GS‐443902 concentrations in peripheral blood mononuclear cells and dried blood spots among pregnant and non‐pregnant women receiving remdesivir for COVID‐19 treatment
Notice bibliographique
Résumé
2 Background: Activation of remdesivir (RDV) has typically been assessed by measuring concentrations of GS-443902 (active triphosphate) in peripheral blood mononuclear cells (PBMC). We previously showed concentrations of RDV, and its major plasma metabolites were similar between pregnant and non-pregnant women with COVID-19, while those of GS-443902 in PBMC were ~50% lower in pregnancy. Dried blood spots (DBS) provide a simpler alternative to PBMC collection and may serve as another surrogate measure of cellular drug activation, but pregnancy status and inflammation due to COVID-19 may impact these measures. Here, we evaluated factors that affect GS-443902 in DBS and PBMC and relationships between cell types in pregnant and non-pregnant women with COVID-19. Methods: IMPAACT 2032 (NCT04582266) was a Phase IV prospective, open label, non-randomized, opportunistic study of hospitalized pregnant and non-pregnant women receiving intravenous RDV as part of clinical care for COVID-19. DBS and PBMC were isolated pre-dose and/or 23-h post-dose on infusion Days 3, 4 and 5. GS-443902 concentrations in each cell type were quantified using LC–MS/MS methods from 2 × 7 mm punches (DBS) or cell pellets (PBMCs). Generalized linear mixed effects modelling (1) evaluated the effect of pregnancy and baseline covariates on log-transformed GS-443902 in DBS and PBMC using forward selection (p < 0.2) and backwards elimination (p < 0.1) after adjusting for infusion number and (2) assessed relationships between GS-443902 in DBS vs. PBMC. Results: About 76 dB and PBMC observations were available from 51 participants (25 pregnant, 26 non-pregnant). Median (interquartile range) GS-443902 concentrations in DBS were 1807 (1395, 2292) and 1633 (1267, 2412) fmol/punch and in PBMC were 2.96 (0.79, 6.89) and 5.15 (3.22, 8.77) μM for pregnant and non-pregnant participants, respectively. In DBS, GS-443902 concentrations were 19% lower (90% CI −33, −2.5%) during pregnancy, increases in SBP (per 10 mmHg) were associated with an 8.1% decrease (90% CI −11.4%, −4.7%) and increases in ALP (per 10 U/L) and platelet count (per 50x109 cells/L) were associated with a 1.4% (90% CI 0.3%, 1.8%) and 5.3% (90% CI 0.8%, 13.4%) increase, respectively. In PBMC, GS-443902 concentrations were 45% lower (90% CI −19.4%, −61.8%) in pregnant vs. non-pregnant participants with each additional infusion. Increases in ALP and ALT (per 10 U/L) were associated with a 4.2% decrease (90% CI −7.7%, −0.6%) and 11.4% decrease (90% CI −18.8%, −3.4%) in GS-443902 concentrations in PBMC, respectively. For DBS vs. PBMC comparisons, GS-443902 concentrations in PBMCs increased by 7.7% (90% CI 4.0%, 11.6%) for every 100 fmol/punch increase in DBS for non-pregnant women; however, in pregnant women, there was a 4.4% decrease (90% CI −7.9%, −0.8%) for GS-443902 in PBMC for every 100 fmol/punch increase in DBS. Conclusions: Cellular disposition of GS-443902 between DBS and PBMCs differed by pregnancy status and other clinical factors associated with COVID-19 disease severity. The mechanisms and clinical significance of these differences are unknown. Further investigation into how pregnancy and other disease-specific factors affect cellular concentrations is warranted.
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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 | 0,001 | 0,002 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».