Determination of Cellular Processing Rates for a Trastuzumab-Maytansinoid Antibody-Drug Conjugate (ADC) Highlights Key Parameters for ADC Design
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Author(s)
Kulkarni, Chethana
Betts, Alison M.
Wittrup, Karl Dane
Maass, Katie F
Date Issued
February 2016
Journal
The AAPS Journal
Publisher
Springer US
Citation
Maass, Katie F., Chethana Kulkarni, Alison M. Betts, and K. Dane Wittrup. “Determination of Cellular Processing Rates for a Trastuzumab-Maytansinoid Antibody-Drug Conjugate (ADC) Highlights Key Parameters for ADC Design.” AAPS J 18, no. 3 (February 24, 2016): 635–646.
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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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Koch Institute for Integrative Cancer Research at MIT
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DOI of Published Version
https://doi.org/10.1208/s12248-016-9892-3
