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The AAPS Journal

, Volume 17, Issue 2, pp 389–399 | Cite as

Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

  • Aman P. Singh
  • Wojciech Krzyzanski
  • Steven W. Martin
  • Gregory Weber
  • Alison Betts
  • Alaa Ahmad
  • Anson Abraham
  • Anup Zutshi
  • John Lin
  • Pratap Singh
Research Article

Abstract

Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R 0), body-weight (BW) normalized central elimination rate (K el/BW−0.25) and central volume (V c/BW1.0). AAFE of less than three-fold was estimated for the binding affinity constant (K D). The other four parameters, i.e., complex turnover rate (K int), target turnover rate (K deg), central to peripheral distribution rate constant (K pt) and peripheral to central rate constant (K tp) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

KEY WORDS

ADME of biologics human translation monoclonal antibodies PK/PD modeling TMDD 

Notes

ACKNOWLEDGMENTS

We thank Dr. Donald E. Mager, University at Buffalo, for helping in the development and review of this manuscript. This work was partially supported by the stipend Aman P. Singh received as a Pharmacokinetic/Pharmacodynamic Summer Intern at PDM Department in Pfizer and partially supported by the NIH grant GM 57980

Supplementary material

12248_2014_9690_MOESM1_ESM.docx (36 kb)
Figure S1 mAb-1 (Efalizumab) pharmacokinetics and %CD11a receptor modulation in chimpanzees fitted with a rapid-binding approximation of TMDD model. (DOCX 35 kb)
12248_2014_9690_MOESM2_ESM.docx (102 kb)
Figure S2 mAb-1 (Efalizumab) pharmacokinetics and %CD11a receptor modulation in Psoriatic patients fitted with a rapid-binding approximation of a TMDD model. (DOCX 101 kb)
12248_2014_9690_MOESM3_ESM.docx (108 kb)
Figure S3 mAb-2 (TRX-1) pharmacokinetics, free and total % CD4+ cells modulation in healthy baboons fitted with a rapid-binding approximation of TMDD model. (DOCX 108 kb)
12248_2014_9690_MOESM4_ESM.docx (104 kb)
Figure S4 mAb-2 (TRX-1) pharmacokinetics, free and total % CD4+ cells modulation in healthy volunteers in phase 1 clinical trial fitted with a rapid-binding approximation of TMDD model. (DOCX 104 kb)
12248_2014_9690_MOESM5_ESM.docx (60 kb)
Figure S5 mAb-3 (MTRX-1) pharmacokinetics, free and total %CD4+ cells modulation in baboons fitted with a rapid-binding approximation of TMDD model. (DOCX 60 kb)
12248_2014_9690_MOESM6_ESM.docx (67 kb)
Figure S6 mAb-3 (MTRX-1) pharmacokinetics, free and total %CD4+ cells modulation fitted with a rapid-binding approximation of TMDD model. (DOCX 67 kb)
12248_2014_9690_MOESM7_ESM.docx (353 kb)
Figure S7 mAb-4 pharmacokinetics in cynomolgus monkeys and healthy volunteers in phase 1 trial fitted with a rapid-binding approximation of a TMDD model. (DOCX 353 kb)
12248_2014_9690_MOESM8_ESM.docx (163 kb)
Figure S8 mAb-5 pharmacokinetics in cynomolgus monkeys and in humans fitted with a rapid-binding approximation of a TMDD model. (DOCX 163 kb)
12248_2014_9690_MOESM9_ESM.docx (51 kb)
Figure S9 mAb-6 pharmacokinetics and total %target modulation in cynomolgus monkeys fitted with a rapid-binding approximation of TMDD model. (DOCX 50 kb)
12248_2014_9690_MOESM10_ESM.docx (52 kb)
Figure S10 mAb-6 pharmacokinetics and total %target modulation in healthy volunteers in a phase-1 trial fitted with a rapid-binding approximation of TMDD model. (DOCX 52 kb)
12248_2014_9690_MOESM11_ESM.docx (104 kb)
Figure S11 TMDD model based predictions for the test drug (mAb-7) PK in a phase-1 clinical trial in healthy volunteers. Note that most of the PK data (except for one subject) for 3 mg SC dose was below the limit of quantification of the assay and could not be presented in Figure 6. Consistent with these findings, model predictions for 3mg SC group were below LLOQ. (DOCX 103 kb)
12248_2014_9690_MOESM12_ESM.docx (106 kb)
Figure S12 Vmax/Km model based predictions for the test drug (mAb-7) PK in a phase-1 clinical trial in healthy volunteers. (DOCX 105 kb)
12248_2014_9690_MOESM13_ESM.docx (218 kb)
Figure S13 Comparative performance of translation rule based predictions vs. empirical (V max/K m) approach. Predicted AUC (A) and Cmax (B) at each dose (3-120mg SC) are shown for the two approaches and compared against the observed data. Solid diagonal line represents perfect agreement. (DOCX 217 kb)

REFERENCES

  1. 1.
    Rodrigues ME, Costa AR, Henriques M, Azeredo J, Oliveira R. Technological progresses in monoclonal antibody production systems. Biotechnol Prog. 2010;26(2):332–51. doi: 10.1002/btpr.348.PubMedGoogle Scholar
  2. 2.
    Lobo ED, Hansen RJ, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004;93(11):2645–68. doi: 10.1002/jps.20178.CrossRefPubMedGoogle Scholar
  3. 3.
    Wang W, Wang EQ, Balthasar JP. Monoclonal antibody pharmacokinetics and pharmacodynamics. Clin Pharmacol Ther. 2008;84(5):548–58. doi: 10.1038/clpt.2008.170.CrossRefPubMedGoogle Scholar
  4. 4.
    Mould DR, Green B. Pharmacokinetics and pharmacodynamics of monoclonal antibodies: concepts and lessons for drug development. BioDrugs. 2010;24(1):23–39. doi: 10.2165/11530560-000000000-00000.CrossRefPubMedGoogle Scholar
  5. 5.
    Dirks NL, Meibohm B. Population pharmacokinetics of therapeutic monoclonal antibodies. Clin Pharmacokinet. 2010;49(10):633–59. doi: 10.2165/11535960-000000000-00000.CrossRefPubMedGoogle Scholar
  6. 6.
    Yousry TA, Major EO, Ryschkewitsch C, Fahle G, Fischer S, Hou J, et al. Evaluation of patients treated with natalizumab for progressive multifocal leukoencephalopathy. N Engl J Med. 2006;354(9):924–33. doi: 10.1056/NEJMoa054693.CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Keymeulen B, Vandemeulebroucke E, Ziegler AG, Mathieu C, Kaufman L, Hale G, et al. Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes. N Engl J Med. 2005;352(25):2598–608. doi: 10.1056/NEJMoa043980.CrossRefPubMedGoogle Scholar
  8. 8.
    Suntharalingam G, Perry MR, Ward S, Brett SJ, Castello-Cortes A, Brunner MD, et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med. 2006;355(10):1018–28. doi: 10.1056/NEJMoa063842.CrossRefPubMedGoogle Scholar
  9. 9.
    Mordenti J, Chen SA, Moore JA, Ferraiolo BL, Green JD. Interspecies scaling of clearance and volume of distribution data for five therapeutic proteins. Pharm Res. 1991;8(11):1351–9. doi: 10.1023/A:1015836720294.CrossRefPubMedGoogle Scholar
  10. 10.
    Mahmood I. Interspecies scaling of protein drugs: prediction of clearance from animals to humans. J Pharm Sci. 2004;93(1):177–85. doi: 10.1002/jps.10531.CrossRefPubMedGoogle Scholar
  11. 11.
    Wang W, Prueksaritanont T. Prediction of human clearance of therapeutic proteins: simple allometric scaling method revisited. Biopharm Drug Dispos. 2010;31(4):253–63. doi: 10.1002/bdd.708.PubMedGoogle Scholar
  12. 12.
    Ling J, Zhou H, Jiao Q, Davis HM. Interspecies scaling of therapeutic monoclonal antibodies: initial look. J Clin Pharmacol. 2009;49(12):1382–402. doi: 10.1177/0091270009337134.CrossRefPubMedGoogle Scholar
  13. 13.
    Deng R, Iyer S, Theil FP, Mortensen DL, Fielder PJ, Prabhu S. Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: what have we learned? MAbs. 2011;3(1):61–6. doi: 10.4161/mabs.3.1.13799.CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Oitate M, Masubuchi N, Ito T, Yabe Y, Karibe T, Aoki T, et al. Prediction of human pharmacokinetics of therapeutic monoclonal antibodies from simple allometry of monkey data. Drug Metab Pharmacokinet. 2011;26(4):423–30. doi: 10.2133/dmpk.DMPK-11-RG-011.CrossRefPubMedGoogle Scholar
  15. 15.
    Oitate M, Nakayama S, Ito T, Kurihara A, Okudaira N, Izumi T. Prediction of human plasma concentration-time profiles of monoclonal antibodies from monkey data by a species-invariant time method. Drug Metab Pharmacokinet. 2012;27(3):354–9.PubMedGoogle Scholar
  16. 16.
    Dong JQ, Salinger DH, Endres CJ, Gibbs JP, Hsu CP, Stouch BJ, et al. Quantitative prediction of human pharmacokinetics for monoclonal antibodies: retrospective analysis of monkey as a single species for first-in-human prediction. Clin Pharmacokinet. 2011;50(2):131–42. doi: 10.2165/11537430-000000000-00000.CrossRefPubMedGoogle Scholar
  17. 17.
    Kagan L, Abraham AK, Harrold JM, Mager DE. Interspecies scaling of receptor-mediated pharmacokinetics and pharmacodynamics of type I interferons. Pharm Res. 2010;27(5):920–32. doi: 10.1007/s11095-010-0098-6.CrossRefPubMedCentralPubMedGoogle Scholar
  18. 18.
    Luu KT, Bergqvist S, Chen E, Hu-Lowe D, Kraynov E. A model-based approach to predicting the human pharmacokinetics of a monoclonal antibody exhibiting target-mediated drug disposition. J Pharmacol Exp Ther. 2012;341(3):702–8. doi: 10.1124/jpet.112.191999.CrossRefPubMedGoogle Scholar
  19. 19.
    Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn. 2001;28(6):507–32. doi: 10.1023/A:1014414520282.CrossRefPubMedGoogle Scholar
  20. 20.
    Mager DE, Krzyzanski W. Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Pharm Res. 2005;22(10):1589–96. doi: 10.1007/s11095-005-6650-0.CrossRefPubMedGoogle Scholar
  21. 21.
    Lavielle M, Mentre F. Estimation of population pharmacokinetic parameters of saquinavir in HIV patients with the MONOLIX software. J Pharmacokinet Pharmacodyn. 2007;34(2):229–49. doi: 10.1007/s10928-006-9043-z.CrossRefPubMedCentralPubMedGoogle Scholar
  22. 22.
    Beal SL. Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn. 2001;28(5):481–504. doi: 10.1023/A:1012299115260.CrossRefPubMedGoogle Scholar
  23. 23.
    Bauer RJ, Dedrick RL, White ML, Murray MJ, Garovoy MR. Population pharmacokinetics and pharmacodynamics of the anti-CD11a antibody hu1124 in human subjects with psoriasis. J Pharmacokinet Biopharm. 1999;27(4):397–420. doi: 10.1023/A:1020917122093.CrossRefPubMedGoogle Scholar
  24. 24.
    Ng CM, Stefanich E, Anand BS, Fielder PJ, Vaickus L. Pharmacokinetics/pharmacodynamics of nondepleting anti-CD4 monoclonal antibody (TRX1) in healthy human volunteers. Pharm Res. 2006;23(1):95–103. doi: 10.1007/s11095-005-8814-3.CrossRefPubMedGoogle Scholar
  25. 25.
    Scheerens H, Su Z, Irving B, Townsend MJ, Zheng Y, Stefanich E, et al. MTRX1011A, a humanized anti-CD4 monoclonal antibody, in the treatment of patients with rheumatoid arthritis: a phase I randomized, double-blind, placebo-controlled study incorporating pharmacodynamic biomarker assessments. Arthritis Res Ther. 2011;13(5):R177. doi: 10.1186/ar3502.CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Zheng Y, Scheerens H, Davis Jr JC, Deng R, Fischer SK, Woods C, et al. Translational pharmacokinetics and pharmacodynamics of an FcRn-variant anti-CD4 monoclonal antibody from preclinical model to phase I study. Clin Pharmacol Ther. 2011;89(2):283–90. doi: 10.1038/clpt.2010.311.CrossRefPubMedGoogle Scholar
  27. 27.
    Hasegawa M, Fujimoto M, Kikuchi K, Takehara K. Elevated serum levels of interleukin 4 (IL-4), IL-10, and IL-13 in patients with systemic sclerosis. J Rheumatol. 1997;24(2):328–32.PubMedGoogle Scholar
  28. 28.
    Hasegawa M, Sato S, Fujimoto M, Ihn H, Kikuchi K, Takehara K. Serum levels of interleukin 6 (IL-6), oncostatin M, soluble IL-6 receptor, and soluble gp130 in patients with systemic sclerosis. J Rheumatol. 1998;25(2):308–13.PubMedGoogle Scholar
  29. 29.
    Machy P, Truneh A. Differential half-life of major histocompatibility complex encoded class I molecules in T and B lymphoblasts. Mol Immunol. 1989;26(8):687–96. doi: 10.1016/0161-5890(89)90027-8.CrossRefPubMedGoogle Scholar
  30. 30.
    Truneh A, Machy P. Detection of very low receptor numbers on cells by flow cytometry using a sensitive staining method. Cytometry. 1987;8(6):562–7. doi: 10.1002/cyto.990080605.CrossRefPubMedGoogle Scholar
  31. 31.
    Betts AM, Clark TH, Yang J, Treadway JL, Li M, Giovanelli MA, et al. The application of target information and preclinical pharmacokinetic/pharmacodynamic modeling in predicting clinical doses of a Dickkopf-1 antibody for osteoporosis. J Pharmacol Exp Ther. 2010;333(1):2–13. doi: 10.1124/jpet.109.164129.CrossRefPubMedGoogle Scholar
  32. 32.
    Tabrizi MA, Tseng CM, Roskos LK. Elimination mechanisms of therapeutic monoclonal antibodies. Drug Discov Today. 2006;11(1–2):81–8. doi: 10.1016/S1359-6446(05)03638-X.CrossRefPubMedGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2014

Authors and Affiliations

  • Aman P. Singh
    • 1
  • Wojciech Krzyzanski
    • 1
  • Steven W. Martin
    • 2
  • Gregory Weber
    • 3
  • Alison Betts
    • 3
  • Alaa Ahmad
    • 4
  • Anson Abraham
    • 3
  • Anup Zutshi
    • 3
  • John Lin
    • 5
  • Pratap Singh
    • 3
    • 6
  1. 1.Department of Pharmaceutical SciencesUniversity at BuffaloBuffaloUSA
  2. 2.Global Clinical Pharmacology, Pfizer, IncCambridgeUSA
  3. 3.Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer IncCambridgeUSA
  4. 4.BioTx Clinical Research, Pfizer, IncCambridgeUSA
  5. 5.Rinat Experimental MedicineSan FranciscoUSA
  6. 6.CambridgeUSA

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