Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis

Edward Cabral1, Mary Katherine Montgomery1, Meaghan Berg2

  • 1Drug Safety R&D, Discovery & Early Development, Pfizer, Inc.

Insights

This study introduces a new workflow using Body Conforming Animal Molds (BCAMs) and a Software-as-a-Service (SaaS) platform for automated Positron Emission Tomography (PET) biodistribution analysis in mice. The method ensures consistent animal positioning and accurate, efficient data quantitation.

Area of Science:

  • Molecular imaging
  • Pharmacological research
  • Drug development

Background:

  • Positron Emission Tomography (PET) is vital for drug development, assessing biomarker modulation, receptor occupancy, and compound biodistribution.
  • Manual PET biodistribution analysis is labor-intensive, time-consuming, and prone to inter-operator variability and inconsistent animal positioning.
  • Consistent animal positioning is critical for accurate longitudinal imaging and biodistribution profiling.

Purpose of the Study:

  • To develop and validate an automated workflow for in vivo PET imaging-based biodistribution analysis in mice.
  • To address limitations of manual analysis, including labor intensity, time consumption, and operator variability.
  • To enhance the reliability and efficiency of drug distribution profiling using PET.

Main Methods:

  • Utilized mouse Body Conforming Animal Molds (BCAMs) for rigid and consistent animal positioning during PET/CT acquisition.
  • Employed a Software-as-a-Service (SaaS) platform with a cloud-based Organ Probability Map (OPM) and AI-powered segmentation.
  • Integrated BCAMs with PET/CT imaging and automated analysis for organ segmentation and biodistribution quantitation.

Main Results:

  • The BCAMs ensured consistent animal positioning, facilitating reproducible imaging.
  • The SaaS platform enabled automated, reliable organ segmentation and biodistribution analysis.
  • Automated analysis yielded results comparable to manual methods, demonstrating accuracy and efficiency.

Conclusions:

  • The presented workflow, combining BCAMs and SaaS, offers an accurate and effective solution for automated PET biodistribution analysis.
  • This automated approach significantly reduces labor, time, and inter-operator variability compared to manual methods.
  • The workflow is adaptable for various tracers and can be executed with minimal training, streamlining drug development processes.

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