|
The
Promise of Pharmacogenomics
December 6, 2004
By Nusrat
Khaleeli and Dennis Fernandez
Why do some
drug therapies work well with some patients but not with others?
Why do some patients experience side effects and others don't? The
answers may lie in our genes.
The field of
pharmacogenomics combines medicine, pharmacology, and genomics to
develop drug therapies that compensate for genetic differences that
cause varied responses in patients.
Pharmacogenomics
stems from a related field, pharmacogenetics, and the two terms
are often used interchangeably. Pharmacogenetics is the decades-old
study of differences in drug absorption, metabolism, elimination
or response, and then examines a few candidate genes for variations
underlying the observed phenotypes. In contrast, pharmacogenomics
casts a wider net to capture complicated patterns of genetic variation
and attempts to correlate these patterns to different drug response
phenotypes. The challenge is to identify genetic differences that
influence drug metabolism and response, and to correlate that data
with drug efficacy and safety information. The goal is to weave
all this information together into something that has enough predictive
value to be used reliably.
Single nucleotide
polymorphisms (SNPs pronounced "snips") are the most prevalent
genetic variations in the human genome. They are single base pair
differences that occur in 1% of the human population. The human
SNP map shows 1.42 million differences, a majority of which occur
in coding regions.
Pharmacogenomics
is the study of how these sequence differences affect the ways in
which people respond to drugs. Variations in the disease-causing
genes, drug targets or the enzymes that metabolize drugs influence
the drug's potency and efficacy. Also, genetic differences between
patients may explain why some patients but not others suffer from
harmful drug side effects.
The primary
goal of pharmacogenomics is to reduce the time and cost of drug
development. Choosing patient candidates for a clinical trial based
on pharmacogenomic knowledge and the patients' genotype is hoped
to eliminate sub-populations for whom drugs are predicted to be
ineffective. This would justify smaller and fewer trials, likely
generate more consistent trial results, and make it easier to gain
FDA approval.
Another goal
of pharmacogenomics is to identify patients who are likely to suffer
drug related adverse events. A 1998 study of hospitalized patients
published in the Journal of the American Medical Association reported
that in 1994, there were more than 2.2 million adverse drug reactions
and 100,000 drug-related deaths, making adverse drug reactions one
of the leading causes of hospitalization and death in the United
States. Moreover, the ability to pre-test patients may have prevented
certain high profile drug withdrawals, including the former Warner-Lambert
Rezulin (troglitazone) and Glaxo Wellcome's Lotronex (alosetron).
Pharmacogenomics
can be used to identify how quickly a patient will metabolize a
drug, and therefore, ensure appropriate dosing. Up to 30% patients
do not respond optimally to certain drugs, this can often be addressed
by merely changing the dose. If these problems were identified and
remedied early in clinical trials, results would be more convincing
and, therefore, approval would be faster and less costly.
Pharmacogenomics
will allow the differentiation of a company's product from others
in the marketplace (e.g., by identifying patients by a genotype
who will respond to product X but not to product Y). One further
benefit to patients is that pharmacogenomic knowledge will also
allow identification of those patients in the population who will
derive no clinical benefit from a prospective treatment. A look
at data from clinical trials in 14 major drug categories reveals
that this "non-responder" subset may be 20-75% of the
general population. Additionally, pharmacogenomic knowledge from
association studies (SNP to disease links) will allow for preventative
screening and preventative treatment.
Currently, costs
limit the widespread use of pharmacogenomics. For example, it costs
approximately one dollar to identify one SNP in a patient sample.
It is estimated that it will require the screening of 100,000 SNPs
per patient to construct an accurate picture of a patient's response
to a drug; this translates to 100,000 dollars per patient.
For this technology
to become practicable, the cost must be reduced to a penny per SNP.
Further, narrowing down a large number of genetic variations to
a number that is amenable to application in a clinical trial would
also prove useful. In this regard, computation methods to categorize
and prioritize SNPs or haplotyping, the identification of closely
associated polymorphisms that tend to occur in clusters, are being
developed.
Other limitations
in the progress of pharmacogenomics include tools used for collecting,
archiving, organizing and interpreting the huge amount of data generated
in a pharmacogenomics study so that data from diverse experiments
can be compared. Also, drug dosage and treatment schedules need
to be standardized in order to accurately compare patient data.
Successful interpretation of data also requires comparison of enormous
quantities of data such as the publicly available databases, Pharmacogenetics
and Pharmacogenomics Knowledge Base (PharmGKB) and the SNP Consortium.
Drug patent
holders in pharmaceutical industry have many incentives to use pharmacogenomic
knowledge to develop genotyping diagnostic tests to be used with
a drug. They have a vested interest in having shorter, less expensive
clinical trials, identifying patients who are expected to have adverse
drug reactions and those requiring tailored dosages of drug. However,
the anticipated loss of sales revenue by identification of the "non-responders"
serves as a strict disincentive for the development of genotyping
diagnostic tests.
Nusrat Khaleeli
and Dennis Fernandez are with Fernandez & Associates, LLP (www.iploft.com).
Go to the
Biotechnology
section of Larta Institute's Research Archives
Return
to this week's issue of VOX
|