Pharmacogenomics and Personalized Medicine
Can doctors predict who should take certain medications and who will suffer side effects? With personalized medicine, physicians may be able to use genetic profiles to make treatment choices.
Personalized medicine is based on using an individual’s genetic profile to make the best therapeutic choice by facilitating predictions about whether that person will benefit from a particular medicine or suffer serious side effects. Drugs are generally tested on a large population of people and the average response is reported. This sort of evidence-based medicine (that is, medical decision making based on empirical data) relies on the law of averages; personalized medicine, on the other hand, recognizes that no two patients are alike.
Basics of Pharmacogenomics
In pharmacogenomics, genomic information is used to study individual responses to drugs. When a gene variant is associated with a particular drug response in a patient, there is the potential for making clinical decisions based on genetics by adjusting the dosage or choosing a different drug, for example. Scientists assess gene variants affecting an individual’s drug response the same way they assess gene variants associated with diseases: by identifying genetic loci associated with known drug responses, and then testing individuals whose response is unknown. Modern approaches include multigene analysis or whole-genome single nucleotide polymorphism (SNP) profiles, and these approaches are just coming into clinical use for drug discovery and development.
When studying drug action in individuals, researchers focus on two major determinants: (1) how much of a drug is needed to reach its target in the body, and (2) how well the target cells, such as heart tissue or neurons, respond to the drug. The scientific terms for these two determinants are pharmacokinetics and pharmacodynamics, and both are critical considerations in the field of pharmacogenomics.
Pharmacokinetics encompasses four processes: absorption, distribution, metabolism, and excretion, which are often abbreviated as ADME (Figure 1). Absorption usually refers to how a drug enters the bloodstream after a person takes a pill or uses an inhalant; intravenous injection circumvents absorption by putting a drug directly into the blood. Distribution describes where the drug travels after absorption and how much of the drug reaches the target site. Many drugs, for example, cannot get past the blood-brain barrier. Metabolism refers to how the drug gets broken down in the body, which can happen immediately by way of enzyme action in the stomach and sometimes involves end products with their own pharmacologic action. Finally, excretion describes how drugs leave the body, whether by urine, bile, or, in some cases, exhalation.
Improving Cancer Outcomes
Both drugs and chemotherapy are used for the treatment of breast cancer, and diagnostic tests have allowed some limited degree of disease typing. For patients with estrogen receptor-sensitive cancer that has not yet spread to the lymph nodes, for example, tamoxifen is the drug of choice, but chemotherapy is frequently offered as an adjunct. However, chemotherapy is known to help only a small number of patients. In fact, a long-term study called the National Surgical Adjuvant Breast and Bowel Project (NSABP) found that only 4% of patients who received chemotherapy had improved outcomes (Paik et al., 2004).
Could patients receive individualized treatment based on their specific type of cancer? It appears that the answer to this question is “yes.” For instance, using real-time PCR methods to study gene expression in breast cancer and taking advantage of a large collection of paraffin-preserved tissue samples, a team of NSABP researchers developed a diagnostic kit that assays 21 genes (Paik et al., 2004). This project has resulted in a diagnostic tool that identifies key genetic components of particular patients’ breast cancer and can improve outcomes. This diagnostic kit predicts the likelihood of cancer recurring for patients who are categorized into one of three risk groups: low, intermediate, and high. In a clinical validation study, the diagnostic data did indeed predict long-term recurrence (Paik et al., 2006). Moreover, in studies to determine treatment benefit, the researchers analyzed tissue samples from the original NSABP study. There was no benefit to adding chemotherapy to tamoxifen in low-risk patients, and only a tiny benefit (2%) in intermediate-risk patients. In high-risk patients, though, the benefit of chemotherapy was clear, with a 28% decrease in recurrence of cancer.