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Enregistrement W2038387558 · doi:10.5414/cpp43485

The bioequivalence of highly variable drugs and drug products

2005· review· en· W2038387558 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueInternational Journal of Clinical Pharmacology and Therapeutics · 2005
Typereview
Langueen
DomaineMathematics
ThématiqueStatistical Methods in Clinical Trials
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésBioequivalenceCmaxConfidence intervalStatisticsMathematicsReplicatePopulationPharmacologyDrugPharmacokineticsGeometric meanMedicineEconometrics

Résumé

récupéré en direct d'OpenAlex

'Highly variable drugs' have been defined as those drugs for which the within-subject variability (WSV) equals or exceeds 30% of the maximum concentration (Cmax) and/or the area under the concentration versus time curve (AUC). Despite the fact that highly variable drugs are generally safe with flat dose response curves, the bioequivalence of their formulations is a problem because the high variability means that large numbers of subjects are required to give adequate statistical power. Highly variable drug products are poor quality formulations where high within-formulation variability (e.g. tablet to tablet variability) poses a problem rather than high innate WSV of the drug itself. A further problem caused by high variability is that a subset of the population may respond differently to the two formulations producing a significant subject x formulation interaction. Practical examples are shown using replicate designs. The methods proposed to deal with the problems posed by highly variable drugs include: (i) Drug regulatory jurisdictions states that the 90% confidence interval (90% CI) around the test to reference geometric mean ratio (GMR) is required to fit with bioequivalence acceptance limits of 0.8 - 1.25 for both Cmax and AUC. The WSV for single point estimation of Cmax is often greater than that for AUC. One strategy therefore is not to require a 90% CI for Cmax of drugs that do not exhibit a toxicity associated with Cmax and merely require the GMR to fall within the acceptance limits. (ii) To arbitrarily broaden the bioequivalence acceptance limits. For example, to permit a sponsor to justify the use of wider limits e.g the 90% CI around the GMR of Cmax values might be required to fit within acceptance limits of 0.75 - 1.33 or even 0.70 - 1.42. (iii) A more systematic approach would be to broaden the acceptance limits by scaling to either the residual variance from a 2-period design or to the WSV of the reference product in a replicate design. Subsequent evaluations of scaling procedures have demonstrated that smaller numbers of subjects are required for bioequivalence studies on formulations of highly variable drugs. A disadvantage of scaling is that the method is less sensitive to differences between the means compared with unscaled treatment, such that the GMR may prove to be unacceptably low or high. This possibility has let to a suggestion that the GMR must fall within acceptance limits of 0.8 - 1.25 in scaled treatments. (iv) A similar method is to scale the metric rather than the acceptance limits. This method was proposed by the United States' Food and Drug Administration in the context of Individual bioequivalence, but may also be applied (v) to average bioequivalence. (vi) To carry out bioequivalence studies at steady state whenever a multiple dose regimen is ethically acceptable for healthy volunteers. This solution is based on the observation that high variability in a single dose study tends to be dampened at steady state, thus increasing statistical power. Drug regulators have not favored this approach on the grounds that bioequivalence testing should be based on the most discriminating test possible. (vii) Finally the use of metabolite data has been proposed since in many (but by no means all) cases, metabolite is less highly variable than that of the parent drug. This subject remains controversial except when the administered substance is a prodrug which converted by metabolism into the active drug.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,016
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,968
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0130,016
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,579
Tête enseignante GPT0,658
Écart entre enseignants0,079 · 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