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Enregistrement W6949599471 · doi:10.5281/zenodo.14583873

Pharmacogenetics in Oncology: Transforming Cancer Treatment - A review

2025· preprint· en· W6949599471 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2025
Typepreprint
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiquePharmacogenetics and Drug Metabolism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPharmacogeneticsDrug responseDrugPersonalized medicinePrecision medicinePharmacogenomicsGenetic testingCancer treatment

Résumé

récupéré en direct d'OpenAlex

Abstract--- Pharmacogenetics has become a “high-profile field” in the advanced field of medicine, as the appellation “pharmacogenetics” or PGx involves the study of the influence of genetic variations on drug responses. PGx has developed personalized therapies which are specific to individuals, as the idiom “one-size-fits-all”, is not for the patients in the medical field due to genetic variations. In oncology, anticancer drugs have been major targets for PGx. This review discusses the current knowledge, limitations, and guidelines published by CPIC (Clinical Pharmacogenetics Implementation Consortium) and other organizations about gene-drug pairs, which can reduce adverse drug responses like tamoxifen/CYP2D6, irinotecan/UGTIA1, thiopurines/TPMT, etc. The main objective of this review is to summarize the application of PGx in oncology, which mainly focuses on germline genetic variations. Keywords: Pharmacogenetics, Oncology, Guidelines, Germline variants, Dosing. Defining Pharmacogenetics In terms of drug administration, the concept of “one size fits all” does not implement for all patients in the medical field. As drugs respond differently in different individuals. There are many causes of variability in drug responses, which include age, sex, body mass, environment, tolerance, and genetic makeup of an individual (Figure.1). Genetic factors affect drug responses greatly. So, in 1959 Freidrich Vogel coined the term “Pharmacogenetics”. Pharmacogenetics has enclasped both fields of genetics and pharmacology into the latest branch. So, pharmacogenetics can be defined as “The branch of genetics and pharmacology that deals with the study of therapeutic responses of an individual due to genetic variation” (Meyer, 2004; Charlab and Zhang, 2013). Figure 1: Variations in drug response between different individuals. In the modern world, pharmacogenetics has become a “high-profile” field. The main goal of pharmacogenetics is to homogenize the heterogeneous pattern of the same drug responses in different individuals. Pharmacogenetics includes two different fields which are “Pharmacodynamics” and “Pharmacokinetics”. Pharmacodynamics is the study of drug activity and its effect on the organism, while pharmacokinetics involves the study of liberation, absorption, distribution, metabolism, and excretion pathways of drugs after delivery (Meyer, 2004; Charlab and Zhang, 2013). The notion of pharmacogenetics has enhanced the chances of better drug responses and minimized the risk of therapeutic failures in individuals. The Clinical Pharmacogenetics Implementation Consortium (CPIC) is an international organization involved in the use of “pharmacogenetics” as a promising tool for testing genetic variability in patients and examining the effect of drugs on individuals. Therefore, CPIC’s goal is to provide instructions or guidelines for the execution of pharmacogenetics in practice (Table.1). There are other numbers of organizations or networks that also provide evidence-based PGx guidelines to optimize the therapies like DPWG (Dutch Pharmacogenetics Working Group), CPNDS (Canadian Pharmacogenomics Network for Drug Safety), etc. However, these guidelines are based on experiments on volunteer individuals (Haidar et al., 2019; Relling et al., 2020). Table 1: Dosing guidelines for various drugs by different organizations (Ji, 2018; Qin, 2020). Drug CPICa DPWGb CPNDSc Irinotecan UGTIA1 Warfarin CYP2C9, VICORC1, CYP4F2 Atazanavir UGT1A1 Tamoxifen CYP2D6 CYP2D6 Cisplatin TPMT Thioguanine TPMT TPMT Ivacaftor CFTR Doxorubicin RARG, SLC8A3, UGT1A6 Phenytoin CYP2C9, HLA-B Tegafur DPYD a Clinical Pharmacogenetics Implementation Consortium. b Dutch Pharmacogenetics Working Group. c Canadian Pharmacogenomics Network for Drug Safety. Pharmacogenetics and Pharmacogenomics Pharmacogenetics is a promising tool for minimizing the adverse side effects of drugs in some individuals and this promising tool has widely investigated in a broader term known as “Pharmacogenomics”. Pharmacogenomics includes the study of whole genomic variations in drug responses in patients, which combines the fields of pharmacology and genomics (Relling et al., 2020). Who should undergo Pharmacogenetic Testing? Healthcare providers recommend pharmacogenetic testing for patients who undergo some adverse drug responses or who have a family history of gene abnormalities, just for getting complete information about patients’ biomarkers. This testing is easy to perform but its result interpretation is not every physician’s cup of tea. National Comprehensive Cancer Network (NCCN) guidelines recommend healthcare providers to prescribe their patients a pharmacogenetic test before prescribing them some medicines like tramadol, codeine, celecoxib, doxepin, amitriptyline, ibuprofen, and meloxicam, as these may cause some adverse side effects in patients (Swarm et al., 2022). The healthcare providers who don’t recommend pharmacogenetic test means that they have no information about it (Just et al., 2017). So, to develop the knowledge of this test, healthcare providers should continue their medical workshops and grand rounds (Just et al., 2017; Liang et al., 2018). About 61.5% of healthcare providers have knowledge about pharmacogenetic tests, while only 51.4% know how to interpret the test results (Just et al., 2017). Applications of Pharmacogenetics As pharmacogenetics has proved an advantageous tool for those individuals who develop adverse side effects after the administration of drugs and the major backbone behind such after-effects of drugs are the genetic variabilities. Pharmacogenetics and pharmacogenomics have found various applications in the medical fields including cardiology, dermatology, gynecology, neurology, hematology, gastroenterology, anesthesiology, dentistry, pulmonology, and oncology. The clinical applications of pharmacogenetics are summarized in the pie graph (Figure.2). The graph shows that pharmacogenetics has major clinical applications in the field of oncology which is 32%, followed by 14% in psychiatry (Jennings et al., 2017; Kalemkerian et al., 2018; Sepulveda et al., 2017). Figure 2: Applications of Pharmacogenetics and Pharmacogenomics in different therapeutic areas. Pharmacogenetics in Oncology for Cancer Management Oncology is the branch of medicine that deals with the study of cancer and tumor, which includes the diagnosis, treatment, and prevention of cancer. There are three different fields in oncology that involve medical oncology, in which cancer patients are treated with drugs only (i.e., Chemotherapy) then the second field is radiation oncology, which involves the use of radiation to kill cancerous cells or radiotherapy, and the third one is surgical oncology, which involves the removal of cancerous cells from the body through surgery. In medical oncology, the same drug and the same dosage of the drug show a miscellaneous pattern of drug responses in different individuals, which may be due to genetic variations in different patients. The treatment of cancer patients in oncology although has developed major advancements but the clinical administration of the same drugs and the same dosage of drugs in different patients has shown variations in drug efficacy (Figure.3) (Evans and Relling, 1999; Fagerlund and Braaten, 2001). These variations in drug responses can result in life-threatening adverse side effects in patients (Rothenberg et al., 2001). So, Pharmacogenetics and Pharmacogenomics have proved very obliging in the field of oncology or we can say that oncology is a major target field for pharmacogenetics, as the main target of pharmacogenetics is to inspect which drugs and doses of a drug are best for a patient which means that genotype-guided protocols have increased the drug efficacy and safety in patients. Figure 3: Individual response to the same drug can vary. Pharmacogenetics and Pharmacogenomics testing in oncology also provide details about the germline variations for anti-cancer drug classes (such as fluoropyrimidines, thiopurines, irinotecan, camptothecins, and others), and the effect of these drugs on drug ability to produce its desired results known as drug efficacy. Scientists evaluated that the alterations in drug pathways i.e., absorption, distribution, metabolism, and excretion (ADME) of enzyme/protein (such as dihydropyrimidine dehydrogenase (DPD), thiopurine S-methyltransferase (TPMT), and UDP-glucuronosyltransferase (UGT1A1) due to genetic or hereditary variations help in predicting adverse side effects, while drug transporters (like ABC or ATP Binding Cassette transporters) are also proved helpful in prediction of drug efficacy (Kaehler and Cascorbi, 2019). Pharmacogenetics in Breast Cancer After lung cancer, breast cancer is the second most common type of cancer worldwide, especially in women. This can also occur in males. Breast cancer has a high rate of mortality (Bray et al., 2018). Breast cancer like other cancer types can be treated in three ways, which include drug therapy, radiotherapy, and surgical therapy. But drug therapies can give different results in different patients, which is the major challenge in breast cancer or any other cancer treatment. Therefore, the studies and research in pharmacogenetics and pharmacogenomics has a tremendous impact on the therapeutic approaches for breast cancer treatment. Healthcare providers should prescribe pharmacogenetic testing before starting drug therapies in breast cancer patients so that these therapies should be effective against breast cancer and reduce the risk of recurrence of breast cancer (Chan et al., 2017). Although there are many anti-cancer drugs but Tamoxifen is the best dru

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0000,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,220
Tête enseignante GPT0,474
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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