Radiosynthesis and preclinical evaluation of [99mTc]Tc-HYNIC–durvalumab for immuno-SPECT imaging of PD-L1 expression in tumor models
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Syed Qaiser Shah
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Ralph Santos-Oliveira
Abstract
Accurate, non-invasive measurement of programmed death-ligand 1 (PD-L1) expression is crucial for the optimization of immune checkpoint inhibitor cancer treatments. We describe the radiosynthesis and preclinical assessment of a new technetium-99m (99mTc) conjugated immuno-SPECT agent, [99mTc]Tc-HYNIC–durvalumab, for PD-L1 imaging in vivo. Durvalumab was conjugated with hydrazinonicotinamide (HYNIC) through lysine residues using an N-hydroxysuccinimide (NHS) ester-based method and subsequently radiolabeled with technetium-99m (99mTc) in the presence of tricine and ethylenediamine-N,N′-diacetic acid (EDDA) as co-ligands. Radiochemical purity, physicochemical stability in physiologically relevant media, and immunoreactivity were determined. Specific binding affinity and kinetics of internalization were studied in PD-L1-positive A549 cells. Biodistribution and SPECT imaging were carried out in A549 xenograft-bearing mice and rabbits. Specificity was confirmed by co-injection with an excess of unlabeled durvalumab. 99mTc-HYNIC-Durvalumab had 98.14 ± 0.27 % radiochemical purity and good stability (>90 % intact at 4 h in serum). The tracer showed high PD-L1 affinity and extensive receptor-mediated binding (28.54 ± 0.65 % at 4 h). In vivo, tumor uptake at 4 h post-injection was 6.42 ± 0.11 %ID/g, significantly reduced by co-administration of excess unlabeled antibody (1.56 ± 0.05 %ID/g, p < 0.01). Tumor-to-background ratio was 3.84, allowing for high-contrast SPECT imaging that accurately defined PD-L1-expressing tumors. These findings support 99mTc-HYNIC-Durvalumab to be a highly specific, stable, and clinically translatable SPECT radiotracer for quantitative PD-L1 expression imaging. This agent has great promise to enable patient stratification and therapeutic monitoring in immuno-oncology. Further studies toward clinical translation is warranted.
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Research ethics: The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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