Pharmacogenomics and American Indian Populations: Drug
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Transcript Pharmacogenomics and American Indian Populations: Drug
12th Annual Summer Public Health Research
Videoconference on Minority Health
Pharmacogenomics and American
Indian Populations:
Drug Development in the Context of
Health Disparities
Morris W. Foster, Ph.D.
Department of Anthropology
University of Oklahoma
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The promise of personalized
medicine
Will it really be individualized?
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Instead of customized therapies for
individuals, pharmacogenomics may
just re-arrange the way in which
the economics of drug development
are calculated, using both historical
and marker-based groupings to
determine potential markets.
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Existing social groups will be
characterized by differing
frequencies of variants that
promote drug response, while
new groupings will be based
on the presence or absence of
those variants.
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To the extent that frequencies of
those variants differ by social
identity, existing health disparities
may be exacerbated by the ways
in which pharmacogenomic drug
development plays out, while
additional disparities may be
created among those assigned to
new, marker-based groups.
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This may result in two kinds of
pharmacogenomically-relevant
groupings:
(1) social groups that are genetically
stereotyped (that is, differences in
frequencies are misunderstood as
the presence or absence of a variant
among all group members), and
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(2) genotyped groups that are
socially identifiable.
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Although not a pharmacogenomic
drug, Bi-Dil is an example of the way
in which identity-based disparities in
the relative frequency of disease
incidence and drug response can drive
drug development and marketing.
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The pre-existence of health disparities
may shape the ways in which
pharmacogenomic drug development
is mapped onto social groups.
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How might the science of
pharmacogenomics play out
across a landscape of social
differentiation and health
disparities?
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American Indian/Alaska Native
(AI/AN) populations make an
interesting model for the groups
that may be affected or created by
that intersection of science and
society.
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AI/AN Populations - 1
1.5%
of total U.S. population
(approx. 4.5 million people)
1.9
million of whom receive their
primary health care through IHS
Significantly
lower life expectancy
and significantly higher rates of
heart disease, diabetes, etc.
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AI/AN Populations - 2
Almost
twice the percentage of
households below poverty as the
general population
Diversity
of specific ancestries
(including European and African)
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Distribution of the benefits of
pharmacogenomics depends
both on population genetics
and market economics.
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Do common drug response
variants found in other populations
also exist in AI/AN populations?
– Yes, as will be the case in all
populations.
15
Do some otherwise rare drug
response variants occur at higher
frequencies in AI/AN populations?
– Probably, as again will be the case
in all populations.
16
Do those otherwise rare variants
occur at different frequencies
between tribal- and village-specific
populations?
– Probably.
17
When will we learn the answers to
the previous three questions?
– Probably after we learn similar
information for other populations,
due in part to internal cultural
reasons and in part to external
economic reasons.
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Pharmacogenomics is itself the
construction of a particular
cultural context, and there will be
barriers to its use when transferred
to other cultural contexts, with
respect both to world view and to
historical relationships with the
culture that created the technology.
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Like many other populations that
experience health disparities,
AI/AN populations have a lesser
economic capacity to:
(1) attract investments in targeted
drug discovery and
(2) pay for access to cutting-edge
diagnostics and drugs.
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If common genetically targeted
drugs do apply, it is likely that
many tribal health facilities and
tribal members will be unable to
afford the diagnostic tests and
what will probably be the higher
costs of new drugs.
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If otherwise rare drug
response variants exist at
higher frequencies for some
AI/AN populations, will drug
companies “personalize”
therapies for those variants?
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Whether or not AI/AN populations
are pharmacogenomically unique,
existing health disparities will be
compounded by the advent of
pharmacogenomics.
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If otherwise rarer variants exist at
higher frequencies for some members
of some AI/AN populations, will this
contribute to a perception that AI/AN
people generally respond differently to
drugs? That is, will differences in
frequency be translated as presence or
absence?
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Does the basis for that perception
already exist due to health disparities
between AI/AN populations and
others?
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The IHS, for example, can be
conceptualized as an example
of ancestry-based medicine.
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If pharmacogenomic drugs are
provided or developed for AI/AN
people through that identityspecific lens, the potential for
confounding the social and the
biological is considerable.
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At the same time, the economic,
cultural, and other challenges
that AI/AN people face in having
access to pharmacogenomic
benefits may most effectively be
addressed through the identity
portal.
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For example, the most effective
argument for developing drugs
targeted to response variants that
are disproportionately found in
AI/AN populations is probably
that of a shared AI/AN identity,
not a shared genotype among
those individuals affected.
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The ideal solution may be to
insist on an identity-neutral
pharmacogenomics. But is
that goal as unrealistic as truly
personalized medicine?
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The engagement between
pharmacogenomics and
historical, social groups and
identities may be unavoidable.
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