Text of DOE proposal:
Introduction – context for the modeling effort
Ionizing radiation holds a special place among environmental carcinogens because of the
extent of information available about radiation effects on humans, on other animals, and
on cells and cell systems. The information available has been enhanced recently with the
availability of new genetic assays that shed light on both low dose effects and the rates of
processes, such as DNA repair, that can modify the expected incidence of effects at low
doses and dose rates. With so much information available along with very substantial
ongoing research efforts, now is a propitious time to attempt syntheses in the form of
biologically based risk models. To the extent that such models can reflect real advances
in understanding, they can provide a number of important benefits.
One type of benefit could come from improvement of the quantitative risk estimates
themselves. Risks from radiation are an important public policy concern: better
estimates, and estimates with a stronger and more credible basis, could have significant
health and economic impact. Achieving such a benefit, however, defines one challenge
to the modeling effort. Society has, at present, a widely used capability for estimating
risks (at least for risks for cancer) from exposure to radiation; that capability is a
projection from the findings of a number of epidemiological studies from people exposed
through atomic weapons, in their occupations, or through medical uses of radiation. The
challenge is to demonstrate added value by showing that the new estimates carry
additional evidentiary weight and more precisely characterize the risks, especially for
parts of the dose response relationship where the epidemiological studies provide
relatively weak direct evidence.
Other benefits could prove just as significant. The rich array of experimental findings
relating to radiation-induced cellular changes offer the prospect that syntheses could
significantly enhance the scientific understanding of carcinogenic processes. Here the
opportunity and the particular challenge are to learn how specific processes relate to each
other as sequences of events which may or may not lead to the development of a tumor.
For example, how are the rates of repair of various types of DNA damage quantitatively
changed in response to changes in exposures to external radiation and other DNA
damaging agents?
A third set of benefits could come from improving risk assessments for other carcinogens
for which the information base is much more limited. For most substances, risk estimates
are based on findings from animal studies with limited numbers, relatively high
exposures, and simple exposure patterns. In contrast, for radiation much information is
available to compare human epidemiology with animal studies and with insights derived
from bio-mechanistic syntheses. The challenge is to develop a bio-mechanistic
understanding of key processes in both humans and animals that will inform the
extrapolations - across species and for low dose exposures; and also to identify which
aspects of these processes are general and which are radiation specific.
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These challenges have implications for the development of biological archetypes, which
we will discuss at the end of the proposal. Briefly, the applications and benefits depend
on findings for humans and whole animals and comparisons with epidemiological and
animal studies. However, most of the exciting new findings with bio-mechanistic
implications are for sub-cellular systems, cells, or collections of cells. We propose in the
next subsection to focus our initial modeling effort on DNA repair processes; we regard
these as a particularly promising arena for using new information to resolve long-standing
puzzles about cancer development. However, we consider it important to embed this
focused cellular and sub-cellular modeling in a hierarchical scheme, so that modeling
effort takes account of the mix of different types of damage that may be repaired fully or
in part, and so that the modeling effort identifies implications at the organism level.
Archetype development is viewed, correctly, in the request for applications as a collective
activity for the various groups; we will urge that such a hierarchical approach be followed
in that effort.
Beyond their implications for model structure, the challenges also have implications for
the detailed activities needed to construct the models These include efforts to account for
inter-individual variability in rates for key biological processes, maintaining control over
uncertainties at each stage of the modeling, developing broadly representative data bases
for calibrating model parameters, documenting the choices and making the data bases
available to other users. Such concerns have informed much of our earlier work, and we
expect to build on our earlier efforts. Thus we have created a substantial data base
reflecting comparable inter-individual variability in many different pharmacokinetic and
pharmacodynamic processes for more than 400 sets of observations in humans. These
data are posted on our web site for others to use http://www2.clarku.edu/faculty/dhattis.
We have made extensive use of Monte Carlo methods to combine uncertainties in a
multiplicity of processes, and within Monte Carlo representations we perform sensitivity
analyses to identify key parameters, and we use self-consistency approaches to ascertain
how well the parameters are determined from the data sets used in model calibration and
validation.
In the next subsection we describe the considerations that led to our proposed approach to
develop a sub-cellular and cellular level model of the dynamics of DNA repair processes,
to embed this model in a general scheme which allows for a mix of inputs reflecting DNA
damage, and to develop outputs relevant to the creation of risk estimates. In the
following subsection, we discuss briefly significant considerations in the complementary
activities of calibrating and validating the models and model components. We conclude
the main body of the proposal with a short subsection discussing considerations in a
hierarchical approach to developing a biological archetype. At the end we provide as a
mini-appendix, an illustration of the quantitative modeling of a highly simplified – one
mechanism – representation of repair processes and discuss its relationship to some
experimental data. We also provide a second mini-appendix that mentions some of the
complexity in the types of damage that are subject to repair.
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Creation of a biologically-based risk model incorporating DNA repair processes
We propose to focus our initial model development on DNA repair processes within
cells. If there were only one unchanging and uniform system for defense of the genome
against a single type of alteration via ionizing radiation it would be relatively
straightforward to derive simple models of the likely effects of that system on dose
response relationships for radiation-induced carcinogenesis. Such simple models have,
for example, been developed in some of our own earlier work (Hattis, 1990). As a short
mini-appendix to this proposal we provide a simple illustration drawn from this work and
discuss its relationship to current experimental findings.
In fact, however there are several defense systems that act to maintain the integrity of
genomic information in the face of exposure to ionizing radiation and other DNAdamaging agents from the diet and from basic oxygen-utilizing metabolic processes.
These defense systems include a host of DNA repair processes specialized to handle
different types of DNA damage (Mohrenweiser et al., 2002). There are also systems to
delay progression of cells through various “check points” in the cell cycle in response to
DNA damage, and allow more time for DNA repair to take place. Additionally there are
systems such as those governed by the p53 protein to send cells with relatively substantial
amounts of DNA damage into “apoptosis” (a particular form of cell suicide) to remove
the hazard that those cells will progress through the sequence of heritable changes that
lead to cancer.
Moreover, of great potential significance for radiation carcinogenesis dose-response
modeling, the activity of these systems is likely to be modulated over time by exposures
to DNA damaging agents of all kinds, including various types of ionizing radiation.
Collectively the perturbations of the levels of genomic protective mechanisms (and
therefore radiation dose response) by radiation exposures themselves can be termed
“adaptive responses” (Joiner et al., 1996; Sasaki et al., 2002; Schlade-Bartusiak, 2002;
Bhattacharjee and Ito, 2001). We can learn about the workings and implications of these
responses by creating general models to describe the system of feedback controls that
modulates the activity of each genomic protective system. [One example of observations
of a relevant feedback system is seen in the work of Lopez et al. (2000)]. Ideally the
models should be calibrated to reflect various circumstances of background DNA damage
rates (e.g., see Povey, 2000) and other characteristics of different people and different cell
types. The models can then be used to elucidate the implications for the net changes in
carcinogenic responses as a function of radiation doses delivered to different population
subgroups over different combinations of duration and intensity fluctuations. (By “net
changes” we mean both increases in damage directly attributable to the fraction of
radiation-related damage in relevant cells that eventually escapes repair and other
genomic defenses, and any decreases in carcinogenesis that may result from increases in
the activity of genomic defense processes in preventing “background” DNA damage.)
Because basal (non-radiation-related) DNA damage, basal levels of genomic defenses,
and the responsiveness of various genomic defenses to added radiation exposure is likely
to vary among population groups and organ/tissue/cell types, it is important to include
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ways of accommodating available data on these differences and their implications for
system response in the overall modeling system.
The goals of our quantitative modeling effort will therefore be to

Build a molecular-level understanding of the system of feedback controls that
governs the activity and effectiveness of the processes involved in cellular
defenses of genomic integrity against various forms of ionizing radiation and
reactive chemical products of radiation. These defense processes include
DNA repair of various types, systems of cell cycle checkpoint controls, and
systems that trigger cell death by apoptosis in response to genomic damage
such as those governed by p53.

Understand the variation in the operation and effectiveness of these systems
--over time
--among tissues and cell types (e.g., hepatocytes, vs epithelial cells in various
organs, e. g. Crompton et al., 2002)
--in various phases of the cell cycle (e.g. McKay et al., 2002)
--along the differentiation sequence from totipotent stem cells through
terminally differentiated cells performing various functions (e.g. BuschfortPapewalis et al., 2002; Qin et al., 2002). (We would initially hypothesize that
activities of genomic defense mechanisms would be greatest for less
differentiated cells, because these are the cells where unrepaired DNA damage
could have effects on the greatest numbers of cellular descendants.)
--with the aging of the organism from embryonic/fetal development through
senescence. Similarly to the previous point, we would hypothesize that
cellular genomic defenses would be most active in early life, although the
needs of rapidly-growing early-life stages to preserve energy resources to grow
and develop other types of differentiated systems may modulate this.
--among people with different genetically-determined forms of various
components of the defensive systems [for example Mohrenweiser and
colleagues have done excellent recent work on heritable differences in
different DNA repair genes, and associations with background and radiationrelated cancer risks (Mohrenweiser et al., 2002 and 2003; Hu et al., 2002;
Duell et al., 2001)]. Among the important findings of this work is that even
relatively modest percentage reductions in measurable DNA repair activity can
be associated with measurable increases in cancer risks (Mohrenweiser et al.,
2003).
--under the influence of different challenges from the external and internal
environment, including various continuous and fluctuating levels of external
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and internal ionizing and other radiation exposures (e.g. Touil et al., 2002;
Murphy et al., 2002; Maeda et al., 2001; Mitchell et al., 2001), oxidative
products of cellular metabolism, and dietary exposures.

Understand the implications of these differences for incremental carcinogenic
responses of systems of cells, organs, and organisms to different doses and
kinds of radiation exposures delivered at different ages to people (and
animals) of different genetic heritage.
We will proceed to build this system in a number of research steps:

Identification of the key molecular mediators of genomic defense (e.g.,
particular DNA repair enzymes, damage receptors and messenger molecules
that combine into signaling systems for either DNA repair or apoptosis—e.g.
Lips and Kaina, 2001),

Identification of the control pathways governing the levels and interactions of
the key molecular mediators,

Identification of basal and radiation-induced activity levels of the various key
molecular mediators (e.g., Brash et al., 2001).

Development of an array of theoretical models of fundamental feedback
control processes for the defense of genomic integrity,

Assembly and interpretation of a database of relevant examples. These will
include studies of the effects of dose fractionation; dose and dose rate
effects—e.g. Fujikawa et al., 2000, and observations of changes in the
activities of genomic defense systems in response to radiation exposures of
various types that can be used for calibration and/or testing of the descriptive
and predictive accuracy of the models as applied to various radiation
exposure/response systems.

Embedding the repair model in a structure that allows for differential inputs in
types and amounts of cell damage, and that interprets the outputs to give
implications for cancer risk estimates for organs and organisms.

Provision of a comprehensive characterization of uncertainties in repair model
parameters, of sensitivity of risks to those parameters, and of the implications
of those uncertainties to the risk estimates themselves.
A key aspect of the initial set of tasks will be to provide a contextual structure for using
the repair model. Because the vulnerability of the genome to radiation-induced damage is
more complex than was originally imagined (see mini-appendix II), inputs will need
specification – types of and amounts of damage - including both radiation exposureinduced damage and background damage. We will adopt a flexible approach so that the
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classification of types of damage can be enlarged and updated as part of future modeling
work and using findings from other modelers.
Similarly we will provide a structure for the use of the outputs of the repair modeling to
inform risk estimates. Overall, the representation system we propose will help integrate
and aid in the interpretation of a large and diverse literature. Systematic assembly and
analysis of information in this framework will also aid in the identification of gaps in
current information that could be helpful in structuring future research efforts.
Model Calibration and Validation
There is a delicate competition in the use of data for developing and applying models,
especially when there are limited amounts of reliable data and there are many uncertain
model parameters. On one hand data are needed for calibration: to identify key
parameters and to establish values and uncertainty estimates for them. On the other hand
comparisons with data sets are needed for validation: to develop confidence (or correct
unfounded confidence) in the validity and serviceability of the model, and point the way
to structural adaptations of the model if indicated. If all of the data were to be used for
calibration, one would obtain, in effect, a glorified example of curve fitting and have little
confidence that the model will provide sensible extrapolated results outside the bounds of
directly available observations. Or, if all the data were used for validation, one would
be, in effect, asking how good the initial guesses were for model parameters; this will
provide little guidance for improving the model or for confidence in applying it. The
middle ground, some use of data for this, some for that, will not automatically resolve this
tension. The key further observation is that calibration and validation raise different
questions and thus pose different demands on data sets.
Thus among the tasks in model development, as enumerated above, are 1)
characterization of available data sets and the conditions under which they were obtained
to assess their suitability for calibration – establishing values and uncertainties for
parameters and combinations of parameters, and their suitability for model validation –
testing the structural relations which give confidence to extrapolations, 2) use of
sensitivity analysis to identify which aspects of model structure can be most easily
distinguished from available alternatives by observations of which parameters and
combinations of parameters, and 3) where data for the needed sensitive parameters are
not readily available, identify “critical experiments” that are capable of yielding data that
could distinguish among model structure options that could have important implications
for risk assessment. We envision performing all three of these tasks in the course of
developing models for cell repair processes. We also note that a collaborative effort at
characterizing calibration and validation opportunities will be appropriate as part of the
development of a biological archetype and the comparison of models for it.
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Some Initial Considerations for Developing a Biological Archetype
The development of a useful biological archetype will require three key ingredients: 1) an
organizing structure based on relevant biological mechanisms and their causal links; 2) a
compendium of data sets that provide information about the processes and links; 3) a
functional interpretation of the data that provides values (with uncertainty) for parameters
used in modeling risks. Our previous work on inter-individual variability provides an
illustration of this sort of archetype construction. Our objective was to shed light on the
influence of inter-individual variability on various kinds of risk estimates for noncancer
effects. We developed a classification of pharmacokinetic and pharmacodynamic
processes which form the causal chains for various types of risks associated with different
types of environmental exposures (Hattis et al., 1999a). This classification provided an
organizational structure for sorting and interpreting a large collection of data sets which
contained information on differences among the human subjects in particular steps or
groups of steps along the causal chains. From these data we then developed a functional
interpretation by creating a derived data base giving the inter-individual variability
associated with each toxin in specific processes or sequences of processes. Variability
was represented by a slope in a fit to a log normal distribution. We have used the derived
data base to create probability distributions that represent the magnitudes of interindividual variability that can be anticipated for various types of toxins, paths for
exposure, and types of health risks. These distributions then provide information about
the range of possible differences between people that should be expected in different sorts
of risk estimates (Hattis et al., 1999b, 2002).
The organizing structure for biological modeling of radiation-induced cancer should
come from considerations about processes involving transformations of genetic material.
Transformations appear in sequences of steps which may lead to development of a cancer
or which instead may lead to non-cancerous benign outcomes. Some of the steps
correspond to deterministic processes; some are stochastic. Transformations are affected
by what happens within the cell, by other cells and by other aspects of the environment.
A suitable classification will identify such elements as types of cells, types of genetic
damage, rates of damage of various types, rates and fidelity of repairs, susceptibility to
further damage and capabilities for future repair, and responsiveness of the activity of
various repair and other genomic defense systems to short and long term dosage of DNA
damaging agents. The classification will also consider influences on rates and types of
damage including internal influences like properties of the stages in the cell replication
cycle and external influences including radiation exposures and other background effects.
Particularly for repair processes and influences on them, it is important to keep track of
feedback mechanisms. Numerous studies and data sets are available to be organized
according to the information they provide about processes and combinations of processes
within the classification. The literature we have cited in this proposal is but a tiny
fraction of the tip of the iceberg. The functional interpretation of these many data sets
can be in the form of probability distributions for numerical characterizations of the
various processes, distributions of rates for particular types of genetic damage at
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particular exposure levels, for instance, or distributions of rates for repair and of fidelity
probabilities.
Because the ultimate goal is to produce better risk estimates for human and other animal
exposures, the organizing classification should embed cellular processes in a hierarchical
scheme which describes systems of cells, organs, and the whole human or animal. The
further advantage of hierarchical embedding is that there is a very substantial literature of
epidemiological and animal studies of effects of radiation exposure. Integrating this
information with that on cell level processes will be facilitated by a scheme that
incorporates all levels of organization on an equal footing. We anticipate that the
information we have accumulated on inter-individual variability in pharmacokinetic and
pharamcodynamic processing, including its specification for cancer related processes
(Hattis and Barlow, 1996), could be used in such a hierarchical scheme.
In general, construction of a biological archetype requires striving for balance in two
dimensions. Attention to the nature of the data must be balanced against attention to the
demands posed by the desired applications. Also realism in descriptions of biological
processes must be balanced against computational tractability. Striking good balances
should be a cooperative endeavor, and our group would like to participate in that.
MINI-APPENDIX I: Illustrative Simple Representation and Discussion of a Repair
Process and Cell Replication Rates
Other things being equal, saturation of DNA repair systems at high rates of DNA lesion
generation would tend to lead to a longer persistence of generated lesions, and greater
opportunity for the lesions to be present at the times of DNA copying when permanent
changes or rearrangements of information coded in DNA can be fixed into the genome.
This would produce an upward turning curve of fixed mutations in relation to carcinogen
dose rate. Inherently, however, DNA repair systems cannot achieve repair 100% of the
time (and so cannot create true "thresholds" with finite damage but zero risk of
mutations) even at very low rates of DNA damage. If (1) if there is a finite number of the
enzyme molecules capable of recognizing a particular kind of DNA lesions and initiating
the repair process and (2) if there is only a finite time period between lesion generation
and potential completion of the mutagenic process through the completion of cell
division, then in general some finite fraction of repairable lesions at low doses must
escape repair.
Assuming that lesion recognition and repair are governed by the same basic principles as
other enzymatic reactions, if there were a single burst of lesion generation at time t = 0,
the rate of lesion repair (-d[L]0/dt) would be given by:
d[L]
-------
Vmax[L]
= - ------------8
dt
K
m
+ [L]
which of course will produce precisely the same consequences for high and low dose
kinetics as detoxification reactions and active tubular secretion (Hattis, 1990). At low
doses (so that the [L] in the denominator of the equation above is much smaller than Km
and can be neglected) the initially generated lesions will decline exponentially with a rate
constant equal to Vmax/Km. If there is continuous low level dosage, then the rate of
lesion generation (k*[C]internal) will come into equilibrium with the rate of lesion repair.
We can then solve for the equilibrium concentration of lesions:
k[C]internalKm
[L]eq =
-------------------Vmax
and the rate of generation of permanent somatic mutations at low dosage must be simply
proportional to this quantity times the replication rate of the relevant cells. For single
high-dose bursts of lesion generation that far exceed the doses required to saturate the
relevant repair enzymes, we can expect that the decline in lesions with time will be linear,
rather than exponential (at the rate given by Vmax), and we can expect as much as a
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[lesion]0 dependence of the concentration X time product of DNA lesions, exacerbated
by whatever enhancement there may be in cell replication at high doses.
Finally, there have been some observations suggesting induction of a DNA repair system
in human lymphoblast cells exposed in vitro to aflatoxin-B (Kaden et al., 1987). In this
system the loss of N7-guanine adducts after exposure was found to be biphasic--initially
showing a half life of 3-4 hours, followed by a slower rate of loss with a half life of about
10 hours. At three different doses, giving three different initial levels of N7-guanine
adducts, the shift from the more rapid to the slower phase of loss (presumably
corresponding to reversion of the enzyme concentration to uninduced levels) occurred at
times when about 1000 total adducts remained per cell. In general, induction of a
relatively low-error repair system, as in this case, can be expected to lead to a high-dose
saturation of mutagenic transitions as a function of dose. By contrast, if the inducible
repair system is more error-prone than the constitutive processes for removal of adducts,
the nonlinearity will be in the direction of producing an upward turning curve of
mutagenic transitions in relation to dose. Similarly, induction of activating or detoxifying
metabolic enzymes would have analogous consequences for dose response relationships.
The interpretation of Kaden et al (1987) in terms of inducible repair is by no means
without uncertainty. Indeed, observations of biphasic kinetics have been made repeatedly
(see, for example, Stenerlow et al., 2000; Radford, 1987; Metzger and Iliakis, 1991;
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Wheeler and Nelson, 1991; Souliotis et al., 1995; Wheeler and Wierowski, 1983; Murray
et al., 1984) with the interpretations that differences in DNA repair rates for similar
adducts can be different for DNA adducts located on relatively condensed vs
uncondensed chromatin, in cells in different phases of the cell cycle, in cycling vs
noncycling cells. or other DNA “compartments”.
Bona fide cases of inducible DNA repair or inducible detoxifying enzyme activities have
one other possible consequence that warrants mention here. It is theoretically possible
that in some cases where there is an appreciable “background” of DNA damage and
carcinogenesis by agents other than the inducing radiation or other exposure over some
dose range the induction of defensive enzyme activities may cause appreciable reductions
in the carcinogenic background processes. In some cases, over some dose range, it is
even possible that these reductions in background carcinogenesis may be quantitatively
greater than the increases in cancer processes that are to be expected from the direct
action of the inducing carcinogen—causing an apparent “hormesis” effect. “Hormesis”
observations, including some apparent examples for carcinogenic endpoints, have been
the subject of considerable discussion (Calabrese et al., 1999; Andersen and Conolly,
1998; Bogen, 1998). In cases where induction or detoxification or repair enzymes occurs
in animal cancer bioassays, and where it can be shown that these produce a discernible
effect on background tumor frequencies, then the appropriate modeling treatment should
be analogous to the treatment of high dose pharmacokinetic nonlinearities. That is, these
extraneous influences on observed tumor frequencies should ideally be statistically
disentangled from the overall dose response observations before assessing the direct
carcinogenic effect of the inducing compound with multistage models.
MINI-APPENDIX II: Complexities in Types of Radiation-Induced Damage
.
The vulnerability of the genome to radiation-induced damage is more complex than was
originally imagined. Cells that are hit by low- or high-LET radiation express damage
themselves but also cause effects on progeny (genomic instability) or on neighboring cells
(bystander effects) There are also observations that suggest that these two processes are
not necessarily independent: that, for instance, the progeny of an irradiated cell can
release a factor that leads to damage or death in neighboring cells (genomic instability
leading to bystander effects as described by Nagar et al 2003) Or an irradiated cell or its
progeny may be able to induce genomic instability in a non-irradiated cell line. Such
possibilities have been recently reviewed by Lorimore & Wright (2003).
The indirect pathways of damage exhibit differences from direct radiation-induced
damage in at least two ways. One concerns dose fractionation. Mothersill and Seymour
(2002) have shown that a fractionated dose (two half doses rather than one whole dose)
causes less cell death in directly irradiated cells. The reverse seems to be the case for
bystander damage; when cells are exposed to the filtered medium of a culture exposed to
a fractionated dose there is more cell death than when they are exposed to a whole-dose
culture medium. A second difference is in the types of damage expressed. DNA in
directly irradiated cells exhibits mainly deletions of various sizes. In contrast, delayed
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mutations or mutations in bystanders are largely point mutations, apparently the result of
oxidative damage, (Lorimore and Wright 2003, Huo et al 2001, Little et al 2003). Double
strand breaks in bystander cells are believed to occur but are believed to be efficiently
repaired, based on the observation that cells deficient in the nonhomologous end rejoining
repair pathway exhibit a substantial increase in unrepaired or misrepaired double strand
breaks (Nagasawa et al 2003).
In summary, the field of indirect radiation-induced damage is evolving rapidly. It
provides important inputs to the dynamic representation of repair by illuminating the
nature of the damage that must be repaired. In turn, understanding mechanisms of repair
is critical to understanding the dynamics of indirect damage. We plan to
comprehensively and periodically assess the current state of this literature. We will
incorporate characterizations (such as the proposal by Nikjoo and Khvostunov (2003))
for these indirect effects in our DNA damage generation and repair modeling efforts.
And we will attempt to identify opportunities for using models of repair mechanisms to
help inform the understanding of indirect processes.
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