The AAPS Journal

, Volume 18, Issue 3, pp 635–646 | Cite as

Determination of Cellular Processing Rates for a Trastuzumab-Maytansinoid Antibody-Drug Conjugate (ADC) Highlights Key Parameters for ADC Design

  • Katie F. Maass
  • Chethana Kulkarni
  • Alison M. Betts
  • K. Dane Wittrup
Research Article Theme: Systems Pharmacokinetics Models for Antibody-Drug Conjugates
Part of the following topical collections:
  1. Theme: Systems Pharmacokinetics Models for Antibody-Drug Conjugates

Abstract

Antibody-drug conjugates (ADCs) are a promising class of cancer therapeutics that combine the specificity of antibodies with the cytotoxic effects of payload drugs. A quantitative understanding of how ADCs are processed intracellularly can illustrate which processing steps most influence payload delivery, thus aiding the design of more effective ADCs. In this work, we develop a kinetic model for ADC cellular processing as well as generalizable methods based on flow cytometry and fluorescence imaging to parameterize this model. A number of key processing steps are included in the model: ADC binding to its target antigen, internalization via receptor-mediated endocytosis, proteolytic degradation of the ADC, efflux of the payload out of the cell, and payload binding to its intracellular target. The model was developed with a trastuzumab-maytansinoid ADC (TM-ADC) similar to trastuzumab-emtansine (T-DM1), which is used in the clinical treatment of HER2+ breast cancer. In three high-HER2-expressing cell lines (BT-474, NCI-N87, and SK-BR-3), we report for TM-ADC half-lives for internalization of 6–14 h, degradation of 18–25 h, and efflux rate of 44–73 h. Sensitivity analysis indicates that the internalization rate and efflux rate are key parameters for determining how much payload is delivered to a cell with TM-ADC. In addition, this model describing the cellular processing of ADCs can be incorporated into larger pharmacokinetics/pharmacodynamics models, as demonstrated in the associated companion paper.

KEY WORDS

antibody-drug conjugate cellular trafficking pharmacokinetics/pharmacodynamics T-DM1 trastuzumab emtansine 

Notes

Acknowledgments

We thank Lindsay King, Nahor Haddish-Berhane, and members of the Wittrup Lab for their technical suggestions. For the gift of the trastuzumab-maytansinoid ADC (TM-ADC), we are grateful to the Pfizer Oncology Bioconjugation group, including William Hu, Ellie Muszynska, Nadira Prashad, Kiran Khandke, and Frank Loganzo. K.F.M. was supported by a Hertz Foundation Fellowship and a National Science Foundation Graduate Research Fellowship. C.K. was supported by the Pfizer Worldwide Research & Development Post-Doctoral Program. This work was also supported by a research grant from Pfizer and in part by the Koch Institute Support (core) grant P30-CA14051 from the National Cancer Institute. We thank the Koch Institute Swanson Biotechnology Center for the technical support, specifically the Flow Cytometry Core.

Supplementary material

12248_2016_9892_Fig6_ESM.gif (26 kb)
Supplemental Figure 1

Curve fits for (A) the apparent KD of Tras-647 on SK-BR-3 cells and (B) koff for Tras-647 on BT-474, N87, and SK-BR-3 cells. (GIF 26 kb)

12248_2016_9892_MOESM1_ESM.eps (46 kb)
High resolution image (EPS 46 kb)
12248_2016_9892_Fig7_ESM.gif (48 kb)
Supplemental Figure 2

Cell growth rates for (A) untreated cells and (B-D) treated cells during efflux rate experiments. For the treated cells, a cell growth rate was only fit for BT-474 cells because the other cell lines did not demonstrate growth. (GIF 47 kb)

12248_2016_9892_MOESM2_ESM.eps (204 kb)
High resolution image (EPS 203 kb)
12248_2016_9892_Fig8_ESM.gif (125 kb)
Supplemental Figure 3

Determination of internalization rate constants for Tras-647 and TM-ADC-647 in three cell lines (BT-474, N87, and SK-BR-3). The linear fit equations are reported for each fit at the top of the graph. Data points represent triplicate independent experiments. (GIF 125 kb)

12248_2016_9892_MOESM3_ESM.eps (63 kb)
High resolution image (EPS 63 kb)
12248_2016_9892_Fig9_ESM.gif (14 kb)
Supplemental Figure 4

Image of native SDS-PAGE gel with cell lysate samples from cells treated for 30 min with 10 nM TM-ADC-647. Lanes are as follows: L – ladder, 1, 2, 3 – cell lysate from BT-474, N87, SK-BR-3 cells (respectively) 19 h after treatment with TM-ADC-647, 4 – positive control of TM-ADC-647 in cell lysis buffer. (GIF 14 kb)

12248_2016_9892_MOESM4_ESM.eps (704 kb)
High resolution image (EPS 703 kb)
12248_2016_9892_Fig10_ESM.gif (50 kb)
Supplemental Figure 5

Plot of species quantity in cells over time as steady state is approached. The species (antibody in complex on the cell surface, internalized antibody, and degraded antibody) over time are shown for three cell lines: (A) BT-474, (B) N87, and (C) SK-BR-3. The vertical dashed line corresponds to the time at which steady state is reached as defined in the methods section. Note that one degraded antibody corresponds to the release of the DAR of drug molecules, i.e. one degraded antibody equals release of two drug molecules if the ADC has a DAR of 2. (GIF 50 kb)

12248_2016_9892_MOESM5_ESM.eps (122 kb)
High resolution image (EPS 121 kb)

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Copyright information

© American Association of Pharmaceutical Scientists 2016

Authors and Affiliations

  • Katie F. Maass
    • 1
    • 2
  • Chethana Kulkarni
    • 3
  • Alison M. Betts
    • 4
  • K. Dane Wittrup
    • 1
    • 2
    • 5
  1. 1.Department of Chemical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.David H. Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Oncology Medicinal Chemistry, Worldwide Medicinal ChemistryPfizerGrotonUSA
  4. 4.Translational Research Group, Department of Pharmacokinetics Dynamics and MetabolismPfizerGrotonUSA
  5. 5.Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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