Interindividual and Intraindividual Variation of Methylphenidate Concentrations in Serum and Saliva of Patients With Attention-Deficit/Hyperactivity Disorder
Notice bibliographique
Résumé
BACKGROUND: Therapeutic drug monitoring is becoming increasingly important in psychiatric therapy, especially in children. However, for several reasons, it cannot yet be implemented as a daily routine in clinical or outpatient settings. To evaluate new, noninvasive procedures, blood and saliva (oral fluid) samples were collected from patients with attention-deficit/hyperactivity disorder (ADHD) who were also being administered methylphenidate (MPH). The study's main purposes were to correlate MPH concentrations in serum and saliva between subjects and to analyze intraindividual variation of serum concentration. METHODS: Thirty-six patients with ADHD (27 children and 9 adults) on MPH medication were included for drug analysis. MPH and its major metabolite ritalinic acid were quantified using liquid chromatography-tandem mass spectrometry measurements. The following correlations were investigated: (1) between drug concentrations in serum and saliva, and (2) between pH value and saliva to serum concentration ratio. Furthermore, the mean intraindividual MPH-concentration fluctuation in saliva under constant frame conditions was analyzed. RESULTS: After quantification, MPH concentrations were approximately 5 times higher in the saliva than in the serum, whereas the concentrations of ritalinic acid were much lower in saliva. We found significant correlations between concentrations of MPH in serum and saliva (r = 0.51, P < 0.05). Saliva MPH measures, compared with serum, were pH-dependent (r = -0.56, P < 0.01). Daily coefficient of variance of saliva concentration in children taking constant medication was 27.3% (11%-42%), whereas the coefficient of variance for the ratio of saliva to serum was 122% (2%-2060%). CONCLUSIONS: Our data indicate that the interindividual variation in saliva to serum concentrations is rather high, whereas the intraindividual variation is fairly low, as already shown in the literature for repeated citalopram serum measurements. Saliva may well serve as an alternative matrix for therapeutic drug monitoring of MPH in patients with ADHD, especially for follow-up examinations. Future research should focus on analyzing the relationship between drug levels in saliva and clinical effects as well as on understanding the mechanisms that generate saliva drug concentrations. These are essential steps before potential clinical use.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».