Adaptive Platform Trial for Personnalisation of Sepsis Treatment in Children and Adults: a Multi-national, Treatable Traits-guided, Adaptive, Bayesian Basket Trial
- Disease/Condition
- Sepsis
- Drug/Device/Intervention
- Tocilizumab
- Study type
- Interventional
- Intervention type
- drug
- Primary sponsor
- Assistance Publique - Hôpitaux de Paris
- Phase
- Phase 3
- Start date
- Apr 01, 2026
- End date
- Apr 01, 2032
- Enrollment
- 2000 participants
All-cause mortality,Number of patients with persistent life-supportive therapies
PALETTE is a perpetual adaptive platform to efficiently study sepsis interventions within
 'treatable traits' in all-ages patients enabling prompt evaluation of pandemic treatments.
 Treatable traits, therapeutic targets identified by phenotypes or endotypes (defined by
 biological mechanism or by treatment response) through validated biomarkers (measurable
 characteristic reflecting normal or pathogenic processes, or treatment responses), may
 include multi-omics, cellular, immune, metabolic, endocrine features, or intelligent
 algorithms. PALETTE Bayesian adaptive design enables parallel investigations of multiple
 interventions for sepsis, and quick inclusion of pandemic pathogens. PALETTE's new conceptual
 model will respond to the challenges of standard approaches, i.e. series of sepsis trials,
 each investigating one or two interventions, expensive, time consuming, and inappropriate in
 pandemic context.
- Registry
- ClinicalTrials.gov
- Trial ID
- NCT06381661
- Type
- Non-Device Trial
Access comprehensive clinical trial information for NCT06381661 through Pure Global AI's free database. This Phase 3 trial is sponsored by Assistance Publique - Hôpitaux de Paris and is currently Not yet recruiting. The study focuses on Sepsis. Target enrollment is 2000 participants.
This page provides complete trial specifications, intervention details, outcomes, and location information. Pure Global AI offers free access to ClinicalTrials.gov data, helping medical device and pharmaceutical companies navigate clinical research efficiently.