Blattnig, Steven R.

Brenner, David J.

Datta, Kamal

Fornace, Albert J.

Norman, Ryan B.

Plante, Ianik

Shuryak, Igor

Slaba, Tony C.

Suman, Shubhankar

English

NASA Technical Reports Server

1ESTIMATING RADIATION QUALITY DEPENDENCIES
FOR GCR-INDUCED TARGETED VS. NON-TARGETED 
HEALTH EFFECTS
David J. Brenner1, Igor Shuryak1, Albert J. Fornace2, 
Shubhankar Suman2, Kamal Datta2, Tony C. Slaba3, 
Steven R. Blattnig3, Ryan B. Norman3, and Ianik Plante4
1Center for Radiological Research, Columbia University; 2Georgetown University; 3NASA 
Langley Research Center, 4 KBRWyle, Houston
https://ntrs.nasa.gov/search.jsp?R=20200002172 2020-05-24T04:35:47+00:00Z
Mars mission astronauts will be exposed to complex 
mixed radiation fields both in flight and on Mars
O’Neill et al 2015,
Tripathi et al 2001
Low-LET risks 
from A bomb 
survivors
Acute
High-LET 
GCR risks
Protracted 
High-LET GCR 
risks
The NASA Radiation Risk Model
3
Our current major problem
❖ Low doses of densely-ionizing GCR radiation 
appear to produce biological damage largely 
through different (non-targeted) mechanisms
as compared to high doses of GCR radiation
4
Non-Targeted Effects (NTE)
 Also called “bystander effects”
 Unirradiated cells respond to signals emitted by nearby irradiated cells
 First noted by Nagasawa & Little (1992): Exposed cells to low doses of alpha 
particles, about 1% of cells were hit, but 30% of cells showed increased 
chromosomal aberrations
 NTE reported for most endpoints, mainly after low doses of high-LET radiation
 Many signaling pathways and reactive oxygen species (ROS) appear to be 
involved, shifting cells into an “activated” stressed state
Zhou et al. Cancer Res, 2008
5
To establish radiation weighting factors for 
targeted effects (TE) and non-targeted effects (NTE), 
and to develop a practical approach for their use in 
complex and time-varying space radiation fields
Relative effects of different radiation qualities 
must be due to the initial track structure
Wright 1982
Track Structure Models
1. Katz Model
• Phenomenological biophysically-based model, 
initially of cell killing, developed by analogy to 
radiation effects in nuclear emulsions
– Model input can’t be directly measured
– Needs large amount of nuclear data to 
calculate model input for every radiation field
Track Structure Models:
2. Microdosimetry
• Microdosimetry: Study of the distribution of deposited 
energy in cell-nucleus sized microscopic volumes
Simulation of single gamma ray 
passing through cell nucleus
Simulation of single gamma ray 
passing through cell nucleus
Microdosimetric Distributions:
Distributions of energy depositions, y,  
in microscopic site sizes
10-2 10-1 100 101 102 103
Lineal Energy, y (keV/mm)
y
 d
(y
)
µ )
y
 d
(y
)
Microdosimetric distributions
can be directly measured or calculated
Response   d(y)   × Q(y) dy
Microdosimetric      
distribution
Track
structure
Biological
response
function
Biological
endpoint
From microdosimetric distributions 
to relative biological response
How do we estimate Q(y),
the Biological Response Function?
Imagine a set of experiments with biological endpoint 
e in which i different radiation types were used: 
These are a series of i Fredholm equations, and given 
the experimental results, ei and the microdosimetric 
spectra, di(y), they can be numerically unfolded to 
produce an estimate of Qe(y)
ei  ∫ di(y) Qe(y) dy 
➢ Fornace et al. measured tumors in APC1638N/+ mice 
exposed at NSRL to: 
➢ Protons (50 to 120 cGy; 1.3 keV/µm)
➢4He (5 to 50 cGy; 2 keV/µm)
➢ 12C (10 to 200 cGy; 13 keV/µm)
➢ 16O (5 to 50 cGy; 22 keV/µm)
➢ 28Si (5 to 140 cGy; 69 keV/µm)
➢ 56Fe (5 to 160 cGy; 148 keV/µm)
➢ g rays (5-200 cGy)
➢ 20-39 mice / radiation type / dose, including zero dose 
14
Quantifying TE vs NTE responses for 
densely-ionizing GCR at low doses
15
Best-Fit Model Parameters for NTE and TE
LET 
(keV/µm)
NTE parameter TE parameter (Gy
-1
)
Gamma 0.3 0.79 [0.18, 16.5] 2.88 [0.00, 3.80]
Protons 1.26 0.94 [0.00, 1.77] 2.88 [0.00, 4.30]
He ions 2 1.29 [0.83, 1.76] 2.88 [0.00, 4.20]
C ions 13 2.64 [1.43, 4.69] 3.47 [2.05, 5.04]
O ions 22 2.72 [1.99, 3.71] 2.88 [0.00, 5.52]
Si ions 69 4.53 [3.15, 6.85] 10.12 [7.68, 12.8]
Fe ions 148 3.94 [2.61, 6.49] 5.06 [2.67, 6.83]
Based on FLUKA or GEANT4 (Beck 2006)
Calculated vs. experimental microdosimetric spectra
Oxygen 400 MeV/u                  Iron 300 MeV/uIron 300 MeV/u
Based on FLUKA or GEANT4 (Beck 2006)
Calculated vs. experimental microdosimetric spectra
Oxygen 400 MeV/u                  Iron 300 MeV/uIron 300 MeV/u
LET approximation LET approximation
18
OP He C Si Fe
y
×
d
(y
)
y (keV/µm)    
LET 
(keV/µm)
NTE parameter TE parameter (Gy
-1
)
Gamma 0.3 0.79 [0.18, 16.5] 2.88 [0.00, 3.80]
Protons 1.26 0.94 [0.00, 1.77] 2.88 [0.00, 4.30]
He ions 2 1.29 [0.83, 1.76] 2.88 [0.00, 4.20]
C ions 13 2.64 [1.43, 4.69] 3.47 [2.05, 5.04]
O ions 22 2.72 [1.99, 3.71] 2.88 [0.00, 5.52]
Si ions 69 4.53 [3.15, 6.85] 10.12 [7.68, 12.8]
Fe ions 148 3.94 [2.61, 6.49] 5.06 [2.67, 6.83]
How do we estimate Q(y),
the Biological Response Function?
Imagine a set of experiments with biological endpoint 
e in which i different radiation types were used: 
These are a series of i Fredholm equations, and given 
the experimental results, ei and the microdosimetric 
spectra, di(y), they can be numerically unfolded to 
produce an estimate of Qe(y)
ei  ∫ di(y) Qe(y) dy 
20
Preliminary Best-Fit Results: 
Qe(y) shapes for mouse GI tumor endpoint 
NTE
TE
Silicon microdosimeter. Rosenfeld et al 2014TE gas microdosimeter. Straume et al 2015
Ongoing…..
1. Generate more detailed d(y) microdosimetric spectra 
(Geant 4+ RITRACKS) and redo this preliminary analysis
2. Generate Qe(y) functions for a variety of different endpoints 
e, both for cancer and non-cancer endpoints
3. Generate consensus Q(y) function(s)
4. Assess in-flight d(y) measurement tools, to allow in-flight 
assessments of Q


Estimating Radiation Quality Dependencies for GCR-Induced Targeted Vs. Non-Targeted Health Effects

Estimating Radiation Quality Dependencies for GCR-Induced Targeted Vs. Non-Targeted Health Effects

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