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J Clin Invest. 2019 Feb 1;129(2):616-630. doi: 10.1172/JCI122216. Epub 2019 Jan 7.

Peptide-based PET quantifies target engagement of PD-L1 therapeutics.

Author information

1
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
2
Center for Translational Medicine, Department of Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, Maryland, USA.
3
Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA.
4
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
5
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
6
The Sidney Kimmel Comprehensive Cancer Center and the Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
7
Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
8
Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Abstract

Immune checkpoint therapies have shown tremendous promise in cancer therapy. However, tools to assess their target engagement, and hence the ability to predict their efficacy, have been lacking. Here, we show that target engagement and tumor-residence kinetics of antibody therapeutics targeting programmed death ligand-1 (PD-L1) can be quantified noninvasively. In computational docking studies, we observed that PD-L1-targeted monoclonal antibodies (atezolizumab, avelumab, and durvalumab) and a high-affinity PD-L1-binding peptide, WL12, have common interaction sites on PD-L1. Using the peptide radiotracer [64Cu]WL12 in vivo, we employed positron emission tomography (PET) imaging and biodistribution studies in multiple xenograft models and demonstrated that variable PD-L1 expression and its saturation by atezolizumab, avelumab, and durvalumab can be quantified independently of biophysical properties and pharmacokinetics of antibodies. Next, we used [64Cu]WL12 to evaluate the impact of time and dose on the unoccupied fraction of tumor PD-L1 during treatment. These quantitative measures enabled, by mathematical modeling, prediction of antibody doses needed to achieve therapeutically effective occupancy (defined as >90%). Thus, we show that peptide-based PET is a promising tool for optimizing dose and therapeutic regimens employing PD-L1 checkpoint antibodies, and can be used for improving therapeutic efficacy.

KEYWORDS:

Cancer immunotherapy; Diagnostic imaging; Oncology; Pharmacology; Therapeutics

PMID:
30457978
PMCID:
PMC6355241
[Available on 2019-05-01]
DOI:
10.1172/JCI122216
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