Integrated mapping of pharmacokinetics and pharmacodynamics in a patient-derived xenograft model of glioblastoma
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s41467-018-07334-3.pdf
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Author(s)
Randall, Elizabeth C.
Laramy, Janice K.
Kim, Minjee
Roos, Alison
Calligaris, David
Regan, Michael S.
Gupta, Shiv K.
Mladek, Ann C.
Carlson, Brett L.
Johnson, Aaron J.
Date Issued
November 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Randall, Elizabeth C., Kristina B. Emdal, Janice K. Laramy, Minjee Kim, Alison Roos, David Calligaris, Michael S. Regan, et al. “Integrated Mapping of Pharmacokinetics and Pharmacodynamics in a Patient-Derived Xenograft Model of Glioblastoma.” Nature Communications 9, no. 1 (November 21, 2018). © 2018 The Authors
Version
Final published version
Abstract
Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Koch Institute for Integrative Cancer Research at MIT
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-018-07334-3
