Genetic Influences on Pharmacokinetics

Drug Metabolism

Drug-metabolism pharmacogenomics has a long history, with work in this field dating to the 1950s. In many cases, patients with extreme response (often toxicity) were found to have excessively high plasma drug concentrations, which were subsequently found to result from variations in the gene for the enzyme important to that drug's metabolism (4). A number of drug-metabolizing enzymes have inactivating mutations, which lead to absent or nonfunctional protein, and for drugs with a high dependence on that particular enzyme, plasma drug concentrations can be 5-10 times that of normal. For drugs with a

Fig. 2. Key components in pharmacogenetics. Figure highlights the two broad areas for research in pharmacogenetics; specifically, pharmacokinetics and pharmacodynamics. Drug-metabolizing enzymes and drug transporters most often contribute to variable pharmacokinetics. Proteins involved in mediating drug effect are broadly defined as drug targets, and include not just the direct protein targets of the drug but any proteins involved in the drug action (e.g., signal transduction proteins, proteins mediating adverse effects). Any of these types of proteins could contribute to interpatient variability in drug efficacy or toxicity, and thus could be candidate genes in pharma-cogenetic studies. (Reproduced with permission from ref. 64).

Fig. 2. Key components in pharmacogenetics. Figure highlights the two broad areas for research in pharmacogenetics; specifically, pharmacokinetics and pharmacodynamics. Drug-metabolizing enzymes and drug transporters most often contribute to variable pharmacokinetics. Proteins involved in mediating drug effect are broadly defined as drug targets, and include not just the direct protein targets of the drug but any proteins involved in the drug action (e.g., signal transduction proteins, proteins mediating adverse effects). Any of these types of proteins could contribute to interpatient variability in drug efficacy or toxicity, and thus could be candidate genes in pharma-cogenetic studies. (Reproduced with permission from ref. 64).

narrow therapeutic index (a small range in which the drug is efficacious, without causing toxicity) these polymorphisms can have profound clinical consequences. One of the best examples of clinical application of pharmacogenomics is with the drug-metabolizing enzyme thiopurine S-methyltransferase (TPMT), which metabolizes thiopurines commonly used in cancer chemotherapy and for immune suppression (5). This enzyme has several inactivating mutations, and genetic testing for TPMT prior to administration of thiopurines is becoming the standard of practice (4,5).

In the case of cardiovascular drugs, there are some that are substrates for the enzymes that exhibit genetic variability, but in a number of cases, these do not have important clinical consequences. For example, approx 70-80% of the metabolism of the P-blocker metoprolol is controlled by the polymorphic cytochrome P450 2D6 (CYP2D6) enzyme, and those who have inactivating mutations on both alleles have no functional protein present. In these patients, called poor metabolizers, their metoprolol plasma concentrations can be more than five times that of normal (6-8). And although such patients may require lower doses of metoprolol, when it is dose-titrated to response, poor metabolizers do not appear to experience significantly greater rates of toxicity from metoprolol, in large part because its concentration-response curve plateaus. As such, the marked increases in metoprolol plasma concentration seen in CYP2D6 poor metabolizers do not lead to a greater maximal therapeutic effect, but rather just a prolongation of effect (8).

A number of other ^-blockers and antiarrhythmic drugs are metabolized by CYP2D6, including timolol, propranolol, carvedilol, propafenone, flecainide, and mexilitene, although none of these are metabolized by CYP2D6 to as great an extent as metoprolol (9,10). CYP2D6 poor metabolizers will have higher concentrations of these drugs as compared to population averages at any given dose, as is seen with metoprolol. However, it is only on occasion that this translates into important problems clinically.

The examples described above are somewhat typical for the cardiovascular drugs in relation to the clinical impact of genetic polymorphisms in the drug-metabolizing enzymes. In general, cardiovascular drugs have very wide therapeutic indexes, so marked increases in plasma drug concentration are typically well tolerated. Thus, drug metabolism pharmacogenomics is less relevant clinically than in areas where the drugs have a much more narrow therapeutic range, such as is the case with cancer chemotherapy, psychiatric medications, and anticonvulsants.

However, there are some interesting exceptions to this rule. Perhaps the one gathering the greatest attention at present is warfarin, an anticoagulant drug with a very narrow therapeutic range, whose metabolism is governed largely by the polymorphic CYP2C9 enzyme. Unlike CYP2D6, CYP2C9 does not have inactivating mutations, but contains several SNPs that change the encoded amino acid, leading to different functional capacities for metabolism. The most common of these polymorphisms are called CYP2C9*2 and CYP2C9*3. Up to 40% of Caucasians (fewer for Asians and those of African descent) carry at least one variant allele, and both variant alleles have significantly reduced catalytic activity (11). Importantly, the presence of one or more variant alleles has been associated in numerous studies with substantially lower warfarin dose requirements, prolongation of the time to stable dosing, and increased risk of bleeding, including serious bleeding episodes (11-14). Given that warfarin requires intensive monitoring and follow-up, the potential benefits of using genetic information to help determine the most appropriate dose for a specific patient are clear. However, studies documenting the benefit of a priori genetic information to guide dosing have not yet been done, and until they are, it is unlikely that CYP2C9 genotyping will be used widely in the clinical setting.

Another example in which drug-metabolism genotype is important is with the enzyme N-acetyl transferase and the drugs procainamide and hydralazine. The relationship between the genetics and pharmacokinetics of these drugs was among the earliest pharmacogenomic examples. Both of these drugs cause a lupus-like syndrome, and it was recognized decades ago that those who developed this lupus-like syndrome were much more likely to have decreased acetylation as compared with those who did not experience this toxicity (4,15,16). Thus, "slow acetylators," a genetically-determined phenotype, are at increased risk of drug-induced lupus. In the case of procainamide, fast acetylators may also experience genotype-related adverse events. Specifically, procainamide is metabolized to N-acetylprocainamide (NAPA), an active metabolite that also possesses antiarrhythmic activity. Fast acetylators accumulate much higher concentrations of NAPA, and as a result are at somewhat increased risk of NAPA-induced QT prolongation and Torsades de Pointes (TdP).

Drug Transporters

Another interesting example that relates to pharmacokinetic differences is digoxin and P-glycoprotein. P-glycoprotein is a membrane-bound adenosine triphosphate (ATP)-dependent efflux pump that is important to the distribution and elimination of a number of drugs, and thus falls into the Fig. 2 "transporter" category (17,18). P-glycoprotein vs gene, ABCBl has numerous genetic polymorphisms, some of which have been associated with differences in digoxin plasma concentrations, potentially a result of genotype-dependent bioavail-ability and renal clearance (19-22). Whereas much of the P-glycopro-tein literature for other drugs is inconsistent, the findings with respect to digoxin pharmacokinetics and P-glycoprotein are fairly consistent, a finding that may be attributed to the fact that most other P-glycoprotein substrates are confounded by significant metabolism by CYP3A4, while digoxin is not. Other cardiovascular drugs that are substrates for P-glycoprotein include amiodarone, atorvastatin, diltiazem, losartan, quini-dine, and verapamil (17). The impact of P-glycoprotein genetic polymorphisms on their pharmacokinetics remains to be elucidated.

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