Combining pharmacometric models with predictive and prognostic biomarkers for precision therapy in Crohn's disease: A case study of brazikumab

Abstract

Abstract Pharmacometric models were used to investigate the utility of biomarkers in predicting the efficacy (Crohn's Disease Activity Index [CDAI]) of brazikumab and provide a data‐driven framework for precision therapy for Crohn's disease (CD). In a phase IIa trial in patients with moderate to severe CD, treatment with brazikumab, an anti‐interleukin 23 monoclonal antibody, was associated with clinical improvement. Brazikumab treatment effect was determined to be dependent on the baseline IL‐22 (BIL22) or baseline C‐reactive protein (BCRP; predictive biomarkers), and placebo effect was found to be correlated with the baseline CDAI (a prognostic biomarker). A maximal total inhibition on CDAI input function of 50.6% and 42.4% was predicted for patients with extremely high BIL22 or BCRP, compared to a maximal total inhibition of 20.9% and 17.8% for patients with extremely low BIL22 or BCRP, respectively, which were mainly due to the placebo effect. We demonstrated that model‐derived baseline biomarker levels that achieve 50% of maximum unbound systemic concentration of 22.8 pg/mL and 8.03 mg/L for BIL22 and BCRP as the cutoffs to select subpopulations can effectively identify high‐response subgroup patients with improved separation of responders when compared to using the median values as the cutoff. This work exemplifies the utility of pharmacometrics to quantify biomarker‐driven responses in biologic therapies and distinguish between predictive and prognostic biomarkers, complementing clinical efforts of identifying subpopulations with higher likelihood of response to brazikumab.