Biology Reference
In-Depth Information
enable one to rapidly and precisely identify off-target
reactions of drugs, thereby providing a powerful approach
to assessing drug toxicities.
Preventive. Systems analyses provide insights into the
dynamics of disease-perturbed networks. A new 'network-
centric' rather than 'gene-centric' approach to choosing drug
targets will employ multiple drugs to 're-engineer' a disease-
perturbed network to make it behave in a more normal
manner. We are now exploring this possibility in microor-
ganisms to learn the fundamental principles of network re-
engineering before applying this strategy to themore complex
requirements of human disease therapies
[59]
. Our conviction
is that the treatment of disease in the future will often require
the combination of two or more drugs. Tools are now being
developed to explore the combinatorial analyses of the
hundreds of drugs that havemet the safety requirement
so-called experiments where patient N equals 1 may open
up powerful new approaches to more effectively dealing
with the individual patient and aggregating the useful data
they generate. Indeed, the first molecularly detailed tracking
of two individuals (the 'quantified self') is already yielding
fascinating insights
[12,25]
. Imagine, in 10 years, 340
million Americans each with billions of data points: this
will potentially create a powerful aggregated data source
from which to infer the predictive medicine of the future.
In this regard, we believe that it will be critical to
champion the idea that it is essential that all patients' data
(with appropriate privacy measures including anonymiza-
tion) be made available through an appropriately consti-
tuted entity for qualified researchers and physicians to mine
for the predictive medicine of the future. After all, this
contribution will enable us to revolutionize the healthcare
for our children and grandchildren
both
those that were efficacious for a particular disease and those
that were not. Hence for the effective and non-useful drugs
perhaps combinations could be identified to more effectively
attack a wide variety of diseases. This couldmake drugs more
effective and far less expensive, because there is a rationale
for the choice of drug targets and their perturbations by drugs.
Through longitudinal multi-biomarker (blood protein,
miRNA, mRNA, metabolites etc.) analyses, P4 medicinewill
be able to predict the potential future emergence of disease-
perturbed networks in patients and then design 'preventive
drugs' that will block the emergence of these disease-per-
turbed networks and their cognate diseases. A systems
approach to the immune response will, in time, give us
a deeper understanding of how to create effective cellular as
well as humoral immune responses, permitting us to create
effective vaccines for scourges such as AIDS. Clearly, stem
cells will provide powerful possibilities in the future for
replacing damaged cellular and even organ components (as
well as being powerful tools for understanding disease
mechanismand stratifying disease). Finally, the digitized data
defining the quantified self will provide powerful new insights
into optimizing wellness for the individual. Indeed, the focus
of P4 medicine will increasingly move from disease to
wellness.
Personalized. On average, humans differ from one
another by about 6 million nucleotides in their genomes
e
a point to which most
patients are responsive. Moreover, laws must be passed to
protect the individual against the exploitation of their
medical data by other elements of society, such as
employers or insurance companies. It is interesting to note
that all of us make available our entire financial histories to
three credit agencies for the convenience of having a credit
card. Surely patients will recognize the benefits of being
able to mine their collective data to pioneer the future of P4
medicine for the benefit of their families.
Participatory.P4medicinereliesgreatlyonthepositive
contributions of activated patients and consumers. Our
existing healthcare system is not well adapted to exploit the
new capabilities of P4 medicine. Physicians as well as
pharmaceutical and medical device companies are
compensated solely for the delivery of specific procedures
and products, hence they have limited financial incentive to
deploy new innovations to predict or prevent disease or to
maintain wellness. Moreover, the healthcare industry is
locked into financial and regulatory models based on large-
scale population studies that ignore crucial genetic and
environmental exposure differences among individuals.
Pressure for change is beginning to be felt as the medical
profession faces the looming challenge of increasingly being
compensated for outcomes as opposed to service delivery.
However, the most important source of pressure for change
will be newly activated and networked patients and
consumers. Collectively, they will constitute a vital new
stakeholder in P4 medicine very different from the passive
recipients (patients) of expert advice characteristic of pre-
digital medicine. Activated and networked consumers will
do more than demand more effective healthcare
e
e
hence we, individually, are genetically unique. Even iden-
tical twins each may exhibit 35 different 'intergenerational
mutational nucleotide differences' from each parent and
from one another
[33]
. Each person must be treated as
a unique individual and not as a statistical average. We must
take account of the fact individuals vary in ways that
significantly affect effective treatment. Individuals should
each serve as their own controls to determine when their
own data reflect transitions from health to disease. More-
over, there is a growing sentiment that observations on
single individuals may collectively provide fundamental
new insights into the disease (or wellness) process. The
they will
e
help direct the changes to achieve it.
Activated and networked consumers are beginning to
push for healthcare that is adapted to their own particular
circumstances, including their individual genome (which is
static and need be sequenced only once) and dynamic
measurements such as from blood (which change over time,
