Skip to main content

Advertisement

SpringerLink
Log in

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates

  • Review Article
  • Published:
The AAPS Journal Aims and scope Submit manuscript

Abstract

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Strebhardt K, Ullrich A. Paul Ehrlich’s magic bullet concept: 100 years of progress. Nat Rev Cancer. 2008;8(6):473–80. https://doi.org/10.1038/nrc2394.

    Article  CAS  PubMed  Google Scholar 

  2. Liu R, Wang RE, Wang F. Antibody-drug conjugates for non-oncological indications. Expert Opin Biol Ther. 2016;16(5):591–3. https://doi.org/10.1517/14712598.2016.1161753.

    Article  PubMed  Google Scholar 

  3. Wang RE, Liu T, Wang Y, Cao Y, Du J, Luo X, et al. An immunosuppressive antibody drug conjugate. J Am Chem Soc. 2015;137:3229–32. https://doi.org/10.1021/jacs.5b00620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lim RKV, Yu S, Cheng B, Li S, Kim NJ, Cao Y, et al. Targeted delivery of LXR agonist using a site-specific antibody−drug conjugate. Bioconjug Chem. 2015;26:2216–22. https://doi.org/10.1021/acs.bioconjchem.5b00203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lehar SM, Pillow T, Xu M, Staben L, Kajihara KK, Vandlen R, et al. Novel antibody–antibiotic conjugate eliminates intracellular S. aureus. Nature. 2015;527:323–8. https://doi.org/10.1038/nature16057.

    Article  CAS  PubMed  Google Scholar 

  6. Thol F, Schlenk RF. Gemtuzumab ozogamicin in acute myeloid leukemia revisited. Expert Opin Biol Ther. 2014 Aug;14(8):1185–95. https://doi.org/10.1517/14712598.2014.922534.

    Article  CAS  PubMed  Google Scholar 

  7. 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. 2004 Oct;10:7063–70. https://doi.org/10.1158/1078-0432.CCR-04-0789.

    Article  CAS  PubMed  Google Scholar 

  8. Ritchie M, Tchistiakova L, Scott N. Implications of receptor-mediated endocytosis and intracellular trafficking dynamics in the development of antibody drug conjugates. MAbs. 2013 Jan 1;5(1):13–21. https://doi.org/10.4161/mabs.22854.

    Article  PubMed  PubMed Central  Google Scholar 

  9. McCombs JR, Owen SC. Antibody drug conjugates: design and selection of linker, payload and conjugation chemistry. AAPS J. 2015 Mar;17(2):339–51. https://doi.org/10.1208/s12248-014-9710-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Klute K, Nackos E, Tasaki S, Nguyen DP, Bander NH, Tagawa ST. Microtubule inhibitor-based antibody–drug conjugates for cancer therapy. Onco Targets Ther. 2014;7:2227–36. https://doi.org/10.2147/OTT.S46887.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fu Y, Ho M. DNA damaging agent-based antibody-drug conjugates for cancer therapy. Antib Ther. 2018 Sep;1(2):43–53. https://doi.org/10.1093/abt/tby007.

    Article  CAS  PubMed Central  Google Scholar 

  12. Senter PD, Sievers EL. The discovery and development of brentuximab vedotin for use in relapsed Hodgkin lymphoma and systemic anaplastic large cell lymphoma. Nat Biotechnol. 2012;30(7):631–7. https://doi.org/10.1038/nbt.2289.

    Article  CAS  PubMed  Google Scholar 

  13. 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. https://doi.org/10.1158/1535-7163.mct-11-0727.

    Article  CAS  PubMed  Google Scholar 

  14. Polakis P. Antibody drug conjugates for cancer therapy. Pharmacol Rev. 2016 Jan;68(1):3–19. https://doi.org/10.1124/pr.114.009373.

    Article  CAS  PubMed  Google Scholar 

  15. Sun X, Widdison W, Mayo M, Wilhelm S, Leece B, Chari R, et al. Design of antibody-maytansinoid conjugates allows for efficient detoxification via liver metabolism. Bioconjug Chem. 2011 Apr 20;22(4):728–35. https://doi.org/10.1021/bc100498q.

    Article  CAS  PubMed  Google Scholar 

  16. Deslandes A. Comparative clinical pharmacokinetics of antibody-drug conjugates in first-in-human phase 1 studies. MAbs. 2014 Jul 1;6(4):859–70. https://doi.org/10.4161/mabs.28965.

  17. Boswell CA, Mundo EE, Zhang C, Bumbaca D, Valle NR, Kozak KR, et al. Impact of drug conjugation on pharmacokinetics and tissue distribution of anti-STEAP1 antibody-drug conjugates in rats. Bioconjug Chem. 2011;22:1994–2004. https://doi.org/10.1021/bc200212a.

    Article  CAS  PubMed  Google Scholar 

  18. Kamath AV, Iyer S. Challenges and advances in the assessment of the disposition of antibody-drug conjugates. Biopharm Drug Dispos. 2016;37:66–74. https://doi.org/10.1002/bdd.1957.

    Article  CAS  PubMed  Google Scholar 

  19. Lyon RP, Bovee TD, Doronina SO, Burke PJ, Hunter JH, Neff-LaFord HD, et al. Reducing hydrophobicity of homogeneous antibody-drug conjugates improves pharmacokinetics and therapeutic index. Nat Biotechnol. 2015;33:733–5. https://doi.org/10.1038/nbt.3212.

    Article  CAS  PubMed  Google Scholar 

  20. Kamath AV, Iyer S. Preclinical pharmacokinetic considerations for the development of antibody drug conjugates. Pharm Res. 2015 Nov;32(11):3470–9. https://doi.org/10.1007/s11095-014-1584-z.

    Article  CAS  PubMed  Google Scholar 

  21. Xie H, Audette C, Hoffee M, Lambert JM, Blattler WA. Pharmacokinetics and biodistribution of the antitumor immunoconjugate, cantuzumab mertansine (huC242-DM1), and its two components in mice. J Pharmacol Exp Ther. 2004;308:1073–82. https://doi.org/10.1124/jpet.103.060533.

    Article  CAS  PubMed  Google Scholar 

  22. Younes A, Bartlett NL, Leonard JP, Kennedy DA, Lynch CM, Sievers EL, et al. Brentuximab vedotin (SGN-35) for relapsed CD30-positive lymphomas. N Engl J Med. 2010 Nov 4;363(19):1812–21. https://doi.org/10.1056/NEJMoa1002965.

    Article  CAS  PubMed  Google Scholar 

  23. Lucas AT, Robinson R, Schorzman AN, Piscitelli JA, Razo JF, Zamboni WC. Pharmacologic considerations in the disposition of antibodies and antibody-drug conjugates in preclinical models and in patients. Antibodies (Basel). 2019 Jan 1;8(1). https://doi.org/10.3390/antib8010003.

  24. Horstmann M, Witthuhn R, Falk M, Stenzl A. Gender-specific differences in bladder cancer: a retrospective analysis. Gend Med. 2008 Dec;5(4):385–94. https://doi.org/10.1016/j.genm.2008.11.002.

    Article  PubMed  Google Scholar 

  25. FDA. ADCETRIS – highlights of prescribing information. 2017. https://www.accessdata.fda.gov/drugsatfda docs/label/2017/125388s094lbl.pdf. Accessed 28 Apr 2020.

  26. FDA. BESPONSA – highlights of prescribing information. 2017. https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/761040s000lbl.pdf. Accessed 28 Apr 2020.

  27. FDA. ENHERTU – highlights of prescribing information. 2019. https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761139s000lbl.pdf. Accessed 28 Apr 2020.

  28. FDA. KADCYLA - highlights of prescribing information. 2013. https://www.accessdata.fda.gov/drugsatfda_docs/label/2013/125427lbl.pdf. Accessed 28 Apr 2020.

  29. FDA. MYLOTARG – highlights of prescribing information. Initial approval 2000. Revised 2017. https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/761060lbl.pdf. Accessed 28 Apr 2020.

  30. FDA. PADCEV – highlights of prescribing information. 2019. https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761137s000lbl.pdf. Accessed 28 Apr 2020.

  31. FDA. POLIVY – highlights of prescribing information. 2019. https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/761121s000lbl.pdf. Accessed 28 Apr 2020.

  32. FDA. TRODELVY – highlights of prescribing information. 2020. https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/761115s000lbl.pdf. Accessed 28 Apr 2020.

  33. FDA. Guidance for industry: pharmacokinetics in patients with impaired renal function—study design data analysis, and impact on dosing and labeling. 2010. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pharmacokinetics-patients-impaired-renal-function-study-design-data-analysis-and-impact-dosing-and. Accessed 28 Apr 2020.

  34. FDA. Guidance for industry: pharmacokinetics in patients with impaired hepatic function: study design data analysis, and impact on dosing and labeling. 2003. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/pharmacokinetics-patients-impaired-hepatic-function-study-design-data-analysis-and-impact-dosing-and. Accessed 28 Apr 2020.

  35. FDA. Guidance for industry, immunogenicity assessment for therapeutic protein products. 2014. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/immunogenicity-assessment-therapeutic-protein-products. Accessed 28 Apr 2020.

  36. Carrasco-Triguero M. Insights on the immunogenicity of antibody-drug conjugates. Bioanalysis. 2015;7(13):1565–8. https://doi.org/10.4155/bio.15.86.

    Article  CAS  PubMed  Google Scholar 

  37. Ali S, Dunmore HM, Karres D, Hay JL, Salmonsson T, Gisselbrecht C, et al. The EMA review of mylotarg (gemtuzumab ozogamicin) for the treatment of acute myeloid leukemia. Oncologist. 2019 May;24(5):e171–9. https://doi.org/10.1634/theoncologist.2019-0025.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kenny JR, Liu MM, Chow AT, Earp JC, Evers R, Slatter JG, et al. Therapeutic protein drug–drug interactions: navigating the knowledge gaps– highlights from the 2012 AAPS NBC roundtable and IQ consortium/FDA workshop. AAPS J. 2013 Oct;15(4):933–40. https://doi.org/10.1208/s12248-013-9495-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bonate PL, Ahamadi M, Budha N, de la Peña A, Earp JC, Hong Y, et al. Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) working group. J Pharmacokinet Pharmacodyn. 2016 Apr;43(2):123–35. https://doi.org/10.1007/s10928-016-9464-2.

    Article  CAS  PubMed  Google Scholar 

  40. Han TH, Gopal KA, Ramchandren R, Goy A, Chen R, Matous JV, et al. CYP3A-mediated drug-drug interaction potential and excretion of brentuximab vedotin, an antibody-drug conjugate, in patients with CD30-positive hematologic malignancies. J Clin Pharmacol. 2013 Aug;53(8):866–77. https://doi.org/10.1002/jcph.116.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Xu K, Liu L, Saad OM, Baudys J, Williams L, Leipold D, et al. Characterization of intact antibody-drug conjugates from plasma/serum in vivo by affinity capture capillary liquid chromatography-mass spectrometry. Anal Biochem. 2011 May 1;412(1):56–66. https://doi.org/10.1016/j.ab.2011.01.004.

    Article  CAS  PubMed  Google Scholar 

  42. Dowell JA, Korth-Bradley J, Liu H, King SP, Berger MS. Pharmacokinetics of gemtuzumab ozogamicin, an antibody-targeted chemotherapy agent for the treatment of patients with acute myeloid leukemia in first relapse. J Clin Pharmacol. 2001 Nov;41(11):1206–14. https://doi.org/10.1177/00912700122012751.

    Article  CAS  PubMed  Google Scholar 

  43. Buckwalter M, Dowell JA, Korth-Bradley J, Gorovits B, Mayer PR. Pharmacokinetics of gemtuzumab ozogamicin as a single-agent treatment of pediatric patients with refractory or relapsed acute myeloid leukemia. J Clin Pharmacol. 2004 Aug;44(8):873–80. https://doi.org/10.1177/0091270004267595.

    Article  CAS  PubMed  Google Scholar 

  44. Kobayashi Y, Tobinai K, Takeshita A, Naito K, Asai O, Dobashi N, et al. Phase I/II study of humanized anti-CD33 antibody conjugated with calicheamicin, gemtuzumab ozogamicin, in relapsed or refractory acute myeloid leukemia: final results of Japanese multicenter cooperative study. Int J Hematol. 2009 May;89(4):460–9. https://doi.org/10.1007/s12185-009-0298-1.

    Article  CAS  PubMed  Google Scholar 

  45. Korth-Bradley JM, Dowell JA, King SP, Liu H, Berger MS, Mylotarg Study Group. Impact of age and gender on the pharmacokinetics of gemtuzumab ozogamicin. Pharmacotherapy. 2001 Oct;21(10):1175–80. https://doi.org/10.1592/phco.21.15.1175.33890.

    Article  CAS  PubMed  Google Scholar 

  46. Gupta M, Lorusso PM, Wang B, Yi JH, Burris HA 3rd, 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 May;52(5):691–703. https://doi.org/10.1177/0091270011403742.

    Article  CAS  PubMed  Google Scholar 

  47. Lu D, Girish S, Gao Y, Wang B, Yi JH, Guardino E, et al. Population pharmacokinetics of trastuzumab emtansine (T-DM1), a HER2-targeted antibody-drug conjugate, in patients with HER2-positive metastatic breast cancer: clinical implications of the effect of covariates. Cancer Chemother Pharmacol. 2014 Aug;74(2):399–410. https://doi.org/10.1007/s00280-014-2500-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Yin O, Xiong Y, Endo S, Yoshihara K, AbuTarif M, Wada R, et al. Population pharmacokinetic analysis of DS-8201a ([Fam-] Trastuzumab Deruxtecan), a HER2-targeting antibody-drug conjugate. In: Patients with HER2-positive breast Cancer or other solid tumors. Washington, D.C: Abstract presented at the American Society for Clinical Pharmacology & Therapeutics Annual Meeting, Marc; 2019.

    Google Scholar 

  49. Garrett M, Ruiz-Garcia A, Parivar K, Hee B, Boni J. Population pharmacokinetics of inotuzumab ozogamicin in relapsed/refractory acute lymphoblastic leukemia and non-Hodgkin lymphoma. J Pharmacokinet Pharmacodyn. 2019 Jun;46(3):211–22. https://doi.org/10.1007/s10928-018-9614-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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 Aug;52(8):657–72. https://doi.org/10.1007/s40262-013-0060-y.

    Article  CAS  PubMed  Google Scholar 

  51. Li H, Han TH, Hunder NN, Jang G, Zhao B. Population pharmacokinetics of brentuximab vedotin in patients with CD30-expressing hematologic malignancies. J Theor Biol. 2018 Apr 14;443:113–24. https://doi.org/10.1016/j.jtbi.2018.01.028.

    Article  CAS  Google Scholar 

  52. Suri A, Mould DR, Liu Y, Jang G, Venkatakrishnan K. Population PK and exposure-response relationships for the antibody-drug conjugate brentuximab vedotin in CTCL patients in the phase III ALCANZA study. Clin Pharmacol Ther. 2018 Nov;104(5):989–99. https://doi.org/10.1002/cpt.1037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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 Dec;106(6):1268–79. https://doi.org/10.1002/cpt.1530.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hibma J, Knight B. Population pharmacokinetic modeling of Gemtuzumab Ozogamicin in adult patients with acute myeloid leukemia. Clin Pharmacokinet. 2019 Mar;58(3):335–47. https://doi.org/10.1007/s40262-018-0699-5.

    Article  CAS  PubMed  Google Scholar 

  55. Lu D, Lu T, Gibiansky L, Li X, Li C, Agarwal P, et al. Integrated two-analyte population pharmacokinetic model of polatuzumab vedotin in patients with non-Hodgkin lymphoma. CPT Pharmacometrics Syst Pharmacol. 2020 Jan;9(1):48–59. https://doi.org/10.1002/psp4.12482.

    Article  CAS  PubMed  Google Scholar 

  56. Lu D, Gibiansky L, Agarwal P, Dere RC, Li C, Chu YW, et al. Integrated two-analyte population pharmacokinetic model for antibody-drug conjugates in patients: implications for reducing pharmacokinetic sampling. CPT Pharmacometrics Syst Pharmacol. 2016 Dec;5(12):665–73. https://doi.org/10.1002/psp4.12137.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Sanderson RJ, Hering MA, James SF, Sun MM, Doronina SO, Siadak AW, et al. In vivo drug-linker stability of an anti-CD30 dipeptide-linked auristatin immunoconjugate. Clin Cancer Res. 2005;11(2 Pt 1):843–52.

    CAS  PubMed  Google Scholar 

  58. Kågedal M, Gibiansky L, Xu J, Wang X, Samineni D, Chen SC, et al. Platform model describing pharmacokinetic properties of vc-MMAE antibody–drug conjugates. J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):537–48. https://doi.org/10.1007/s10928-017-9544-y.

    Article  CAS  PubMed  Google Scholar 

  59. Bauer RJ. NONMEM 7.3 users guides. ICON plc, Gaithersburg, MD, 1989–2013. https://nonmem.iconplc.com/nonmem730/nm730.pdf. Accessed 28 April 2020.

  60. 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 Sep;16(5):994–1008. https://doi.org/10.1208/s12248-014-9618-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sukumaran S, Zhang C, Leipold DD, Saad OM, Xu K, Gadkar K, et al. Development and translational application of an integrated, mechanistic model of antibody-drug conjugate pharmacokinetics. AAPS J. 2017 Jan;19(1):130–40. https://doi.org/10.1208/s12248-016-9993-z.

    Article  CAS  PubMed  Google Scholar 

  62. 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 Jun;32(6):1884–93. https://doi.org/10.1007/s11095-014-1582-1.

    Article  CAS  PubMed  Google Scholar 

  63. 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 Dec;39(6):643–59. https://doi.org/10.1007/s10928-012-9276-y.

    Article  PubMed  Google Scholar 

  64. Wada R, Erickson HK, Lewis Phillips GD, Provenzano CA, Leipold DD, Mai E, et al. Mechanistic pharmacokinetic/pharmacodynamic modeling of in vivo tumor uptake, catabolism, and tumor response of trastuzumab maytansinoid conjugates. Cancer Chemother Pharmacol. 2014 Nov;74(5):969–80. https://doi.org/10.1007/s00280-014-2561-2.

    Article  CAS  PubMed  Google Scholar 

  65. 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 Feb 1;64(3):1094–101. https://doi.org/10.1158/0008-5472.can-03-2524.

    Article  CAS  PubMed  Google Scholar 

  66. 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. https://doi.org/10.1208/s12248-014-9576-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Betts AM, Haddish-Berhane N, Tolsma J, Jasper P, King LE, Sun Y, et al. Preclinical to clinical translation of antibody-drug conjugates using pk/pd modeling: a retrospective analysis of inotuzumab ozogamicin. AAPS J. 2016 Sep;18(5):1101–16. https://doi.org/10.1208/s12248-016-9929-7.

    Article  CAS  PubMed  Google Scholar 

  68. Singh AP, Sharma S, Shah DK. Quantitative characterization of in vitro bystander effect of antibody-drug conjugates. J Pharmacokinet Pharmacodyn. 2016 Dec;43(6):567–82. https://doi.org/10.1007/s10928-016-9495-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Vasalou C, Helmlinger G, Gomes B. A mechanistic tumor penetration model to guide antibody drug conjugate design. PLoS One. 2015 Mar 18;10(3):e0118977. https://doi.org/10.1371/journal.pone.0118977.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Menezes B, Cilliers C, Wessler T, Thurber GM, Linderman JJ. An agent-based systems pharmacology model of the antibody-drug conjugate Kadcyla to predict efficacy of different dosing regimens. AAPS J. 2020 Jan 15;22(2):29. https://doi.org/10.1208/s12248-019-0391-1.

    Article  CAS  PubMed  Google Scholar 

  71. Burton JK, Bottino D, Secomb TW. A systems pharmacology model for drug delivery to solid tumors by antibody-drug conjugates: implications for bystander effects. AAPS J. 2019 Dec 11;22(1):12. https://doi.org/10.1208/s12248-019-0390-2.

    Article  PubMed  Google Scholar 

  72. Han TH, Gopal AK, Ramchandren R, Goy A, Chen R, Matous JV, et al. CYP3A-mediated drug-drug interaction potential and excretion of brentuximab vedotin, an antibody-drug conjugate, in patients with CD30-positive hematologic malignancies. J Clin Pharmacol. 2013 Aug;53(8):866–77. https://doi.org/10.1002/jcph.116.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Ferl GZ, Kenanova V, Wu AM, DiStefano JJ 3rd. A two-tiered physiologically based model for dually labeled single-chain Fv-fc antibody fragments. Mol Cancer Ther. 2006 Jun;5(6):1550–8. https://doi.org/10.1158/1535-7163.MCT-06-0072.

    Article  CAS  PubMed  Google Scholar 

  74. 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 Feb;39(1):67–86. https://doi.org/10.1007/s10928-011-9232-2.

    Article  CAS  PubMed  Google Scholar 

  75. 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 Sep;18(5):1117–30. https://doi.org/10.1208/s12248-016-9940-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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 Nov;19(6):1715–34. https://doi.org/10.1208/s12248-017-0131-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shen BQ, Bumbaca D, Yue Q, Saad O, Tibbitts J, Khojasteh SC, et al. Non-clinical disposition and metabolism of DM1, a component of trastuzumab emtansine (T-DM1), in Sprague Dawley rats. Drug Metab Lett. 2015;9(2):119–31. https://doi.org/10.2174/1872312809666150602151922.

    Article  CAS  PubMed  Google Scholar 

  78. 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. 2015 Jan;54(1):81–93. https://doi.org/10.1007/s40262-014-0182-x.

    Article  CAS  PubMed  Google Scholar 

  79. SimCYP Version 12 Release Note. 2012. https://www.businesswire.com/news/home/20120801005699/en/Simcyp-Releases-Version-12-Simulator-Extending-Lead. Accessed 28 Apr 2020.

  80. SimCYP Version 15 Release Note. 2015. https://support.certara.com/news/certara-launches-version-15-of-its-simcyp-population-based-simulator. Accessed 28 Apr 2020.

  81. Beck A, Goetsch L, Dumontet C, Corvaïa N. Strategies and challenges for the next generation of antibody-drug conjugates. Nat Rev Drug Discov. 2017 May;16(5):315–37. https://doi.org/10.1038/nrd.2016.268.

    Article  CAS  PubMed  Google Scholar 

  82. Gunawardena J. Models in biology: ‘accurate descriptions of our pathetic thinking’. BMC Biol. 2014 Apr 30;12:29. https://doi.org/10.1186/1741-7007-12-29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The author wishes to express appreciation to Peter Bonate, Stacey Tannenbaum, and Mary Choules for their valuable comments about this manuscript. The author also thanks the anonymous referees for their constructive suggestions.

Author information

Authors and Affiliations

  1. Pharmacometrics US, Clinical Pharmacology & Exploratory Development, Astellas Pharma, Inc., USA, 1 Astellas Way, Northbrook, Illinois, 60062, USA

    Peiying Zuo

Authors
  1. Peiying Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peiying Zuo.

Additional information

Publisher’s Note

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

Electronic supplementary material

Table S1

List of clinical trials from clinicaltrials.gov that involve ADC as intervention (assessed June 1st, 2020). (XLSX 199 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zuo, P. Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates. AAPS J 22, 105 (2020). https://doi.org/10.1208/s12248-020-00475-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12248-020-00475-8

Key Words

Search

Navigation