An Agent-Based Systems Pharmacology Model of the Antibody-Drug Conjugate Kadcyla to Predict Efficacy of Different Dosing Regimens

  • 19 Accesses


The pharmaceutical industry has invested significantly in antibody-drug conjugates (ADCs) with five FDA-approved therapies and several more showing promise in late-stage clinical trials. The FDA-approved therapeutic Kadcyla (ado-trastuzumab emtansine or T-DM1) can extend the survival of patients with tumors overexpressing HER2. However, tumor histology shows that most T-DM1 localizes perivascularly, but coadministration with its unconjugated form (trastuzumab) improves penetration of the ADC into the tumor and subsequent treatment efficacy. ADC dosing schedule, e.g., dose fractionation, has also been shown to improve tolerability. However, it is still not clear how coadministration with carrier doses impacts efficacy in terms of receptor expression, dosing regimens, and payload potency. Here, we develop a hybrid agent-based model (ABM) to capture ADC and/or antibody delivery and to predict tumor killing and growth kinetics. The results indicate that a carrier dose improves efficacy when the increased number of cells targeted by the ADC outweighs the reduced fractional killing of the targeted cells. The threshold number of payloads per cell required for killing plays a pivotal role in defining this cutoff. Likewise, fractionated dosing lowers ADC efficacy due to lower tissue penetration from a reduced maximum plasma concentration. It is only beneficial when an increase in tolerability from fractionation allows a higher ADC/payload dose that more than compensates for the loss in efficacy from fractionation. Overall, the multiscale model enables detailed depictions of heterogeneous ADC delivery, cancer cell death, and tumor growth to show how carrier dosing impacts efficacy to design the most efficacious regimen.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Coats S, Williams M, Kebble B, Dixit R, Tseng L, Yao NS, et al. Antibody-drug conjugates: future directions in clinical and translational strategies to improve the therapeutic Index. Clin Cancer Res. 2019;25:5441–8.

  2. 2.

    Cilliers C, Guo H, Liao J, Christodolu N, Thurber GM. Multiscale modeling of antibody-drug conjugates: connecting tissue and cellular distribution to whole animal pharmacokinetics and potential implications for efficacy. AAPS J. 2016;18(5):1117–30.

  3. 3.

    Bhatnagar S, Deschenes E, Liao J, Cilliers C, Thurber GM. Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors. J Pharm Sci. 2014;103(10):3276–86.

  4. 4.

    Baker JHE, Kyle AH, Reinsberg SA, Moosvi F, Patrick HM, Cran J, et al. Heterogeneous distribution of trastuzumab in HER2-positive xenografts and metastases: role of the tumor microenvironment. Clin Exp Metastasis. 2018;35(7):691–705.

  5. 5.

    Cilliers C, Menezes B, Nessler I, Linderman J, Thurber GM. Improved tumor penetration and single-cell targeting of antibody–drug conjugates increases anticancer efficacy and host survival. Cancer Res. 2018;78(3):758–68.

  6. 6.

    Hinrichs MJM, Ryan PM, Zheng B, Afif-Rider S, Yu XQ, Gunsior M, et al. Fractionated dosing improves preclinical therapeutic index of pyrrolobenzodiazepine-containing antibody drug conjugates. Clin Cancer Res. 2017;23(19):5858–68.

  7. 7.

    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.

  8. 8.

    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.

  9. 9.

    Thurber GM, Dane WK. A mechanistic compartmental model for total antibody uptake in tumors. J Theor Biol. 2012;314:57–68.

  10. 10.

    Vasalou C, Helmlinger G, Gomes B. A mechanistic tumor penetration model to guide antibody drug conjugate design. PLoS One. 2015;10(3):e0118977.

  11. 11.

    Singh AP, Shah DK. A “dual” cell-level systems PK-PD model to characterize the bystander effect of ADC. J Pharm Sci. 2019;108:2465–75.

  12. 12.

    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(5):994–1008.

  13. 13.

    Cilliers C, Nessler I, Christodolu N, Thurber GM. Tracking antibody distribution with near-infrared fluorescent dyes: impact of dye structure and degree of labeling on plasma clearance. Mol Pharm. 2017;14(5):1623–33.

  14. 14.

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

  15. 15.

    Nugent L, Jain RK. Extravascular diffusion in normal and neoplastic tissues. Cancer Res. 1984;44:238–44.

  16. 16.

    Thurber GM, Schmidt MM, Wittrup KD. Antibody tumor penetration: transport opposed by systemic and antigen-mediated clearance. Adv Drug Deliv Rev. 2008;60(12):1421–34.

  17. 17.

    Thurber GM, Weissleder R. Quantitating antibody uptake in vivo: conditional dependence on antigen expression levels. Mol Imaging Biol. 2011;13(4):623–32.

  18. 18.

    Bostrom J, Haber L, Koenig P, Kelley RF, Fuh G. High affinity antigen recognition of the dual specific variants of herceptin is entropy-driven in spite of structural plasticity. PLoS One. 2011;6(4):e17887.

  19. 19.

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

  20. 20.

    Maass KF, Kulkarni C, Betts AM, Wittrup KD. Determination of cellular processing rates for a trastuzumab-maytansinoid antibody-drug conjugate (ADC) highlights key parameters for ADC design. AAPS J. 2016;18(3):635–46.

  21. 21.

    Khera E, Cilliers C, Bhatnagar S, Thurber GM. Computational transport analysis of antibody-drug conjugate bystander effects and payload tumoral distribution: implications for therapy. Mol Syst Des Eng. 2018;3(1):73–88.

  22. 22.

    Poon KA, Flagella K, Beyer J, Tibbitts J, Kaur S, Saad O, et al. Preclinical safety profile of trastuzumab emtansine (T-DM1): mechanism of action of its cytotoxic component retained with improved tolerability. Toxicol Appl Pharmacol. 2013;273(2):298–313.

  23. 23.

    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.

  24. 24.

    Yuan F, Dellian M, Fukumura D, Leunig M, Berk DA, Torchilin VP, et al. Vascular permeability in a human tumor xenograft: molecular size dependence and cutoff size. Cancer Res. 1995;55(17):3752–6.

  25. 25.

    Cilfone NA, Kirschner DE, Linderman JJ. Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems. Cell Mol Bioeng. 2015;8(1):119–36.

  26. 26.

    Forster JC, Harriss-Phillips WM, Douglass MJ, Bezak E. A review of the development of tumor vasculature and its effects on the tumor microenvironment. Hypoxia (Auckl). 2017;5:21–32.

  27. 27.

    Cardillo TM, Govindan SV, Sharkey RM, Trisal P, Arrojo R, Liu D, et al. Sacituzumab govitecan (IMMU-132), an anti-Trop-2/SN-38 antibody-drug conjugate: characterization and efficacy in pancreatic, gastric, and other cancers. Bioconjug Chem. 2015;26(5):919–31.

  28. 28.

    Ahmed S, Ellis M, Li H, Pallucchini L, Stein AM. Guiding dose selection of monoclonal antibodies using a new parameter (AFTIR) for characterizing ligand binding systems. J Pharmacokinet Pharmacodyn. 2019;46(3):287–304.

  29. 29.

    Prabhu S, Boswell CA, Leipold D, Khawli L, Li D, Lu D, et al. Antibody delivery of drugs and radionuclides: factors influencing clinical pharmacology. Ther Deliv. 2011;6(2):769–91.

  30. 30.

    Shah DK, Loganzo F, Haddish-Berhane N, Musto S, Wald HS, Barletta F, et al. Establishing in vitro-in vivo correlation for antibody drug conjugate efficacy: a PK/PD modeling approach. J Pharmacokinet Pharmacodyn. 2018;45(2):339–49.

  31. 31.

    Hamblett K, Senter P, Chace D, Sun M, Lenox J, Cerveny C, et al. Effects of drug loading on the antitumor activity of a monoclonal antibody drug conjugate. Clin Cancer Res. 2004;10:7063–70.

  32. 32.

    Sukumaran S, Gadkar K, Zhang C, Bhakta S, Liu L, Xu K, et al. Mechanism-based pharmacokinetic/pharmacodynamic model for THIOMAB drug conjugates. Pharm Res. 2015;32(6):1884–93.

  33. 33.

    Rosenberg JE, O’Donnell PH, Balar AV, McGregor BA, Heath EI, Yu EY, et al. Pivotal trial of enfortumab vedotin in urothelial carcinoma after platinum and anti-programmed death 1/programmed death ligand 1 therapy. J Clin Oncol. 2019:37(29):2592–600.

  34. 34.

    Wang J, Seebacher N, Shi H, Kan Q, Zhenfeng D. Novel strategies to prevent the development of multidrug resistance (MDR) in cancer. Oncotarget. 2017;8(48):84559–71.

  35. 35.

    Erickson HK, Lewis Phillips GD, Leipold DD, Provenzano CA, Mai E, Johnson HA, et al. The effect of different linkers on target cell catabolism and pharmacokinetics/pharmacodynamics of trastuzumab maytansinoid conjugates. Mol Cancer Ther. 2012;11(5):1133–42.

  36. 36.

    Wittrup KD. Antitumor antibodies can drive therapeutic T cell responses. Trends Cancer. 2017;3(9):615–20.

  37. 37.

    Rios-Doria J, Harper J, Rothstein R, Wetzel L, Chesebrough J, Marrero A, et al. Antibody-drug conjugates bearing pyrrolobenzodiazepine or tubulysin payloads are immunomodulatory and synergize with multiple immunotherapies. Cancer Res. 2017;77(10):2686–98.

  38. 38.

    Pienaar E, Sarathy J, Prideaux B, Dietzold J, Dartois V, Kirschner DE, et al. Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLoS Comput Biol. 2017;13(8):e1005650.

  39. 39.

    Cilfone NA, Ford CB, Marino S, Mattila JT, Gideon HP, Flynn JL, et al. Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection. J Immunol. 2015;194(2):664–77.

  40. 40.

    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(1):148–63.

Download references


The authors acknowledge funding from NIH R01 CA196018 (JJL) and R35 GM128819 (GMT). The authors also thank Paul Wolberg for technical assistance.

Author information

Correspondence to Jennifer J. Linderman.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(DOCX 1765 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Menezes, B., Cilliers, C., Wessler, T. et al. An Agent-Based Systems Pharmacology Model of the Antibody-Drug Conjugate Kadcyla to Predict Efficacy of Different Dosing Regimens. AAPS J 22, 29 (2020) doi:10.1208/s12248-019-0391-1

Download citation


  • Antibody-Drug Conjugates
  • Pharmacokinetics and Pharmacodynamics
  • Multiscale Agent-Based Model
  • Kadcyla
  • Trastuzumab