Combining Pharmacometric Models with Predictive and Prognostic Biomarkers for Precision Therapy in Crohn's Disease: A Case Study of Brazikumab.

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. In a phase 2a trial in patients with moderate to severe Crohn's disease, 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 (BCDAI) (a prognostic biomarker). A maximal total inhibition (I ) on CDAI input function (k ) 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 IB (baseline biomarker levels that achieve 50% of I ) 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 sub-group 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.