Predictive enrichment strategies for immune-targeted interventions in depression. 01/11/2024 - 31/10/2026

Abstract

BACKGROUND: 30% of Major Depression Disorder patients display an immune-mediated subtype, associated with poor response to first-line antidepressant treatments. Immune-targeted augmentation with anti-inflammatory compounds shows promise and may be more effective for the immune-mediated subgroup. For optimal clinical trial designs, we require guidance on the selection of patients who may benefit, the outcome measures that capture the clinical benefits, and the subtype-specific effect sizes per compound. AIM: Identify baseline predictive blood-based and clinical biomarkers, define the optimal outcome measures for immune-targeted interventions, and rank these based on subtype-specific effect sizes. Facilitate science-to-policy translation by integrating research findings into clinical recommendations and predictive enrichment strategies for future clinical trials. APPROACH: I will address these questions through a dual approach, integrating insights (WP3) gained with stratification meta-analyses and individual participant data (of at least n=10 RCTs) (WP1) with the results of our pre-stratified clinical trial (WP2). IMPACT: My project will innovate MDD treatment guidelines and optimise future RCT protocols. This will result in a decreased number of failed RCTs, which will lead to cost-effective benefits and more interest among pharmaceutical industry players. The predictive enrichment strategies can then be used to innovate intervention trials also in other mental disorders.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Predictive enrichment strategies for immune-targeted interventions in depression. 01/11/2023 - 31/10/2024

Abstract

BACKGROUND: 30% of Major Depression Disorder patients display an immune-mediated subtype that is associated with poor response to first-line antidepressant treatments. Immune-targeted augmentation with anti-inflammatory compounds shows promise and may be more effective for the immune-mediated subgroup. For optimal clinical trial designs, we require guidance on the selection of patients who may benefit, on the outcome measures that capture the clinical benefits, and the subtype-specific effect sizes per compound. AIM: Identify baseline predictive blood-based and clinical biomarkers to facilitate predictive enrichment strategies for future clinical trials on immune-mediated depression, define the optimal outcome measures for immune-targeted pharmacological interventions, and ranking these based on their subtype-specific effect sizes. APPROACH: I will address these questions through a dual approach, combining insights gained during the preparation of a new RCT with stratification meta-analyses, design harmonisation and machine learning strategies in existing datasets (n=9 RCTs) within an international consortium. IMPACT: My project will optimise future RCT protocols. This will result in a decreased number of failed RCTs, which leads to cost-effective benefits and more interest among pharmaceutical industry players. The predictive enrichment strategies can then be used to innovate intervention trials also in other mental disorders.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project