# Development of FAST-DOSE assay system for the rapid assessment of acute radiation exposure, individual radiosensitivity and injury in victims for a large-scale radiological incident

> **NIH NIH U01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $485,237

## Abstract

Summary
Following a large scale radiological or nuclear event, hundreds of thousands of people may be exposed to
ionizing radiation/s and require subsequent dose-dependent medical management. It will be crucial to collect
and analyze human biofluids (such as blood, urine, saliva) as soon as possible within the first week for
accurate dose prediction and early triage decision. There is a need for FDA-approved in vitro diagnostic
high-throughput biodosimetry devices with the ability to determine past radiation exposure with precision
and accuracy. At the Center for High Throughput Radiation Biodosimetry, the Columbia University Center
for Medical Countermeasures against Radiation (CMCR), we have developed FAST-DOSE (Fluorescent
Automated Screening Tool for Dosimetry) assay system, to measure radiation-responsive proteins in
human peripheral blood samples for retrospective estimation of radiation dose. The protein panel also
includes biomarkers for blood leukocyte subtypes to reflect hematological sensitivity and injury. The FAST-
DOSE assay system is intended as an in vitro diagnostic device (IVD) as defined by 21 CFR 809.3. The
platform uses a commercial imaging flow cytometry system (ImageStream®X) and associated Image Data
Exploration and Analysis Software (IDEAS®) to rapidly quantify changes in biomarker expression levels within
specific cellular structures using fluorescent imagery and algorithms for estimation of absorbed dose. The
studies planned here are designed to develop and optimize our FAST-DOSE assay system to accurately
estimate absorbed dose and assess hematopoietic injury in human lymphocytes after ionizing irradiation. The
first objective is to build on our current biomarker validation data for early engagement with the FDA via the
pre-submission process. We have used the human ex vivo model and humanized mouse (Hu-NSG) and non-
human primate (NHP) models to validate biomarker expression and radiosensitivity in blood leukocytes after
acute ionizing radiation exposure. The Specific Aims proposed here are designed to: optimize the assay
protocol and identify biomarker dose/time kinetics for accurate dose predictions in vitro and test 1) inter-donor
variation, 2) intra-donor variation and 3) inter-laboratory variability (Aim 1); test the effect of specific
confounders: age and sex, inheritance with germline BRCA1/2 pathogenic variant, and inflammation and trauma
on the biomarker response, before and after irradiation (Aim 2); measure biomarker levels and time kinetics in
vivo and correlate with hematopoietic injury, based on peripheral blood leukocyte counts, and stem and
progenitor cell levels in the bone marrow of Hu-NSG mice (Aim 3) and, develop mathematical models (using
machine learning and regression techniques) to select the best FAST-DOSE biomarkers and their combinations
for generating dose predictions based on the ex vivo and in vivo dose response of these biomarkers (Aim 4).
Our vision for future development is to develop ...

## Key facts

- **NIH application ID:** 9870417
- **Project number:** 1U01AI148309-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Helen C Turner
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $485,237
- **Award type:** 1
- **Project period:** 2020-02-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9870417

## Citation

> US National Institutes of Health, RePORTER application 9870417, Development of FAST-DOSE assay system for the rapid assessment of acute radiation exposure, individual radiosensitivity and injury in victims for a large-scale radiological incident (1U01AI148309-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9870417. Licensed CC0.

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