharmacol. 2018;84(5):911–25.

    Article  CAS  Google Scholar 

  18. Frances N, Claret L, Bruno R, Iliadis A. Tumor growth modeling from clinical trials reveals synergistic anticancer effect of the capecitabine and docetaxel combination in metastatic breast cancer. Cancer Chemother Pharmacol. 2011;68(69):1413–9.

    Article  CAS  PubMed  Google Scholar 

  19. Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, et al. A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy. Clin Cancer Res. 2012;18:5071–80.

    Article  CAS  PubMed  Google Scholar 

  20. Solans BP, López-Díaz de Cerio A, Elizalde A, Pina LJ, Inogés S, Espinós J, et al. Assessing the impact of the addition of dendritic cell vaccination to neoadjuvant chemotherapy in breast cancer patients: a model-based characterization approach. Br J Clin Pharmacol. 2019;85:1670–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. de Vries Schultink AHM, Boekhout AH, Gietema JA, Burylo AM, Dorlo TPC, van Hasselt JGC, et al. Pharmacodynamic modeling of cardiac biomarkers in breast cancer patients treated with anthracycline and trastuzumab regimens. J Pharmacokinet Pharmacodyn. 2018;45:431–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Sostelly A, Payen L, Guitton J, Di Pietro A, Falson P, Honorat M, et al. Quantitative evaluation of the combination between cytotoxic drug and efflux transporter inhibitors based on a tumour growth inhibition model. Fundam Clin Pharmacol. 2014;28(2):161–9.

    Article  CAS  PubMed  Google Scholar 

  23. Ye SF, Li J, Ji SM, Zeng HH, Lu W. Dose-biomarker-response modeling of the anticancer effect of ethaselen in a human non-small cell lung cancer xenograft mouse model. Acta Pharmacol Sin. 2017;38(2):223–32.

    Article  CAS  PubMed  Google Scholar 

  24. Buil-Bruna N, López-Picazo JM, Moreno-Jiménez M, Martín-Algarra S, Ribba B, Trocóniz IF. A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients. AAPS J. 2014;16(3):609–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Radunskaya A, Kim R, Woods T. Mathematical modeling of tumor immune interactions: a closer look at the role of a PD-L1 inhibitor in cancer immunotherapy. Spora. 2018;4:23–41.

    Article  Google Scholar 

  26. Chigutsa E, Long AJ, Wallin JE. Exposure-response analysis of necitumumab efficacy in squamous non-small cell lung cancer patients. CPT Pharmacometrics Syst Pharmacol. 2017;6:560–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tate SC, Andre V, Enas N, Ribba B, Gueorguieva I. Early change in tumour size predicts overall survival in patients with first-line metastatic breast cancer. Eur J Cancer. 2016;66:95–103.

    Article  PubMed  Google Scholar 

  28. Mangas-Sanjuan V, Buil-Bruna N, Garrido MJ, Soto E, Trocóniz IF. Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules. J Pharmacol Exp Ther. 2015;354:55–64.

    Article  CAS  PubMed  Google Scholar 

  29. Ouerdani A, Goutagny S, Kalamarides M, Trocóniz IF, Ribba B. Mechanism-based modeling of the clinical effects of bevacizumab and everolimus on vestibular schwannomas of patients with neurofibromatosis type 2. Cancer Chemother Pharmacol. 2016;77:1263–73.

    Article  CAS  PubMed  Google Scholar 

  30. Netterberg I, Karlsson MO, Nielsen EI, Quartino AL, Lindman H, Friberg LE. The risk of febrile neutropenia in breast cancer patients following adjuvant chemotherapy is predicted by the time course of interleukin-6 and C-reactive protein by modelling. Br J Clin Pharmacol. 2018;84:490–500.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Quartino AL, Li H, Jin JY, Wada DR, Benyunes MC, McNally V, et al. Pharmacokinetic and exposure-response analyses of pertuzumab in combination with trastuzumab and docetaxel during neoadjuvant treatment of HER2 + early breast cancer. Cancer Chemother Pharmacol. 2017;79(2):353–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang L, Beal SL, Sheiner LB. Simultaneous vs sequential analysis for population PK/PD data I: best-case performance. J Pharmacokinet Pharmacodyn. 2003;30:387–404.

    Article  PubMed  Google Scholar 

  33. Joerger M, Hess D, Delmonte A, Gallerani E, Fasolo A, Gianni L, et al. Integrative population pharmacokinetic and pharmacodynamic dose finding approach of the new campothecin compound namitecan (ST1968). Br J Clin Pharmacol. 2015;80(1):128–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tate SC, Sykes AK, Kulanthaivel P, Chan EM, Turner PK, Cronier DM. A population pharmacokinetic and pharmacodynamic analysis of abemaciclib in a phase I clinical trials in cancer patients. Clin Pharmacokinet. 2018;57:335–44.

    Article  CAS  PubMed  Google Scholar 

  35. Liang S, Brundage RC, Jacobson PA, Blaes A, Kirstein MN. Pharmacokinetic–pharmacodynamic modelling of acute N-terminal pro B-type natriuretic peptide after doxorubicin infusion in breast cancer. Br J Clin Pharmacol. 2016;82(3):773–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Imbs DC, Diéras V, Bachelot T, Campone M, Isambert N, Joly F, et al. Pharmacokinetic interaction between pazopanib and cisplatin regimen. Cancer Chemother Pharmacol. 2016;77:385–92.

    Article  CAS  PubMed  Google Scholar 

  37. Li X, Mao Y, Li K, Shi T, Yao H, Yao J, et al. Pharmacokinetics and tissue distribution in mice of triptolide-loaded lipid emulsion and accumulation effect of pancreas. Drug Deliv. 2016;23:1344–54.

    CAS  PubMed  Google Scholar 

  38. Zhang J, Zhang L, Yan Y, Li S, Xie L, Zhang W, et al. Are capecitabine and the active metabolite 5-Fu CNS penetrable to treat breast cancer brain metastasis? Drug Metab Dispos. 2015;43:411–7.

    Article  PubMed  CAS  Google Scholar 

  39. Bulitta JB, Zhao P, Arnold RD, Kessler DR, Daifuki R, Pratt K, et al. Multiple-pool cell lifespan models for neutropenia to assess the population pharmacodynamics of unbound paclitaxel from two formulations in cancer patients. Cancer Chemother Pharmacol. 2009;63:1035–48.

    Article  CAS  PubMed  Google Scholar 

  40. Joerger M, Huitema AD, Boogerd W, van der Sande JJ, Schellens JH, Beijnen JH. Interactions of serum albumin, valproic acid and carbamazepine with the pharmacokinetics of phenytoin in cancer pantients. Basic Clin Pharmacol Toxicol. 2006;99(2):133–40.

    Article  CAS  PubMed  Google Scholar 

  41. Haouala A, Widmed N, Guidi M, Montemuro M, Lyvraz S, Buclin T, et al. Prediction of free imatinib concentrations based on total plasma concentrations in patients with gastrointestinal stromal tumours. Br J Clin Pharmacol. 2013;75:1007–18.

    Article  CAS  PubMed  Google Scholar 

  42. Miles D, Jumbe NL, Lacy S, Nguyen L. Population pharmacokinetic model of cabozantinib in patients with medullary thyroid carcinoma and its application to an exposure-response analysis. Clin Pharmacokinet. 2016;55:93–105.

    Article  CAS  PubMed  Google Scholar 

  43. Fukudo M, Ikemi Y, Togashi Y, Masago K, Kim YH, Mio T, et al. Population pharmacokinetics/pharmacodynamics of erlotinib and pharmacogenomic analysis of plasma and cerebroespinal fluid drug concentrations in Japanese patients with non-small cell lung cancer. Clin Pharmacokinet. 2013;52(7):593–609.

    Article  CAS  PubMed  Google Scholar 

  44. Marostica E, Sukbuntherng J, Loury D, de Jong J, de Trixhe XW, Vermeulen A, et al. Population pharmacokinetic model of ibrutinib, a Bruton tyrosine kinase inhibitor, in patients with B cell malignancies. Cancer Chemother Pharmacol. 2015;75:111–21.

    Article  CAS  PubMed  Google Scholar 

  45. Solans BP, Fleury A, Freiwald M, Fritsch H, Haug K, Troconiz IF. Population pharmacokinetics of volasertib administered in patients with acute myeloid leukaemia as a single agent or in combination with cytarabine. Clin Pharmacokinet. 2018;57(3):379–92.

    Article  CAS  Google Scholar 

  46. Buil-Bruna N, Garrido MJ, Dehez M, Manon A, Nguyen TX, Gomez-Panzani EL, et al. Population pharmacokinetic analysis of lanreotide autogel/depot in the treatment of neuroendocrine tumors: pooled analysis of four clinical trials. Clin Pharmacokinet. 2016;55(4):461–73.

    Article  CAS  PubMed  Google Scholar 

  47. Puisset F, Alexandre J, Treluyer JM, Raoul V, Roché H, Goldwasser F, et al. Clinical pharmacodynamic factors in docetaxel toxicity. Br J Cancer. 2007;97(3):290–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bihorel S, Raddad E, Fiedler-Kelly J, Stille JR, Hing J, Ludwig E. Population pharmacokinetic and pharmacodynamic modeling of LY2510924 in patients with advanced cancer. CPT Pharmacometrics Syst Pharmacol. 2017;6:614–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Pérez-Blanco JS, Santos-Buelga D, Fernández de Gatta MD, Hernández-Rivas JM, Martín A, García MJ. Population pharmacokinetics of doxorubicin and doxorubicinol in patients diagnosed with non-Hodgkin’s lymphoma. Br J Clin Pharmacol. 2016;82:1517–27.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Oosten AW, Abrantes JA, Jönsson S, de Bruijn P, Kuip EJ, Falcão A, et al. Treatment with subcutaneous and transdermal fentanyl: results from a population pharmacokinetic study in cancer patients. Eur J Clin Pharmacol. 2016;72(4):459–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Joerger M, Huitema AD, Meenhorst PL, Schellens JH, Beijnen JH. Pharmacokinetics of low-dose doxorubicin and metabolites in patients with AIDS-related Kaposi sarcoma. Cancer Chemother Pharmacol. 2005;55:488–96.

    Article  CAS  PubMed  Google Scholar 

  52. Duoung JK, Griffin MJ, Hargrave D, Vormoor J, Edwards D, Boddy AV. A population pharmacokinetic model of AT9283 in adults and children to predict the maximum tolerated dose in children with leukaemia. Br J Clin Pharmacol. 2017;83(8):1713–22.

    Article  CAS  Google Scholar 

  53. Bartelink IH, Lalmohamed A, van Reij EM, Dvorak CC, Savic RM, Zwaveling J, et al. Association of busulfan exposure with survival and toxicity after haematopoietic cell transplantation in children and young adults: a multicenter, retrospective cohort analysis. Lancet Haematol. 2016;3:e526–36.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Ouerdani A, Struemper H, Suttle AB, Ouellet D, Ribba B. Preclinical modeling of tumor growth and angiogenesis inhibition to describe pazopanib clinical effects in renal cell carcinoma. CPT Pharmacometrics Syst Pharmacol. 2015;4:660–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Singh R, Mehrotra S, Gopalakrishnan M, Gojo I, Karp JE, Greer JM, et al. Population pharmacokinetics and exposure-response assessment of veliparib co-administered with temozolomide in patients with myeloid leukemias. Cancer Chemother Pharmacol. 2019;83(2):319–28.

    Article  CAS  PubMed  Google Scholar 

  56. Suri A, Mould DR, Song G, Collins GP, Endres CJ, Gomez-Navarro J, et al. Population pharmacokinetic modeling and exposure-response assessment for the antibody-drug conjugate brentuximab vedotin in Hodgkin’s lymphoma in the Phase III ECHELON-1 Study. Clin Pharmacol Ther. 2019. https://doi.org/10.1002/cpt.1530.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Quartino AL, Hillenchach C, Li J, Wada RD, Visich J, Li C, et al. Population pharmacokinetic and exposure-response analysis for trastuzumab administered using a subcutaneous “manual syringe” injection or intravenously in women with HER2-positive early breast cancer. Cancer Chemother Pharmacol. 2016;77:77–88.

    Article  CAS  PubMed  Google Scholar 

  58. Blasco H, Senecal D, Le Gouge A, Pinard E, Benz-de Bretagne I, Colombat P, et al. Influence of methotrexate exposure on outcome in patients treated with MBVP chemotherapy for primary central nervous system lymphoma. Br J Clin Pharmacol. 2010;70(3):367–75.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Brown K, Comisar C, Witjes H, Maringwa J, de Greef R, Vishwanathan K, et al. Population pharmacokinetics and exposure-response of osimertinib in patients with non-small cell lung cancer. Br J Clin Pharmacol. 2017;83(6):1216–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mehrota S, Gopalakrishnan M, Gobburu J, Ji J, Greer JM, Piekarz R, et al. Exposure-response of veliparib to inform phase II trial design in refractory or relapsed patients with hematological malignancies. Clin Cancer Res. 2017;23:6421–9.

    Article  CAS  Google Scholar 

  61. Abou-Alfa GK, Lewis LD, LoRusso P, Maitland M, Chandra P, Cheeti S, et al. Pharmacokinetics and safety of vismodegib in patients with advanced solid malignancies and hepatic impairment. Cancer Chemother Pharmacol. 2017;80:29–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Freshwater R, Kondic A, Ahamadi M, Li CH, de Greef R, de Alwis D, et al. Evaluation of dosing strategy for pembrolizumab for oncology indications. J Immunother Cancer. 2017;5:43.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Tamai T, Hayato S, Hojo S, Suzuki T, Okusaka T, Ikeda K, et al. Dose finding of lenvatinib in subjects with advanced hepatocellular carcinoma based on population pharmacokinetic and exposure-response analyses. J Clin Pharmacol. 2017;57(9):1138–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Puszkiel A, White-Koning M, Dupin N, Kramkimel N, Thomas-Schoemann A, Noé G, et al. Plasma vemurafenib exposure and pre-treatment hepatocyte growth factor level are two factors contributing to the early peripheral lymphocytes depletion in BRAF-mutated melanoma patients. Pharmacol Res. 2016;113:709–18.

    Article  CAS  PubMed  Google Scholar 

  65. Houk BE, Bello CL, Poland B, Rosen LS, Demetri GD, Motzer RJ. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother Pharmacol. 2010;66:357–71.

    Article  CAS  PubMed  Google Scholar 

  66. García-Cremades M, Pitou C, Iversen PW, Troconiz IF. Predicting tumour growth and its impact on survival in gemcitabine-treated patients with advanced pancreatic cancer. Eur J Pharm Sci. 2018;115:296–303.

    Article  PubMed  CAS  Google Scholar 

  67. Han K, Chanu P, Jonsson F, Winter H, Bruno R, Jin J, et al. Exposure-response and tumor growth inhibition analyses of the monovalent anti-c-MET antibody onartuzumab (MetMAb) in the second- and third-line non-small cell lung cancer. AAPS J. 2017;19:527–33.

    Article  CAS  PubMed  Google Scholar 

  68. Joerger M, von Pawel J, Kraff S, Fischer JR, Eberhardt W, Gauler TC, et al. Open-label, randomized study of individualized, pharmacokinetically (PK)-guided dosing of paclitaxel combined with carboplatin or cisplatin in patients with advanced non-small-cell lung cancer (NSCLC). Ann Oncol. 2016;27(10):1895–902.

    Article  CAS  PubMed  Google Scholar 

  69. Ahamadi M, Freshwater T, Prohn M, Li CH, de Alwis DP, de Greef R, et al. Model-based characterization of the pharmacometrics of pembrolizumab: a humanized anti-PD1 monoclonal antibody in advanced solid tumours. CPT Pharmacometrics Syst Pharmacol. 2017;6:49–57.

    Article  CAS  PubMed  Google Scholar 

  70. Bruno R, Hille D, Riva A, Vivier N, ten Bokkel Huinnink WW, van Oosterom AT, et al. Population pharmacokinetics/pharmacodynamics of docetaxel in phase II studies in patients with cancer. J Clin Oncol. 1998;16(1):187–96.

    Article  CAS  PubMed  Google Scholar 

  71. Li C, Wang B, Chen SC, Wada R, Lu D, Wang X, et al. Exposure-response analyses of trastuzumab emtansine in patients with HER2-positive advanced breast cancer previously treated with trastuzumab and a taxane. Cancer Chemother Pharmacol. 2017;80(6):1079–90.

    Article  CAS  PubMed  Google Scholar 

  72. Dahl SG, Aaribs Km Gundert-Remy U, Karlsson MO, Schneider YJ, Steimer JL, et al. Incorporating physiological and biochemical mechanism into pharmacokinetic–pharmacodynamic models: a conceptual framework. Basic Clin Pharmacol Toxicol. 2010;106(1):2–12.

    CAS  PubMed  Google Scholar 

  73. Shardlow CE, Generaux GT, Patel AH, Tai G, Tran T, Bloomer JC. Impact of physiologically based pharmacokinetic modeling and simulation in drug development. Drug Metab Dispos. 2013;41(12):1994–2003.

    Article  CAS  PubMed  Google Scholar 

  74. Jones H, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013;2:e63.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Rowland M, Lesko LJ, Rostami-Hodjegan A. Physiologically based pharmacokinetics is impacting drug development and regulatory decision making. CPT Pharmacometrics Syst Pharmacol. 2015;4(6):313–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Yoshida K, Budha N, Jin JY. Impact of physiologically based pharmacokinetic models on regulatory reviews and product labels: frequent utilization in the field of oncology. Clin Pharmacol Ther. 2017;101(5):597–602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Bi Y, Deng J, Murry DJ, An G. A whole-body physiologically based pharmacokinetic model of gefitinib in mice and scale-up to humans. AAPS J. 2016;18(1):228–38.

    Article  CAS  PubMed  Google Scholar 

  78. Takahashi RH, Choo EF, Ma S, Wong S, Halladay J, Deng Y, et al. Absorption, metabolism, excretion, and the contribution of intestinal metabolism to the oral disposition of [14C]Cobimetinib, a MEK inhibitor, in humans. Drug Metab Dispos. 2016;44(1):28–39.

    Article  CAS  PubMed  Google Scholar 

  79. Bradshaw-Pierce EL, Eckhardt SG, Gustafson DL. A physiologically based pharmacokinetic model of docetaxel disposition: from mouse to man. Clin Cancer Res. 2007;13(9):2768–76.

    Article  CAS  PubMed  Google Scholar 

  80. Chen Y, Zhao K, Liu F, Xie Q, Zhong Z, Miao M, et al. Prediction of deoxypodophyllotoxin disposition in mouse, rat, monkey, and dog by physiologically based pharmacokinetic model and the extrapolation to human. Front Pharmacol. 2016;7:488.

    PubMed  PubMed Central  Google Scholar 

  81. Diestelhorst C, Boos J, McCune JS, Russell J, Kangarloo SB, Hempel G. Physiologically based pharmacokinetic modelling of busulfan: a new approach to describe and predict the pharmacokinetics in adults. Cancer Chemother Pharmacol. 2013;72(5):991–1000.

    Article  CAS  PubMed  Google Scholar 

  82. Hu ZY, Lu J, Zhao Y. A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans. Br J Pharmacol. 2014;171(11):2778–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hudachek SF, Gustafson DL. Customized in silico population mimics actual population in docetaxel population pharmacokinetic analysis. J Pharm Sci. 2011;100(3):1156–66.

    Article  CAS  PubMed  Google Scholar 

  84. Kletting P, Kull T, Bunjes D, Mahren B, Luster M, Reske SN, et al. Radioimmunotherapy with anti-CD66 antibody: improving the biodistribution using a physiologically based pharmacokinetic model. J Nucl Med. 2010;51(3):484–91.

    Article  CAS  PubMed  Google Scholar 

  85. Hardiansyah D, Maass C, Attarwala AA, Müller B, Kletting P, Mottaghy FM, et al. The role of patient-based treatment planning in peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. 2016;43(5):871–80.

    Article  CAS  PubMed  Google Scholar 

  86. Xia B, Heimbach T, Lin TH, He H, Wang Y, Tan E. Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions. Cancer Chemother Pharmacol. 2012;69(6):1567–82.

    Article  CAS  PubMed  Google Scholar 

  87. Lu XF, Bi K, Chen X. Physiologically based pharmacokinetic model of docetaxel and interspecies scaling: comparison of simple injection with folate receptor-targeting amphiphilic copolymer-modified liposomes. Xenobiotica. 2016;46(12):1093–104.

    Article  CAS  PubMed  Google Scholar 

  88. Glassman PM, Balthasar JP. Physiologically-based modeling to predict the clinical behavior of monoclonal antibodies directed against lymphocyte antigens. MAbs. 2017;9(2):297–306.

    Article  CAS  PubMed  Google Scholar 

  89. Malik PRV, Hamadeh A, Phipps C, Edginton AN. Population PBPK modelling of trastuzumab: a framework for quantifying and predicting inter-individual variability. J Pharmacokinet Pharmacodyn. 2017;44(3):277–90.

    Article  CAS  PubMed  Google Scholar 

  90. Kyle AH, Huxham LA, Yeoman DM, Minchinton AI. Limited tissue penetration of taxanes: a mechanism for resistance in solid tumours. Clin Cancer Res. 2007;13:2804–10.

    Article  CAS  PubMed  Google Scholar 

  91. Joyce JA. Therapeutic targeting of the tumor microenvionment. Cancer Cell. 2005;7:513–20.

    Article  CAS  PubMed  Google Scholar 

  92. De Morrée E, van Soest R, Aghai A, de Ridder C, de Brujin P, Ghobadi Moghaddam-Helmantel I, et al. Understanding taxanes in prostate cancer: importance of intratumoral drug accumulation. Prostate. 2016;76(10):927–36.

    Article  PubMed  CAS  Google Scholar 

  93. Sharma J, Lv H, Gallo JM. Intratumoral modeling of gefitinib pharmacokinetics and pharmacodynamics in an orthotopic mouse model of glioblastoma. Cancer Res. 2016;73(16):5242–52.

    Article  CAS  Google Scholar 

  94. Thurber HM, Weissleder R. A systems approach for tumor pharmacokinetics. PLoS One. 2011;6(9):e24696.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wagner M, Wiig H. Tumor interstitial fluid formation, characterization, and clinical implications. Front Oncol. 2015;5:115.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Liu G, Kolesar J, McNeel DG, Leith C, Schell K, Eickhoff K, et al. A phase I pharmacokinetic and pharmacodynamic correlative study of antisense Bcl-2 oligonucleotide g3139 in combination with carboplatin and paclitaxel, in patients with advanced solid tumors. Clin Cancer Res. 2008;1:2732–9.

    Article  Google Scholar 

  97. Pigatto MC, Roman RM, Carrara L, Buffon A, Magni P, Dalla Costa T. Pharmacokinetic/pharmacodynamic modeling of etoposide tumor growth inhibitory effect in Walker-256 tumor-bearing rat model using free intratumoral drug concentrations. Eur J Pharm Sci. 2017;97:70–8.

    Article  CAS  PubMed  Google Scholar 

  98. Rizk ML, Zou L, Savic ML, Dooley KE. Importance of drug pharmacokinetics at the site of action. Clin Trans Sci. 2017;10(3):133–42.

    Article  CAS  Google Scholar 

  99. Smith JA, Mathew L, Burney M, Nyshadham P, Coleman RL. Equivalency challenge: evaluation of Lipodox as the generic equivalent for Doxil in a human ovarian cancer orthotopic mouse model. Gynecol Oncol. 2016;141:357–63.

    Article  CAS  PubMed  Google Scholar 

  100. Hunz M, Jetter A, Warm M, Pantke E, Tusxher M, Hempel G, et al. Plasma and tissue pharmacokinetics of epirubicin and paclitaxel in patients receiving neoadjuvant chemotherapy for locally advanced primary breast cancer. Clin Pharmacol Ther. 2007;81:659–68.

    Article  CAS  PubMed  Google Scholar 

  101. Apparaju SK, Gudelsky GA, Desai PB. Pharmacokinetics of gemcitabine in tumor and non-tumor extracellular fluid of brain: as in vivo assessment in rats employing intracerebral microdialysis. Cancer Chemother Pharmacol. 2008;61:223–9.

    Article  CAS  PubMed  Google Scholar 

  102. Dave N, Gudelsky GA, Desai PB. The pharmacokinetics of letrozole in brain and brain tumor in rats with orthotopically implanted C6 glioma, assessed using intracerebral microdialysis. Cancer Chemother Pharmacol. 2013;72(2):349–57.

    Article  CAS  PubMed  Google Scholar 

  103. Yingchoncharoen P, Kalinowski DS, Richardson DR. Lipid-based drug delivery system in cancer therapy: what is available and what is yet to come. Pharmacol Rev. 2016;68:701–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Patel H, Joshi A, Joshi A, Stagni G. Transdermal delivery of etoposide phosphate I: in vitro and in vivo evaluation. J Pharm Sci. 2016;105:2114–22.

    Article  CAS  PubMed  Google Scholar 

  105. Ribba B, Boetsch C, Nayak T, Grimm HP, Charo J, Evers S, et al. Prediction of the optimal dosing regimen using a mathematical model of tumor uptake for immunocytokine-based cancer immunotherapy. Clin Can Res. 2018;24(14):3325–33.

    Article  CAS  Google Scholar 

  106. Schmidt MM, Wittrup KD. A modeling analysis of the effects of molecular size and binding affinity on tumour targeting. Mol Cancer Ther. 2009;8:2861–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Thurber GM, Wittrup KD. A mechanistic compartmental model for total antibody uptake in tumours. J Theor Biol. 2012;314:57–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Ruiz-Cerdá L, Asín-Prieto E, Parra-Guillen ZP, Trocóniz IF. The long neglected player: modeling tumour uptake to guide optimal dosing. Clin Cancer Res. 2018;24(14):3236–8.

    Article  PubMed  Google Scholar 

  109. Chen C, He ZC, Shi Y, Zhou W, Zhang X, Xiao HL, et al. Microvascular fractal dimension predicts prognosis and response to chemotherapy in glioblastoma: an automatic image analysis study. Lab Investig. 2018;98:924–34.

    Article  CAS  PubMed  Google Scholar 

  110. Brandhonneur N, Noury F, Bruyère A, Saint-Jalmes H, Le Corre P. PBPK model of methotrexate in cerebrospinal fluid ventricles using a combined microdialysis and MRI acquisition. Eur J Pharm Biopharm. 2016;104:117–30.

    Article  CAS  PubMed  Google Scholar 

  111. Gustafson DL, Thamm DH. Pharmacokinetic modeling of doxorubicin pharmacokinetics in dogs deficient in ABCB1 drug transporters. J Vet Intern Med. 2010;24(3):579–86.

    Article  CAS  PubMed  Google Scholar 

  112. Khot A, Tibbitts J, Rock D, Shah DK. Development of a translational physiologically based pharmacokinetic model for antibody drug conjugates: a case study with T-DM1. AAPS J. 2017;19:1715–34.

    Article  CAS  PubMed  Google Scholar 

  113. Schiltmeyer B, Klingebiel T, Schwab M, Mürdter TE, Ritter CA, Jenke A, et al. Population pharmacokinetics of oral busulfan in children. Cancer Chemother Pharmacol. 2003;52(3):209–16.

    Article  CAS  PubMed  Google Scholar 

  114. Diestelhorst C, Boos J, McCune JS, Russell J, Kangarloo SB, Hempel G. Predictive performance of a physiologically based pharmacokinetic model of busulfan in children. Pediatr Hematol Oncol. 2014;31(8):731–42.

    Article  CAS  PubMed  Google Scholar 

  115. US Food and Drug Administration, Center for Drug Evaluation and Research. Guidance for industry: bioavailability and bioequivalence studies for orally administered drug products: general considerations. Rockville (MD): Office of Training and Communications, Division of Communications Management, Drug Information Branch; 2000.

  116. US Food and Drug Administration. 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. 2016. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidance/UCM531207.pdf. Accessed 11 Dec 2018.

  117. European Medicines Agency. Guidance for quantification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation (draft guidance). Committee for Medicinal Products for Human Use (CHMP). 2016. https://www.ema.europa.eu/documents/scientific-guideline/draft-guideline-qualification-reporting-physiologically-based-pharmacokinetic-pbpk-modelling_en.pdf. Accessed 11 Dec 2018.

  118. European Medicines Agency. Concept paper on extrapolation of efficacy and safety in medicine development: human medicines development and evaluation. 2013. https://www.ema.europa.eu/documents/scientific-guideline/adopted-reflection-paper-use-extrapolation-development-medicines-paediatrics-revision-1_en.pdf. Accessed 12 Dec 2018.

  119. Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, et al. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumour dynamics. J Clin Oncol. 2009;27:4103–8.

    Article  CAS  PubMed  Google Scholar 

  120. Panetta JC, Kirstein MN, Gajjar AJ, Nair G, Fouladi M, Stewart CF. A mechanistic mathematical model of temozolomide myelosuppression in children with high-grade gliomas. Math Biosci. 2003;186:29–41.

    Article  PubMed  Google Scholar 

  121. Bartelink IH, Jones EF, Shahidi-Latham SK, Lee PRE, Zheng Y, Vicini P, et al. Tumor drug penetration measurements could be the neglected piece of the personalized cancer treatment puzzle. Clin Pharmacol Ther. 2019;106:148–63.

    Article  PubMed  Google Scholar 

  122. Li J, Sausville EA, Klein PJ, Morgenstern D, Leamon CP, Messmann RA, et al. Clinical pharmacokinetics and exposure-toxicity relationships of a folate-Vinca alkaloid conjugate EC145 in cancer patients. J Clin Pharmacol. 2009;49(12):1467–76.

    Article  CAS  PubMed  Google Scholar 

  123. Li J, Ramírez-Valle F, Xue Y, Ventura JI, Gouedard O, Mei J, et al. Population pharmacokinetics and exposure response assessment of CC-292, a potent BTK inhibitor, in patients with chronic lymphocytic leukemia. J Clin Pharmacol. 2017;57(19):1279–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Rodon J, Poster-Vinay D, Hollebecque A, Nuciforo P, Azaro A, Cattan V, et al. First-in-human phase I study of oral A49076, a unique MET/AXL/FGFR inhibitor, in advanced solid tumours. Eur J Cancer. 2017;81:142–50.

    Article  CAS  PubMed  Google Scholar 

  125. Tran B, Carvajar RD, Marabelle A, Patel SP, LoRusso PM, Pasmussen E, et al. Dose escalation results from a first-in-human, phase 1 study of glucocorticoid-induced TNF receptor-related protein agonist AMG 228 in patients with advanced solid tumors. J Immunother Cancer. 2018;6(1):93.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Quartino AL, Li H, Kirschbrown WP, Mangat R, Wada DR, Garg A, et al. Population pharmacokinetic and covariate analysis of intravenous trastuzumab (Herceptin®), a HER-2 targeted monoclonal antibody, in patients with a variety of solid tumors. Cancer Chemother Pharmacol. 2019;83(2):329–40. https://doi.org/10.1007/s00280-018-3728-z.

    Article  CAS  PubMed  Google Scholar 

  127. Kojima T, Yamazaki K, Kato K, Muro K, Hara H, Chin K, et al. Phase I dose-escalataion trial of SYM004, an anti-EGFR antibody mixture, in Japanese patients with advanced solid tumors. Cancer Sci. 2018;109(10):3253–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Oude Munnink TH, Henstra MJ, Segerink LI, Movig KL, Brummelhuis-Visser P. Therapeutic drug monitoring of monoclonal antibodies in inflammatory and malignant disease: translating TNF-α experience to oncology. Clin Pharmacol Ther. 2016;99(4):419–36.

    Article  CAS  PubMed  Google Scholar 

  129. Minasian L, Rosen O, Auclair D, Rahman A, Pazdur R, Schilsky RL. Optimizing dosing for oncology drugs. Clin Pharmacol Ther. 2014;96:572–9.

    Article  CAS  PubMed  Google Scholar 

  130. Fiedler W, Cresta S, Schulze-Berkgkamen H, De Dosso S, Weidmann J, Tessari A, et al. Phase I study of tomuzotoximab, a glycoengineered therapeutic antibody against epidermal growth factor receptor, in patients with advanced carcinomas. ESMO Open. 2018;3(2):e00D303.

    Article  Google Scholar 

  131. Centanni M, Moes DJAR, Trocóniz IF, Ciccolini J, van Hasselt JGC. Clinical pharmacokinetics and pharmacodynamics of immune checkpoint inhibitors. Clin Pharmacokinet. 2019;58:835–57.

    Article  PubMed  PubMed Central  Google Scholar 

  132. Bajaj G, Wang X, Agrawal S, Gupta M, Roy A, Feng Y. Model-based population pharmacokinetic analysis of nivolumab in patients with solid tumors. CPT Pharmacometrics Syst Pharmacol. 2016;6:58–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Stroh M, Winter H, Marchand M, Claret L, Eppler S, Ruppel J, et al. Clinical Pharmacokinetics and pharmacodynamics of atezolizumab in metastatic urothelial carcinoma. Clin Pharmacol Ther. 2017;102:305–12.

    Article  CAS  PubMed  Google Scholar 

  134. Center for Drug Evaluation and Research [ICDER], US FDA. Clinical multi-discipline review: avelumab. Silver Spring (MD): US FDA; 2017.

  135. Center for Drug Evaluation and Research (ICDER), US FDA. Clinical pharmacology and biopharmaceutics review: durvalumab. Silver Spring (MD): US FDA; 2017.

  136. Feng Y, Masson E, Dai D, Parker SM, Berman D, Roy A. Model-based clinical pharmacology profiling of ipilimumab in patients with advanced melanoma. Br J Clin Pharmacol. 2014;78:106–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Latz JE, Karlsson MO, Rusthoven JJ, Ghosh A, Johnson RD. A semimechanistic-physiologic population pharmacokinetic/pharmacodynamic model for neutropenia following pemetrexed therapy. Cancer Chemother Pharmacol. 2006;57(4):412–26.

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Belén P. Solans and Iñaki F. Trocóniz contributed equally to the paper seniorship.

Authors and Affiliations

  1. Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea s/n, 31008, Pamplona, Navarra, Spain

    Belén P. Solans, María Jesús Garrido & Iñaki F. Trocóniz

  2. Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain

    Belén P. Solans, María Jesús Garrido & Iñaki F. Trocóniz

Authors
  1. Belén P. Solans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Jesús Garrido
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iñaki F. Trocóniz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Belén P. Solans or Iñaki F. Trocóniz.

Ethics declarations

Funding

La Caixa Banking Foundation and the Asociación de Amigos de la Universidad de Navarra provided the funding for this research.

Conflict of interest

Belén P. Solans, María Jesús Garrido and Iñaki F. Trocóniz have no conflicts of interest that are directly relevant to the content of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Solans, B.P., Garrido, M.J. & Trocóniz, I.F. Drug Exposure to Establish Pharmacokinetic–Response Relationships in Oncology. Clin Pharmacokinet 59, 123–135 (2020). https://doi.org/10.1007/s40262-019-00828-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-019-00828-3

Search

Navigation