Pharmacogenomics may be defined as the genome wide analysis of genetic determinants of drug efficacy and toxicity. This branch of science began as
pharmacogenetics when Vogel gave its name in 1959.The main goal of pharmacogenomics is evaluating the role of genetic variants in drug metabolism
and effect, developing innovative ways of minimizing harmful drug effects and optimising care for individual patients. Various tools like gene mapping, gene
sequencing, statistical genetics and gene expression are used to derive information. Various factors influence the drug response like age, genetics, sex, race/ethnicity, disease state, absorption, distribution, metabolism, excretion, body weight, height, receptor sensitivity, organ dysfunction, accompanying medications, smoking, diet, alcohol, stress, pollution, socio-economic status, drug adherence, physiological changes including pregnancy, lactation, unmeasured nucleotide or structural variation, complex methylation/epigenetic mechanisms and gene-environment interactions. Around 30 to 60% of medication response rate for treating many diseases like depression, schizophrenia, rheumatoid arthritis has been observed. It has been reported that genetic factors can account 20 to 95% variation in drug response. Patients are classified into poor, normal and rapid metabolizers depending on the response to the drug intake. When a standard dose is given to the poor metabolizer, the drug is metabolized slowly resulting in an increased risk of toxicity and for the ultra metabolizer, the standard dose may be ineffective.
Genetic variations related to drug response can be classified in to three types namely pharmacokinetic, pharmacodynamic and idiosyncratic, based on their
mechanism of action. Variations involving pharmacokinetics are associated with drug transporters and metabolizing enzymes and lead to alterations in the uptake, distribution and elimination of drugs. The Pharmacodynamic type of variations occur in the drug target or a component of the target pathway leading to altered drug efficacy. The target of pharmacodynamics includes receptors, ion channels, enzymes, transducer and regulatory proteins and immune molecules. A third type of variations known as idiosyncratic involved in unintended actions of a drug outside its therapeutic indication.
Inter individual variations in drug response have been reported based on genetic variations in drug metabolizing enzymes, receptors, transporters and pathways. The drug metabolizing enzymes are heterogeneous group of proteins involved in metabolism of drugs. They are two groups namely oxidative drug metabolizing enzymes and conjugative drug metabolizing enzymes. The oxidative drug metabolizing enzymes include cytochrome P450 and Flavin mono oxygenase; these catalyze the introduction of an oxygen atom into substrate molecules leading to hydroxylation or demethylation. The conjugative enzymes catalyze the coupling of endogenous small molecules to xenobiotics that results in the formation of soluble compounds that are more readily excreted. This enzyme family has UDP glycosyltransferases (UGTs), glutathione transferases, sulfotransferases and N acetyltransferases as members.
Classical examples of variation in drug response: Though many examples are available, few are given below.
(a) Warfarin: It is an anticoagulant used to prevent stroke and venous thromboembolism. Its use is limited by narrow therapeutic window, variability in dose–response, interactions with drugs and diet and risk of serious bleeding. The dose is prescribed based on anticoagulation response measured by laboratory
assay like internalised normalised ratio. Clinical factors account for 17-21% of variation and genetic polymorphism in genes such as Cytochrome P450(CYP)2C9 and vitamin K expoxide reductase complex subunit 1(VKORC1) are responsible for 30-35% variation in warfarin dosing. Over coagulation and risk of bleeding
was observed in carriers of at least one or more variant alles of the CYP2C9 genotype. Incease risk of adverse cardiac events were observed in those who possess the variant VKORC1. A 42.6% benefit of warfarin treatment was observed after genotype guided drug regimen.
(b) Clopidogrel: In cardiac patients, who are undergoing percutaneous coronary interventions (PTCA) clopidogreal is a standard medication. Clopidogreal is a
prodrug that requires metabolic activation in a reaction catalyzed by chytochrome P450 enzyme CYP2C19 into its active metabolite. It has been reported that around 25% of patients experience subtherapeutic antiplatlet response. A lower capacity to metabolize clopidogrel into its active metabolite and inhibit platelet activation and higher risk of adverse cardiovascular events were observed variant allele carriers when compared to wild type allele of CYP2C19 .Another mediator of Clopidogrel platelet effect has been reported and it was Paraoxagenase 1(PON1). It was found to drive the conversion of the drug into the active metabolite. A polymorphism in PON1 (PON1Q192R) was observed to affect the platelet response, clopidogrel pharmacokientics and the risk for thrombosis. Limited platelet inhibition and decreased plasma levels of both active PON1 and clopidogrel metabolites were observed homozygous individuals of PON1QQ192.
(c) Flaxacillin: It is an antibiotic used for the treatment of staphylococcal infections. The usage of this drug has been associated with cholestatic hepatitis in
approximately 8.5 cases per 100,000 patients. A single nucleotide polymorphism in HLA-B5701 has shown strong association with hepatic injury. Abacavir: It is a nucleoside analogue used to treat patients with HIV type 1 infection. Within six weeks of teatment, approximately 5% of patients developed a hypersensitivity reaction involving multisystem with symptoms of fever, rash and gastrointestinal discomforts which subsided within 72 hours of discontinuation of the drug. Approximately 74% carriers of haplotype HLAB5701 showed hypersensitivity when administered with abacavir.
(d) Ribavirin: Chronic hepatitis C is a liver disease characterised by hepatitis C infection. The patients with this infection are treated with pegylated interferon
and ribavirin. Approximately 50% of patients depending on ethnic origin show positive response to this treatment and become virus free. Pharmacogenomic
studies have shown that CC genotype at the SNP rs12979860, 3kb upstream of the IL28B gene is associated with response to pegylated interferon and ribavirin
for patients with chronic genotype 1 infection and natural clearance and the presence of G allele at rs8099917 is associated with non response.
Approaches of Pharmacogenomics
Candidate gene approach: Candidate gene use experimentally derived a priori knowledge about a disease or a drug involving both public and proprietary databases for identifying candidate genes whose expression may impact drug action or disease pathogenesis. In this approach, genes are identified based on
metabolic pathways, molecular targets, biological response pathways and/or disease risk. Based on the perceived likelihood of involvement of drug response
the genes are ranked. Though this approach is tested in unrelated subjects, family studies, but population based studies are commonly employed as they detect
relative risk as low as 1.5. The main focus of this approach is finding whether there are differences between the case (non responder) and the control (responder) in genetic variation that is assumed to be functional and involved in the observed phenotypic variability. One of the successful examples of this approach is the identification of individual response to the drug 6-mercaptopurine. The drawbacks of this approach is that it fails to consider a potential contribution of other genes particularly those whose function is yet to be understood; it is always not possible to have a information on the functionality of genetic variability or may be unreliable; it relies on the variability of specific point in the whole gene sequence.
Genome wide approach: This approach does not require a prior knowledge of the target gene. It attempts to identify the association between genetic variants
and a given disorder by directing marker SNPs and analysing differences between case and control groups. This approach has been successful in mapping rare
highly penetrant diseases in family pedigrees and identifying genes for monogenic traits. The strategies in this approach are based on the Linkage Disequilibrium relationships and structuring of haplotype blocks in the genome. It requires thousands of single nucleotide polymorphism (SNP) as genetic markers and it has been estimated that a minimum of 300,000 to 500,000 evenly spaced SNPs needed to find a marker within the range of disequilibrium. One of the successful examples of genome wide approach is identification of genetic variant in the drug transporter gene SLCO1b1 responsible for statin induced myopathy. The success of genome wide approach depends on study design, sample size, quality control of genotype, collection bias, individual sample data, and ability of high throughput technologies to produce volume of data and ethnicity details. Validating data in an independent cohort in unbiased approach is requisite. The reported odds ratios are relatively small. The practical utility of information generated by using this approach remains controversial. Whole genome studies involve usage of microarrays which is out of reach of routine clinical practice. Complexity in analysis and requirement of additional guarantees in diagnosis make this approach far from applicable. Matching control groups to factors such as underlying disease and ancestry, contributions of genetic variants not detected by current platforms, analysis of gene-gene and gene-environment interactions in determining phenotype, affordable sequencing, storing of sequence data, information management and methods of genome analysis are the challenging issues in this approach.
Applications of Pharmacogenomics
1) Pharmacogenomic studies are helpful in developing therapeutic agents suitable for genetically identifiable human sub group populations.
2) Pharmacogenomics research can decrease the time and number of subjects needed for clinical trials.
3) Pharmacogenomics may facilitate the identification of biomarkers to optimize drug selection, dose and treatment duration and avert adverse drug reactions. 4) Pharmacogenomics may be helpful in reducing national health care bills in developing countries by taking into consideration of genomic variations between populations.
5) Study of the genotypes of populations with little admixture may be helpful in predicting drug responses without testing each individual.
6) Pharmacogenomics may help to improve our understanding of the mechanisms underlying variability in human physiology and its response to drug therapy with a final goal of improving therapy.
7) Information generated from pharmacogenomics studies may help health professionals and patients to make informed decisions about treatment
options.
8) Generation of data on new and existing drugs will help in effective utilisation of scarce resources.
9) Pharmacogenomics may be helpful in reducing the costs associated with inappropriate drug treatments or hospitalisations due to serious adverse reactions.
10) Pharmacogenomics testing may produce collateral information which may be medically beneficial for ex. polymorphism in dopime receptor though pharmacogenomics information may help in smoking cessation.
11) Long term applications of pharmacogenomics include reducing the burden of disease, improving the economic efficiency of the health care system and reducing some disparities in health care acess and health outcomes.
12) Pharmacogenomics may be helpful in offering alternatives to the traditional drug development.
Challenges: The challenges of pharmacogenomics include
- establishing the clinical utility in order to support the value of genotyping; unaffordability of
technology for wider application of pharmacogenomics outside the research and development setting; - unequal treatment or health disparities due to social and the consequent ethical and legal issues connected to pharmacogenomics testing;
- measurement challenges such as presence of multiple pathways involved in drug effects, multiple polymorphisms, gene-environment interactions, length of time between testing and clinical outcomes and multiple determinants of clinical outcomes;
- time and cost intensivity;
- developing technology to findout specific SNP; finding patients who fit into the criteria and possible users of the drugs;
- defining cut-off within adverse drug reponse distributions;
- lack of reproducibility of some gene-drug pairs and the questionable utility of the findings in a large population.