By Associate Professor Mirette Saad
Published November 2024
Pharmacogenetics (PGx) and Personalised Patient Care
The incorporation of genetic information obtained from pharmacogenetic testing holds substantial promise for improving therapeutic decision-making through enhanced efficacy and reduced adverse events. Considerations for clinical implementation are crucial for patient health.
PGx Testing at Clinical Labs
Clinical Labs offers an extensive range of pharmacogenetic testing panels to provide clinicians and healthcare providers with important information to help determine the most appropriate treatment for each individual, particularly in areas such as mental health, pain management, cardiology, and oncology.
PGx Testing Recommended by Guidelines
Clinical pharmacotherapeutic guidelines, including the Dutch Pharmacogenetics Working Group (DPWG) and the Clinical Pharmacogenetics Implementation Consortium (CPIC), have been implemented to assist in reducing healthcare costs associated with adverse drug events (ADEs) and poor responses to pharmacotherapy.
When to Order the Test
Physicians may order pharmacogenetic testing at the time of drug prescribing and dispensing for patients with genotypes that require action, such as dose reductions. The preemptive use of testing could significantly optimise drug outcomes and be particularly useful for patients undergoing multiple treatments or experiencing poor drug responses.
The Cytochrome P450 (CYP450) and Differences in Drug Metabolism
A family of enzymes catalyses the metabolism of many drugs and xenobiotics. The genes that code for cytochrome P450 enzymes are highly polymorphic, which can significantly affect drug metabolism in certain individuals and influence plasma levels of both the active drug and its metabolites. CYP2D6, CYP2C19, CYP2C9, and SLCO1B1 variants are responsible for the metabolism of a large number of commonly prescribed medications.
The UDP-glucuronosyltransferase gene (UGT1A1) is involved in the metabolism of irinotecan-based chemotherapy, which is used in patients with advanced solid tumours, including colorectal and lung cancers.
Thiopurine methyltransferase (TPMT) is the primary enzyme responsible for the metabolism of thiopurine drugs (azathioprine, 6-mercaptopurine, and 6-thioguanine). It is recommended that physicians order TPMT genotyping before prescribing thiopurines to avoid bone marrow toxicity and subsequent neutropenia.
Mutations in the dihydropyrimidine dehydrogenase gene (DPYD) interfere with the breakdown of chemotherapeutic cancer drugs with structures similar to pyrimidines, such as 5-fluorouracil and capecitabine (click button below for Table 1). As a result, these drugs can accumulate in the body, leading to severe reactions and neurological manifestations due to DPYD deficiency.
Genetic Variations Can Render Some Medications Ineffective or Toxic
Pharmacogenetic variants result in four distinct phenotypes: Normal Metabolisers (NMs), Intermediate Metabolisers (IMs), Poor Metabolisers (PMs), and Ultrarapid Metabolisers (UMs), which provide guidance for drug dosing and selection.
Overall, wild-type alleles and normal metabolisers are usually associated with functional enzyme-mediated metabolism. Ultrarapid metabolisers may not achieve therapeutic plasma levels due to decreased trough drug concentrations, whereas poor metabolisers treated with drugs that are metabolised by these enzymes are at increased risk for prolonged therapeutic effects or toxicity due to elevated trough levels of therapeutic drugs.
For a full listing of genes tested and drugs metabolised, click the button below.
Listing of Genes Tested and Drugs Metabolised
For detailed information on the PGx panels available at Clinical Labs, as well as guidance on how to order PGx testing, please visit our dedicated webpage.
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References
Guidelines – CPIC (cpicpgx.org).
Caudle et al. 2017.
Deneer and van Schaik, 2013.
Hicks et al. 2015.
John J. Lima et al. 2020.
Johnson et al. 2017.
Klein et al. 2009.
Liou et al. 2012.
Mary V. Relling et al. 2018.
Matthew P. Goetz et al. 2018.
Ramsey et al. 2014.
Scott et al. 2013.
Swen et al. 2011.