The Promise of Personalized Medicine

Adverse events caused by drug therapy are a significant burden to the healthcare system and society in general. Many of these adverse events are preventable, a consequence of an individual’s genetic makeup as described in the next sections. Personalized medicine certainly has a significant role to play in the avoidance of harm; however, the real promise of pharmacogenetics may be in optimal prescribing. From a health economics perspective, the use of personalized medicine may be one of the most effective ways to manage the seemingly intractable problem of rising drug costs and access to life-saving pharmaceuticals.

Take the example of a new anticancer drug, considered to be a breakthrough therapy for lung cancer. As a collection of diseases rooted in genetics, cancer has become one of the clearest examples of the potential for pharmacogenomics in predicting pharmacodynamics of a given drug in a given patient, as outlined in the following example.

An anticancer drug may be considered successful if 10% to 20% of patients respond to it. From a clinician’s perspective and from the perspective of those 10% to 20% of patients, this is a success, but from a health economist’s perspective, a $30,000-per-year drug that fails in 80% to 90% of patients is not cost-effective. If the annual cost for a new anticancer drug is going to be around $30,000 to $60,000, the only way to make these promising new drugs cost-effective is to administer them to only the 10% to 20% of patients who will benefit from them.

Pharmacogenetics and Pharmacogenomics

Pharmacogenetics and pharmacogenomics are often used interchangeably but do have distinct meanings. Although either term refers to the use of genetic information to guide therapeutic decision-making:

Pharmacogenetics focuses on the influence of single genes on drug response.

Pharmacogenomics takes a broader view of the influence of an individual’s entire genome on his or her response to drug therapy.

One can consider pharmacogenetics to be the original term for this field, coined in the days when tools such as microarrays did not exist and the human genome had not been mapped. In those early days it seemed unlikely or impossible that an entire genome could be analyzed in an efficient enough manner to become a useful tool in everyday medicine. Now that we have the means and the knowledge to envision a genome-wide approach to everyday prescribing, the term pharmacogenomics is often used to refer to this field. Table 6-1 lists some of the terms commonly used in pharmacogenetics.

TABLE 6-1 Definitions of Some Common Terms Used in Pharmacogenetics

The most common basis for genetic variation, and thus the basis for a pharmacogenomic approach to drug therapy, is the single nucleotide polymorphism, or SNP (Figure 6-1). An SNP occurs when a single nucleotide is exchanged for another at a point in an individual’s genome. It is estimated that the human genome consists of approximately 3 billion nucleotides, which in specific combinations form 25,000 to 40,000 genes and encode approximately 100,000 proteins (at last count). SNPs that occur in coding regions of the genome have the potential to influence protein expression by altering an amino acid within the protein.

Nonsynonymous coding SNPs (cSNPs) or missense SNPs change the identity of an amino acid. Substitution of this amino acid may change the structure or function of the protein.

Synonymous coding SNPs or sense SNPs do not change the identity of an amino acid and are not likely to affect the structure or function of the protein.

Nonsense mutations are SNPs that lead to a stop codon. A stop codon will halt translation of nucleotides into a protein.

Figure 6-1 Single nucleotide polymorphism.

These variants are often indicated with an asterisk followed by a number indicating the specific mutation in that allele (e.g., CYP450 2D6*4). Certain alleles occur more commonly in some ethnic groups than in others. The impact of these SNPs on phenotypes and their subsequent clinical consequences can again be divided into the two fundamental branches of pharmacology: those that influence pharmacokinetics and those that influence pharmacodynamics.

Pharmacokinetics

The field of pharmacogenomics began with the observations of dramatic differences in the way that certain individuals metabolize drugs. This, along with the fact that adverse drug reactions (ADRs) were the first and most obvious application of pharmacogenomics, has meant that the influence of pharmacogenomics on pharmacokinetics has been much more extensively studied than the impact of pharmacogenomics on pharmacodynamics.

Until recently, the identification of genetically based aberrant metabolism would invariably begin with the observation of an ADR. An ADR can occur because of either higher- (more frequent) or lower-than-expected plasma levels of a given drug. A physician would note toxicities to therapeutic doses of a given drug and discover that plasma levels of that drug were much higher than expected. When other factors such as drug interactions or liver or renal disease were factored out, the clinician was left with genetics as the most viable option for explaining the unexpectedly high plasma levels.

A classic example occurred with the antituberculosis drug isoniazid (Figure 6-2). Isoniazid is acetylated (metabolized) by N-acetyltransferase 2 (NAT2), and it was observed that some patients metabolized this drug slowly (i.e., were slow metabolizers), whereas others were rapid metabolizers.

The clinical consequence is that rapid metabolizers would have low plasma levels of the drug, increasing the potential for therapeutic failure, whereas slow metabolizers were predisposed to toxicities associated with the drug.

This wide variation in response to isoniazid led researchers to speculate that genetics might be playing a role. Isoniazid became one of the first drugs to be identified as having a genetic component to its pharmacologic actions.

Figure 6-2 Influence of genetics on isoniazid metabolism.

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