Pharmacogenomics (a portmanteau of pharmacology and genomics) is the study of the role of genetics in drug response. It deals with the influence of acquired and inherited genetic variation on drug response in patients by correlating gene expression or single-nucleotide polymorphisms with drug absorption, distribution, metabolism and elimination, as well as drug receptor target effects.[1][2][3] The term pharmacogenomics is often used interchangeably with pharmacogenetics. Although both terms relate to drug response based on genetic influences, pharmacogenetics focuses on single drug-gene interactions, while pharmacogenomics encompasses a more genome-wide association approach, incorporating genomics and epigenetics while dealing with the effects of multiple genes on drug response.[4][5][6]
Pharmacogenomics aims to develop rational means to optimize drug therapy, with respect to the patients' genotype, to ensure maximum efficacy with minimal adverse effects.[7] Through the utilization of pharmacogenomics, it is hoped that drug treatments can deviate from what is dubbed as the one-dose-fits-all approach. It attempts to eliminate the trial-and-error method of prescribing, allowing physicians to take into consideration their patients genes, the functionality of these genes, and how this may affect the efficacy of the patients current and/or future treatments (and where applicable, provide an explanation for the failure of past treatments).[4] Such approaches promise the advent of "personalized medicine"; in which drugs and drug combinations are optimized for each individual's unique genetic makeup.[8][9] Whether used to explain a patients response or lack thereof to a treatment, or act as a predictive tool, it hopes to achieve better treatment outcomes, greater efficacy, minimization of the occurrence of drug toxicities and adverse drug reactions (ADRs). For patients who have lack of therapeutic response to a treatment, alternative therapies can be prescribed that would best suit their requirements. In order to provide pharmacogenomic-based 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]
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. Interestingly, this identification was later validated and attributed to deficiency of G6PD in the 1950s and called favism.[11][12] Although the first official publication dates back to 1961,[13] circa 1950s marked the unofficial beginnings of this science. Reports of prolonged paralysis and fatal reactions linked to genetic variants in patients who lacked butyryl-cholinesterase (pseudocholinesterase) following administration of succinylcholine injection during anesthesia were first reported in 1956.[1][14] The term pharmacogenetic was first coined in 1959 by Friedrich Vogel of Heidelberg, Germany (although some papers suggest it was 1957). In the late 1960s, twin studies supported the inference of genetic involvement in drug metabolism, with identical twins sharing remarkable similarities to drug response compared to fraternity twins.[15] The term pharmacogenomics first began appearing around the 1990s.[11]
There are several known genes which are largely responsible for variances in drug metabolism and response. The focus of this article will remain on the genes that are more widely accepted and utilized clinically for brevity.
The most prevalent drug-metabolizing enzymes (DME) are the Cytochrome P450 (CYP) enzymes. The term Cytochrome P450 was coined by Omura and Sato in 1962 to describe the membrane-bound, heme-containing protein characterized by 450nm spectral peak when complexed with carbon monoxide.[16] The human CYP family consists of 57 genes, with 18 families and 44 subfamilies. CYP proteins are conveniently arranged into these families and subfamilies on the basis of similarities identified between the amino acid sequences. Enzymes that share 35-40% identity are assigned to the same family by an Arabic numeral, and those that share 55-70% make up a particular subfamily with a designated letter.[17] For example, CYP2D6 refers to family 2, subfamily D, and gene number 6.
From a clinical perspective, the most commonly tested CYPs include: CYP2D6, CYP2C19, CYP2C9, CYP3A4 and CYP3A5. These genes account for the metabolism of approximately 80-90% of currently available prescription drugs.[18][19] The table below provides a summary for some of the medications that take these pathways.
Also known as debrisoquine hydroxylase (named after the drug that led to its discovery), CYP2D6 is the most well-known and extensively studied CYP gene.[22] It is a gene of great interest also due to its highly polymorphic nature, and involvement in a high number of medication metabolisms (both as a major and minor pathway). More than 100 CYP2D6 genetic variants have been identified.[21]
Discovered in the early 1980s, CYP2C19 is the second most extensively studied and well understood gene in pharmacogenomics.[20] Over 28 genetic variants have been identified for CYP2C19,[23] of which affects the metabolism of several classes of drugs, such as antidepressants and proton pump inhibitors.[24]
CYP2C9 constitutes the majority of the CYP2C subfamily, representing approximately 20% of the liver content. It is involved in the metabolism of approximately 10% of all drugs, which include medications with narrow therapeutic windows such as warfarin and tolbutamide.[24][25] There are approximately 57 genetic variants associated with CYP2C9.[23]
The CYP3A family is the most abundantly found in the liver, with CYP3A4 accounting for 29% of the liver content.[20] These enzymes also cover between 40-50% of the current prescription drugs, with the CYP3A4 accounting for 40-45% of these medications.[12]CYP3A5 has over 11 genetic variants identified at the time of this publication.[23]
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Pharmacogenomics - Wikipedia, the free encyclopedia
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