Utility of PK-PD Modeling and Simulation to Improve Decision Making for Antibody-Drug Conjugate Development

  • Aman P. Singh
  • Dhaval K. ShahEmail author
Part of the Cancer Drug Discovery and Development book series (CDD&D)


Comprehension of the pharmacokinetics (PK) and pharmacodynamics (PD) of Antibody-drug Conjugates (ADCs) can be challenging as it requires integration of the information stemming from various moieties (i.e. the antibody, the drug, and the conjugate). Computational modeling provides an excellent tool to overcome these challenges by providing an opportunity to integrate all the available information within a mathematical framework. With an ever-increasing pipeline of more than 60 ADC molecules currently in the clinic, plenty of resources and time are invested towards discerning some key questions associated with PK, efficacy, and toxicity of the most promising candidates. In order to streamline the process of finding the answers to these questions and to expedite the development of ADCs, mathematical modeling and simulation (M&S) can be employed at different stages of ADC development. Successful application of this tool can not only enhance the scientific understanding of the processes underlying PK-PD of ADCs but can also provide comprehensive model-derived outcomes that can help accelerate the decision-making process. Within this book chapter, we have discussed an array of different PK-PD models and modeling strategies that could be employed at discovery, preclinical, or clinical stages, to make rational decisions for the development of ADCs. In addition, suitable examples from the literature are discussed where M&S has been utilized to make key go/no-go decisions.


PK-PD Modeling Antibody-Drug Conjugate Model-Based Drug Development Preclinical-to-Clinical Translation Decision Making Population PK-PD Analysis 



This work was supported by NIH grant GM114179 to D.K.S., and the Centre for Protein Therapeutics at the State University of New York at Buffalo. Authors would also like to thank Dr. Amrita V. Kamath (Genentech®, Inc) for her helpful discussion while conception of this book chapter.


  1. 1.
    Kimko H, Pinheiro J (2015) Model-based clinical drug development in the past, present and future: a commentary. Br J Clin Pharmacol 79(1):108–116CrossRefGoogle Scholar
  2. 2.
    Seruga B, Ocana A, Amir E, Tannock IF (2015) Failures in phase III: causes and consequences. Clin Cancer Res 21(20):4552–4560CrossRefPubMedGoogle Scholar
  3. 3.
    Singh AP, Shah DK (2017) Application of a PK-PD modeling and simulation-based strategy for clinical translation of antibody-drug conjugates: a case study with Trastuzumab Emtansine (T-DM1). AAPS J 19(4):1054–1070CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kamath AV, Iyer S (2015) Preclinical pharmacokinetic considerations for the development of antibody drug conjugates. Pharm Res 32(11):3470–3479CrossRefPubMedGoogle Scholar
  5. 5.
    Lin K, Tibbitts J (2012) Pharmacokinetic considerations for antibody drug conjugates. Pharm Res 29(9):2354–2366CrossRefPubMedGoogle Scholar
  6. 6.
    Sapra P, Betts A, Boni J (2013) Preclinical and clinical pharmacokinetic/pharmacodynamic considerations for antibody-drug conjugates. Expert Rev Clin Pharmacol 6(5):541–555CrossRefPubMedGoogle Scholar
  7. 7.
    Behrens CR, Liu B (2014) Methods for site-specific drug conjugation to antibodies. MAbs 6(1):46–53CrossRefPubMedGoogle Scholar
  8. 8.
    Singh AP, Shin YG, Shah DK (2015) Application of pharmacokinetic-pharmacodynamic modeling and simulation for antibody-drug conjugate development. Pharm Res 32(11):3508–3525CrossRefPubMedGoogle Scholar
  9. 9.
    Singh AP, Shah DK (2017) Measurement and mathematical characterization of cell-level pharmacokinetics of antibody-drug conjugates: a case study with Trastuzumab-vc-MMAE. Drug Metab Dispos 45(11):1120–1132CrossRefPubMedGoogle Scholar
  10. 10.
    Khot A, Sharma S, Shah DK (2015) Integration of bioanalytical measurements using PK-PD modeling and simulation: implications for antibody-drug conjugate development. Bioanalysis 7(13):1633–1648CrossRefPubMedGoogle Scholar
  11. 11.
    Shah DK, Barletta F, Betts A, Hansel S (2013) Key bioanalytical measurements for antibody-drug conjugate development: PK/PD modelers’ perspective. Bioanalysis 5(9):989–992CrossRefPubMedGoogle Scholar
  12. 12.
    Shah DK, Haddish-Berhane N, Betts A (2012) Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin. J Pharmacokinet Pharmacodyn 39(6):643–659CrossRefPubMedGoogle Scholar
  13. 13.
    Chudasama VL, Schaedeli Stark F, Harrold JM, Tibbitts J, Girish SR, Gupta M et al (2012) Semi-mechanistic population pharmacokinetic model of multivalent trastuzumab emtansine in patients with metastatic breast cancer. Clin Pharmacol Ther 92(4):520–527CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lu D, Jin JY, Girish S, Agarwal P, Li D, Prabhu S et al (2015) Semi-mechanistic multiple-analyte pharmacokinetic model for an antibody-drug-conjugate in cynomolgus monkeys. Pharm Res 32(6):1907–1919CrossRefPubMedGoogle Scholar
  15. 15.
    Betts AM, Haddish-Berhane N, Tolsma J, Jasper P, King LE, Sun Y et al (2016) Preclinical to clinical translation of antibody-drug conjugates using PK/PD modeling: a retrospective analysis of inotuzumab ozogamicin. AAPS J 18(5):1101–1116CrossRefPubMedGoogle Scholar
  16. 16.
    Workgroup EM, Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M et al (2016) Good practices in model-informed drug discovery and development: practice, application, and documentation. CPT Pharmacometrics Syst Pharmacol 5(3):93–122CrossRefGoogle Scholar
  17. 17.
    Baumann A (2008) Preclinical development of therapeutic biologics. Exp Opin Drug Discov 3(3):289–297CrossRefGoogle Scholar
  18. 18.
    Singh AP, Maass KF, Betts AM, Wittrup KD, Kulkarni C, King LE et al (2016) Evolution of antibody-drug conjugate tumor disposition model to predict preclinical tumor pharmacokinetics of trastuzumab-emtansine (T-DM1). AAPS J 18(4):861–875CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Singh AP, Sharma S, Shah DK (2016) Quantitative characterization of in vitro bystander effect of antibody-drug conjugates. J Pharmacokinet Pharmacodyn 43(6):567–582CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Shah DK, King LE, Han X, Wentland JA, Zhang Y, Lucas J et al (2014) A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate. AAPS J 16(3):452–463CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Khot A, Tibbitts J, Rock D, Shah DK (2017) Development of a translational physiologically based pharmacokinetic model for antibody-drug conjugates: a case study with T-DM1. AAPS J 19(6):1715–1734. doi:  10.1208/s12248-017-0131-3 CrossRefPubMedGoogle Scholar
  22. 22.
    Shah DK, Betts AM (2012) Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J Pharmacokinet Pharmacodyn 39(1):67–86CrossRefPubMedGoogle Scholar
  23. 23.
    Haddish-Berhane N, Shah DK, Ma D, Leal M, Gerber HP, Sapra P et al (2013) On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach. J Pharmacokinet Pharmacodyn 40(5):557–571CrossRefPubMedGoogle Scholar
  24. 24.
    Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO (2002) Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol 20(24):4713–4721CrossRefPubMedGoogle Scholar
  25. 25.
    Tatipalli MDH (2012) Semi-physiological population PK/PD model of ADC neutropenia. University of Florida, GainesvilleGoogle Scholar
  26. 26.
    Ait-Oudhia S, Zhang W, Mager DEA (2017) Mechanism-based PK/PD model for hematological toxicities induced by antibody-drug conjugates. AAPS J 19(5):1436–1448. doi:  10.1208/s12248-017-0113-5 CrossRefPubMedGoogle Scholar
  27. 27.
    Bender BC, Schaedeli-Stark F, Koch R, Joshi A, Chu YW, Rugo H et al (2012) 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 70(4):591–601CrossRefPubMedGoogle Scholar
  28. 28.
    Sadekar S, Figueroa I, Tabrizi M (2015) Antibody drug conjugates: application of quantitative pharmacology in modality design and target selection. AAPS J 17(4):828–836CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Maass KF, Kulkarni C, Betts AM, Wittrup KD (2016) Determination of cellular processing rates for a trastuzumab-maytansinoid antibody-drug conjugate (ADC) highlights key parameters for ADC design. AAPS J 18(3):635–646CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Maass KF, Kulkarni C, Quadir MA, Hammond PT, Betts AM, Wittrup KDA (2015) Flow cytometric clonogenic assay reveals the single-cell potency of doxorubicin. J Pharm Sci 104(12):4409–4416CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bender B, Leipold DD, Xu K, Shen BQ, Tibbitts J, Friberg LEA (2014) mechanistic pharmacokinetic model elucidating the disposition of trastuzumab emtansine (T-DM1), an antibody-drug conjugate (ADC) for treatment of metastatic breast cancer. AAPS J 16(5):994–1008CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Sukumaran S, Gadkar K, Zhang C, Bhakta S, Liu L, Xu K et al (2015) Mechanism-based pharmacokinetic/pharmacodynamic model for THIOMAB drug conjugates. Pharm Res 32(6):1884–1893CrossRefPubMedGoogle Scholar
  33. 33.
    Zhao B ZS, Alley SC (2011) Physiologically-based pharmacokinetic modeling of an anti-CD70 auristatin antibody-drug conjugate in tumor-bearing mice. In: American conference on pharmacometrics (ACoP), San DiegoGoogle Scholar
  34. 34.
    Chen Y, Samineni D, Mukadam S, Wong H, Shen BQ, Lu D et al (2015) Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates. Clin Pharmacokinet 54(1):81–93CrossRefPubMedGoogle Scholar
  35. 35.
    Cilliers C, Guo H, Liao J, Christodolu N, Thurber GM (2016) Multiscale modeling of antibody-drug conjugates: connecting tissue and cellular distribution to whole animal pharmacokinetics and potential implications for efficacy. AAPS J 18(5):1117–1130CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Ferl GZ, AM W, JJ DS 3rd (2005) A predictive model of therapeutic monoclonal antibody dynamics and regulation by the neonatal Fc receptor (FcRn). Ann Biomed Eng 33(11):1640–1652CrossRefPubMedGoogle Scholar
  37. 37.
    Jumbe NL, Xin Y, Leipold DD, Crocker L, Dugger D, Mai E et al (2010) Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice. J Pharmacokinet Pharmacodyn 37(3):221–242CrossRefPubMedGoogle Scholar
  38. 38.
    Donaghy H (2016) Effects of antibody, drug and linker on the preclinical and clinical toxicities of antibody-drug conjugates. MAbs 8(4):659–671CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Bender BC, Schindler E, Friberg LE (2015) Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response. Br J Clin Pharmacol 79(1):56–71CrossRefPubMedGoogle Scholar
  40. 40.
    Gupta M, Lorusso PM, Wang B, Yi JH, Burris HA 3rd, Beeram M et al (2012) 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 52(5):691–703CrossRefPubMedGoogle Scholar
  41. 41.
    Lu D, Joshi A, Wang B, Olsen S, Yi JH, Krop IE et al (2013) 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 52(8):657–672CrossRefPubMedGoogle Scholar
  42. 42.
    Lu D, Gibiansky L, Agarwal P, Dere RC, Li C, Chu YW et al (2016) Integrated two-analyte population pharmacokinetic model for antibody-drug conjugates in patients: implications for reducing pharmacokinetic sampling. CPT Pharmacometrics Syst Pharmacol 5(12):665–673CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Kagedal M, Gibiansky L, Xu J, Wang X, Samineni D, Chen SC et al (2017) Platform model describing pharmacokinetic properties of vc-MMAE antibody-drug conjugates. J Pharmacokinet Pharmacodyn 44(6):537–548. doi:  10.1007/s10928-017-9544-y CrossRefPubMedGoogle Scholar
  44. 44.
    Luu KVE, Volkert A, Ogura M, Goy G, Boni J (2012) Antitumor response to inotuzumab ozogamicin (INO) in patients with refractory or relapsed indolent B-cell non-Hodgkin’ s l ymphomas (NHL): pharmacokinetic-pharmacodynamic (PK-PD) modeling and interim results from a phase II study. In: AACR 103rd annual meeting, ChicagoGoogle Scholar
  45. 45.
    Li C, Wang B, Chen SC, Wada R, Lu D, Wang X et al (2017) Exposure-response analyses of trastuzumab emtansine in patients with HER2-positive advanced breast cancer previously treated with trastuzumab and a taxane. Cancer Chemother Pharmacol 80(6):1079–1090. doi:  10.1007/s00280-017-3440-4 CrossRefPubMedGoogle Scholar
  46. 46.
    Mugundu GVE, Boni J (2012) Use of pharmacokineticpharmacodynamic modeling to characterize platelet response following inotuzumab ozogamicin treatment in patients with follicular or diffuse large B-cell non-Hodgkin’s lymphoma. In: AACR 103rd annual meeting, ChicagoGoogle Scholar
  47. 47.
    Li CLD, Samineni D, Kaagedal M, Chen C, Jin J, Girish S (eds) (2017) PK/PD modeling strategy to support the development of antibody drug conjugates. In: AAPS national biotechnology conference, San DiegoGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical SciencesThe State University of New York at BuffaloBuffaloUSA

Personalised recommendations