The coming‐of‐age of pharmacogenetic testing in clinical psychiatry
Notice bibliographique
Résumé
Pharmacogenetic (PGx) testing is key to addressing the issue of the prevailing ‘trial-and-error’ prescribing of medications in psychiatry. Expert groups have reported high evidence levels for several gene–drug pairs related to psychotropic medications with most of the focus related to antidepressants.1 For instance, several commercially available PGx tests have been launched in psychiatric care that typically examine genetic variations of at least the CYP2C19 and CYP2D6 genes. Some companies have conducted prospective evaluation trials examining the benefits of genetic testing versus treatment as usual (TAU). The largest trial to date was recently completed and consisted of an 8-week double-blind, multicenter, randomized controlled trial (RCT) of the Genesight commercial test versus TAU in depression (N = 1167).2 While using symptom improvement as the outcome variable showed no significance (P = 0.107), patients guided by the test achieved a significant improvement in response (≥50% decrease in 17-item Hamilton Rating Scale for Depression [HAMD-17]; P = 0.013) and remission (HAMD-17 ≤ 7; P = 0.007), compared with those who received TAU. Notably, a recent meta-analysis of multigene PGx tests (incorporating the study by Greden et al.2 and four other RCTs) reported that the PGx test-guided groups (n = 887) showed significantly better remission rates (odds ratio of 1.7) compared to the non-guided (TAU) group (n = 850).3 Given that a high proportion of patients with depression is treated by primary care providers, the utility of PGx testing was evaluated in primary (N = 810) and psychiatric (N = 1061) care settings in a subset of the IMPACT project (a 7-year naturalistic study examining the effects of PGx testing).4 The response rate at follow up tended to be better in patients taking medications congruent with their combinatorial PGx test report (28.0%), in comparison with the patients taking incongruent medications (21.4%, P = 0.057). This study suggested that PGx testing appears to be at least as effective in primary care as in psychiatric care settings, though the response rate was higher in patients treated by primary care providers than in those treated by psychiatrists in this sample (response rate: 30.1% vs 22.3%, respectively, P < 0.01). In summary, PGx testing is promising for psychiatric treatment and can provide substantial benefit to patients; however, there are still several issues to be addressed. First, most studies have evaluated patients who have not responded to previous treatment, and it remains unclear whether these tests are beneficial and cost-effective enough to be applied pre-emptively for the entire patient population at the beginning of their treatment. More prospective RCTs are needed that will evaluate factors such as level of treatment resistance and cost–benefit analyses. Second, it is important to keep in mind that test panels across academic centers and industry differ substantially. For instance, the level of agreement in psychotropic medication recommendations across four PGx tests was reported to be modest as follows: antidepressants (56%), anxiolytics/hypnotics (56%), and antipsychotics (55%), with the exception of mood stabilizers (84%).5 This finding indicates that PGx tests cannot be considered as interchangeable, which highlights the need for test standardization and further prospective RCTs.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».