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Why is Pharmacogenomics suddenly in the news?

Why haven’t I heard of it before?   What is it about?

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. 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.

Pharmacogenomics aims to develop rational means to optimize drug therapy, with respect to the patients’ genotype, to ensure maximum efficacy with minimal adverse effects. 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 patient’s genes, the functionality of these genes, and how this may affect the efficacy of the patient’s current and/or future treatments (and where applicable, provide an explanation for the failure of past treatments).   Such approaches promise the advent of “personalized medicine”; in which drugs and drug combinations are optimized for each individual’s unique genetic makeup. Whether used to explain a patient’s 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. Sequencing provides many more data points, including detection of mutations that prematurely terminate the synthesized protein (early stop codon).

Pharmacogenomics was first recognized around 510 BC when a connection was made 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. Although the first official publication dates back to 1961, 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. 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. The term pharmacogenomics first began appearing around the 1990s.

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. (a) Cytochrome P450s; (b) VKORC1; and (c) TPMT

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 450 nm spectral peak when complexed with carbon monoxide. 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.   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. 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. 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.   Discovered in the early 1980s, CYP2C19 is the second most extensively studied and well understood gene in pharmaco-genomics. Over 28 genetic variants have been identified for CYP2C19, of which affects the metabolism of several classes of drugs, such as antidepressants and protein pump inhibitors.    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. There are approximately 57 genetic variants associated with CYP2C9.   The CYP3A family is the most abundantly found in the liver, with CYP3A4 accounting for 29% of the liver content. These enzymes also cover between 40-50% of the current prescription drugs, with the CYP3A4 accounting for 40-45% of these medications.   The vitamin K epoxide reductase complex subunit 1 (VKPRC1) is responsible for the pharmacodynamics of warfarin.   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.

Patient genotypes are usually categorized into the following predicted phenotypes: (1) Ultra-Rapid Metabolizer: Patients with substantially increased metabolic activity; (2) Extensive Metabolizer: Normal metabolic activity; (3) Intermediate Metabolizer: Patients with reduced metabolic activity; and (4) Poor Metabolizer: Patients with little to no functional metabolic activity.

The two extremes of this spectrum are the Poor Metabolizers and Ultra-Rapid Metabolizers. Efficacy of a medication is not only based on the above metabolic statuses, but also the type of drug consumed. Drugs can be classified into two main groups: active drugs and pro-drugs. Active drugs refer to drugs that are inactivated during metabolism, and Pro-Drugs are inactive until they are metabolized.

Each phenotype is based upon the allelic variation within the individual genotype. However, several genetic events can influence a same phenotypic trait, and establishing genotype-to-phenotype relationships can thus be far from consensual with many enzymatic patterns. For instance, the influence of the CYP2D6*1/*4 allelic variant on the clinical outcome in patients treated with Tamoxifen remains debated today. In oncology, genes coding for DPD, UGT1A1, TPMT and CDA are involved in the pharmacokinetics of 5-FU/capecitabine, irinotecam, 6-mercaptopurine and gemcitabine/cytarabine, respectively, have all been described as being highly polymorphic. A strong body of evidence suggests that patients affected by these genetic polymorphisms will experience severe/lethal toxicities upon drug intake, and that pre-therapeutic screening does help to reduce the risk of treatment-related toxicities through adaptive dosing strategies.

The list below provides a few more commonly known applications of pharmacogenomics: (1) Improve drug safety, and reduce ADRs; (2) Tailor treatments to meet patients’ unique genetic pre-disposition, identifying optimal dosing; (3) Improve drug discovery targeted to human disease; and (4) 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.

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. 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.

A potential role pharmacogenomics may play would be to reduce the occurrence of poly-pharmacy. It is theorized that with tailored drug treatments, patients will not have the need to take several medications that are intended to treat the same condition. In doing so, they could potentially minimize the occurrence of ADRs, have improved treatment outcomes, and can save costs by avoiding purchasing extraneous medications. An example of this can be found in Psychiatry, where patients tend to be receiving more medications then even age-matched non-psychiatric patients. This has been associated with an increased risk of inappropriate prescribing.

The U.S. Food and Drug Administration (FDA) appears to be very invested in the science of pharmacogenomics as is demonstrated through the 120 and more FDA-approved drugs that include pharmacogenomic biomarkers in their labels. On May 22, 2005, the FDA issued its first Guidance for Industry: Pharmacogenomic Data Submissions, which clarified the type of pharmacogenomic data required to be submitted to the FDA and when. Experts recognized the importance of the FDA’s acknowledgement that pharmacogenomics experiments will not bring negative regulatory consequences. The FDA had released its latest guide Clinical Pharmacogenomics (PGx): Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling in January, 2013. The guide is intended to address the use of genomic information during drug development and regulatory review processes.

Although there appears to be a general acceptance of the basic tenet of pharmacogenomics amongst physicians and healthcare professionals, several challenges exist that slow the uptake, implementation, and standardization of pharmacogenomics. Some of the concerns raised by physicians include:   (1) Limitation on how to apply the test into clinical practices and treatment; (2) A general feeling of lack of availability of the test; (3) The understanding and interpretation of evidence-based research; and (4) Ethical, legal and social issues.

Issues surrounding the availability of the test include: (a) 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 (b) 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.

Computational advances in Pharmacogenomics has proven to be a blessing in research. As a simple example, for nearly a decade the ability to store more information on a hard drive has enabled us to investigate a human genome sequence cheaper and in more detail with regards to the effects/risks/safety concerns of drugs and other such substances. Such computational advances are expected to continue in the future. The aim is to use the genome sequence data to effectively make decisions in order to minimize the negative impacts on, say, a patient or the health industry in general. A large amount of research in the biomedical sciences regarding Pharmacogenomics as of late stems from combinatorial chemistry, genomic mining, omic technologies and high throughput screening. In order for the field to grow rich knowledge enterprises and business must work more closely together and adopt simulation strategies. Consequently more importance must be placed on the role of computational biology with regards to safety and risk assessments. Here we can find the growing need and importance of being able to manage large, complex data sets, being able to extract information by integrating disparate data so that developments can be made in improving human health.

 Kathy Kiefer


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