C-reactive protein and response to lurasidone in patients with bipolar depression

Abstract

Prior studies suggest that the inflammatory biomarker c-reactive protein (CRP) holds promise for predicting antidepressant response in patients with major depressive disorder. The objective of this study was to evaluate whether CRP might similarly predict antidepressant responses to lurasidone in patients with bipolar I depression. Serum CRP concentration was measured prior to, and following, 6 weeks of treatment in 485 outpatients with bipolar I depression. Patients were randomized to receive monotherapy with lurasidone 20-60 mg/day (N = 161), lurasidone 80-120 mg/day (N = 162) or placebo (N = 162). CRP was assessed using the wide-range CRP assay (wr-CRP). The primary efficacy endpoint was change from baseline to week 6 in Montgomery-Åsberg Depression Rating Scale (MADRS) score. Mixed models and statistical interaction tests were applied to investigate the moderating effects of pre-treatment wr-CRP on clinical endpoints. CRP was evaluated as a log-transformed continuous variable and by clinically-relevant cut-points. Increasing pre-treatment wr-CRP level predicted a larger overall antidepressant response to lurasidone, as well as an increased response for a number of individual depressive symptoms. These moderating effects of pre-treatment wr-CRP remained significant after adjustment for potential confounds (e.g. baseline BMI and weight change). Treatment with lurasidone did not affect serum concentrations of CRP compared to placebo during the study. Elevated CRP level prior to treatment was associated with an enhanced clinical response to lurasidone in patients with bipolar I depression. If confirmed in future studies, CRP may represent a clinically useful diagnostic and predictive biomarker supporting a precision medicine approach to the treatment of bipolar depression.

Trial registration: ClinicalTrials.gov NCT00868699.

Keywords: Atypical antipsychotics; Bipolar depression; C-reactive protein; Inflammation; Lurasidone; Predictive biomarkers.