A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease.

Objectives: Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase.

Methods: We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect.

Results: Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients.

Conclusion: This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.