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Pharmacogenomics is the field
of correlating DNA or gene expression variations
to pharmacological function and/or patient response
to drug therapies.
Pharmacogenomic data can help
develop individualized therapies based on patient
stratification (identifying potential responders
and non-responders to a therapeutic regimen).
Patient stratification can allow scientists to
develop a trial design comprised of a genetically
differentiated patient pool, using genomic biomarkers
to predict response of a group of individuals
to a therapeutic. In undifferentiated patient
pools, the number of non-responders could jeopardize
a trial’s endpoint, thereby possibly preventing
advancement of a therapeutic to a genetically
responsive subpopulation.
A genetically differentiated trial
design could allow for more efficient clinical
trials, may decrease the cost of drug development,
and could help improve the probability of a successful
New Drug Application (NDA).
For example, the drug Herceptin
may have initially been considered a failed drug
due to its impact on only 25% of the patient population
during clinical trials. However, the 25% for whom
Herceptin was effective were all found to overexpress
the HER-2 gene. Using pharmacogenomic testing
to predict HER-2 overexpression as a means to
stratify breast cancer patients led to a successful
NDA for Herceptin, and has allowed Genentech to
build a $500MM business with this drug.
Additional success stories are beginning
to emerge from the use of and the incorporation
of pharmacogenomics in drug development. In a
Phase III clinical trial designed to evaluate
CML patient response to Gleevac, pharmacogenomic
testing identified a 31-gene biomarker within
the patient population that predicted clinical
response with 94% accuracy. In a Phase II clinical
trial designed to evaluate Myeloma patient response
to Velcade, pharmacogenomic testing identified
a 30-gene biomarker that predicted responders
with 71% accuracy and non-responders with 84%
accuracy.
Pharmacogenomic biomarker identification
and validation is growing rapidly. Patient stratification based on pharmacogenomic
tests may reduce costs by targeting treatments to those individuals mostly likely to benefit, moving
us another step closer to personalized medicines.
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HERE to read more on Expression Analysis' collaborations with the US Food & Drug Administration.
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