Геном и реакция на лекарственные препараты: фармакогеномика и персонализированная медицина.

Study of the role of the genome in drug response
the journal

Pharmacogenomics, often abbreviated "PGx," is the study of the role of the genome in drug response. Its name (pharmaco + genomics) reflects its combining of pharmacology and genomics. Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs. It deals with the influence of acquired and inherited genetic variation on drug response, by correlating DNA mutations (including point mutations, copy number variations, and structural variations) with pharmacokinetic (drug absorption, distribution, metabolism, and elimination), pharmacodynamic (effects mediated through a drug's biological targets), and/or immunogenic endpoints. Pharmacogenomics aims to develop rational means to optimize drug therapy, with regard to the patients' genotype, to achieve maximum efficiency with minimal adverse effects. It is hoped that by using pharmacogenomics, pharmaceutical drug treatments can deviate from what is dubbed as the "one dose fits all" approach. Pharmacogenomics also attempts to eliminate trial and error in prescribing, allowing physicians to take into consideration their patient's genes, the functionality of these genes, and how this may affect the effectiveness of the patient's current or future treatments (and where applicable, provide an explanation for the failure of past treatments). Such approaches promise the advent of precision medicine and even personalized medicine, in which drugs and drug combinations are optimized for narrow subsets of patients or even for each individual's unique genetic makeup. Whether used to explain a patient's response (or lack of it) to a treatment, or to act as a predictive tool, it hopes to achieve better treatment outcomes and greater efficacy, and reduce drug toxicities and adverse drug reactions (ADRs). For patients who do not respond to a treatment, alternative therapies can be prescribed that would best suit their requirements. In order to provide pharmacogenomic recommendations for a given drug, two possible types of input can be used: genotyping, or exome or whole genome sequencing. Sequencing provides many more data points, including detection of mutations that prematurely terminate the synthesized protein (early stop codon). CYP3A4, CYP2C9, CYP2C19, and CYP2D6 are major CYP enzymes involved in drug metabolism and are all known to be highly polymorphic.

Many medications rely on transporters to cross cellular membranes in order to move between body fluid compartments such as the blood, gut lumen, bile, urine, brain, and cerebrospinal fluid. The major transporters include the solute carrier, ATP binding cassette, and organic anion transporters.

Pharmacodynamics refers to the impact a medication has on the body, or its mechanism of action.

Drug targets are the specific sites where a medication carries out its pharmacological activity. The interaction between the drug and this site results in a modification of the target that may include inhibition or potentiation. Most of the pharmacogenetic interactions that involve drug targets are within the field of oncology and include targeted therapeutics designed to address somatic mutations (see also Cancer Pharmacogenomics). For example, EGFR inhibitors like gefitinib (Iressa) or erlotinib (Tarceva) are only indicated in patients carrying specific mutations to EGFR. Germline mutations in drug targets can also influence response to medications, though this is an emerging subfield within pharmacogenomics. One well established gene drug interaction involving a germline mutation to a drug target is warfarin (Coumadin) and VKORC1, which codes for vitamin K epoxide reductase (VKOR). Warfarin binds to and inhibits VKOR, which is an important enzyme in the vitamin K cycle. Inhibition of VKOR prevents reduction of vitamin K, which is a cofactor required in the formation of coagulation factors II, VII, IX and X, and inhibitors protein C and S.

Medications can have off target effects (typically unfavorable) that arise from an interaction between the medication and/or its metabolites and a site other than the intended target. Genetic variation in the off target sites can influence this interaction. The main example of this type of pharmacogenomic interaction is glucose 6 phosphate dehydrogenase (G6PD). G6PD is the enzyme involved in the first step of the pentose phosphate pathway which generates NADPH (from NADP). NADPH is required for the production of reduced glutathione in erythrocytes and it is essential for the function of catalase. Glutathione and catalase protect cells from oxidative stress that would otherwise result in cell lysis. Certain variants in G6PD result in G6PD deficiency, in which cells are more susceptible to oxidative stress. When medications that have a significant oxidative effect are administered to individuals who are G6PD deficient, they are at an increased risk of erythrocyte lysis that presents as hemolytic anemia.

The human leukocyte antigen (HLA) system, also referred to as the major histocompatibility complex (MHC), is a complex of genes important for the adaptive immune system. Mutations in the HLA complex have been associated with an increased risk of developing hypersensitivity reactions in response to certain medications.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) is "an international consortium of individual volunteers and a small dedicated staff who are interested in facilitating use of pharmacogenetic tests for patient care. CPIC’s goal is to address barriers to clinical implementation of pharmacogenetic tests by creating, curating, and posting freely available, peer reviewed, evidence based, updatable, and detailed gene/drug clinical practice guidelines. CPIC guidelines follow standardized formats, include systematic grading of evidence and clinical recommendations, use standardized terminology, are peer reviewed, and are published in a journal (in partnership with Clinical Pharmacology and Therapeutics) with simultaneous posting to cpicpgx. org, where they are regularly updated." For the gene drug pairs included in the table, "the FDA has evaluated and believes there is sufficient scientific evidence to suggest that subgroups of patients with certain genetic variants, or genetic variant inferred phenotypes (such as affected subgroup in the table below), are likely to have altered drug metabolism, and in certain cases, differential therapeutic effects, including differences in risks of adverse events." "The information in this Table is intended primarily for prescribers, and patients should not adjust their medications without consulting their prescriber. This version of the table is limited to pharmacogenetic associations that are related to drug metabolizing enzyme gene variants, drug transporter gene variants, and gene variants that have been related to a predisposition for certain adverse events. The FDA recognizes that various other pharmacogenetic associations exist that are not listed here, and this table will be updated periodically with additional pharmacogenetic associations supported by sufficient scientific evidence."

The Pharmacogenomics Knowledgebase (PharmGKB) is an "NIH funded resource that provides information about how human genetic variation affects response to medications. PharmGKB collects, curates and disseminates knowledge about clinically actionable gene drug associations and genotype phenotype relationships."

There are many commercial laboratories around the world who offer pharmacogenomic testing as a laboratory developed test (LDTs). The tests offered can vary significantly from one lab to another, including genes and alleles tested for, phenotype assignment, and any clinical annotations provided. With the exception of a few direct to consumer tests, all pharmacogenetic testing requires an order from an authorized healthcare professional. In order for the results to be used in a clinical setting in the United States, the laboratory performing the test much be CLIA certified. Other regulations may vary by country and state.

Direct to consumer (DTC) pharmacogenetic tests allow consumers to obtain pharmacogenetic testing without an order from a prescriber. DTC pharmacogenetic tests are generally reviewed by the FDA to determine the validity of test claims. The FDA maintains a list of DTC genetic tests that have been approved.

There are multiple ways to represent a pharmacogenomic genotype. A commonly used nomenclature system is to report haplotypes using a star (*) allele (e. g., CYP2C19 *1/*2). Single nucleotide polymorphisms (SNPs) may be described using their assignment reference SNP cluster ID (rsID) or based on the location of the base pair or amino acid impacted.

In 2017 CPIC published results of an expert survey to standardize terms related to clinical pharmacogenetic test results. Consensus for terms to describe allele functional status, phenotype for drug metabolizing enzymes, phenotype for drug transporters, and phenotype for high risk genotype status was reached.

Initiatives to spur adoption by clinicians include the Ubiquitous Pharmacogenomics (U PGx) program in Europe and the Clinical Pharmacogenetics Implementation Consortium (CPIC) in the United States. In a 2017 survey of European clinicians, in the prior year two thirds had not ordered a pharmacogenetic test. In 2010, Vanderbilt University Medical Center launched Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT); in 2015 survey, two thirds of the clinicians had ordered a pharmacogenetic test. In 2019, the largest private health insurer, UnitedHealthcare, announced that it would pay for genetic testing to predict response to psychiatric drugs. In 2020, Canada's 4th largest health and dental insurer, Green Shield Canada, announced that it would pay for pharmacogenetic testing and its associated clinical decision support software to optimize and personalize mental health prescriptions.

A potential role for pharmacogenomics is to reduce the occurrence of polypharmacy: it is theorized that with tailored drug treatments, patients will not need to take several medications to treat the same condition. Thus they could potentially reduce the occurrence of adverse drug reactions, improve treatment outcomes, and save costs by avoiding purchase of some medications. For example, maybe due to inappropriate prescribing, psychiatric patients tend to receive more medications than age matched non psychiatric patients. The need for pharmacogenomically tailored drug therapies may be most evident in a survey conducted by the Slone Epidemiology Center at Boston University from February 1998 to April 2007. The study elucidated that an average of 82% of adults in the United States are taking at least one medication (prescription or nonprescription drug, vitamin/mineral, herbal/natural supplement), and 29% are taking five or more. The study suggested that those aged 65 years or older continue to be the biggest consumers of medications, with 17 19% in this age group taking at least ten medications in a given week. Polypharmacy has also shown to have increased since 2000 from 23% to 29%.

In the early 2000s, handling genetic information as exceptional, including legal or regulatory protections, garnered strong support. It was argued that genomic information may need special policy and practice protections within the context of electronic health records (EHRs). In 2008, the Genetic Information Nondiscrimination Act (GINA) was enacted to protect patients from health insurance companies discriminating against an individual based on genetic information. More recently it has been argued that genetic exceptionalism is past its expiration date as we move into a blended genomic/big data era of medicine, yet exceptionalism practices continue to permeate clinical healthcare today. Garrison et al. recently relayed a call to action to update verbiage from genetic exceptionalism to genomic contextualism in that we recognize a fundamental duality of genetic information. This allows room in the argument for different types of genetic information to be handled differently while acknowledging that genomic information is similar and yet distinct from other health related information. Additionally, Evans et al. argue that the EHR has sufficient privacy standards to hold other sensitive information such as social security numbers and that the fundamental nature of an EHR is to house highly personal information. Rigorous protection and masking of genetic information is argued to impede further scientific progress and clinical translation into routine clinical practices.

Pharmacogenomics was first recognized by Pythagoras around 510 BC when he made a connection between the dangers of fava bean ingestion with hemolytic anemia and oxidative stress. In the 1950s, this identification was validated and attributed to deficiency of G6PD and is called favism. Although the first official publication was not until 1961, the unofficial beginnings of this science were around the 1950s. Reports of prolonged paralysis and fatal reactions linked to genetic variants in patients who lacked butyrylcholinesterase ('pseudocholinesterase') following succinylcholine injection during anesthesia were first reported in 1956. The term pharmacogenetics was first coined in 1959 by Friedrich Vogel of Heidelberg, Germany (although some papers suggest it was 1957 or 1958). In the late 1960s, twin studies supported the inference of genetic involvement in drug metabolism, with identical twins sharing remarkable similarities in drug response compared to fraternal twins. The term pharmacogenomics first began appearing around the 1990s. Research has focused on combinatorial chemistry, genomic mining, omic technologies, and high throughput screening. As the cost per genetic test decreases, the development of personalized drug therapies will increase. Technology now allows for genetic analysis of hundreds of target genes involved in medication metabolism and response in less than 24 hours for under $1,000. This a huge step towards bringing pharmacogenetic technology into everyday medical decisions. Likewise, companies like deCODE genetics, MD Labs Pharmacogenetics, Navigenics and 23andMe offer genome scans. The companies use the same genotyping chips that are used in GWAS studies and provide customers with a write up of individual risk for various traits and diseases and testing for 500,000 known SNPs. Costs range from $995 to $2500 and include updates with new data from studies as they become available. The more expensive packages even included a telephone session with a genetics counselor to discuss the results.

Pharmacogenetics has become a controversial issue in the area of bioethics. Privacy and confidentiality are major concerns. The evidence of benefit or risk from a genetic test may only be suggestive, which could cause dilemmas for providers.

+Web Resources for Pharmacogenomics Data source Main use Citation PharmVar A central repository for pharmacogene variation that focuses on haplotype structure and allelic variation SuperCYP Bioinformatics Tool Containing 1170 drugs with more than 3800 interactions, and approximately 2000 known SNPs. These SNPs are listed and ordered according to their effect on expression and/or activity. PharmGKB The Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation affects drug response. dbSNP database A repository of SNPs and other variants that have been reported after discovery, compiled and officially named. These are SNPs across the board. FINDbase Repository of allele frequencies of pharmacogenetic markers in different populations Pharmacogenomics Biomarkers in Drug Labelling A table that identifies which FDA approved drugs have pharmacogenomics related warning labels SNPedia A wiki based bioinformatics database of SNPs Pharmacogenomics Research Network (PGRN)The PGRN hosts resources and information to stimulate collaborative research in pharmacogenomics and precision medicine.