A Revolution in R&D

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genetics-based form of pharmacogenomics (see
sidebar, “Pharmacogenomics—Some Definitions”),
and comes into play later, in the development
phase: it involves predicting the efficacy and side
effects of candidate drugs.
The data explosion detonated by genomics technology
has created vast amounts of genetic information,
ready for sifting. The findings of the
Human Genome Project and related endeavors are
merely the starting point. The ultimate goal is to
elucidate the genetic basis of human disease and
drug response. In the short term, genetics research
will enable scientists to predict disease susceptibility
and likely drug response in individuals; in the
longer term, it should help to improve the quality
of pharmaceuticals and medical diagnoses.
PHARMACOGENOMICS—SOME DEFINITIONS
Pharmacogenomics is the use of genomics
approaches to elucidate drug response. There are
three relevant approaches: via DNA, via RNA, and
via proteins, and three corresponding forms of pharmacogenomics:
pharmacogenomics using genetic
approaches (or pharmacogenetics), expression profiling
(or expression pharmacogenomics), and proteomics
(or proteomic pharmacogenomics).
Pharmacogenetics predicts patients’ drug response
by analyzing the genetic variations in their DNA. It is
the form of pharmacogenomics discussed in the
main text here.
Expression pharmacogenomics predicts patients’
drug response by analyzing their RNA levels—specifically,
by comparing the amounts of RNA found in
different samples to determine which genes are
expressed at different levels. An example: a research
group at The Whitehead Institute studying two very
similar leukemias (AML and ALL) has observed a
distinct difference in expression levels of specific
genes, and thereby provided a quick and reliable
method for differentiating them. Patients are now
less at risk of being misdiagnosed and being given
Attaining the short-term goal is, conceptually, simple
enough. The genetic codes of individuals differ
in tiny, but sometimes decisive, details. By comparing
an individual’s genetic variations against the
“standard” genome, scientists should be able to predict
whether that individual is at risk for a specific
disease, and, if so, how well suited he or she is to a
particular drug therapy—the work respectively of
disease genetics and pharmacogenetics.
The two approaches benefit R&D economics in different
ways. Disease genetics will improve efficiency
in target discovery and, by leading to the discovery
of particularly high-quality targets, will bring about
improved success rates in validation and downstream.
Pharmacogenetics, by enabling scientists to
select patients more appropriately for clinical trials,
an incorrect, and possibly lethal, drug treatment: in
effect, the test screens for adverse drug response.
Expression pharmacogenomics seems to be moving
from academic studies and biotechs into more mainstream
pharmaceutical R&D. Witness the recent purchase
by Merck and Co. of Rosetta Inpharmatics, a
biotech founded specifically to develop expression
pharmacogenomics.
Finally, proteomic pharmacogenomics predicts
patients’ drug response by analyzing their protein
levels—specifically, by comparing protein readings
in different tissue samples to identify proteins that
differ either in structure or in expression levels.
Consider the example of an aberrant fusion of two
proteins called Bcr and Abl, which occurs in more
than 95 percent of patients with CML (chronic
myeloid leukemia, which accounts for about 20 percent
of all cases of adult leukemia). This aberrant
fusion protein is present only in cancer cells. It distinguishes
itself from its normal counterparts by its
increased size. It can be used not only to monitor the
progression of the disease but also to test whether
Gleevec, a revolutionary new drug, would provide an
effective therapy.