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Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Antibody-Drug Conjugate Development

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ABSTRACT

Characterization and prediction of the pharmacokinetics (PK) and pharmacodynamics (PD) of Antibody-Drug Conjugates (ADCs) is challenging, since it requires simultaneous quantitative understanding about the PK-PD properties of three different molecular species i.e., the monoclonal antibody, the drug, and the conjugate. Mathematical modeling and simulation provides an excellent tool to overcome these challenges, as it can simultaneously integrate the PK-PD of ADCs and their components in a quantitative manner. Additionally, the computational PK-PD models can also serve as a cornerstone for the model-based drug development and preclinical-to-clinical translation of ADCs. To provide an overview of this subject matter, this manuscript reviews the PK-PD models applicable to ADCs. Additionally, the usage of these models during different drug development stages (i.e., discovery, preclinical development, and clinical development) is also emphasized. The importance of PK-PD modeling and simulation in making rationale go/no-go decisions throughout the drug development process is also highlighted. There is an array of PK-PD models available, ranging from the systems models specifically developed for ADCs to the empirical models applicable to all chemotherapeutic agents, which one can employ for ADCs. The decision about which model to choose depends on the questions to be answered, time at hand, and resources available.

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Abbreviations

ADC:

Antibody-drug conjugates

ADME:

Absorption, distribution, metabolism and elimination

AUC:

Area under the curve

DAR:

Drug: antibody ratio

ELISA:

Enzyme-linked immunosrbent assay

IgG:

Immunoglobulin G

IV:

Intravenous

IVIVC:

In vitro-in vivo correlation

LC-MS:

Liquid chromatography-mass spectrometry

mAb:

Monoclonal antibody

MBDD:

Model-based drug development

mPBPK:

Minimal physiologically-based pharmacokinetic

MTD:

Maximum tolerated dose

NCA:

Non-compartmental analysis

ORR:

Objective response rate

PBPK:

Physiologically-based pharmacokinetic

PD:

Pharmacodynamics

PFS:

Progression free survival

PK:

Pharmacokinetics

PK-PD:

Pharmacokinetics-pharmacodynamics

PK-TD:

Pharmacokinetics-toxicodynamics

PP:

Proliferating population

TDC:

ThioMab-drug conjugates

T-DM1:

Trastuzumab-emtansine

TGI:

Tumor growth inhibition

TI:

Therapeutic index

TSC:

Tumor static concentration

TV:

Tumor volume

REFERENCES

  1. Sassoon I, Blanc V. Antibody-drug conjugate (ADC) clinical pipeline: a review. Methods Mol Biol. 2013;1045:1–27.

    Article  PubMed  Google Scholar 

  2. Minich SS. Brentuximab vedotin: a new age in the treatment of Hodgkin lymphoma and anaplastic large cell lymphoma. Ann Pharmacother. 2012;46(3):377–83.

    Article  PubMed  Google Scholar 

  3. Gerber HP, Koehn FE, Abraham RT. The antibody-drug conjugate: an enabling modality for natural product-based cancer therapeutics. Nat Prod Rep. 2013;30(5):625–39.

    Article  CAS  PubMed  Google Scholar 

  4. Mullard A. Maturing antibody-drug conjugate pipeline hits 30. Nat Rev Drug Discov. 2013;12(5):329–32.

    Article  CAS  PubMed  Google Scholar 

  5. Shah DK, Barletta F, Betts A, Hansel S. Key bioanalytical measurements for antibody-drug conjugate development: PK/PD modelers’ perspective. Bioanalysis. 2013;5(9):989–92.

    Article  CAS  PubMed  Google Scholar 

  6. Milligan PA, Brown MJ, Marchant B, Martin SW, van der Graaf PH, Benson N, et al. Model-based drug development: a rational approach to efficiently accelerate drug development. Clin Pharmacol Ther. 2013;93(6):502–14.

    Article  CAS  PubMed  Google Scholar 

  7. Shah DK, Haddish-Berhane N, Betts A. Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin. J Pharmacokinet Pharmacodyn. 2012;39(6):643–59.

    Article  PubMed  Google Scholar 

  8. Jackson D, Atkinson J, Guevara CI, Zhang C, Kery V, Moon SJ, et al. In vitro and in vivo evaluation of cysteine and site specific conjugated herceptin antibody-drug conjugates. PLoS ONE. 2014;9(1):e83865.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Okeley NM, Miyamoto JB, Zhang X, Sanderson RJ, Benjamin DR, Sievers EL, et al. Intracellular activation of SGN-35, a potent anti-CD30 antibody-drug conjugate. Clin Cancer Res Off J Am Assoc Cancer Res. 2010;16(3):888–97.

    Article  CAS  Google Scholar 

  10. Haddish-Berhane N, Shah DK, Ma D, Leal M, Gerber HP, Sapra P, et al. On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach. J Pharmacokinet Pharmacodyn. 2013;40(5):557–71.

    Article  CAS  PubMed  Google Scholar 

  11. Hamed SS, Roth CM. Mathematical modeling to distinguish cell cycle arrest and cell killing in chemotherapeutic concentration response curves. J Pharmacokinet Pharmacodyn. 2011;38(3):385–403.

    Article  PubMed  Google Scholar 

  12. Muller PY, Milton MN. The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov. 2012;11(10):751–61.

    Article  CAS  PubMed  Google Scholar 

  13. Gorovits B, Alley SC, Bilic S, Booth B, Kaur S, Oldfield P, et al. Bioanalysis of antibody-drug conjugates: American Association of Pharmaceutical Scientists Antibody-Drug Conjugate Working Group position paper. Bioanalysis. 2013;5(9):997–1006.

    Article  CAS  PubMed  Google Scholar 

  14. Wagner-Rousset E, Janin-Bussat MC, Colas O, Excoffier M, Ayoub D, Haeuw JF, et al. Antibody-drug conjugate model fast characterization by LC-MS following IdeS proteolytic digestion. mAbs. 2014;6(1):273–85.

    Article  PubMed  Google Scholar 

  15. Bender B, Leipold DD, Xu K, Shen BQ, Tibbitts J, Friberg LE. A mechanistic pharmacokinetic model elucidating the disposition of trastuzumab emtansine (T-DM1), an antibody-drug conjugate (ADC) for treatment of metastatic breast cancer. AAPS J. 2014.

  16. Hamblett KJ, Senter PD, Chace DF, Sun MM, Lenox J, Cerveny CG, et al. Effects of drug loading on the antitumor activity of a monoclonal antibody drug conjugate. Clin Cancer Res Off J Am Assoc Cancer Res. 2004;10(20):7063–70.

    Article  CAS  Google Scholar 

  17. Panowksi S, Bhakta S, Raab H, Polakis P, Junutula JR. Site-specific antibody drug conjugates for cancer therapy. mAbs. 2014;6(1):34–45.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Sukumaran S, Gadkar K, Zhang C, Bhakta S, Xu K, Raab H, et al. Mechanism-based pharmacokinetic/pharmacodynamics model for THIOMAB drug conjugates. Annual conference of American Association of Pharmaceutical Sciences (AAPS). Texas; 2013. Poster # T3237.

  19. Shah DK, King LE, Han X, Wentland JA, Zhang Y, Lucas J, et al. A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate. AAPS J. 2014;16(3):452–63.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Schmidt MM, Wittrup KD. A modeling analysis of the effects of molecular size and binding affinity on tumor targeting. Mol Cancer Ther. 2009;8(10):2861–71.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Thurber GM, Schmidt MM, Wittrup KD. Factors determining antibody distribution in tumors. Trends Pharmacol Sci. 2008;29(2):57–61.

    PubMed Central  CAS  PubMed  Google Scholar 

  22. Thurber GM, Zajic SC, Wittrup KD. Theoretic criteria for antibody penetration into solid tumors and micrometastases. J Nucl Med Off Publ, Soc Nucl Med. 2007;48(6):995–9.

    CAS  Google Scholar 

  23. Thurber GM, Weissleder R. A systems approach for tumor pharmacokinetics. PLoS ONE. 2011;6(9):e24696.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Shah DK, Balthasar JP. Physiologically based pharmacokinetic model for topotecan in mice. J Pharmacokinet Pharmacodyn. 2011;38(1):121–42.

    Article  CAS  PubMed  Google Scholar 

  25. Shah DK, Betts AM. Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J Pharmacokinet Pharmacodyn. 2012;39(1):67–86.

    Article  CAS  PubMed  Google Scholar 

  26. Shah DK, Balthasar JP. Predicting the effects of 8C2, a monoclonal anti-topotecan antibody, on plasma and tissue disposition of topotecan. J Pharmacokinet Pharmacodyn. 2014;41(1):55–69.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Shah DK, Balthasar JP. PK/TD modeling for prediction of the effects of 8C2, an anti-topotecan mAb, on topotecan-induced toxicity in mice. Int J Pharm. 2014;465(1–2):228–38.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Garg A, Balthasar JP. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007;34(5):687–709.

    Article  CAS  PubMed  Google Scholar 

  29. Shah DK, Betts AM. Antibody biodistribution coefficients: inferring tissue concentrations of monoclonal antibodies based on the plasma concentrations in several preclinical species and human. mAbs. 2013;5(2):297–305.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Zhao B, Zheng S, Alley SC. Physiologically-based pharmacokinetic modeling of an anti-CD70 auristatin antibody-drug conjugate in tumor-bearing mice. American Conference on Pharmacometrics (ACoP). San Diego; 2011.

  31. Chen Y, Samineni D, Mukadam S, Wong H, Shen BQ, Lu D, et al. Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates. Clin Pharmacokinet. 2014.

  32. Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 2004;64(3):1094–101.

    Article  CAS  PubMed  Google Scholar 

  33. Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPPS Pharmsci. 2002;4(4):E42.

    Google Scholar 

  34. Jumbe NL, Xin Y, Leipold DD, Crocker L, Dugger D, Mai E, et al. Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice. J Pharmacokinet Pharmacodyn. 2010;37(3):221–42.

    Article  CAS  PubMed  Google Scholar 

  35. Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Onco Off J Am Soc Clin Oncol. 2002;20(24):4713–21.

    Article  Google Scholar 

  36. Tatipalli M, Derendorf H. Semi-physiological population PK/PD model of ADC neutropenia. M.S. Thesis. Pharmaceutical Sciences. Gainesville: University of Florida; 2012.

  37. Gupta M, Lorusso PM, Wang B, Yi JH, Burris 3rd HA, Beeram M, et al. Clinical implications of pathophysiological and demographic covariates on the population pharmacokinetics of trastuzumab emtansine, a HER2-targeted antibody-drug conjugate, in patients with HER2-positive metastatic breast cancer. J Clin Pharmacol. 2012;52(5):691–703.

    Article  CAS  PubMed  Google Scholar 

  38. Bender BC, Schaedeli-Stark F, Koch R, Joshi A, Chu YW, Rugo H, et al. A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer. Cancer Chemother Pharmacol. 2012;70(4):591–601.

    Article  CAS  PubMed  Google Scholar 

  39. Lu D, Joshi A, Wang B, Olsen S, Yi JH, Krop IE, et al. An integrated multiple-analyte pharmacokinetic model to characterize trastuzumab emtansine (T-DM1) clearance pathways and to evaluate reduced pharmacokinetic sampling in patients with HER2-positive metastatic breast cancer. Clin Pharmacokinet. 2013;52(8):657–72.

    Article  CAS  PubMed  Google Scholar 

  40. Chudasama VL, Schaedeli Stark F, Harrold JM, Tibbitts J, Girish SR, Gupta M, et al. Semi-mechanistic population pharmacokinetic model of multivalent trastuzumab emtansine in patients with metastatic breast cancer. Clin Pharmacol Ther. 2012;92(4):520–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn. 2001;28(6):507–32.

    Article  CAS  PubMed  Google Scholar 

  42. Gibiansky L, Gibiansky E. Target-mediated drug disposition model and its approximations for antibody-drug conjugates. J Pharmacokinet Pharmacodyn. 2014;41(1):35–47.

    Article  CAS  PubMed  Google Scholar 

  43. Luu K, Vandendries E, Volkert A, Ogura M, Goy G, Boni J. Antitumor response to inotuzumab ozogamicin (INO) in patients with refractory or relapsed indolent B-cell non-Hodgkin’s lymphomas (NHL): pharmacokinetic-pharmacodynamic (PK-PD) modeling and interim results from a phase II study AACR 103rd Annual Meeting Chicago; 2012. Poster # 7114.

  44. Mugundu G, Vandendries E, Boni J. Use of pharmacokinetic-pharmacodynamic modeling to characterize platelet response following inotuzumab ozogamicin treatment in patients with follicular or diffuse large B-cell non-Hodgkin’s lymphoma AACR 103rd Annual Meeting Chicago; 2012. Poster # 3776.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, New York, 14214-8033, USA

    Aman P. Singh & Dhaval K. Shah

  2. College of Pharmacy, Chungnam National University, Daejeon, South Korea

    Young G. Shin

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  1. Aman P. Singh
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Correspondence to Dhaval K. Shah.

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Singh, A.P., Shin, Y.G. & Shah, D.K. Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Antibody-Drug Conjugate Development. Pharm Res 32, 3508–3525 (2015). https://doi.org/10.1007/s11095-015-1626-1

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