Impact of pharmacodynamic biomarkers in immuno-oncology (IO) phase 1 clinical trials.
Notice bibliographique
Résumé
2653 Background: Pharmacodynamic biomarkers (PD) are considered fundamental for go/no-go decisions in phase 1 trials. Despite an increase in the availability of blood-based biomarker assays, the requirement of invasive non-diagnostic research tumor biopsies for trial eligibility remains common. In the immuno-oncology (IO) era, the impact of PD analysis for the confirmation of biologic activity and recommended phase 2 dose (RP2D) has not been investigated. Methods: Phase 1 studies from 01/2014 to 12/2018 were reviewed. Among 12053 abstracts screened, a total of 143 phase I-IO trials were identified. Characteristics of studies that included on-treatment PD biomarkers (tissue-derived, blood-based and radiomic) were extracted and analyzed. Outcomes from the biomarker data in terms of proof of mechanism/biologic activity and statistically significant correlation with clinical benefit (objective response or survival) were collected. Authors’ statements on the influence of PD results on RP2D were also noted. Results: Out of 143 phase 1 IO trials, 107 (75%) were monotherapy. The most frequent IO evaluated were vaccines (41%), cell therapy (16%), immunomodulators (13%) and cytokines (7%). Of the 36 combination studies, 20 (61%) included a second IO drug while 16 (39%) included molecular-targeted agents. Only 18 of 143 studies (12%) did not report any PD data. Of the remaining 125 studies, tissue-derived PD (t-PD) biomarkers alone, blood-based PD (b-PD) biomarkers alone, both t-PD and b-PD biomarkers, and imaging biomarkers were tested in 3 (2%), 97 (78%), 25 (20%), and 7 (6%), respectively. Demonstration of proof of mechanism/biologic activity only were reported in 16/28 (57%), 80/122 (66%) and 4/7 (57%) of the t-PD, b-PD and imaging biomarker studies, respectively. Significant correlation with clinical benefit was reported in 2/28 (7%), 7/122 (6%) and 0/7 (0%) of the t-PD, b-PD and imaging biomarker studies, respectively; these involved 4 vaccines (1 in combination with PD1 blockade), 1 cell therapy and 1 oncolytic virus (in combination with CTLA4 blockade). Among 35 b-PD studies with negative results, 5 also performed t-PD biomarkers, all with negative results. Notably, 3 out of 10 t-PD studies with negative results reported concurrent positive b-PD results. Based on the published reports, authors stated that biomarker results helped with RP2D determination in 16/28 (57%) of t-PD and 78/122 (64%) of b-PD studies. Conclusions: Our results suggest that in the IO era, most studies perform PD analysis, with similar proportions of t-PD and b-PD showing proof of mechanism/biologic activity. IO PD biomarkers have limited correlation with clinical benefit. Many authors considered IO PD biomarkers to be relevant in RP2D decisions, but this needs confirmation by other measures of impact. With continued technological developments utilizing circulating biomarkers, b-PD may ultimately replace many t-PD tests in future IO studies.
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 | 0,105 | 0,209 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».