# Development of completely automated quality control procedures for 4 PET Imaging tracers that will increase production throughput and lead to expanded diversity of PET imaging available to patients.

> **NIH NIH R44** · TRACE-ABILITY, INC. · 2020 · $810,570

## Abstract

Long-term objective of this project is increased availability of Positron Emission Tomography (PET)
imaging to patients across different disease areas. The approach focuses on eliminating complexity and
quality risks associated with production of radioactive contrast agents, PET tracers relied upon in neurology
and oncology. The specific solution developed in this project is a platform that will afterwards deliver the
same benefits in production of PET tracers used in an expanded variety of disease areas.
 PET Tracers rely on radionuclides with inherently short half-lives such as F-18 (110 min), Ga-68 (68
min), that have to be produced multiple times a day from a cyclotron or generator. Quality Control (QC)
of PET tracers is by far the most labor-, skill- and risk-intensive part of the cGMP production process. It
requires assessment of 10-14 different parameters for the PET tracer to be released for patient use. The
complex current state of PET tracer QC presents a barrier to the expansion of PET imaging, particularly to
smaller hospitals and clinics in the rural US, often limits the number of PET tracers that may be produced
in one facility, and ultimately limits the availability of PET imaging to patients.
 The innovation is in the disposable kit that enables multiple tests from a single sample on a single
platform composed of a microplate reader and automated pipettor. Tracer-QC has been successfully
benchmarked with FDG, the most common PET tracer used in multiple applications. It has delivered the
desired simplification, efficiency and traceability of data. Four PET tracers chosen for this project are: [F-
18]Florbetaben – FDA approved PET tracer for amyloid imaging in Alzheimer’s Disease diagnostics, [F-
18]Flortaucipir, – a tau protein marker with highest predictive potential for Alzheimer’s Disease and other
neurodegenerative diseases such as TBI/CTE, [F-18]FMISO – leading marker for detection and
management of sarcomas, [Ga-68]DOTATATE – FDA-approved PET tracer for neuroendocrine tumors.
The impacts of this work will be: (1) improved availability of these 4 PET tracers to patients, (2) streamlined
procedures for transition of future tracers onto Tracer-QC platform, and (3) freed up capacity in PET
production facilities to add more PET tracers to their offerings to the imaging centers.
 Each Phase I Specific Aim focuses on one of 2 riskiest new tests. Milestone is experimental
demonstration of the desired limit of detection. Specific Aim 1: Acetone (LOD = 2000 ppm). Specific
Aim 2: DMSO (LOD = 2000 ppm). Phase II Specific Aims 1-4: Clinical Production of new tracer using
Tracer-QC for quality control. 1 ([F-18]Florbetaben, 2 ([F-18]Flortaucipir, 3 ([F-18]FMISO), 4 ([Ga-
68]DOTATATE): Milestones are (a) Tracer-QC method validated for each PET tracer QC and (b)
IND/NDA amendment filed. The project will yield a fully-automated QC solution that has been validated,
de-risked and is positioned for easy implementation with DMF cross-reference fo...

## Key facts

- **NIH application ID:** 9983832
- **Project number:** 5R44MH119110-03
- **Recipient organization:** TRACE-ABILITY, INC.
- **Principal Investigator:** Arkadij Elizarov
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $810,570
- **Award type:** 5
- **Project period:** 2018-08-08 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9983832

## Citation

> US National Institutes of Health, RePORTER application 9983832, Development of completely automated quality control procedures for 4 PET Imaging tracers that will increase production throughput and lead to expanded diversity of PET imaging available to patients. (5R44MH119110-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9983832. Licensed CC0.

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