Pharmacogenomics for Psychiatry: Focusing on Drug Metabolizing Enzymes and Transporters, with Validated Methodology for CYP2D6 and CYP2C19 Including for a Novel Sub-Haplotype
Notice bibliographique
Résumé
Pharmacogenomics (PGx) is interested in the impact individual genetic makeup has on a patient’s response to pharmacological agents. In clinical practice, PGx has the potential of reducing adverse drug reactions (ADRs), which cost Canada $65 million dollars in the year 2018, as well as enhancing treatment outcomes. Implementation of PGx in the clinic depends on, among other factors, a) knowledge of enzymes/transporters responsible for absorption, distribution, metabolism and excretion (ADME) and their genetic variation; b) the possible gene-drug pairs and drug interaction effects based on the genes that encode such enzymes/transporters; as well as c) robust methodology that can be applied in the genotyping efforts in order to generate individual data. The aim of my thesis is to address the aforementioned for advancement of PGx in psychiatric practice. With these aims in mind, in the first part of Chapter 2, I review the main enzymes involved in phase I and II metabolism, as well as the transporters involved in phase III (excretion). The second part of the review presents pharmacogenetic associations important to psychiatry, that is, different examples of antipsychotics and antidepressants, as well as atomoxetine, are reviewed in relation to their metabolic pathway, introducing the gene-drug pairs that are of interest for devising pharmacogenetic guidelines. On this topic, existing guidelines by both the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) are also presented. In Chapter 3, innovative methodology is introduced for the clinical genotyping of the genes CYP2D6 and CYP2C19 in a subset (N=95) of samples from the Genome-based therapeutic drugs for depression (GENDEP) clinical trial designed to investigate pharmacogenomic predictors of response to antidepressants. In it, the technologies used were: TaqMan copy number variant (CNV) and single nucleotide variant (SNV) assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel and the Agena MassARRAY. Through the employment of these different technologies, which were cross- validated, we were able to resolve samples that had been previously genotyped, but for which no data had resulted. This was enabled through the use of the above technologies and long-range polymerase chain reaction (L-PCR) with Sanger sequencing. An important contribution of the methodology described in the chapter is a validated methodology for a comprehensive range of CYP2D6 haplotypes, including a larger range of hybrids and hybrid tandems compared to previous reports in the field. Building on the work described above, Chapter 4 describes the genotyping of an individual sample that was initially detected from the genotyping work in Chapter 3. The sample of interest here was not concordant across the technologies used in terms of the genotypic automated call, which put into question the haplotypes present in the sample. Similar to Chapter 3, through the application of L-PCR and sequencing work, we were able to interrogate the SNVs present in the sample and, from the data generated, present a previously unreported sub-haplotype of CYP2D6*41.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,005 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».