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01.12.2017 | Original research | Ausgabe 1/2017 Open Access

EJNMMI Research 1/2017

High-throughput high-volume nuclear imaging for preclinical in vivo compound screening§

Zeitschrift:
EJNMMI Research > Ausgabe 1/2017
Autoren:
Sven Macholl, Ciara M. Finucane, Jacob Hesterman, Stephen J. Mather, Rachel Pauplis, Deirdre Scully, Jane K. Sosabowski, Erwan Jouannot
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13550-017-0281-4) contains supplementary material, which is available to authorized users.
§Presented at the EANM 2016 [1]

Abstract

Background

Preclinical single-photon emission computed tomography (SPECT)/CT imaging studies are hampered by low throughput, hence are found typically within small volume feasibility studies. Here, imaging and image analysis procedures are presented that allow profiling of a large volume of radiolabelled compounds within a reasonably short total study time. Particular emphasis was put on quality control (QC) and on fast and unbiased image analysis.

Methods

2–3 His-tagged proteins were simultaneously radiolabelled by 99mTc-tricarbonyl methodology and injected intravenously (20 nmol/kg; 100 MBq; n = 3) into patient-derived xenograft (PDX) mouse models. Whole-body SPECT/CT images of 3 mice simultaneously were acquired 1, 4, and 24 h post-injection, extended to 48 h and/or by 0–2 h dynamic SPECT for pre-selected compounds. Organ uptake was quantified by automated multi-atlas and manual segmentations. Data were plotted automatically, quality controlled and stored on a collaborative image management platform. Ex vivo uptake data were collected semi-automatically and analysis performed as for imaging data.

Results

>500 single animal SPECT images were acquired for 25 proteins over 5 weeks, eventually generating >3500 ROI and >1000 items of tissue data. SPECT/CT images clearly visualized uptake in tumour and other tissues even at 48 h post-injection. Intersubject uptake variability was typically 13% (coefficient of variation, COV). Imaging results correlated well with ex vivo data.

Conclusions

The large data set of tumour, background and systemic uptake/clearance data from 75 mice for 25 compounds allows identification of compounds of interest. The number of animals required was reduced considerably by longitudinal imaging compared to dissection experiments. All experimental work and analyses were accomplished within 3 months expected to be compatible with drug development programmes. QC along all workflow steps, blinding of the imaging contract research organization to compound properties and automation provide confidence in the data set. Additional ex vivo data were useful as a control but could be omitted from future studies in the same centre. For even larger compound libraries, radiolabelling could be expedited and the number of imaging time points adapted to increase weekly throughput. Multi-atlas segmentation could be expanded via SPECT/MRI; however, this would require an MRI-compatible mouse hotel. Finally, analysis of nuclear images of radiopharmaceuticals in clinical trials may benefit from the automated analysis procedures developed.
Zusatzmaterial
Additional file 1: Detailed descriptions of the radiolabelling procedure, image analysis, γ-counting procedure and analysis, and intersubject variability estimation. Further results of the γ-counter calibration, ex vivo/imaging comparison and intersubject variability. (DOCX 171 kb)
13550_2017_281_MOESM1_ESM.docx
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