Goodman and Gilman Manual of Pharmacology and Therapeutics
Pharmacogenetics is the study of the genetic basis for variation in drug response. In this broadest sense, pharmacogenetics encompasses pharmacogenomics, which employs tools for surveying the entire genome to assess multigenic determinants of drug response. Individuals differ from each other approximately every 300-1000 nucleotides, with an estimated total of 10 million single nucleotide polymorphisms (SNPs; single base pair substitutions found at frequencies ≥1% in a population) and thousands of copy number variations in the genome. Identifying which of these variants or combinations of variants have functional consequence for drug effects is the task of modern pharmacogenetics.
IMPORTANCE OF PHARMACOGENETICS TO VARIABILITY IN DRUG RESPONSE
Drug response is considered to be a gene-by-environment phenotype. An individual’s response to a drug depends on the complex interplay between environmental factors (e.g., diet, age, infections, drugs, exercise level, occupation, exposure to toxins, tobacco, and alcohol use) and genetic factors (e.g., gender, variants of drug transporters, and drug metabolizing enzymes expressed). Variation in drug response therefore may be explained by variation in environmental and genetic factors, alone or in combination.
Drug metabolism is highly heritable, with genetic factors accounting for most of the variation in metabolic rates for many drugs.
Comparison of intra-twin vs inter-pair variability suggests that ~75-85% of the variability in pharmacokinetic half-lives for drugs that are eliminated by metabolism is heritable. Extended kindreds may be used to estimate heritability. Inter- vs. intra-family variability and relationships among members of a kindred are used to estimate heritability. Using this approach with lymphoblastoid cells, cytotoxicity from chemotherapeutic agents was shown to be heritable, with ~20-70% of the variability in sensitivity to 5-fluorouracil, cisplatin, docetaxel, and other anticancer agents estimated as inherited.
For “monogenic” phenotypic traits, it is often possible to predict phenotype based on genotype. Several genetic polymorphisms of drug metabolizing enzymes result in monogenic traits. Based on a retrospective study, 49% of adverse drug reactions were associated with drugs that are substrates for polymorphic drug metabolizing enzymes, a proportion larger than estimated for all drugs (22%) or for top-selling drugs (7%). Prospective genotype determinations may result in the ability to prevent adverse drug reactions. Defining multigenic contributors to drug response will be much more challenging. For some multigenic phenotypes, such as response to antihypertensives, the large numbers of candidate genes will necessitate a large patient sample size to produce the statistical power required to solve the “multigene” problem.
GENOMIC BASIS OF PHARMACOGENETICS
A trait (e.g., CYP2D6 “poor metabolism”) is deemed autosomal recessive if the responsible gene is located on an autosome (i.e., it is not sex-linked) and a distinct phenotype is evident only with nonfunctional alleles on both the maternal and paternal chromosomes. An autosomal recessive trait does not appear in heterozygotes. A trait is deemed codominantif heterozygotes exhibit a phenotype that is intermediate to that of homozygotes for the common allele and homozygotes for the variant allele. With the advances in molecular characterization of polymorphisms and a genotype-to-phenotype approach, many polymorphic traits (e.g., CYP2C19 metabolism of drugs such as mephenytoin and omeprazole) are now recognized to exhibit some degree of codominance. Two major factors complicate the historical designation of recessive, codominant, and dominant traits. First, even within a single gene, a vast array of polymorphisms (promoter, coding, non-coding, completely inactivating, or modestly modifying) are possible. Each polymorphism may produce a different effect on gene function and therefore differentially affect a measured trait. Second, most traits (pharmacogenetic and otherwise) are multigenic, not monogenic. Thus, even if the designations of recessive, codominant, and dominant are informative for a given gene, their utility in describing the genetic variability that underlies variability in drug response phenotype is diminished, because variability is likely to be multigenic.
TYPES OF GENETIC VARIANTS
A polymorphism is a variation in the DNA sequence that is present at an allele frequency of 1% or greater in a population. Two major types of sequence variation have been associated with variation in human phenotype: single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). In comparison to base pair substitutions, indels are much less frequent in the genome and are of particularly low frequency in coding regions of genes. Single base pair substitutions that are present at frequencies ≥ 1% in a population are termed SNPs and are present in the human genome at ~1 SNP every few hundred to a thousand base pairs.
SNPs in the coding region are termed cSNPs (coding SNPs), and are further classified as nonsynonymous (or missense) or synonymous (or sense). Coding nonsynonymous SNPs result in a nucleotide substitution that changes the amino acid codon (e.g., proline [CCG] to glutamine [CAG]), which could change protein structure, stability, substrate affinities, or introduce a stop codon. Coding synonymous SNPs do not change the amino acid codon, but may have functional consequences (transcript stability, splicing). Typically, substitutions of the third base pair, termed the wobble position, in a 3 base pair codon, such as the G to A substitution in proline (CCG → CCA), do not alter the encoded amino acid. Base pair substitutions that lead to a stop codon are termed nonsense mutations. In addition, ~10% of SNPs can have more than 2 possible alleles (e.g., a C can be replaced by either an A or G), so that the same polymorphic site can be associated with amino acid substitutions in some alleles but not others.
Synonymous polymorphisms have sometimes been found to contribute directly to a phenotypic trait. One of the most notable examples is a polymorphism in ABCB1, which encodes P-glycoprotein, an efflux pump that interacts with many clinically used drugs. The synonymous polymorphism, C3435T, is associated with various phenotypes and results in a change from a preferred codon for isoleucine to a less preferred codon. Presumably, the less preferred codon is translated at a slower rate, which apparently changes the folding of the protein, its insertion into the membrane, and its interaction with drugs.
Noncoding SNPs may be in promoters, introns, or other regulatory regions that may affect transcription factor binding, enhancers, transcript stability, or splicing. Polymorphisms in noncoding regions of genes may occur in the 3′ and 5′ untranslated regions, in promoter or enhancer regions, in intronic regions, or in large regions between genes, intergenic regions (for nomenclature guide, see Figure 7–1). Noncoding SNPs in promoters or enhancers may alter cis- or trans-acting elements that regulate gene transcription or transcript stability. Noncoding SNPs in introns or exons may create alternative exon splicing sites, and the altered transcript may have fewer or more exons, or shorter or larger exons, than the wild-type transcript. Introduction or deletion of exonic sequence can cause a frame shift in the translated protein and thereby change protein structure or function, or result in an early stop codon, which makes an unstable or nonfunctional protein. Because 95% of the genome is intergenic, most polymorphisms are unlikely to directly affect the encoded transcript or protein. However, intergenic polymorphisms may have biological consequences by affecting DNA tertiary structure, interaction with chromatin and topoisomerases, or DNA replication. Thus, intergenic polymorphisms cannot be assumed to be without pharmacogenetic importance.
Figure 7–1 Nomenclature of genomic regions.
The second major type of polymorphism is indels. SNP indels can have any of the same effects as SNP substitutions: short repeats in the promoter (which can affect transcript amount), or insertions/deletions that add or subtract amino acids. A remarkable diversity of indels is tolerated as germline polymorphisms. A common glutathione-S-transferase M1 (GSTM1) polymorphism is caused by a 50-kilobase (kb) germline deletion, and the null allele has a population frequency of 0.3-0.5. Biochemical studies indicate that livers from homozygous null individuals have only ~50% of the glutathione conjugating capacity of those with at least 1 copy of the GSTM1 gene. The number of TA repeats in the UGT1A1 promoter affects the quantitative expression of this crucial glucuronosyltransferase in liver; 6 or 7 repeats constitute the most common alleles.
Some deletion and duplication polymorphisms can be seen as a special case of copy number variations (CNVs). Copy number variations involve large segments of genomic DNA that may involve gene duplications (stably transmitted inherited germline gene replication that causes increased protein expression and activity), gene deletions that result in the complete lack of protein production, or inversions of genes that may disrupt gene function. CNVs range in size from 1 kb to many megabases. CNVs appear to occur in ~10% of the human genome and in 1 study accounted for ~18% of the detected genetic variation in expression of around 15,000 genes in lymphoblastoid cell lines. There are notable examples of CNVs in pharmacogenetics; gene duplications of CYP2D6 are associated with an ultra-rapid metabolizer phenotype.
A haplotype, which is defined as a series of alleles found at a linked locus on a chromosome, specifies the DNA sequence variation in a gene or a gene region on 1 chromosome. For example, consider 2 SNPs in ABCB1, which encodes for the multidrug resistance protein, P-glycoprotein. One SNP is a T-to-A base pair substitution at position 3421 and the other is a C-to-T change at position 3435. Possible haplotypes would be T3421C3435, T3421T3435, A3421C3435, and A3421T3435. For any gene, individuals will have 2 haplotypes, 1 maternal and 1 paternal in origin. A haplotype represents the constellation of variants that occur together for the gene on each chromosome. In some cases, this constellation of variants, rather than the individual variant or allele, may be functionally important. In others, however, a single mutation may be functionally important regardless of other linked variants within the haplotype(s).
Two terms describe the relationship of genotypes at 2 loci: linkage equilibrium and linkage disequilibrium. Linkage equilibrium occurs when the genotype present at 1 locus is independent of the genotype at the second locus. Linkage disequilibrium occurs when the genotypes at the 2 loci are not independent of one another. In complete linkage disequilibrium, genotypes at 2 loci always occur together. Patterns of linkage disequilibrium are population specific and as recombination occurs linkage disequilibrium between 2 alleles will decay and linkage equilibrium will result.
ETHNIC DIVERSITY. Polymorphisms differ in their frequencies within human populations and have been classified as either cosmopolitan or population (or race and ethnic) specific. Cosmopolitan polymorphisms are those polymorphisms present in all ethnic groups, although frequencies may differ among ethnic groups. Likely to have arisen before migrations of humans from Africa, cosmopolitan polymorphisms are generally older than population-specific polymorphisms. The presence of ethnic and race-specific polymorphisms is consistent with geographical isolation of human populations. These polymorphisms probably arose in isolated populations and then reached a certain frequency because they are advantageous (positive selection) or, more likely, neutral to a population. African Americans have the highest number of population-specific polymorphisms in comparison to European Americans, Mexican Americans, and Asian Americans.
PHARMACOGENETIC STUDY DESIGN CONSIDERATIONS
A pharmacogenetic trait is any measurable or discernible trait associated with a drug. Thus, enzyme activity, drug or metabolite levels in plasma or urine, blood pressure or lipid lowering produced by a drug, and drug-induced gene expression patterns are examples of pharmacogenetic traits. Directly measuring a trait (e.g., enzyme activity) has the advantage that the net effect of the contributions of all genes that influence the trait is reflected in the phenotypic measure. However, it has the disadvantage that it is also reflective of nongenetic influences (e.g., diet, drug interactions, diurnal, or hormonal fluctuation) and thus, may be “unstable.”
For CYP2D6, if a patient is given an oral dose of dextromethorphan, and the urinary ratio of parent drug to metabolite is assessed, the phenotype is reflective of the genotype for CYP2D6. However, if dextromethorphan is given with quinidine, a potent inhibitor of CYP2D6, the phenotype may be consistent with a poor metabolizer genotype, even though the subject carries wild-type CYP2D6 alleles. In this case, quinidine administration results in a drug-induced haploinsufficiency, and the assignment of a CYP2D6 poor metabolizer phenotype would not be accurate for that subject in the absence of quinidine. If a phenotypic measure, such as the erythromycin breath test (for CYP3A), is not stable within a subject, this is an indication that the phenotype is highly influenced by nongenetic factors, and may indicate a multigenic or weakly penetrant effect of a monogenic trait. Most pharmacogenetic traits are multigenic rather than monogenic (Figure 7–2), and considerable effort is being made to identify the important polymorphisms that influence variability in drug response.
Figure 7–2 Monogenic versus multigenic pharmacogenetic traits. Possible alleles for a monogenic trait (upper left), in which a single gene has a low-activity (1a) and a high-activity (1b) allele. The population frequency distribution of a monogenic trait (bottom left), here depicted as enzyme activity, may exhibit a trimodal frequency distribution among low activity (homozygosity for 1a), intermediate activity (heterozygote for 1a and 1b), and high activity (homozygosity for 1b). This is contrasted with multigenic traits (e.g., an activity influenced by up to 4 different genes, genes 2 through 5), each of which has 2, 3, or 4 alleles (a through d). The population histogram for activity is unimodal-skewed, with no distinct differences among the genotypic groups. Multiple combinations of alleles coding for low activity and high activity at several of the genes can translate into low-, medium-, and high-activity phenotypes.
GENETIC TESTING. Most genotyping methods use constitutional or germline DNA, that is, DNA extracted from any somatic, diploid cells, usually white blood cells or buccal cells. DNA is extremely stable if appropriately extracted and stored and DNA sequence is generally invariant throughout an individual’s lifetime. Because genotyping tests are directed at specific known polymorphic sites, and because not all known functional polymorphisms are likely to be known for any particular gene, it is critical to understand the methodology for interrogating the polymorphic sites. One method to assess the reliability of any specific genotype determination in a group of individuals is to assess whether the relative number of homozygotes to heterozygotes is consistent with the overall allele frequency at each polymorphic site. Hardy-Weinberg equilibrium is maintained when mating within a population is random and there is no natural selection effect on the variant. Such assumptions are described mathematically when the proportions of the population that are observed to be homozygous for the variant genotype (q2), homozygous for the wild-type genotype (p2), and heterozygous (2*p*q) are not significantly different from that predicted from the overall allele frequencies (p = frequency of wild-type allele; q = frequency of variant allele) in the population. Proportions of the observed 3 genotypes must add up to 1.
CANDIDATE GENE VERSUS GENOME-WIDE APPROACHES
After genes in drug response pathways are identified, the next step in the design of a candidate gene association pharmacogenetic study is to identify the genetic polymorphisms that are likely to contribute to the therapeutic and/or adverse responses to the drug. There are several databases that contain information on polymorphisms and mutations in human genes (Table 7–1); these databases allow the investigator to search by gene for reported polymorphisms. Some of the databases, such as the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB), include phenotypic as well as genotypic data.
Databases Containing Information on Human Genetic Variation
In candidate gene association studies, specific genes are prioritized as playing a role in response or adverse response to a drug, it is important to select polymorphisms in those genes for association studies. For this purpose, there are 2 categories of polymorphisms. The first are polymorphisms that do not, in and of themselves, cause altered function or expression level of the encoded protein (e.g., an enzyme that metabolizes the drug or the drug receptor). Rather, these polymorphisms are linked to the variant allele(s) that produces the altered function. The second type of polymorphism is the causative polymorphism, which directly precipitates the phenotype. For example, a causative SNP may change an amino acid residue at a site that is highly conserved throughout evolution. This substitution may result in a protein that is nonfunctional or has reduced function. If biological information indicates that a particular polymorphism alters function, e.g., in cellular assays of nonsynonymous variants, this polymorphism is an excellent candidate to use in an association study. When causative SNPs are unknown, tag SNPs can be typed to represent important, relatively common blocks of variation within a gene. Once a tag SNP is found to associate with a drug response phenotype, the causative variant or variants, which may be in linkage with the tag SNP, should be identified. Because the causative variant may be an unknown variant, sequencing the gene may be necessary to identify potential causative variants. These additional causative variants may be uncovered by further deep resequencing of the gene.
GENOME-WIDE AND ALTERNATIVE LARGE-SCALE APPROACHES. A potential drawback of the candidate gene approach is that the wrong genes may be studied. Genome-wide approaches, using gene expression arrays, genome-wide scans, or proteomics, can complement and feed into the candidate gene approach by providing a relatively unbiased survey of the genome to identify previously unrecognized candidate genes. For example, RNA, DNA, or protein from patients who have unacceptable toxicity from a drug can be compared with identical material from identically treated patients who did not have such toxicity. Differences in gene expression, DNA polymorphisms, or relative amounts of proteins can be ascertained using computational tools, to identify genes, genomic regions, or proteins that can be further assessed for germline polymorphisms differentiating the phenotype. Gene expression and proteomic approaches have the advantage that the abundance of signal may itself directly reflect some of the relevant genetic variation; however, both types of expression are highly influenced by choice of tissue type, which may not be available from the relevant tissue; for example, it may not be feasible to obtain biopsies of brain tissue for studies on CNS toxicity. DNA has the advantage that it is readily available and independent of tissue type, but the vast majority of genomic variation is not in genes, and the large number of polymorphisms presents the danger of type I error (finding differences in genome-wide surveys that are false positives). Current research challenges include prioritizing among the many possible differentiating variations in genome-wide surveys of RNA, DNA, and protein to focus on those that hold the most promise for future pharmacogenomic utility.
FUNCTIONAL STUDIES OF POLYMORPHISMS
For most polymorphisms, functional information is not available. Therefore, to select polymorphisms that are likely to be causative, it is important to predict whether a polymorphism may result in a change in expression level of a protein or a change in protein function, stability, or subcellular localization. One way to gain an understanding of the functional effects of various types of genomic variations is to survey the mutations that have been associated with human Mendelian disease. The greatest numbers of DNA variations associated with Mendelian diseases or traits are missense and nonsense mutations, followed by deletions.
Functional genomics studies of numerous variants in membrane transporters suggest that the variants that alter function are likely to change an evolutionarily conserved amino acid residue and to be at low allele frequencies. These data indicate that SNPs that alter evolutionarily conserved residues are most deleterious. For example, substitution of a charged amino acid (Arg) for a nonpolar, uncharged amino acid (Cys) is more likely to affect function than substitution of residues that are more chemically similar (e.g., Arg to Lys). The data also suggest that rare SNPs, at least in the coding region, are likely to alter function.
Among the first pharmacogenetic examples to be discovered was glucose-6-phosphate dehydrogenase (G6PD) deficiency, an X-linked monogenic trait that results in severe hemolytic anemia in individuals after ingestion of fava beans or various drugs, including many antimalarial agents. G6PD is normally present in red blood cells and helps to regulate levels of glutathione (GSH), an antioxidant. Antimalarials such as primaquine increase red blood cell fragility in individuals with G6PD deficiency, leading to profound hemolytic anemia. The severity of the deficiency syndrome varies among individuals and is related to the amino acid variant in G6PD. The severe form of G6PD deficiency is associated with changes at residues that are highly conserved across evolutionary history. Collectively, studies of Mendelian traits and polymorphisms suggest that non-synonymous SNPs that alter residues that are highly conserved among species and those that result in more radical changes in the nature of the amino acid are likely to be the best candidates for causing functional changes. The information in Table 7–2 can be used as a guide for prioritizing polymorphisms in candidate gene association studies.
Predicted Functional Effect and Relative Risk That a Variant Will Alter Function of SNP Types in the Human Genome
With the increasing number of SNPs that have been identified, it is clear that computational methods are needed to predict the functional consequences of SNPs. To this end, predictive algorithms have been developed to identify potentially deleterious amino acid substitutions. These methods can be classified into 2 groups. The first group relies on sequence comparisons alone to identify and score substitutions according to their degree of conservation across multiple species; different scoring matrices have been used (e.g., BLOSUM62, SIFT, and PolyPhen). The second group of methods relies on mapping of SNPs onto protein structures, in addition to sequence comparisons. For example, rules have been developed that classify SNPs in terms of their impact on folding and stability of the native protein structure as well as shapes of its binding sites.
Functional activity of amino acid variants for many proteins can be studied in cellular assays. An initial step in characterizing the function of a non-synonymous variant is to isolate the variant gene or construct the variant by site-directed mutagenesis, express it in cells, and compare its functional activity to that of the reference or most common form of the protein. For many proteins, including enzymes, transporters, and receptors, the mechanisms by which amino acid substitutions alter function have been characterized in kinetic studies. Figure 7–3 shows simulated curves depicting the rate of metabolism of a substrate by 2 amino acid variants of an enzyme and the most common genetic form of the enzyme.
Figure 7–3 Concentration-dependence curves showing the rate of metabolism of a hypothetical substrate by the common genetic form of an enzyme and 2 nonsynonymous variants. Variant A exhibits an increased Km and likely reflects a change in the substrate binding site of the protein by the substituted amino acid. Variant B exhibits a change in the maximum rate of metabolism (Vmax) of the substrate. This may be due to reduced expression level of the enzyme.
In contrast to the studies with SNPs in coding regions, we know much less about noncoding region SNPs. SNPs identified in genome-wide association studies as being associated with clinical phenotypes including drug response phenotypes have largely been in noncoding regions, either intergenic or intronic regions, of the genome. An example of profound functional effect of a noncoding SNP is provided by CYP3A5; a common noncoding intronic SNP in CYP3A5 accounts for its polymorphic expression in humans. The SNP accounting for variation in CYP3A5 protein creates an alternative splice site, resulting in a transcript with a larger exon 3 but also the introduction of an early stop codon (Figure 7–4).
Figure 7–4 An intronic SNP can affect splicing and account for polymorphic expression of CYP3A5. A common polymorphism (A > G) in intron 3 of CYP3A5 defines the genotypes associated with the wild-type CYP3A5*1 allele, or the variant nonfunctional CYP3A5*3 allele. This intronic SNP creates an alternative splice site that results in the production of an alternative CYP3A5 transcript carrying an additional intron 3B (panel B), with an early stop codon and truncated CYP3A5 protein. The wild-type gene (more common in African than Caucasian or Asian populations) results in production of active CYP3A5 protein (panel A); the *3 variant results in a truncated and inactive protein. Thus, metabolism of CYP3A5 substrates is diminished in vitro (panel C) and blood concentrations of such medications are higher in vivo (panel D) for those with the *3 than the *1 allele.
Candidate genes for therapeutic and adverse response can be divided into 3 categories: pharmacokinetic, receptor/target, and disease modifying.
PHARMACOKINETIC ALTERATIONS. Germline variability in genes that encode determinants of the pharmacokinetics of a drug, in particular metabolizing enzymes and transporters, affect drug concentrations, and are therefore major determinants of therapeutic and adverse drug response (Table 7–3). Multiple enzymes and transporters may be involved in the pharmacokinetics of a single drug. Several polymorphisms in drug metabolizing enzymes were discovered as monogenic phenotypic trait variations.
Examples of Genetic Polymorphisms Influencing Drug Response
For example, a very large number of medications (estimated at 15-25% of all medicines in use) have been shown to be substrates for CYP2D6 (see Table 7–3 and Figure 6–3A). Phenotypic consequences of the deficient CYP2D6 phenotype include increased risk of toxicity of antidepressants or antipsychotics (catabolized by the enzyme), lack of analgesic effects of codeine (anabolized by the enzyme), and lack of activation of tamoxifen, leading to a greater risk of relapse or recurrence in breast cancer. Conversely, the ultrarapid phenotype is associated with extremely rapid clearance and thus inefficacy of antidepressants.
A promoter region variant in the enzyme UGT1A1, UGT1A1*28, which has an additional TA in comparison to the more common form of the gene, has been associated with a reduced transcription rate ofUGT1A1 and lower glucuronidation activity of the enzyme. This reduced activity has been associated with higher levels of the active metabolite SN38 of the cancer chemotherapeutic agent irinotecan (seeChapter 6), which is associated with the increased risk of toxicity (see Figures 6–5 and 6–6). CYP2C19, historically termed mephenytoin hydroxylase, displays penetrant pharmacogenetic variability, with just a few SNPs accounting for the majority of the deficient, poor metabolizer phenotype. The deficient phenotype is much more common in Chinese and Japanese populations. Several proton pump inhibitors, including omeprazole and lansoprazole, are inactivated by CYP2C19. Thus, the deficient patients have higher exposure to active parent drug, a greater pharmacodynamic effect (higher gastric pH), and a higher probability of ulcer cure than heterozygotes or homozygous wild-type individuals.
Both pharmacokinetic and pharmacodynamic polymorphisms affect warfarin dosing. The anticoagulant warfarin is catabolized by CYP2C9, and its action is partly dependent upon the baseline level of reduced vitamin K (catalyzed by vitamin K epoxide reductase; Figure 7–5 and see Figure 30–6). Inactivating polymorphisms in CYP2C9 are common, with 2-10% of most populations being homozygous for low-activity variants, and are associated with lower warfarin clearance, a higher risk of bleeding complications, and lower dose requirements (see Table 30–2). Combined with genotyping for a common polymorphism in VKORC1, inherited variation in these 2 genes account for 20-60% of the variability in warfarin doses needed to achieve the desired coagulation level.
Figure 7–5 Pharmacogenetics of warfarin dosing. Warfarin is metabolized by CYP2C9 to inactive metabolites and exerts its anticoagulant effect partly via inhibition of VKORC1 (vitamin K epoxide hydrolase), an enzyme necessary for reduction of inactive to active vitamin K. Common polymorphisms in both genes, CYP2C9 and VKORC1, impact on warfarin pharmacokinetics and pharmacodynamics, respectively, to affect the population mean therapeutic doses of warfarin necessary to maintain the desired degree of anticoagulation (often measured by the international normalized ratio [INR] blood test) and minimize the risk of too little anticoagulation (thrombosis) or too much anticoagulation (bleeding). See also Figure 30–6 and Table 30–2.
DRUG RECEPTOR/TARGET ALTERATIONS. Gene products that are direct targets for drugs have an important role in pharmacogenetics. Highly penetrant variants with profound functional consequences in some genes may cause disease phenotypes that confer negative selective pressure; more subtle variations in the same genes can be maintained in the population without causing disease, but nonetheless causing variation in drug response.
For example, complete inactivation by means of rare point mutations in methylenetetrahydrofolate reductase (MTHFR) causes severe mental retardation, cardiovascular disease, and a shortened lifespan. Conversely, the 677C → T SNP causes an amino acid substitution that is maintained in the population at a high frequency (frequency in most white populations = 0.4) and is associated with modestly lower MTHFR activity (~30% less than the 677C allele) and modest but significantly elevated plasma homocysteine concentrations (~25% higher). This polymorphism does not alter drug pharmacokinetics, but does appear to modulate pharmacodynamics by predisposing to GI toxicity to the antifolate drug methotrexate in stem cell transplant recipients.
FACTORS MODIFYING METHOTREXATE ACTION. The methotrexate pathway involves metabolism, transport, drug modifier, and drug target polymorphisms. Methotrexate is a substrate for transporters and anabolizing enzymes that affect its intracellular pharmacokinetics and that are subject to common polymorphisms. Several of the direct targets (dihydrofolate reductase, purine transformylases, and thymidylate synthase [TYMS]) are also subject to common polymorphisms. A polymorphic indel in TYMS (2 vs. 3 repeats of a 28-base pair repeat in the enhancer) affects the amount of enzyme expression in both normal and tumor cells. The TYMS polymorphism can affect both toxicity and efficacy of anticancer agents (e.g., fluorouracil and methotrexate) that target TYMS. Thus, the genetic contribution to variability in the pharmacokinetics and pharmacodynamics of methotrexate cannot be understood without assessing genotypes at a number of different loci.
OTHER EXAMPLES OF DRUG TARGET POLYMORPHISMS. Many drug target polymorphisms have been shown to predict responsiveness to drugs (Table 7–3). Serotonin receptor polymorphisms predict not only the responsiveness to antidepressants, but also the overall risk of depression. β Adrenergic receptor polymorphisms have been linked to asthma responsiveness, renal function following angiotensin-converting enzyme (ACE) inhibitors, and heart rate following β blockers. Polymorphisms in HMG-CoA reductase have been linked to the degree of lipid lowering following statins (see Chapter 31), and to the degree of positive effects on high-density lipoproteins among women on estrogen replacement therapy. Ion channel polymorphisms have been linked to a risk of cardiac arrhythmias in the presence and absence of drug triggers.
POLYMORPHISM-MODIFYING DISEASES. Some genes may be involved in an underlying disease being treated, but do not directly interact with the drug. Modifier polymorphisms are important for the de novo risk of some events and for the risk of drug-induced events.
For example, the MTHFR polymorphism is linked to homocysteinemia, which in turn affects thrombosis risk. The risk of a drug-induced thrombosis is dependent not only on the use of prothrombotic drugs, but on environmental and genetic predisposition to thrombosis, which may be affected by germline polymorphisms in MTHFR, factor V, and prothrombin. These polymorphisms do not directly act on the pharmacokinetics or pharmacodynamics of prothrombotic drugs, such as glucocorticoids, estrogens, and asparaginase, but may modify the risk of the phenotypic event (thrombosis) in the presence of the drug. Likewise, polymorphisms in ion channels (e.g., HERG, KvLQT1, Mink, and MiRP1) may affect the overall risk of cardiac dysrhythmias, risk that may be accentuated by a drug that can prolong the QT interval in some circumstances (e.g., macrolide antibiotics, antihistamines).
CANCER AS A SPECIAL CASE. Cancer pharmacogenetics have an unusual aspect in that tumors exhibit somatically acquired mutations in addition to the underlying germline variation of the host. Thus, the efficacy of some anticancer drugs depends on the genetics of both the host and the tumor.
For example, non-small-cell lung cancer is treated with an inhibitor of epidermal growth factor receptor (EGFR), gefitinib. Patients whose tumors have activating mutations in the tyrosine kinase domain ofEGFR appear to respond better to gefitinib than those without the mutations. Breast cancer patients with expression of the Her2 antigen (as an acquired genetic changes) are more likely to benefit from the antibody trastuzumab than are those who are negative for Her2 expression, and this results in a common tailoring of anticancer therapy in patients with breast cancer based on tumor genetics. Some genetic alterations affects both tumor and host: the presence of 2 instead of 3 copies of a TYMS enhancer repeat polymorphism increases the risk of host toxicity but also increases the chance of tumor susceptibility to thymidylate synthase inhibitors.
PHARMACOGENETICS IN CLINICAL PRACTICE
Three major types of evidence should accumulate to implicate a polymorphism in clinical care:
1. Screens of tissues from multiple humans linking the polymorphism to a trait
2. Complementary preclinical functional studies indicating that the polymorphism is plausibly linked with the phenotype
3. Multiple supportive clinical phenotype/genotype association studies
Most drug dosing relies on a population “average” dose of drug. Adjusting dosages for variables such as renal or liver dysfunction is often accepted in drug dosing. Even though there are many examples of significant effects of polymorphisms on drug disposition (e.g., see Table 7–3), there is much more hesitation from clinicians to adjust doses based on genetic testing than on indirect clinical measures of renal and liver function. The frequency of functionally important polymorphisms means that complexity of dosing will be likely to increase substantially in the postgenomic era. Even if every drug has only 1 important polymorphism to consider when dosing, the scale of complexity could be large. The potential utility of pharmacogenetics to optimize drug therapy is great. With continued incorporation of pharmacogenetics into clinical trials, the important genes and polymorphisms will be identified, and data will demonstrate whether dosage individualization can improve outcomes and decrease short- and long-term adverse effects.
There are useful resources that permit clinicians to access information on pharmacogenetics, (see Table 7–1). Passage of laws to prevent genetic discrimination may assuage concerns that genetic data placed in medical records could penalize those with “unfavorable” genotypes.