Exposure–response relationship of guselkumab and the potential of serum proteomics in identifying predictive biomarker candidates in psoriasis

Abstract

AbstractBackgroundResponse to biologics in psoriasis varies in real‐world settings. Serum biomarkers could aid biologic selection and dose modifications to improve patient outcomes while encouraging cost‐effective care.ObjectivesTo explore the exposure–response relationship for guselkumab (GUS), to define a GUS concentration target for optimal response and to evaluate the potential of serum protein levels as predictive biomarker candidates.MethodsThis is a prospective, multicentric, cohort study in psoriasis patients treated with GUS. Serum GUS trough concentrations (TCs) collected at multiple timepoints were measured using an in‐house immunoassay. Next, proximity extension assay technology (Target 96 Inflammation Panel Olink®) was used to measure serum protein levels in a subcohort including 38 GUS patients (week 0 and week 4), six psoriasis patients naive for systemic treatment and four healthy controls.ResultsSeventy‐five patients participated and 400 samples were collected. Guselkumab TCs and clinical response were correlated at week 4, week 12 and in steady‐state (≥20 weeks). Optimal responders (Psoriasis Area and Severity Index [PASI] ≤ 2) had significantly higher TCs than suboptimal responders from week 4 onwards in treatment. An optimal steady‐state TC of 1.6 μg/mL was defined. Although TC and absolute PASI were lower and worse, respectively, in patients weighing ≥90 kg, clinical outcomes referred to desirable to excellent PASI ranges. Therefore, we do not recommend systematically higher GUS doses in obese patients. We could not reveal early differentially expressed proteins to distinguish future optimal from suboptimal responders.ConclusionsWe demonstrated an exposure–response relationship for GUS and an optimal steady‐state TC of 1.6 μg/mL in real‐world psoriasis patients. Hereby, we deliver more evidence that therapeutic drug monitoring poses a promising strategy in optimizing GUS treatment. No biomarker candidates were identified through serum proteomics. We propose protein screening should be repeated in larger cohorts to continue the quest for predictive biomarkers.