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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.^[1] 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.^[2]^[3]^[4]

Pharmacogenomics aims to develop rational means to optimize drug therapy, with regard to the patients' genotype, to achieve maximum efficiency with minimal adverse effects.^[5] 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).^[6]^[7] 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.^[8]^[9]

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.^[10] Sequencing provides many more data points, including detection of mutations that prematurely terminate the synthesized protein (early stop codon).^[10]

Pharmacogenetics vs. pharmacogenomics

The term pharmacogenomics is often used interchangeably with pharmacogenetics. Although both terms relate to drug response based on genetic influences, there are differences between the two. Pharmacogenetics is limited to monogenic phenotypes (i.e., single gene-drug interactions). Pharmacogenomics refers to polygenic drug response phenotypes and encompasses transcriptomics, proteomics, and metabolomics.

Mechanisms of pharmacogenetic interactions

Pharmacokinetics

Pharmacokinetics involves the absorption, distribution, metabolism, and elimination of pharmaceutics. These processes are often facilitated by enzymes such as drug transporters or drug metabolizing enzymes (discussed in-depth below). Variation in DNA loci responsible for producing these enzymes can alter their expression or activity so that their functional status changes. An increase, decrease, or loss of function for transporters or metabolizing enzymes can ultimately alter the amount of medication in the body and at the site of action. This may result in deviation from the medication's therapeutic window and result in either toxicity or loss of effectiveness.

Drug-metabolizing enzymes

The majority of clinically actionable pharmacogenetic variation occurs in genes that code for drug-metabolizing enzymes, including those involved in both phase I and phase II metabolism. The cytochrome P450 enzyme family is responsible for metabolism of 70-80% of all medications used clinically.^[11] CYP3A4, CYP2C9, CYP2C19, and CYP2D6 are major CYP enzymes involved in drug metabolism and are all known to be highly polymorphic.^[11] Additional drug-metabolizing enzymes that have been implicated in pharmacogenetic interactions include UGT1A1 (a UDP-glucuronosyltransferase), DPYD, and TPMT.^[12]

Drug transporters

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.^[13] The major transporters include the solute carrier, ATP-binding cassette, and organic anion transporters.^[13] Transporters that have been shown to influence response to medications include OATP1B1 (SLCO1B1) and breast cancer resistance protein (BCRP) (ABCG2).^[14]

Pharmacodynamics

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

Drug targets

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.^[15] 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.^[16]^[17]

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.^[18] 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.^[18]^[19]

Off-target sites

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.^[20] 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.^[21] 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.^[22]

Immunologic

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.^[23]

Clinical pharmacogenomics resources

Clinical Pharmacogenetics Implementation Consortium (CPIC)

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."^[12]

The CPIC guidelines are "designed to help clinicians understand HOW available genetic test results should be used to optimize drug therapy, rather than WHETHER tests should be ordered. A key assumption underlying the CPIC guidelines is that clinical high-throughput and pre-emptive (pre-prescription) genotyping will become more widespread, and that clinicians will be faced with having patients’ genotypes available even if they have not explicitly ordered a test with a specific drug in mind. CPIC's guidelines, processes and projects have been endorsed by several professional societies."^[12]

U.S. Food and Drug Administration

Table of Pharmacogenetic Associations

In February 2020 the FDA published the Table of Pharmacogenetic Associations.^[24] 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."^[25]

"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."^[25]

Table of Pharmacogenomic Biomarkers in Drug Labeling

The FDA Table of Pharmacogenomic Biomarkers in Drug Labeling lists FDA-approved drugs with pharmacogenomic information found in the drug labeling. "Biomarkers in the table include but are not limited to germline or somatic gene variants (polymorphisms, mutations), functional deficiencies with a genetic etiology, gene expression differences, and chromosomal abnormalities; selected protein biomarkers that are used to select treatments for patients are also included."^[26]

PharmGKB

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."^[27]

Commercial Pharmacogenetic Testing Laboratories

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 Pharmacogenetic Testing

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.^[28] The FDA maintains a list of DTC genetic tests that have been approved.

Common Pharmacogenomic-Specific Nomenclature

Genotype

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.^[29]

Phenotype

In 2017 CPIC published results of an expert survey to standardize terms related to clinical pharmacogenetic test results.^[30] 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.

Applications

The list below provides a few more commonly known applications of pharmacogenomics:^[31]

Improve drug safety, and reduce ADRs;
Tailor treatments to meet patients' unique genetic pre-disposition, identifying optimal dosing;
Improve drug discovery targeted to human disease; and
Improve proof of principle for efficacy trials.

Pharmacogenomics may be applied to several areas of medicine, including pain management, cardiology, oncology, and psychiatry. A place may also exist in forensic pathology, in which pharmacogenomics can be used to determine the cause of death in drug-related deaths where no findings emerge using autopsy.^{[citation needed]}

In cancer treatment, pharmacogenomics tests are used to identify which patients are most likely to respond to certain cancer drugs. In behavioral health, pharmacogenomic tests provide tools for physicians and care givers to better manage medication selection and side effect amelioration. Pharmacogenomics is also known as companion diagnostics, meaning tests being bundled with drugs. Examples include KRAS test with cetuximab and EGFR test with gefitinib. Beside efficacy, germline pharmacogenetics can help to identify patients likely to undergo severe toxicities when given cytotoxics showing impaired detoxification in relation with genetic polymorphism, such as canonical 5-FU.^[32] In particular, genetic deregulations affecting genes coding for DPD, UGT1A1, TPMT, CDA and CYP2D6 are now considered as critical issues for patients treated with 5-FU/capecitabine, irinotecan, mercaptopurine/azathioprine, gemcitabine/capecitabine/AraC and tamoxifen, respectively.^[33]

In cardiovascular disorders, the main concern is response to drugs including warfarin, clopidogrel, beta blockers, and statins.^[10] In patients with CYP2C19, who take clopidogrel, cardiovascular risk is elevated, leading to medication package insert updates by regulators.^[34] In patients with type 2 diabetes, haptoglobin (Hp) genotyping shows an effect on cardiovascular disease, with Hp2-2 at higher risk and supplemental vitamin E reducing risk by affecting HDL.^[35]

In psychiatry, as of 2010, research has focused particularly on 5-HTTLPR and DRD2.^[36]

Clinical implementation

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.^[37] In a 2017 survey of European clinicians, in the prior year two-thirds had not ordered a pharmacogenetic test.^[38]

In 2010, Vanderbilt University Medical Center launched Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT);^[39] in 2015 survey, two-thirds of the clinicians had ordered a pharmacogenetic test.^[40]

In 2019, the largest private health insurer, UnitedHealthcare, announced that it would pay for genetic testing to predict response to psychiatric drugs.^[41]

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.^[42]

Reduction of polypharmacy

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.^[43]

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%.^[44]

Example case studies

Case A – Antipsychotic adverse reaction^[45]

Patient A has schizophrenia. Their treatment included a combination of ziprasidone, olanzapine, trazodone and benztropine. The patient experienced dizziness and sedation, so they were tapered off ziprasidone and olanzapine, and transitioned to quetiapine. Trazodone was discontinued. The patient then experienced excessive sweating, tachycardia and neck pain, gained considerable weight and had hallucinations. Five months later, quetiapine was tapered and discontinued, with ziprasidone re-introduced into their treatment, due to the excessive weight gain. Although the patient lost the excessive weight they had gained, they then developed muscle stiffness, cogwheeling, tremors and night sweats. When benztropine was added they experienced blurry vision. After an additional five months, the patient was switched from ziprasidone to aripiprazole. Over the course of 8 months, patient A gradually experienced more weight gain and sedation, and developed difficulty with their gait, stiffness, cogwheeling and dyskinetic ocular movements. A pharmacogenomics test later proved the patient had a CYP2D6 *1/*41, which has a predicted phenotype of IM and CYP2C19 *1/*2 with a predicted phenotype of IM as well.

Case B – Pain Management^[46]

Patient B is a woman who gave birth by caesarian section. Her physician prescribed codeine for post-caesarian pain. She took the standard prescribed dose, but she experienced nausea and dizziness while she was taking codeine. She also noticed that her breastfed infant was lethargic and feeding poorly. When the patient mentioned these symptoms to her physician, they recommended that she discontinue codeine use. Within a few days, both the patient's and her infant's symptoms were no longer present. It is assumed that if the patient had undergone a pharmacogenomic test, it would have revealed she may have had a duplication of the gene CYP2D6, placing her in the Ultra-rapid metabolizer (UM) category, explaining her reactions to codeine use.

Case C – FDA Warning on Codeine Overdose for Infants^[47]

On February 20, 2013, the FDA released a statement addressing a serious concern regarding the connection between children who are known as CYP2D6 UM, and fatal reactions to codeine following tonsillectomy and/or adenoidectomy (surgery to remove the tonsils and/or adenoids). They released their strongest Boxed Warning to elucidate the dangers of CYP2D6 UMs consuming codeine. Codeine is converted to morphine by CYP2D6, and those who have UM phenotypes are in danger of producing large amounts of morphine due to the increased function of the gene. The morphine can elevate to life-threatening or fatal amounts, as became evident with the death of three children in August 2012.

Challenges

Although there appears to be a general acceptance of the basic tenet of pharmacogenomics amongst physicians and healthcare professionals,^[49] several challenges exist that slow the uptake, implementation, and standardization of pharmacogenomics. Some of the concerns raised by physicians include:^[50]^[49]^[51]

Limitation on how to apply the test into clinical practices and treatment;
A general feeling of lack of availability of the test;
The understanding and interpretation of evidence-based research;
Combining test results with other patient data for prescription optimization; and
Ethical, legal and social issues.

Issues surrounding the availability of the test include:^[48]

The lack of availability of scientific data: Although there are a considerable number of drug-metabolizing enzymes involved in the metabolic pathways of drugs, only a fraction have sufficient scientific data to validate their use within a clinical setting; and
Demonstrating the cost-effectiveness of pharmacogenomics: Publications for the pharmacoeconomics of pharmacogenomics are scarce, therefore sufficient evidence does not at this time exist to validate the cost-effectiveness and cost-consequences of the test.

Although other factors contribute to the slow progression of pharmacogenomics (such as developing guidelines for clinical use), the above factors appear to be the most prevalent. Increasingly substantial evidence and industry body guidelines for clinical use of pharmacogenetics have made it a population wide approach to precision medicine. Cost, reimbursement, education, and easy use at the point of care remain significant barriers to widescale adoption.

Controversies

Race-based medicine

There has been call to move away from race and ethnicity in medicine and instead use genetic ancestry as a way to categorize patients.^[52] Some alleles that vary in frequency between specific populations have been shown to be associated with differential responses to specific drugs. As a result, some disease-specific guidelines only recommend pharmacogenetic testing for populations where high-risk alleles are more common^[53] and, similarly, certain insurance companies will only pay for pharmacogenetic testing for beneficiaries of high-risk populations.^[54]

Genetic exceptionalism

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).^[55] 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.^[56]^[57]

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.^[58]^[59] 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.^[60] 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.^[60] Genomic contextualism would allow for a case-by-case analysis of the technology and the context of its use (e.g., clinical practice, research, secondary findings).

Others argue that genetic information is indeed distinct from other health-related information but not to the extent of requiring legal/regulatory protections, similar to other sensitive health-related data such as HIV status.^[61] 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.^[58] Similarly, a systematic review reported that the public had concern over privacy of genetic information, with 60% agreeing that maintaining privacy was not possible; however, 96% agreed that a direct-to-consumer testing company had protected their privacy, with 74% saying their information would be similarly or better protected in an EHR. With increasing technological capabilities in EHRs, it is possible to mask or hide genetic data from subsets of providers and there is not consensus on how, when, or from whom genetic information should be masked.^[55]^[62] Rigorous protection and masking of genetic information is argued to impede further scientific progress and clinical translation into routine clinical practices.^[63]

History

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.^[64]^[65] Although the first official publication was not until 1961,^[66] 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.^[2]^[67] The term pharmacogenetics was first coined in 1959 by Friedrich Vogel of Heidelberg, Germany (although some papers suggest it was 1957 or 1958).^[68] 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.^[69] The term pharmacogenomics first began appearing around the 1990s.^[64]

The first FDA approval of a pharmacogenetic test was in 2005^[9] (for alleles in CYP2D6 and CYP2C19)

Future

Computational advances have enabled cheaper and faster sequencing.^[70] Research has focused on combinatorial chemistry,^[71] genomic mining, omic technologies, and high throughput screening.

As the cost per genetic test decreases, the development of personalized drug therapies will increase.^[72] 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.^[73]

Ethics

Pharmacogenetics has become a controversial issue in the area of bioethics. Privacy and confidentiality are major concerns.^[74] The evidence of benefit or risk from a genetic test may only be suggestive, which could cause dilemmas for providers.^[74]^: 145 Drug development may be affected, with rare genetic variants possibly receiving less research.^[74] Access and patient autonomy are also open to discussion.^[75]^: 680

Web-based resources

Web Resources for Pharmacogenomics^[76]^[77]
Data source	Main use	Citation
PharmVar	A central repository for pharmacogene variation that focuses on haplotype structure and allelic variation	^[78]
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.	^[79]
PharmGKB	The Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation affects drug response.	^[80]
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.	^[81]^[82]
FINDbase	Repository of allele frequencies of pharmacogenetic markers in different populations	^[83]
Pharmacogenomics Biomarkers in Drug Labelling	A table that identifies which FDA-approved drugs have pharmacogenomics-related warning labels	^[84]
SNPedia	A wiki-based bioinformatics database of SNPs	^[85]^[86]
Pharmacogenomics Research Network (PGRN)	The PGRN hosts resources and information to stimulate collaborative research in pharmacogenomics and precision medicine.	^[87]

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^ ^a ^b Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, Kirchheiner J, Guchelaar HJ (August 2007). "Translating pharmacogenomics: challenges on the road to the clinic". PLOS Medicine. 4 (8): e209. doi:10.1371/journal.pmed.0040209. PMC 1945038. PMID 17696640.
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External links

"Pharmacogenomics Factsheet". National Center for Biotechnology Information (NCBI), U.S. National Library of Medicine. Retrieved 2011-07-11. a quick introduction to customised drugs
"Pharmacogenomics Education Initiatives". U.S. Food and Drug Administration. 2010-09-24. Retrieved 2011-07-11.
"Personalized Medicine (Pharmacogenetics)". University of Utah's Genetic Science Learning Center. Archived from the original on 2011-05-19. Retrieved 2011-07-11.
"Center for Pharmacogenomics and Individualized Therapy". University of North Carolina at Chapel Hill Center for Pharmacogenomics and Individualized Therapy. Archived from the original on 2014-08-06. Retrieved 2014-06-25.

Journals:

"Pharmacogenomics". Future Medicine Ltd. 24 August 2023.
"Pharmacogenetics and Genomics". Lippincott Williams & Wilkins. ISSN 1744-6872.
"The Pharmacogenomics Journal". The Pharmacogenomics Journal. Nature Publishing Group. 20 October 2021. ISSN 1470-269X.

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[UNC_Center_for_Pharmacogenomics_and_Individualized_Therapy-3] "Center for Pharmacogenomics and Individualized Therapy". Unc Eshelman School of Pharmacy. Retrieved 2014-06-25.

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[pmid19530963-5] Becquemont L (June 2009). "Pharmacogenomics of adverse drug reactions: practical applications and perspectives". Pharmacogenomics. 10 (6): 961–969. doi:10.2217/pgs.09.37. PMID 19530963.

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[:2-25] Center for Devices and Radiological Health (2022-10-26). "Table of Pharmacogenetic Associations". FDA.

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[30] Caudle KE, Dunnenberger HM, Freimuth RR, Peterson JF, Burlison JD, Whirl-Carrillo M, et al. (February 2017). "Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)". Genetics in Medicine. 19 (2): 215–223. doi:10.1038/gim.2016.87. PMC 5253119. PMID 27441996.

[31] Cohen N (November 2008). Pharmacogenomics and Personalized Medicine (Methods in Pharmacology and Toxicology). Totowa, NJ: Humana Press. p. 6. ISBN 978-1934115046.

[pmid20920994-32] Ciccolini J, Gross E, Dahan L, Lacarelle B, Mercier C (October 2010). "Routine dihydropyrimidine dehydrogenase testing for anticipating 5-fluorouracil-related severe toxicities: hype or hope?". Clinical Colorectal Cancer. 9 (4): 224–228. doi:10.3816/CCC.2010.n.033. PMID 20920994.

[pmid202043652-33] Yang CG, Ciccolini J, Blesius A, Dahan L, Bagarry-Liegey D, Brunet C, et al. (January 2011). "DPD-based adaptive dosing of 5-FU in patients with head and neck cancer: impact on treatment efficacy and toxicity". Cancer Chemotherapy and Pharmacology. 67 (1): 49–56. doi:10.1007/s00280-010-1282-4. PMID 20204365. S2CID 25362813.

[34] Dean L (2012). "Clopidogrel Therapy and CYP2C19 Genotype". In Pratt VM, McLeod HL, Rubinstein WS, et al. (eds.). Medical Genetics Summaries. National Center for Biotechnology Information (NCBI). PMID 28520346. Bookshelf ID: NBK84114.

[35] Bale BF, Doneen AL, Vigerust DJ (2018). "Precision Healthcare of Type 2 Diabetic Patients Through Implementation of Haptoglobin Genotyping". Frontiers in Cardiovascular Medicine. 5: 141. doi:10.3389/fcvm.2018.00141. PMC 6198642. PMID 30386783.

[36] Malhotra AK (2010). "The state of pharmacogenetics". Psychiatr Times. 27 (4): 38–41, 62.

[37] Williams MS (August 2019). "Early Lessons from the Implementation of Genomic Medicine Programs". Annual Review of Genomics and Human Genetics. 20 (1): 389–411. doi:10.1146/annurev-genom-083118-014924. PMID 30811224. S2CID 73460688.

[38] Just KS, Steffens M, Swen JJ, Patrinos GP, Guchelaar HJ, Stingl JC (October 2017). "Medical education in pharmacogenomics-results from a survey on pharmacogenetic knowledge in healthcare professionals within the European pharmacogenomics clinical implementation project Ubiquitous Pharmacogenomics (U-PGx)". European Journal of Clinical Pharmacology. 73 (10): 1247–1252. doi:10.1007/s00228-017-2292-5. PMC 5599468. PMID 28669097.

[39] Carlson B (2012). "Vanderbilt pioneers bedside genetics". Biotechnology Healthcare. 9 (2): 31–32. PMC 3411230. PMID 22876213.

[40] Peterson JF, Field JR, Shi Y, Schildcrout JS, Denny JC, McGregor TL, et al. (August 2016). "Attitudes of clinicians following large-scale pharmacogenomics implementation". The Pharmacogenomics Journal. 16 (4): 393–398. doi:10.1038/tpj.2015.57. PMC 4751074. PMID 26261062.

[41] "Pharmacogenetic test makers cheer UnitedHealth coverage. Other payers aren't there yet". MedTech Dive. Retrieved 2019-12-29.

[42] "Personalized drug treatment through pharmacogenetics".

[polypharmacy_book-43] Ritsner M (2013). Polypharmacy in Psychiatry Practice, Volume I. Multiple Medication Strategies. Dordrecht: Springer Science and Business Media. ISBN 978-94-007-5804-9.

[Slone_Study-44] "Patterns of Medication Use in the United States". Boston University, Slone Epidemiology Center. 2006.

[PMC2656342-45] Foster A, Wang Z, Usman M, Stirewalt E, Buckley P (December 2007). "Pharmacogenetics of antipsychotic adverse effects: Case studies and a literature review for clinicians". Neuropsychiatric Disease and Treatment. 3 (6): 965–973. doi:10.2147/ndt.s1752. PMC 2656342. PMID 19300635.

[url_AMA_Pharmacogenomic_Factsheet-46] "Pharmacogenetics: increasing the safety and effectiveness of drug therapy [Brochure]" (PDF). American Medical Association. 2011.

[url_FDA_Codeine_Warning-47] "FDA Drug Safety Communication: Safety review update of codeine use in children; new Boxed Warning and Contraindication on use after tonsillectomy and/or adenoidectomy". United States Food and Drug Administration. 2013-02-20.

[sven2007-48] Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, Kirchheiner J, Guchelaar HJ (August 2007). "Translating pharmacogenomics: challenges on the road to the clinic". PLOS Medicine. 4 (8): e209. doi:10.1371/journal.pmed.0040209. PMC 1945038. PMID 17696640.

[pmid22278335-49] Stanek EJ, Sanders CL, Taber KA, Khalid M, Patel A, Verbrugge RR, et al. (March 2012). "Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey". Clinical Pharmacology and Therapeutics. 91 (3): 450–458. doi:10.1038/clpt.2011.306. PMID 22278335. S2CID 21366195.

[Pharmacogenetics_and_Pharmacogenomics_Factsheet.-50] "Center for Genetics Education".^{[permanent dead link‍]}

[pmid22689709-51] Ma JD, Lee KC, Kuo GM (August 2012). "Clinical application of pharmacogenomics". Journal of Pharmacy Practice. 25 (4): 417–427. doi:10.1177/0897190012448309. PMID 22689709. S2CID 1212666.

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@@ Line 1: / Line 1: @@
 {{Short description|Study of the role of the genome in drug response}}
 {{for|the journal|Pharmacogenomics (journal)}}
+{{Technical|date=May 2024}}
 {{Genetics sidebar}}
-'''Pharmacogenomics''' is the study of the role of the [[genome]] in [[drug]] response. Its name (''[[wikt:pharmaco-#Prefix|pharmaco-]]'' + ''[[genomics]]'') reflects its combining of [[pharmacology]] and [[genomics]]. Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs.<ref>{{Cite book|title=Emerging Medical Technologies| vauthors = Ermak G |publisher=World Scientific|year=2015|isbn=978-981-4675-80-2}}</ref> It deals with the influence of [[Somatic mutation|acquired]] and [[Germline mutation|inherited]] genetic variation on drug response, by correlating DNA [[mutation]]s (including [[single-nucleotide polymorphism]]s, [[copy number variation]]s, and [[Indel|insertions/deletions]]) with [[pharmacokinetics|pharmacokinetic]] (drug [[absorption (pharmacokinetics)|absorption]], [[distribution (pharmacology)|distribution]], [[metabolism]], and [[Clearance (medicine)|elimination]]), [[pharmacodynamics|pharmacodynamic]] (effects mediated through a drug's [[biological target]]s), and/or [[Immunogenicity|immunogenic]] endpoints.<ref name="pmid14585618">{{cite journal | vauthors = Johnson JA | title = Pharmacogenetics: potential for individualized drug therapy through genetics | journal = Trends in Genetics | volume = 19 | issue = 11 | pages = 660–666 | date = November 2003 | pmid = 14585618 | doi = 10.1016/j.tig.2003.09.008 }}</ref><ref name="UNC Center for Pharmacogenomics and Individualized Therapy">{{cite news| title= Center for Pharmacogenomics and Individualized Therapy| newspaper=Unc Eshelman School of Pharmacy| url=https://pharmacy.unc.edu/research/centers/cpit/| access-date=2014-06-25}}</ref><ref>{{cite web| title=overview of pharmacogenomics|publisher= Up-to-Date| url =http://www.uptodate.com/contents/overview-of-pharmacogenomics|date=May 16, 2014|access-date=2014-06-25}}</ref>
+'''Pharmacogenomics''', often abbreviated "PGx," is the study of the role of the [[genome]] in [[drug]] response. Its name (''[[wikt:pharmaco-#Prefix|pharmaco-]]'' + ''[[genomics]]'') reflects its combining of [[pharmacology]] and [[genomics]]. Pharmacogenomics analyzes how the genetic makeup of a patient affects their response to drugs.<ref>{{Cite book|title=Emerging Medical Technologies| vauthors = Ermak G |publisher=World Scientific|year=2015|isbn=978-981-4675-80-2}}</ref> It deals with the influence of [[Somatic mutation|acquired]] and [[Germline mutation|inherited]] genetic variation on drug response, by correlating DNA [[mutation]]s (including [[point mutation]]s, [[copy number variation]]s, and [[Structural variation in the human genome|structural variations]]) with [[pharmacokinetics|pharmacokinetic]] (drug [[absorption (pharmacokinetics)|absorption]], [[distribution (pharmacology)|distribution]], [[metabolism]], and [[Clearance (medicine)|elimination]]), [[pharmacodynamics|pharmacodynamic]] (effects mediated through a drug's [[biological target]]s), and/or [[Immunogenicity|immunogenic]] endpoints.<ref name="pmid14585618">{{cite journal | vauthors = Johnson JA | title = Pharmacogenetics: potential for individualized drug therapy through genetics | journal = Trends in Genetics | volume = 19 | issue = 11 | pages = 660–666 | date = November 2003 | pmid = 14585618 | doi = 10.1016/j.tig.2003.09.008 | s2cid = 15195039 }}</ref><ref name="UNC Center for Pharmacogenomics and Individualized Therapy">{{cite news| title= Center for Pharmacogenomics and Individualized Therapy| newspaper=Unc Eshelman School of Pharmacy| url=https://pharmacy.unc.edu/research/centers/cpit/| access-date=2014-06-25}}</ref><ref>{{cite web| title=overview of pharmacogenomics|publisher= Up-to-Date| url =http://www.uptodate.com/contents/overview-of-pharmacogenomics|date=May 16, 2014|access-date=2014-06-25}}</ref>
-Pharmacogenomics aims to develop rational means to optimize [[pharmacotherapy|drug therapy]], with regard to the patients' [[genotype]], to achieve maximum efficiency with minimal [[adverse effect (medicine)|adverse effects]].<ref name="pmid19530963">{{cite journal | vauthors = Becquemont L | title = Pharmacogenomics of adverse drug reactions: practical applications and perspectives | journal = Pharmacogenomics | volume = 10 | issue = 6 | pages = 961–969 | date = June 2009 | pmid = 19530963 | doi = 10.2217/pgs.09.37 }}</ref> 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 efficacy of the patient's current or future treatments (and where applicable, provide an explanation for the failure of past treatments).<ref name="pmid19565025"/><ref>{{cite journal | vauthors = Hauser AS, Chavali S, Masuho I, Jahn LJ, Martemyanov KA, Gloriam DE, Babu MM | title = Pharmacogenomics of GPCR Drug Targets | journal = Cell | volume = 172 | issue = 1–2 | pages = 41–54.e19 | date = January 2018 | pmid = 29249361 | pmc = 5766829 | doi = 10.1016/j.cell.2017.11.033 }}</ref> 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.<ref>{{cite web|url=https://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm126957.pdf|title=Guidance for Industry Pharmacogenomic Data Submissions|date=March 2005|publisher=[[U.S. Food and Drug Administration]]|access-date=2008-08-27}}</ref><ref name="Squassina2010">{{cite journal | vauthors = Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, Mitropoulos K, Del Zompo M, Patrinos GP | display-authors = 6 | title = Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice | journal = Pharmacogenomics | volume = 11 | issue = 8 | pages = 1149–1167 | date = August 2010 | pmid = 20712531 | doi = 10.2217/pgs.10.97 }}</ref>
+Pharmacogenomics aims to develop rational means to optimize [[pharmacotherapy|drug therapy]], with regard to the patients' [[genotype]], to achieve maximum efficiency with minimal [[adverse effect (medicine)|adverse effects]].<ref name="pmid19530963">{{cite journal | vauthors = Becquemont L | title = Pharmacogenomics of adverse drug reactions: practical applications and perspectives | journal = Pharmacogenomics | volume = 10 | issue = 6 | pages = 961–969 | date = June 2009 | pmid = 19530963 | doi = 10.2217/pgs.09.37 }}</ref> 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).<ref name="pmid19565025">{{cite journal |vauthors=Sheffield LJ, Phillimore HE |date=May 2009 |title=Clinical use of pharmacogenomic tests in 2009 |journal=The Clinical Biochemist. Reviews |volume=30 |issue=2 |pages=55–65 |pmc=2702214 |pmid=19565025}}</ref><ref>{{cite journal | vauthors = Hauser AS, Chavali S, Masuho I, Jahn LJ, Martemyanov KA, Gloriam DE, Babu MM | title = Pharmacogenomics of GPCR Drug Targets | journal = Cell | volume = 172 | issue = 1–2 | pages = 41–54.e19 | date = January 2018 | pmid = 29249361 | pmc = 5766829 | doi = 10.1016/j.cell.2017.11.033 }}</ref> 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.<ref>{{cite web|url=https://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm126957.pdf|title=Guidance for Industry Pharmacogenomic Data Submissions|date=March 2005|publisher=[[U.S. Food and Drug Administration]]|access-date=2008-08-27}}</ref><ref name="Squassina2010">{{cite journal | vauthors = Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, Mitropoulos K, Del Zompo M, Patrinos GP | display-authors = 6 | title = Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice | journal = Pharmacogenomics | volume = 11 | issue = 8 | pages = 1149–1167 | date = August 2010 | pmid = 20712531 | doi = 10.2217/pgs.10.97 }}</ref>
 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]].<ref name=eval>{{cite journal | vauthors = Huser V, Cimino JJ | title = Providing pharmacogenomics clinical decision support using whole genome sequencing data as input | journal = AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science | volume = 2013 | pages = 81 | year = 2013 | pmid = 24303303 }}</ref> Sequencing provides many more data points, including detection of mutations that prematurely terminate the synthesized protein (early [[stop codon]]).<ref name=eval/>
-== History ==
-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 called [[favism]].<ref name="pmid11678777"/><ref name="pmid22461095">{{cite journal | vauthors = Prasad K | title = Role of regulatory agencies in translating pharmacogenetics to the clinics | journal = Clinical Cases in Mineral and Bone Metabolism | volume = 6 | issue = 1 | pages = 29–34 | date = January 2009 | pmid = 22461095 | pmc = 2781218 }}</ref> Although the first official publication was not until 1961,<ref name="Evans_1961">{{cite journal | vauthors = Evans DA, Clarke CA | title = Pharmacogenetics | journal = British Medical Bulletin | volume = 17 | issue = 3 | pages = 234–240 | date = September 1961 | pmid = 13697554 | doi = 10.1093/oxfordjournals.bmb.a069915 }}</ref> 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.<ref name="pmid14585618"/><ref name="pmid16415920">{{cite journal | vauthors = Kalow W | title = Pharmacogenetics and pharmacogenomics: origin, status, and the hope for personalized medicine | journal = The Pharmacogenomics Journal | volume = 6 | issue = 3 | pages = 162–165 | year = 2006 | pmid = 16415920 | doi = 10.1038/sj.tpj.6500361 | doi-access = free }}</ref> The term pharmacogenetic was first coined in 1959 by [[Friedrich Vogel (human geneticist)|Friedrich Vogel]] of [[Heidelberg]], Germany (although some papers suggest it was 1957 or 1958).<ref>{{cite journal | vauthors = Vogel F | title = Moderne probleme der humangenetik. | trans-title = Modern human genetics problems | journal = Ergebnisse der Inneren Medizin und Kinderheilkunde | trans-journal = Results of internal medicine and pediatrics | language = German | date = 1959 | pages = 52–125 | publisher = Springer | location = Berlin, Heidelberg }}</ref> 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.<ref name="pmid16421980">{{cite journal | vauthors = Motulsky AG, Qi M | title = Pharmacogenetics, pharmacogenomics and ecogenetics | journal = Journal of Zhejiang University. Science. B | volume = 7 | issue = 2 | pages = 169–170 | date = February 2006 | pmid = 16421980 | pmc = 1363768 | doi = 10.1631/jzus.2006.B0169 }}</ref> The term pharmacogenomics first began appearing around the 1990s.<ref name="pmid11678777">{{cite journal | vauthors = Pirmohamed M | title = Pharmacogenetics and pharmacogenomics | journal = British Journal of Clinical Pharmacology | volume = 52 | issue = 4 | pages = 345–347 | date = October 2001 | pmid = 11678777 | pmc = 2014592 | doi = 10.1046/j.0306-5251.2001.01498.x }}</ref>
-The first FDA approval of a pharmacogenetic test was in 2005<ref name=Squassina2010/> (for [[allele]]s in CYP2D6 and CYP2C19).
 == Pharmacogenetics vs. pharmacogenomics ==
+The term ''pharmacogenomics'' is often used interchangeably with ''pharmacogenetics''. Although both terms relate to drug response based on genetic influences, there are differences between the two. '''Pharmacogenetics''' is limited to [[Monogenic (genetics)|monogenic]] phenotypes (i.e., single gene-drug interactions). '''Pharmacogenomics''' refers to polygenic drug response phenotypes and encompasses [[Transcriptome|transcriptomics]], [[proteomics]], and [[metabolomics]].
-The term ''pharmacogenomics'' is often used interchangeably with ''pharmacogenetics''. Although both terms relate to drug response based on genetic influences. '''Pharmacogenomics''' encompasses a more genome-wide association approach, incorporating genomics and [[epigenetics]] while dealing with the effects of multiple genes or even [[chromosome]]s on drug response.<ref name="pmid19565025">{{cite journal | vauthors = Sheffield LJ, Phillimore HE | title = Clinical use of pharmacogenomic tests in 2009 | journal = The Clinical Biochemist. Reviews | volume = 30 | issue = 2 | pages = 55–65 | date = May 2009 | pmid = 19565025 | pmc = 2702214 }}</ref><ref name="pmid19299369">{{cite journal | vauthors = Shin J, Kayser SR, Langaee TY | title = Pharmacogenetics: from discovery to patient care | journal = American Journal of Health-System Pharmacy | volume = 66 | issue = 7 | pages = 625–637 | date = April 2009 | pmid = 19299369 | doi = 10.2146/ajhp080170 }}</ref><ref name="Pharmacogenetics and Pharmacogenomics Factsheet.">{{cite web| title= Center for Genetics Education| url= http://www.genetics.edu.au/Publications-and-Resources/Genetics-Fact-Sheets/pharmacogenetics-pharmacogenomics=2014-09-03}}{{Dead link|date=May 2020 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> Pharmacogenomics study the inherited [[genetics|genetic]] differences in drug [[metabolic pathway]]s (and other pharmacological principles, like enzymes, messengers and receptors) which can affect individual responses to drugs, both in terms of therapeutic effect as well as adverse effects.<ref name="Klotz-2007">{{cite journal | vauthors = Klotz U | title = The role of pharmacogenetics in the metabolism of antiepileptic drugs: pharmacokinetic and therapeutic implications | journal = Clinical Pharmacokinetics | volume = 46 | issue = 4 | pages = 271–279 | year = 2007 | pmid = 17375979 | doi = 10.2165/00003088-200746040-00001 | s2cid = 30702170 }}</ref> '''Pharmacogenetics''' in the other hand focuses on single drug-[[gene]] interactions taking in count [[allele]] genes, [[Dominance (genetics)|dominance]] and [[gene polymorphism]] in order to understand the better use of a drug on a single patient or population.
 == Mechanisms of pharmacogenetic interactions ==
 === Pharmacokinetics ===
-[[Pharmacokinetics]] involves the absorption, distribution, metabolism, and elimination of pharmaceutics. These processes are often facilitated by enzymes such as drug transporters or drug metabolizing enzymes (discussed more in-depth below). Variation in DNA loci responsible for producing these enzymes can alter their expression or activity so that their functional status changes. An increase, decrease, or loss of function for transporters or metabolizing enzymes can ultimately alter the amount of medication within the body. This may result in deviation from the medication's [[Therapeutic index|therapeutic window]] and result in either toxicity or loss of effectiveness.
+[[Pharmacokinetics]] involves the absorption, distribution, metabolism, and elimination of pharmaceutics. These processes are often facilitated by enzymes such as drug transporters or drug metabolizing enzymes (discussed in-depth below). Variation in DNA loci responsible for producing these enzymes can alter their expression or activity so that their functional status changes. An increase, decrease, or loss of function for transporters or metabolizing enzymes can ultimately alter the amount of medication in the body and at the site of action. This may result in deviation from the medication's [[Therapeutic index|therapeutic window]] and result in either toxicity or loss of effectiveness.
 ==== Drug-metabolizing enzymes ====
-The majority of clinically actionable pharmacogenetic variation occurs in genes that code for drug-metabolizing enzymes, including those involved in both [[Phase I metabolism|phase I]] and [[Phase II metabolism|phase II]] metabolism. The [[cytochrome P450]] enzymes are important for the hepatic metabolism of many medications and are highly polymorphic. Additional drug-metabolizing enzymes that have been implicated in pharmacogenetic interactions include [[UGT1A1]] (a [[Glucuronosyltransferase|UDP-glucuronosyltransferase]]), [[Dihydropyrimidine dehydrogenase (NADP+)|DPYD]], and [[Thiopurine methyltransferase|TPMT]].<ref name=":0">{{Cite web |title=Clinical Pharmacogenetics Implementation Consortium |url=https://cpicpgx.org/ |access-date=2022-12-13 |website=cpicpgx.org |language=en-US}}</ref>
+The majority of clinically actionable pharmacogenetic variation occurs in genes that code for drug-metabolizing enzymes, including those involved in both [[Phase I metabolism|phase I]] and [[Phase II metabolism|phase II]] metabolism. The [[cytochrome P450]] enzyme family is responsible for metabolism of 70-80% of all medications used clinically.<ref name=":7">{{Cite journal |last1=Zanger |first1=Ulrich M. |last2=Schwab |first2=Matthias |date=2013-04-01 |title=Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation |journal=Pharmacology & Therapeutics |volume=138 |issue=1 |pages=103–141 |doi=10.1016/j.pharmthera.2012.12.007 |pmid=23333322 |issn=0163-7258|doi-access=free }}</ref> [[CYP3A4]], [[CYP2C9]], [[CYP2C19]], and [[CYP2D6]] are major CYP enzymes involved in drug metabolism and are all known to be highly polymorphic.<ref name=":7" /> Additional drug-metabolizing enzymes that have been implicated in pharmacogenetic interactions include [[UGT1A1]] (a [[Glucuronosyltransferase|UDP-glucuronosyltransferase]]), [[Dihydropyrimidine dehydrogenase (NADP+)|DPYD]], and [[Thiopurine methyltransferase|TPMT]].<ref name=":0">{{Cite web |title=Clinical Pharmacogenetics Implementation Consortium |url=https://cpicpgx.org/ |access-date=2022-12-13 |website=cpicpgx.org |language=en-US}}</ref>
 ==== Drug transporters ====
-Many medications rely on [[transport protein|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.<ref name=":1">{{cite journal | vauthors = Nigam SK | title = What do drug transporters really do? | journal = Nature Reviews. Drug Discovery | volume = 14 | issue = 1 | pages = 29–44 | date = January 2015 | pmid = 25475361 | pmc = 4750486 | doi = 10.1038/nrd4461 }}</ref> The major transporters include the [[Solute carrier family|solute carrier]], [[ATP-binding cassette transporter|ATP-binding cassette]], and [[Organo anion transporter family|organic anion transporters]].<ref name=":1" />
+Many medications rely on [[transport protein|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.<ref name=":1">{{cite journal | vauthors = Nigam SK | title = What do drug transporters really do? | journal = Nature Reviews. Drug Discovery | volume = 14 | issue = 1 | pages = 29–44 | date = January 2015 | pmid = 25475361 | pmc = 4750486 | doi = 10.1038/nrd4461 }}</ref> The major transporters include the [[Solute carrier family|solute carrier]], [[ATP-binding cassette transporter|ATP-binding cassette]], and [[Organo anion transporter family|organic anion transporters]].<ref name=":1" /> Transporters that have been shown to influence response to medications include [[OATP1B1]] (''SLCO1B1'') and breast cancer resistance protein (BCRP) (''[[ABCG2]]).''<ref>{{Cite journal |last1=Cooper-DeHoff |first1=Rhonda M. |last2=Niemi |first2=Mikko |last3=Ramsey |first3=Laura B. |last4=Luzum |first4=Jasmine A. |last5=Tarkiainen |first5=E. Katriina |last6=Straka |first6=Robert J. |last7=Gong |first7=Li |last8=Tuteja |first8=Sony |last9=Wilke |first9=Russell A. |last10=Wadelius |first10=Mia |last11=Larson |first11=Eric A. |last12=Roden |first12=Dan M. |last13=Klein |first13=Teri E. |last14=Yee |first14=Sook Wah |last15=Krauss |first15=Ronald M. |date=May 2022 |title=The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and Statin-Associated Musculoskeletal Symptoms |journal=Clinical Pharmacology and Therapeutics |volume=111 |issue=5 |pages=1007–1021 |doi=10.1002/cpt.2557 |issn=1532-6535 |pmc=9035072 |pmid=35152405}}</ref>
 === Pharmacodynamics ===
+[[Pharmacodynamics]] refers to the impact a medication has on the body, or its mechanism of action.
+==== Drug targets ====
+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.<ref>{{Cite book |last=Zanders |first=Edward D. |chapter=Introduction to Drugs and Drug Targets |date=2011-03-21 |title=The Science and Business of Drug Discovery |pages=11–27 |doi=10.1007/978-1-4419-9902-3_2 |pmc=7120710|isbn=978-1-4419-9901-6 }}</ref> Most of the pharmacogenetic interactions that involve drug targets are within the field of [[oncology]] and include [[Targeted therapy|targeted therapeutics]] designed to address [[somatic mutation]]s (see also [[Cancer pharmacogenomics|Cancer Pharmacogenomics]]). For example, [[Epidermal growth factor receptor|EGFR]] inhibitors like [[gefitinib]] (Iressa) or [[erlotinib]] (Tarceva) are only indicated in patients carrying specific mutations to ''EGFR''.<ref>Iressa [package insert]. Wilmington, DE: Astra Zeneca; 2021.</ref><ref>Tarceva [package insert]. Northbrook, IL: OSI Pharmaceuticals, LLC; 2016.  https://www.accessdata.fda.gov/drugsatfda_docs/label/2016/021743s025lbl.pdf</ref>
+[[Germline mutation]]s 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|vitamin K epoxide reductase (VKOR)]]. Warfarin binds to and inhibits VKOR, which is an important enzyme in the vitamin K cycle.<ref name=":8">{{Cite journal |last1=Oldenburg |first1=Johannes |last2=Marinova |first2=Milka |last3=Müller-Reible |first3=Clemens |last4=Watzka |first4=Matthias |date=2008 |title=The vitamin K cycle |url=https://pubmed.ncbi.nlm.nih.gov/18374189/ |journal=Vitamins and Hormones |volume=78 |pages=35–62 |doi=10.1016/S0083-6729(07)00003-9 |issn=0083-6729 |pmid=18374189|isbn=9780123741134 }}</ref> Inhibition of VKOR prevents [[Redox|reduction]] of [[vitamin K]], which is a [[Cofactor (biochemistry)|cofactor]] required in the formation of [[coagulation]] factors [[Thrombin|II]], [[Coagulation factor VII|VII]], [[Factor IX|IX]] and [[Factor X|X]], and inhibitors [[protein C]] and [[Protein S|S]].<ref name=":8" /><ref>{{Cite journal |last1=Mijares |first1=M. E. |last2=Nagy |first2=E. |last3=Guerrero |first3=B. |last4=Arocha-Piñango |first4=C. L. |date=September 1998 |title=[Vitamin K: biochemistry, function, and deficiency. Review] |url=https://pubmed.ncbi.nlm.nih.gov/9780555/ |journal=Investigacion Clinica |volume=39 |issue=3 |pages=213–229 |issn=0535-5133 |pmid=9780555}}</ref>
-==== Example: VKORC1 ====
+==== Off-target sites ====
+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.<ref>{{Cite journal |last=Rudmann |first=Daniel G. |date=February 2013 |title=On-target and off-target-based toxicologic effects |url=https://pubmed.ncbi.nlm.nih.gov/23085982/ |journal=Toxicologic Pathology |volume=41 |issue=2 |pages=310–314 |doi=10.1177/0192623312464311 |issn=1533-1601 |pmid=23085982|s2cid=11858945 }}</ref> 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|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 [[Red blood cell|erythrocytes]] and it is essential for the function of [[catalase]].<ref>{{Cite journal |last1=Recht |first1=Judith |last2=Chansamouth |first2=Vilada |last3=White |first3=Nicholas J. |last4=Ashley |first4=Elizabeth A. |date=2022-05-03 |title=Nitrofurantoin and glucose-6-phosphate dehydrogenase deficiency: a safety review |journal=JAC-Antimicrobial Resistance |volume=4 |issue=3 |pages=dlac045 |doi=10.1093/jacamr/dlac045 |issn=2632-1823 |pmc=9070801 |pmid=35529053}}</ref> Glutathione and catalase protect cells from oxidative stress that would otherwise result in cell [[lysis]]. Certain variants in ''G6PD'' result in [[Glucose-6-phosphate dehydrogenase deficiency|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]].<ref>{{Cite journal |last1=Gammal |first1=Roseann S. |last2=Pirmohamed |first2=Munir |last3=Somogyi |first3=Andrew A. |last4=Morris |first4=Sarah A. |last5=Formea |first5=Christine M. |last6=Elchynski |first6=Amanda L. |last7=Oshikoya |first7=Kazeem A. |last8=McLeod |first8=Howard L. |last9=Haidar |first9=Cyrine E. |last10=Whirl-Carrillo |first10=Michelle |last11=Klein |first11=Teri E. |last12=Caudle |first12=Kelly E. |last13=Relling |first13=Mary V. |date=May 2023 |title=Expanded Clinical Pharmacogenetics Implementation Consortium Guideline for Medication Use in the Context of G6PD Genotype |journal=Clinical Pharmacology & Therapeutics |language=en |volume=113 |issue=5 |pages=973–985 |doi=10.1002/cpt.2735 |issn=0009-9236 |pmc=10281211 |pmid=36049896}}</ref>
-The vitamin K epoxide reductase complex subunit 1 ([[VKORC1]]) is responsible for the pharmacodynamics of warfarin.<ref name="pmid21507031">{{cite journal | vauthors = Teh LK, Langmia IM, Fazleen Haslinda MH, Ngow HA, Roziah MJ, Harun R, Zakaria ZA, Salleh MZ | display-authors = 6 | title = Clinical relevance of VKORC1 (G-1639A and C1173T) and CYP2C9*3 among patients on warfarin | journal = Journal of Clinical Pharmacy and Therapeutics | volume = 37 | issue = 2 | pages = 232–236 | date = April 2012 | pmid = 21507031 | doi = 10.1111/j.1365-2710.2011.01262.x | s2cid = 22671298 | doi-access = free }}</ref> VKORC1 along with CYP2C9 are useful for identifying the risk of bleeding during warfarin administration. Warfarin works by inhibiting VKOR, which is encoded by the VKORC1 gene. Individuals with polymorphism in this have an affected response to warfarin treatment.<ref name="tableFDA">{{cite web |author=U.S. Food and Drug Administration (FDA) |title=Table of Pharmacogenomic Biomarkers in Drug Labels. |url=https://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm |access-date=2014-09-03 |website=[[Food and Drug Administration]]}}</ref>
 === Immunologic ===
-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 a medication.<ref>{{cite journal | vauthors = Pavlos R, Mallal S, Phillips E | title = HLA and pharmacogenetics of drug hypersensitivity | journal = Pharmacogenomics | volume = 13 | issue = 11 | pages = 1285–1306 | date = August 2012 | pmid = 22920398 | doi = 10.2217/pgs.12.108 }}</ref>
+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.<ref>{{cite journal | vauthors = Pavlos R, Mallal S, Phillips E | title = HLA and pharmacogenetics of drug hypersensitivity | journal = Pharmacogenomics | volume = 13 | issue = 11 | pages = 1285–1306 | date = August 2012 | pmid = 22920398 | doi = 10.2217/pgs.12.108 }}</ref>
 == Clinical pharmacogenomics resources ==
@@ Line 49: / Line 49: @@
 ==== Table of Pharmacogenetic Associations ====
-In February 2020 the [[Food and Drug Administration|FDA]] published the [https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations Table of Pharmacogenetic Associations].<ref>{{Cite web | author = Office of the Commissioner|date=2020-03-24 |title=FDA Announces Collaborative Review of Scientific Evidence to Support Associations Between Genetic Information and Specific Medications |url=https://www.fda.gov/news-events/press-announcements/fda-announces-collaborative-review-scientific-evidence-support-associations-between-genetic |access-date=2022-12-13 |website=FDA |language=en}}</ref> 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."<ref name=":2">{{Cite journal | author = Center for Devices and Radiological Health|date=2022-10-26 |title=Table of Pharmacogenetic Associations |url=https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations |journal=FDA |language=en}}</ref>
+In February 2020 the [[Food and Drug Administration|FDA]] published the Table of Pharmacogenetic Associations.<ref>{{Cite web | author = Office of the Commissioner|date=2020-03-24 |title=FDA Announces Collaborative Review of Scientific Evidence to Support Associations Between Genetic Information and Specific Medications |url=https://www.fda.gov/news-events/press-announcements/fda-announces-collaborative-review-scientific-evidence-support-associations-between-genetic |access-date=2022-12-13 |website=FDA |language=en}}</ref> 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."<ref name=":2">{{Cite journal | author = Center for Devices and Radiological Health|date=2022-10-26 |title=Table of Pharmacogenetic Associations |url=https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations |journal=FDA |language=en}}</ref>
 "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."<ref name=":2" />
 ==== Table of Pharmacogenomic Biomarkers in Drug Labeling ====
+The FDA Table of Pharmacogenomic Biomarkers in Drug Labeling lists FDA-approved drugs with pharmacogenomic information found in the drug labeling. "Biomarkers in the table include but are not limited to germline or somatic gene variants (polymorphisms, mutations), functional deficiencies with a genetic etiology, gene expression differences, and chromosomal abnormalities; selected protein biomarkers that are used to select treatments for patients are also included."<ref>{{Cite journal | author = Center for Drug Evaluation and Research |date=2022-08-11 |title=Table of Pharmacogenomic Biomarkers in Drug Labeling |url=https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling |journal=FDA |language=en}}</ref>
-[[File:Sequence2Script_Overview.jpg|thumb|416x416px]]
-The FDA [https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling Table of Pharmacogenomic Biomarkers in Drug Labeling] lists FDA-approved drugs with pharmacogenomic information found in the drug labeling. "Biomarkers in the table include but are not limited to germline or somatic gene variants (polymorphisms, mutations), functional deficiencies with a genetic etiology, gene expression differences, and chromosomal abnormalities; selected protein biomarkers that are used to select treatments for patients are also included."<ref>{{Cite journal | author = Center for Drug Evaluation and Research |date=2022-08-11 |title=Table of Pharmacogenomic Biomarkers in Drug Labeling |url=https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling |journal=FDA |language=en}}</ref>
-=== Sequence2Script ===
-[https://sequence2script.com/#/ Sequence2Script] is a "free online tool to help healthcare providers and clinical laboratories translate pharmacogenetic test results into clinically useful recommendations."<ref name=":3">{{Cite web |title=seq2script-ui |url=https://sequence2script.com/#/ |access-date=2022-12-13 |website=sequence2script.com}}</ref><ref>{{cite journal | vauthors = Bousman CA, Wu P, Aitchison KJ, Cheng T | title = Sequence2Script: A Web-Based Tool for Translation of Pharmacogenetic Data Into Evidence-Based Prescribing Recommendations | journal = Frontiers in Pharmacology | volume = 12 | pages = 636650 | date = 2021 | pmid = 33815120 | doi = 10.3389/fphar.2021.636650 | pmc = 8015939 | doi-access = free }}</ref> This tool "creates a report that outlines medication and dosing recommendations based on a patient’s known genotypes. The recommendations made by this tool are based on expert, peer-reviewed guidelines developed by the CPIC, the Dutch Pharmacogenetics Working Group (DPWG), and US Food & Drug Administration (FDA). All you need to do is enter the patient’s known genetic information and any medications they are currently taking, and Sequence2Script will compile the recommendations from these sources into a single report that can be reviewed online or printed out."
-Sequence2Script was developed by the Psychiatric Pharmacogenomics Laboratory in the Department of Medical Genetics at the University of Calgary.<ref name=":3" />
 === PharmGKB ===
 The [[PharmGKB|Pharmacogenomics Knowledgebase (PharmGKB)]] is an "[[National Institutes of Health|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."<ref>{{Cite web |title=PharmGKB |url=https://www.pharmgkb.org/about |access-date=2022-12-13 |website=PharmGKB |language=en}}</ref>
-=== Phenoconversion calculator ===
-The University of Florida College of Pharmacy developed the PROP™ Pharmacogenetics Calculator to "help clinicians integrate a standardized method of assessing CYP2D6 phenoconversion into practice when a CYP2D6 genotype is available."<ref>{{Cite web |title=CYP2D6 Phenoconversion Calculator » Precision Medicine Program » UF Health » University of Florida |url=https://precisionmedicine.ufhealth.org/phenoconversion-calculator/ |access-date=2022-12-13 |language=en}}</ref>
 == Commercial Pharmacogenetic Testing Laboratories ==
@@ Line 78: / Line 69: @@
 === Genotype ===
-There are multiple ways to represent a pharmacogenomic [[genotype]]. A commonly used [[nomenclature]] system is to report [[haplotype]]s using a star (*) allele (e.g., [[CYP2C19]] *1/*2). [[Single-nucleotide polymorphism]]s (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.<ref>{{cite journal | vauthors = Poo DC, Cai S, Mah JT | title = UASIS: Universal Automatic SNP Identification System | journal = BMC Genomics | volume = 12 | issue = Suppl 3 | pages = S9 | date = November 2011 | pmid = 22369494 | pmc = 3333510 | doi = 10.1186/1471-2164-12-S3-S9 }}</ref>
+There are multiple ways to represent a pharmacogenomic [[genotype]]. A commonly used [[nomenclature]] system is to report [[haplotype]]s using a star (*) allele (e.g., [[CYP2C19]] *1/*2). [[Single-nucleotide polymorphism]]s (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.<ref>{{cite journal | vauthors = Poo DC, Cai S, Mah JT | title = UASIS: Universal Automatic SNP Identification System | journal = BMC Genomics | volume = 12 | issue = Suppl 3 | pages = S9 | date = November 2011 | pmid = 22369494 | pmc = 3333510 | doi = 10.1186/1471-2164-12-S3-S9 | doi-access = free }}</ref>
 === Phenotype ===
@@ Line 94: / Line 85: @@
 In [[cancer treatment]], pharmacogenomics tests are used to identify which patients are most likely to respond to certain [[cancer drugs]]. In behavioral health, pharmacogenomic tests provide tools for physicians and care givers to better manage medication selection and side effect amelioration. Pharmacogenomics is also known as companion diagnostics, meaning tests being bundled with drugs. Examples include [[KRAS]] test with [[cetuximab]] and [[Epidermal growth factor receptor|EGFR]] test with [[gefitinib]]. Beside efficacy, germline pharmacogenetics can help to identify patients likely to undergo severe toxicities when given cytotoxics showing impaired detoxification in relation with genetic polymorphism, such as canonical 5-FU.<ref name="pmid20920994">{{cite journal | vauthors = Ciccolini J, Gross E, Dahan L, Lacarelle B, Mercier C | title = Routine dihydropyrimidine dehydrogenase testing for anticipating 5-fluorouracil-related severe toxicities: hype or hope? | journal = Clinical Colorectal Cancer | volume = 9 | issue = 4 | pages = 224–228 | date = October 2010 | pmid = 20920994 | doi = 10.3816/CCC.2010.n.033 }}</ref> In particular, genetic deregulations affecting genes coding for [[Dihydropyrimidine dehydrogenase|DPD]], [[UGT1A1]], [[TPMT]], [[Cytidine deaminase|CDA]] and [[CYP2D6]] are now considered as critical issues for patients treated with 5-FU/capecitabine, irinotecan, mercaptopurine/azathioprine, gemcitabine/capecitabine/AraC and tamoxifen, respectively.<ref name="pmid202043652">{{cite journal | vauthors = Yang CG, Ciccolini J, Blesius A, Dahan L, Bagarry-Liegey D, Brunet C, Varoquaux A, Frances N, Marouani H, Giovanni A, Ferri-Dessens RM, Chefrour M, Favre R, Duffaud F, Seitz JF, Zanaret M, Lacarelle B, Mercier C | display-authors = 6 | title = DPD-based adaptive dosing of 5-FU in patients with head and neck cancer: impact on treatment efficacy and toxicity | journal = Cancer Chemotherapy and Pharmacology | volume = 67 | issue = 1 | pages = 49–56 | date = January 2011 | pmid = 20204365 | doi = 10.1007/s00280-010-1282-4 | s2cid = 25362813 }}</ref>
-In [[cardiovascular disease|cardiovascular disorders]], the main concern is response to drugs including [[warfarin]], [[clopidogrel]], [[beta blocker]]s, and [[statin]]s.<ref name=eval/> In patients with CYP2C19, who take clopidogrel, cardiovascular risk is elevated, leading to [[medication package insert]] updates by regulators.<ref>{{cite book | title=Medical Genetics Summaries | chapter=Clopidogrel Therapy and CYP2C19 Genotype | chapter-url=https://www.ncbi.nlm.nih.gov/books/NBK84114/ | veditors=Pratt VM, McLeod HL, Rubinstein WS, Scott SA, Dean LC, Kattman BL, Malheiro AJ | display-editors=3 | publisher=[[National Center for Biotechnology Information]] (NCBI) | year=2012 | pmid=28520346 | id=Bookshelf ID: NBK84114 | vauthors=Dean L | url=https://www.ncbi.nlm.nih.gov/books/NBK61999/ }}</ref> In patients with [[type 2 diabetes]], [[haptoglobin]] (Hp) genotyping shows an effect on cardiovascular disease, with Hp2-2 at higher risk and supplemental vitamin E reducing risk by affecting [[High-density lipoprotein|HDL]].<ref>{{cite journal | vauthors = Bale BF, Doneen AL, Vigerust DJ | title = Precision Healthcare of Type 2 Diabetic Patients Through Implementation of Haptoglobin Genotyping | journal = Frontiers in Cardiovascular Medicine | volume = 5 | pages = 141 | date = 2018 | pmid = 30386783 | pmc = 6198642 | doi = 10.3389/fcvm.2018.00141 | doi-access = free }}</ref>
+In [[cardiovascular disease|cardiovascular disorders]], the main concern is response to drugs including [[warfarin]], [[clopidogrel]], [[beta blocker]]s, and [[statin]]s.<ref name="eval" /> In patients with CYP2C19, who take clopidogrel, cardiovascular risk is elevated, leading to [[medication package insert]] updates by regulators.<ref>{{cite book | title=Medical Genetics Summaries | chapter=Clopidogrel Therapy and CYP2C19 Genotype | chapter-url=https://www.ncbi.nlm.nih.gov/books/NBK84114/ | veditors=Pratt VM, McLeod HL, Rubinstein WS, Scott SA, Dean LC, Kattman BL, Malheiro AJ | display-editors=3 | publisher=[[National Center for Biotechnology Information]] (NCBI) | year=2012 | pmid=28520346 | id=Bookshelf ID: NBK84114 | vauthors=Dean L | url=https://www.ncbi.nlm.nih.gov/books/NBK61999/ }}</ref> In patients with [[type 2 diabetes]], [[haptoglobin]] (Hp) genotyping shows an effect on cardiovascular disease, with Hp2-2 at higher risk and supplemental vitamin E reducing risk by affecting [[High-density lipoprotein|HDL]].<ref>{{cite journal | vauthors = Bale BF, Doneen AL, Vigerust DJ | title = Precision Healthcare of Type 2 Diabetic Patients Through Implementation of Haptoglobin Genotyping | journal = Frontiers in Cardiovascular Medicine | volume = 5 | pages = 141 | date = 2018 | pmid = 30386783 | pmc = 6198642 | doi = 10.3389/fcvm.2018.00141 | doi-access = free }}</ref>
 In psychiatry, as of 2010, research has focused particularly on [[5-HTTLPR]] and [[Dopamine receptor D2|DRD2]].<ref>{{cite journal| vauthors = Malhotra AK |year=2010|title=The state of pharmacogenetics|url=http://www.psychiatrictimes.com/neuropsychiatry/content/article/10168/1550787|journal=Psychiatr Times|volume=27|issue=4|pages=38–41, 62}}</ref>
@@ Line 100: / Line 91: @@
 === Clinical implementation ===
 {{Further|Education in personalized medicine}}
-Initiatives to spur adoption by clinicians include the [https://upgx.eu/ Ubiquitous Pharmacogenomics (U-PGx)] program in [[Europe]] and the Clinical Pharmacogenetics Implementation Consortium (CPIC) in the United States.<ref>{{cite journal | vauthors = Williams MS | title = Early Lessons from the Implementation of Genomic Medicine Programs | journal = Annual Review of Genomics and Human Genetics | volume = 20 | issue = 1 | pages = 389–411 | date = August 2019 | pmid = 30811224 | doi = 10.1146/annurev-genom-083118-014924 | s2cid = 73460688 }}</ref> In a 2017 survey of European clinicians, in the prior year two-thirds had not ordered a pharmacogenetic test.<ref>{{cite journal | vauthors = Just KS, Steffens M, Swen JJ, Patrinos GP, Guchelaar HJ, Stingl JC | title = Medical education in pharmacogenomics-results from a survey on pharmacogenetic knowledge in healthcare professionals within the European pharmacogenomics clinical implementation project Ubiquitous Pharmacogenomics (U-PGx) | journal = European Journal of Clinical Pharmacology | volume = 73 | issue = 10 | pages = 1247–1252 | date = October 2017 | pmid = 28669097 | pmc = 5599468 | doi = 10.1007/s00228-017-2292-5 }}</ref>
+Initiatives to spur adoption by clinicians include the [https://upgx.eu/ Ubiquitous Pharmacogenomics (U-PGx)] program in [[Europe]] and the Clinical Pharmacogenetics Implementation Consortium (CPIC) in the United States.<ref>{{cite journal | vauthors = Williams MS | title = Early Lessons from the Implementation of Genomic Medicine Programs | journal = Annual Review of Genomics and Human Genetics | volume = 20 | issue = 1 | pages = 389–411 | date = August 2019 | pmid = 30811224 | doi = 10.1146/annurev-genom-083118-014924 | s2cid = 73460688 | doi-access = free }}</ref> In a 2017 survey of European clinicians, in the prior year two-thirds had not ordered a pharmacogenetic test.<ref>{{cite journal | vauthors = Just KS, Steffens M, Swen JJ, Patrinos GP, Guchelaar HJ, Stingl JC | title = Medical education in pharmacogenomics-results from a survey on pharmacogenetic knowledge in healthcare professionals within the European pharmacogenomics clinical implementation project Ubiquitous Pharmacogenomics (U-PGx) | journal = European Journal of Clinical Pharmacology | volume = 73 | issue = 10 | pages = 1247–1252 | date = October 2017 | pmid = 28669097 | pmc = 5599468 | doi = 10.1007/s00228-017-2292-5 }}</ref>
 In 2010, [[Vanderbilt University Medical Center]] launched Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT);<ref>{{cite journal | vauthors = Carlson B | title = Vanderbilt pioneers bedside genetics | journal = Biotechnology Healthcare | volume = 9 | issue = 2 | pages = 31–32 | date = 2012 | pmid = 22876213 | pmc = 3411230 }}</ref> in 2015 survey, two-thirds of the clinicians had ordered a pharmacogenetic test.<ref>{{cite journal | vauthors = Peterson JF, Field JR, Shi Y, Schildcrout JS, Denny JC, McGregor TL, Van Driest SL, Pulley JM, Lubin IM, Laposata M, Roden DM, Clayton EW | display-authors = 6 | title = Attitudes of clinicians following large-scale pharmacogenomics implementation | journal = The Pharmacogenomics Journal | volume = 16 | issue = 4 | pages = 393–398 | date = August 2016 | pmid = 26261062 | pmc = 4751074 | doi = 10.1038/tpj.2015.57 }}</ref>
@@ Line 110: / Line 101: @@
 === Reduction of polypharmacy ===
-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 reaction]]s, improve treatment outcomes, and save costs by avoiding purchase of some medications. For example, maybe due to inappropriate subscribing, [[psychiatry|psychiatric]] patients tend to receive more medications than age-matched non-psychiatric patients.<ref name="polypharmacy_book">{{cite book | vauthors = Ritsner M | date=2013 |title=Polypharmacy in Psychiatry Practice, Volume I. Multiple Medication Strategies | location=Dordrecht |publisher=Springer Science and Business Media |isbn=978-94-007-5804-9}}</ref>
+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 reaction]]s, improve treatment outcomes, and save costs by avoiding purchase of some medications. For example, maybe due to inappropriate prescribing, [[psychiatry|psychiatric]] patients tend to receive more medications than age-matched non-psychiatric patients.<ref name="polypharmacy_book">{{cite book | vauthors = Ritsner M | date=2013 |title=Polypharmacy in Psychiatry Practice, Volume I. Multiple Medication Strategies | location=Dordrecht |publisher=Springer Science and Business Media |isbn=978-94-007-5804-9}}</ref>
 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%.<ref name="Slone_Study">{{cite web | url = http://www.bu.edu/slone/research/studies/slone-survey/ | title = Patterns of Medication Use in the United States | date = 2006  | publisher = Boston University, Slone Epidemiology Center}}</ref>
@@ Line 116: / Line 107: @@
 == Example case studies ==
-''Case A – Antipsychotic adverse reaction''<ref name="PMC2656342">{{cite journal | vauthors = Foster A, Wang Z, Usman M, Stirewalt E, Buckley P | title = Pharmacogenetics of antipsychotic adverse effects: Case studies and a literature review for clinicians | journal = Neuropsychiatric Disease and Treatment | volume = 3 | issue = 6 | pages = 965–973 | date = December 2007 | pmid = 19300635 | pmc = 2656342 | doi = 10.2147/ndt.s1752 }}</ref>
+''Case A – Antipsychotic adverse reaction''<ref name="PMC2656342">{{cite journal | vauthors = Foster A, Wang Z, Usman M, Stirewalt E, Buckley P | title = Pharmacogenetics of antipsychotic adverse effects: Case studies and a literature review for clinicians | journal = Neuropsychiatric Disease and Treatment | volume = 3 | issue = 6 | pages = 965–973 | date = December 2007 | pmid = 19300635 | pmc = 2656342 | doi = 10.2147/ndt.s1752 | doi-access = free }}</ref>
 Patient A has schizophrenia. Their treatment included a combination of ziprasidone, olanzapine, trazodone and [[benzatropine|benztropine]]. The patient experienced dizziness and sedation, so they were tapered off ziprasidone and olanzapine, and transitioned to quetiapine. Trazodone was discontinued. The patient then experienced excessive sweating, tachycardia and neck pain, gained considerable weight and had hallucinations. Five months later, quetiapine was tapered and discontinued, with ziprasidone re-introduced into their treatment, due to the excessive weight gain. Although the patient lost the excessive weight they had gained, they then developed muscle stiffness, [[cogwheeling]], tremors and night sweats. When benztropine was added they experienced blurry vision. After an additional five months, the patient was switched from ziprasidone to aripiprazole. Over the course of 8 months, patient A gradually experienced more weight gain and sedation, and developed difficulty with their gait, stiffness, cogwheeling and dyskinetic ocular movements. A pharmacogenomics test later proved the patient had a CYP2D6 *1/*41, which has a predicted phenotype of IM and CYP2C19 *1/*2 with a predicted phenotype of IM as well.
@@ Line 130: / Line 121: @@
 == Challenges ==
-[[File:Pharmacogenomics challenges from research to practice.jpg|thumb|upright=2| Consecutive phases and associated challenges in Pharmacogenomics.<ref name="sven2007">{{cite journal | vauthors = Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, Kirchheiner J, Guchelaar HJ | title = Translating pharmacogenomics: challenges on the road to the clinic | journal = PLOS Medicine | volume = 4 | issue = 8 | pages = e209 | date = August 2007 | pmid = 17696640 | pmc = 1945038 | doi = 10.1371/journal.pmed.0040209 }}</ref>]]
+[[File:Pharmacogenomics challenges from research to practice.jpg|thumb|upright=2| Consecutive phases and associated challenges in Pharmacogenomics.<ref name="sven2007">{{cite journal | vauthors = Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, Kirchheiner J, Guchelaar HJ | title = Translating pharmacogenomics: challenges on the road to the clinic | journal = PLOS Medicine | volume = 4 | issue = 8 | pages = e209 | date = August 2007 | pmid = 17696640 | pmc = 1945038 | doi = 10.1371/journal.pmed.0040209 | doi-access = free }}</ref>]]
-Although there appears to be a general acceptance of the basic tenet of pharmacogenomics amongst physicians and healthcare professionals,<ref name="pmid22278335">{{cite journal | vauthors = Stanek EJ, Sanders CL, Taber KA, Khalid M, Patel A, Verbrugge RR, Agatep BC, Aubert RE, Epstein RS, Frueh FW | display-authors = 6 | title = Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey | journal = Clinical Pharmacology and Therapeutics | volume = 91 | issue = 3 | pages = 450–458 | date = March 2012 | pmid = 22278335 | doi = 10.1038/clpt.2011.306 | s2cid = 21366195 }}</ref> several challenges exist that slow the uptake, implementation, and standardization of pharmacogenomics. Some of the concerns raised by physicians include:<ref name="Pharmacogenetics and Pharmacogenomics Factsheet."/><ref name="pmid22278335"/><ref name="pmid22689709">{{cite journal | vauthors = Ma JD, Lee KC, Kuo GM | title = Clinical application of pharmacogenomics | journal = Journal of Pharmacy Practice | volume = 25 | issue = 4 | pages = 417–427 | date = August 2012 | pmid = 22689709 | doi = 10.1177/0897190012448309 | s2cid = 1212666 }}</ref>
+Although there appears to be a general acceptance of the basic tenet of pharmacogenomics amongst physicians and healthcare professionals,<ref name="pmid22278335">{{cite journal | vauthors = Stanek EJ, Sanders CL, Taber KA, Khalid M, Patel A, Verbrugge RR, Agatep BC, Aubert RE, Epstein RS, Frueh FW | display-authors = 6 | title = Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey | journal = Clinical Pharmacology and Therapeutics | volume = 91 | issue = 3 | pages = 450–458 | date = March 2012 | pmid = 22278335 | doi = 10.1038/clpt.2011.306 | s2cid = 21366195 }}</ref> several challenges exist that slow the uptake, implementation, and standardization of pharmacogenomics. Some of the concerns raised by physicians include:<ref name="Pharmacogenetics and Pharmacogenomics Factsheet.">{{cite web |title=Center for Genetics Education |url=http://www.genetics.edu.au/Publications-and-Resources/Genetics-Fact-Sheets/pharmacogenetics-pharmacogenomics=2014-09-03}}{{Dead link|date=May 2020|bot=InternetArchiveBot|fix-attempted=yes}}</ref><ref name="pmid22278335" /><ref name="pmid22689709">{{cite journal | vauthors = Ma JD, Lee KC, Kuo GM | title = Clinical application of pharmacogenomics | journal = Journal of Pharmacy Practice | volume = 25 | issue = 4 | pages = 417–427 | date = August 2012 | pmid = 22689709 | doi = 10.1177/0897190012448309 | s2cid = 1212666 }}</ref>
 * Limitation on how to apply the test into clinical practices and treatment;
 * A general feeling of lack of availability of the test;
 * The understanding and interpretation of evidence-based research;
 * Combining test results with other patient data for prescription optimization; and
 * Ethical, legal and social issues.
-Issues surrounding the availability of the test include:<ref name="sven2007"/>
+Issues surrounding the availability of the test include:<ref name="sven2007" />
-* ''The lack of availability of scientific data'': Although there are considerable number of DME involved in the metabolic pathways of drugs, only a fraction have sufficient scientific data to validate their use within a clinical setting; and
+* ''The lack of availability of scientific data'': Although there are a considerable number of drug-metabolizing enzymes involved in the metabolic pathways of drugs, only a fraction have sufficient scientific data to validate their use within a clinical setting; and
 * ''Demonstrating the cost-effectiveness of pharmacogenomics'': Publications for the [[pharmacoeconomics]] of pharmacogenomics are scarce, therefore sufficient evidence does not at this time exist to validate the cost-effectiveness and cost-consequences of the test.
@@ Line 149: / Line 140: @@
 === Race-based medicine ===
-There has been call to move away from race and ethnicity in medicine and instead use genetic ancestry as a way to categorize patients.<ref>{{cite journal | vauthors = Borrell LN, Elhawary JR, Fuentes-Afflick E, Witonsky J, Bhakta N, Wu AH, Bibbins-Domingo K, Rodríguez-Santana JR, Lenoir MA, Gavin JR, Kittles RA, Zaitlen NA, Wilkes DS, Powe NR, Ziv E, Burchard EG | display-authors = 6 | title = Race and Genetic Ancestry in Medicine - A Time for Reckoning with Racism | journal = The New England Journal of Medicine | volume = 384 | issue = 5 | pages = 474–480 | date = February 2021 | pmid = 33406325 | pmc = 8979367 | doi = 10.1056/NEJMms2029562 }}</ref> Some [[allele]]s that vary in frequency between specific populations have been shown to be associated with differential responses to specific [[drugs]]. As a result, some disease-specific guidelines only recommend pharmacogenetic testing for populations where high-risk alleles are more common<ref>{{cite journal | vauthors = FitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, Gelber AC, Harrold LR, Khanna D, King C, Levy G, Libbey C, Mount D, Pillinger MH, Rosenthal A, Singh JA, Sims JE, Smith BJ, Wenger NS, Bae SS, Danve A, Khanna PP, Kim SC, Lenert A, Poon S, Qasim A, Sehra ST, Sharma TS, Toprover M, Turgunbaev M, Zeng L, Zhang MA, Turner AS, Neogi T | display-authors = 6 | title = 2020 American College of Rheumatology Guideline for the Management of Gout | journal = Arthritis Care & Research | volume = 72 | issue = 6 | pages = 744–760 | date = June 2020 | pmid = 32391934 | doi = 10.1002/acr.24180 | s2cid = 218583019 | hdl = 2027.42/155497 | hdl-access = free }}</ref> and, similarly, certain insurance companies will only pay for pharmacogenetic testing for beneficiaries of high-risk populations.<ref>{{Cite web |title=LCD - Molecular Pathology Procedures (L35000) |url=https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=35000&ver=133&bc=0 |access-date=2022-12-13 |website=www.cms.gov}}</ref>
+There has been call to move away from race and ethnicity in medicine and instead use genetic ancestry as a way to categorize patients.<ref>{{cite journal | vauthors = Borrell LN, Elhawary JR, Fuentes-Afflick E, Witonsky J, Bhakta N, Wu AH, Bibbins-Domingo K, Rodríguez-Santana JR, Lenoir MA, Gavin JR, Kittles RA, Zaitlen NA, Wilkes DS, Powe NR, Ziv E, Burchard EG | display-authors = 6 | title = Race and Genetic Ancestry in Medicine - A Time for Reckoning with Racism | journal = The New England Journal of Medicine | volume = 384 | issue = 5 | pages = 474–480 | date = February 2021 | pmid = 33406325 | pmc = 8979367 | doi = 10.1056/NEJMms2029562 }}</ref> Some [[allele]]s that vary in frequency between specific populations have been shown to be associated with differential responses to specific [[drugs]]. As a result, some disease-specific guidelines only recommend pharmacogenetic testing for populations where high-risk alleles are more common<ref>{{cite journal | vauthors = FitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, Gelber AC, Harrold LR, Khanna D, King C, Levy G, Libbey C, Mount D, Pillinger MH, Rosenthal A, Singh JA, Sims JE, Smith BJ, Wenger NS, Bae SS, Danve A, Khanna PP, Kim SC, Lenert A, Poon S, Qasim A, Sehra ST, Sharma TS, Toprover M, Turgunbaev M, Zeng L, Zhang MA, Turner AS, Neogi T | display-authors = 6 | title = 2020 American College of Rheumatology Guideline for the Management of Gout | journal = Arthritis Care & Research | volume = 72 | issue = 6 | pages = 744–760 | date = June 2020 | pmid = 32391934 | doi = 10.1002/acr.24180 | pmc = 10563586 | s2cid = 218583019 | hdl = 2027.42/155497 | hdl-access = free }}</ref> and, similarly, certain insurance companies will only pay for pharmacogenetic testing for beneficiaries of high-risk populations.<ref>{{Cite web |title=LCD - Molecular Pathology Procedures (L35000) |url=https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=35000&ver=133&bc=0 |access-date=2022-12-13 |website=www.cms.gov}}</ref>
 === Genetic exceptionalism ===
@@ Line 157: / Line 148: @@
 Others argue that genetic information is indeed distinct from other health-related information but not to the extent of requiring legal/regulatory protections, similar to other sensitive health-related data such as HIV status.<ref>{{cite journal | vauthors = Sulmasy DP | title = Naked bodies, naked genomes: the special (but not exceptional) nature of genomic information | journal = Genetics in Medicine | volume = 17 | issue = 5 | pages = 331–336 | date = May 2015 | pmid = 25232853 | doi = 10.1038/gim.2014.111 | s2cid = 34092673 | doi-access = free }}</ref> 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.<ref name=":5" /> Similarly, a systematic review reported that the public had concern over privacy of genetic information, with 60% agreeing that maintaining privacy was not possible; however, 96% agreed that a direct-to-consumer testing company had protected their privacy, with 74% saying their information would be similarly or better protected in an EHR. With increasing technological capabilities in EHRs, it is possible to mask or hide genetic data from subsets of providers and there is not consensus on how, when, or from whom genetic information should be masked.<ref name=":4" /><ref>{{cite journal | vauthors = Caraballo PJ, Sutton JA, Giri J, Wright JA, Nicholson WT, Kullo IJ, Parkulo MA, Bielinski SJ, Moyer AM | display-authors = 6 | title = Integrating pharmacogenomics into the electronic health record by implementing genomic indicators | journal = Journal of the American Medical Informatics Association | volume = 27 | issue = 1 | pages = 154–158 | date = January 2020 | pmid = 31591640 | pmc = 6913212 | doi = 10.1093/jamia/ocz177 }}</ref>  Rigorous protection and masking of genetic information is argued to impede further scientific progress and clinical translation into routine clinical practices.<ref>{{cite journal | vauthors = Martani A, Geneviève LD, Pauli-Magnus C, McLennan S, Elger BS | title = Regulating the Secondary Use of Data for Research: Arguments Against Genetic Exceptionalism | journal = Frontiers in Genetics | volume = 10 | pages = 1254 | date = 2019-12-20 | pmid = 31956328 | pmc = 6951399 | doi = 10.3389/fgene.2019.01254 | doi-access = free }}</ref>
+== History ==
+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]].<ref name="pmid11678777">{{cite journal |vauthors=Pirmohamed M |date=October 2001 |title=Pharmacogenetics and pharmacogenomics |journal=British Journal of Clinical Pharmacology |volume=52 |issue=4 |pages=345–347 |doi=10.1046/j.0306-5251.2001.01498.x |pmc=2014592 |pmid=11678777}}</ref><ref name="pmid22461095">{{cite journal |vauthors=Prasad K |date=January 2009 |title=Role of regulatory agencies in translating pharmacogenetics to the clinics |journal=Clinical Cases in Mineral and Bone Metabolism |volume=6 |issue=1 |pages=29–34 |pmc=2781218 |pmid=22461095}}</ref> Although the first official publication was not until 1961,<ref name="Evans_1961">{{cite journal |vauthors=Evans DA, Clarke CA |date=September 1961 |title=Pharmacogenetics |journal=British Medical Bulletin |volume=17 |issue=3 |pages=234–240 |doi=10.1093/oxfordjournals.bmb.a069915 |pmid=13697554}}</ref> 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.<ref name="pmid14585618" /><ref name="pmid16415920">{{cite journal |vauthors=Kalow W |year=2006 |title=Pharmacogenetics and pharmacogenomics: origin, status, and the hope for personalized medicine |journal=The Pharmacogenomics Journal |volume=6 |issue=3 |pages=162–165 |doi=10.1038/sj.tpj.6500361 |pmid=16415920 |s2cid=21761285 |doi-access=}}</ref> The term pharmacogenetics was first coined in 1959 by [[Friedrich Vogel (human geneticist)|Friedrich Vogel]] of [[Heidelberg]], [[Germany]] (although some papers suggest it was 1957 or 1958).<ref>{{cite journal |vauthors=Vogel F |date=1959 |title=Moderne probleme der humangenetik. |trans-title=Modern human genetics problems |journal=Ergebnisse der Inneren Medizin und Kinderheilkunde |language=German |location=Berlin, Heidelberg |publisher=Springer |pages=52–125 |trans-journal=Results of internal medicine and pediatrics}}</ref> In the late 1960s, [[Twin study|twin studies]] supported the inference of genetic involvement in drug metabolism, with identical twins sharing remarkable similarities in drug response compared to fraternal twins.<ref name="pmid16421980">{{cite journal |vauthors=Motulsky AG, Qi M |date=February 2006 |title=Pharmacogenetics, pharmacogenomics and ecogenetics |journal=Journal of Zhejiang University. Science. B |volume=7 |issue=2 |pages=169–170 |doi=10.1631/jzus.2006.B0169 |pmc=1363768 |pmid=16421980}}</ref> The term pharmacogenomics first began appearing around the 1990s.<ref name="pmid11678777" />
+The first FDA approval of a pharmacogenetic test was in 2005<ref name="Squassina2010" /> (for [[allele]]s in ''CYP2D6'' and ''CYP2C19'')
 == Future ==
-Computational advances have enabled cheaper and faster sequencing.<ref name="pharmacogenomic_example_book">{{cite book | vauthors = Kalow W | title = Pharmacogenomics | url = https://archive.org/details/pharmacogenomics00kalo | url-access = limited | publisher = Taylor & Francis | location = New York | year = 2005 | pages = [https://archive.org/details/pharmacogenomics00kalo/page/n564 552]–3| isbn = 978-1-57444-878-8 }}</ref> Research has focused on [[combinatorial chemistry]],<ref name="pmid11908762">{{cite journal | vauthors = Thorpe DS | title = Combinatorial chemistry: starting the second decade | journal = The Pharmacogenomics Journal | volume = 1 | issue = 4 | pages = 229–232 | year = 2001 | pmid = 11908762 | doi = 10.1038/sj.tpj.6500045 | doi-access = free }}</ref> genomic mining, omic technologies, and [[High-throughput screening|high throughput screening]].
+Computational advances have enabled cheaper and faster sequencing.<ref name="pharmacogenomic_example_book">{{cite book | vauthors = Kalow W | title = Pharmacogenomics | url = https://archive.org/details/pharmacogenomics00kalo | url-access = limited | publisher = Taylor & Francis | location = New York | year = 2005 | pages = [https://archive.org/details/pharmacogenomics00kalo/page/n564 552]–3| isbn = 978-1-57444-878-8 }}</ref> Research has focused on [[combinatorial chemistry]],<ref name="pmid11908762">{{cite journal | vauthors = Thorpe DS | title = Combinatorial chemistry: starting the second decade | journal = The Pharmacogenomics Journal | volume = 1 | issue = 4 | pages = 229–232 | year = 2001 | pmid = 11908762 | doi = 10.1038/sj.tpj.6500045 | s2cid = 1740692 | doi-access =  }}</ref> genomic mining, omic technologies, and [[High-throughput screening|high throughput screening]].
 As the cost per genetic test decreases, the development of personalized drug therapies will increase.<ref name="pmid17168846">{{cite journal | vauthors = Paul NW, Fangerau H | title = Why should we bother? Ethical and social issues in individualized medicine | journal = Current Drug Targets | volume = 7 | issue = 12 | pages = 1721–1727 | date = December 2006 | pmid = 17168846 | doi = 10.2174/138945006779025428 }}</ref> 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.<ref name="isbn0-465-02550-1">{{cite book | vauthors = Topol E |title=The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care|publisher=Basic Books|year=2012|isbn=978-0-465-02550-3|location=New York|url-access=registration|url=https://archive.org/details/creativedestruct0000topo}}</ref>
@@ Line 177: / Line 174: @@
 | ''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.|| <ref name="pmid19934256">{{cite journal | vauthors = Preissner S, Kroll K, Dunkel M, Senger C, Goldsobel G, Kuzman D, Guenther S, Winnenburg R, Schroeder M, Preissner R | display-authors = 6 | title = SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions | journal = Nucleic Acids Research | volume = 38 | issue = Database issue | pages = D237–D243 | date = January 2010 | pmid = 19934256 | pmc = 2808967 | doi = 10.1093/nar/gkp970 }}</ref>
 |-
-| ''PharmGKB'' || The Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation affects drug response.|| <ref name="pmid23824865">{{cite book | vauthors = Thorn CF, Klein TE, Altman RB | title = Pharmacogenomics | chapter = PharmGKB: the Pharmacogenomics Knowledge Base | series = Methods in Molecular Biology | location = Clifton, N.J. | volume = 1015 | pages = 311–20 | date = 2013 | pmid = 23824865 | pmc = 4084821 | doi = 10.1007/978-1-62703-435-7_20 | isbn = 978-1-62703-434-0 }}</ref>
+| ''PharmGKB'' || The Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation affects drug response.|| <ref name="pmid23824865">{{cite book | vauthors = Thorn CF, Klein TE, Altman RB | chapter = PharmGKB: The Pharmacogenomics Knowledge Base | title = Pharmacogenomics | series = Methods in Molecular Biology | location = Clifton, N.J. | volume = 1015 | pages = 311–20 | date = 2013 | pmid = 23824865 | pmc = 4084821 | doi = 10.1007/978-1-62703-435-7_20 | isbn = 978-1-62703-434-0 }}</ref>
 |-
 | ''dbSNP database'' || A repository of [[single nucleotide polymorphism|SNPs]] and other variants that have been reported after discovery, compiled and officially named. These are SNPs across the board.|| <ref>{{Cite web|url=https://www.ncbi.nlm.nih.gov/SNP/|title=DBSNP Home Page | work = National Center for Biotechnology Information, U.S. National Library of Medicine }}</ref><ref name="pmid10592272">{{cite journal | vauthors = Smigielski EM, Sirotkin K, Ward M, Sherry ST | title = dbSNP: a database of single nucleotide polymorphisms | journal = Nucleic Acids Research | volume = 28 | issue = 1 | pages = 352–355 | date = January 2000 | pmid = 10592272 | pmc = 102496 | doi = 10.1093/nar/28.1.352 }}</ref>
@@ Line 219: / Line 216: @@
 {{refbegin}}
 * {{cite journal | vauthors =  Katsnelson A | title =  A Drug to Call One's Own: Will medicine finally get personal? | journal = Scientific American |date=August 2005 | url = http://www.scientificamerican.com/article.cfm?id=a-drug-to-call-ones-own  }}
-* {{cite journal | vauthors = Karczewski KJ, Daneshjou R, Altman RB | title = Chapter 7: Pharmacogenomics | journal = PLOS Computational Biology | volume = 8 | issue = 12 | pages = e1002817 | year = 2012 | pmid = 23300409 | pmc = 3531317 | doi = 10.1371/journal.pcbi.1002817 | bibcode = 2012PLSCB...8E2817K }}
+* {{cite journal | vauthors = Karczewski KJ, Daneshjou R, Altman RB | title = Chapter 7: Pharmacogenomics | journal = PLOS Computational Biology | volume = 8 | issue = 12 | pages = e1002817 | year = 2012 | pmid = 23300409 | pmc = 3531317 | doi = 10.1371/journal.pcbi.1002817 | bibcode = 2012PLSCB...8E2817K | doi-access = free }}
 {{refend}}
@@ Line 228: / Line 225: @@
 * {{cite web | url = https://pharmacy.unc.edu/research/centers/center-for-pharmacogenomics-and-individualized-therapy | archive-url = https://web.archive.org/web/20140806192211/https://pharmacy.unc.edu/research/centers/center-for-pharmacogenomics-and-individualized-therapy | url-status = dead | archive-date = 2014-08-06 | title = Center for Pharmacogenomics and Individualized Therapy | publisher = University of North Carolina at Chapel Hill Center for Pharmacogenomics and Individualized Therapy | access-date = 2014-06-25 }}
 Journals:
-* {{cite web | url = http://www.futuremedicine.com/loi/pgs | title = Pharmacogenomics | publisher = Future Medicine Ltd}}
+* {{cite web | url = http://www.futuremedicine.com/loi/pgs | title = Pharmacogenomics | date = 24 August 2023 | publisher = Future Medicine Ltd}}
 * {{cite web | url = http://www.jpharmacogenetics.com/ | title = Pharmacogenetics and Genomics | publisher = Lippincott Williams & Wilkins | issn = 1744-6872 }}
-* {{cite journal | url = http://www.nature.com/tpj/index.html | title = The Pharmacogenomics Journal | journal = The Pharmacogenomics Journal | publisher = Nature Publishing Group | issn = 1470-269X }}
+* {{cite journal | url = http://www.nature.com/tpj/index.html | title = The Pharmacogenomics Journal | journal = The Pharmacogenomics Journal | date = 20 October 2021 | publisher = Nature Publishing Group | issn = 1470-269X }}
 {{Genomics}}
@@ Line 237: / Line 234: @@
 {{Authority control}}
+{{DEFAULTSORT:Pharmacogenomics/Pharmacogenetics}}
 [[Category:Genomics]]
 [[Category:Pharmacology]]

v t e Genomics
Fields	Cognitive genomics Computational genomics Comparative genomics Functional genomics Genome project Human Genome Project Metagenomics Human Microbiome Project Pangenomics Personal genomics Population genomics Sociogenomics Structural genomics
Bioinformatics	Biochip Cheminformatics Chemogenomics Connectomics Human Connectome Project Epigenomics Human Epigenome Project Glycomics Immunomics Lipidomics Metabolomics Microbiomics Nutrigenomics Paleopolyploidy Pharmacogenetics Pharmacogenomics Systems biology Toxicogenomics Transcriptomics
Structural biology	Proteomics Human proteome project Call-map proteomics Structure-based drug design Expression proteomics
Research tools	2-D electrophoresis Mass spectrometer Electrospray ionization Matrix-assisted laser desorption ionization Matrix-assisted laser desorption ionization-time of flight mass spectrometer Microfluidic-based tools Isotope affinity tags Chromosome conformation capture
Organizations	DNA Data Bank of Japan (JP) European Molecular Biology Laboratory (EU) National Institutes of Health (USA) Wellcome Sanger Institute (UK)
List Category

v t e Pharmacy
General	Compounding History of pharmacy Prehistoric medicine Medication Prescription drug Pharmacy Pharmacological activity Separation of prescribing and dispensing
Pharmaceutical sciences	Pharmacology Pharmacokinetics & Pharmacodynamics Pharmacometrics Pharmacogenomics Toxicology Pharmaceutical chemistry Pharmaceutics Pharmacognosy Pharmacoepidemiology Pharmacovigilance Pharmacocybernetics
Professions	Pharmacist List Pharmaconomist Pharmacy residency Pharmacy technician Pharmacy school
Practice areas	Clinical pharmacy Community pharmacy shop Consultant pharmacist Hospital pharmacy Nuclear pharmacy Pharmacist prescriber Pharmacy informatics Specialty pharmacy Veterinary pharmacy
Pharmaceutical industry	Drug development Drug discovery Investigational New Drug Pharmacy benefit management List of pharmaceutical companies Medication costs Pharmacy in China Pharmacies of Norway Pharmacies in the United States History
Category