adverse drug response. Drugs predicted to be less effective or likely to cause an adverse
event could be avoided. For example, a person predicted to be at high risk for diabetes
might be tested for hyperglycemia more frequently, placed on a medical diet earlier, and
treated more aggressively for glucose intolerance.
Persons with the same type of cancer might respond differently to therapy because the
genetic abnormalities in their tumors are different
or because of differences, for
example, in the way that chemotherapeutic drugs are metabolized.
Examples of polymorphisms that influence drug metabolism include variants of CYP2C9 and
VKORC1 that influence the metabolism of warfarin, an anticoagulant drug; CYP2D6 variants
that affect the biotransformation of β-adrenergic receptor antagonists, neuroleptics, and
tricyclic antidepressants; NAT2 variants that affect the inactivation of isoniazid, a drug
commonly used to treat tuberculosis; and G6PD that influences sensitivity to the antimalarial
drug, primaquine. Response to antihypertensive β-blockers has been associated with
variants in genes that encode subunits of the β-adrenergic receptor.
Potential obstacles to the use of genetic information in personalized medicine include an
inability to identify genetic and environmental risk factors (and their interactions) that enable
accurate prediction of clinically significant risk; lack of evidence demonstrating that individual
risk assessment improves diagnostic accuracy and treatment outcome; lack of technologies
for cost-efficient assessment of an individual’s genome; building an infrastructure for
clinicians to access risk data, interpret risk information, and explain risk estimates to
patients; and the need for guidelines and policies for how risk assessment information
should be used in clinical and research applications.
The use of individual genetic variants to predict risk of disease and/or response to
pharmacologic agents can be considered the practice of
, whereas the
assessment of the action of many genes simultaneously to predict disease risk or drug
Potential uses of whole-genome data from an individual include screening for inborn errors,
metabolism in newborns (i.e., newborn screening), testing for carriers of genetic disorders
(e.g., sickle cell disease, cystic fibrosis), assessing risk for common diseases, predicting
drugs that might influence risk for a serious adverse drug, and forensic identification.
The answer is B.