Objective: The individualization of treatments through pharmacokinetic monitoring is a therapeutic strategy aimed at improving both the effectiveness and safety of drug therapy. For tumor necrosis factor inhibitors used in Crohn's disease, a robust correlation has been demonstrated between drug exposure and clinical response. However, evidence regarding ustekinumab concentrations and their relationship with treatment effectiveness remains limited. The objective of this study was to analyze ustekinumab concentrations at week 8 of treatment for Crohn's disease and to evaluate their association with biochemical remission at week 24 and treatment persistence.
Methods: This prospective study included patients with Crohn's disease who initiated ustekinumab between 2020 and 2023. Ustekinumab trough plasma concentrations were measured at week 8. Quartile analysis and binary logistic regression were performed to assess the relationship between ustekinumab concentrations and biochemical remission rates at week 24. Treatment persistence across concentration quartiles was evaluated using Kaplan-Meier analysis.
Results: A total of 36 patients were included. Individuals achieving biochemical remission at week 24 had higher ustekinumab concentrations at week 8 compared with non-responders (10.64 vs. 5.83 μg/mL; p = 0.016). Patients within the quartile-4 (>13.29 μg/mL) at week 8 showed significantly higher biochemical remission rates at week 24 compared with the remaining quartiles (100% vs. 57.1%; p = 0.023); Odds Ratio = 1.20; 95%-Confidence Interval [1.02-1.42]; p = 0.027. The 2-year treatment persistence rate was significantly greater among patients in quartiles 3-4 (100%) compared with those in quartile-1 (55.6%) and quartile 2 (50%) (p = 0.014).
Conclusion: This study confirms the association between ustekinumab concentrations at week 8 and treatment effectiveness in Crohn's disease. A concentration >13.29 μg/mL at week 8 was identified as a predictive marker of biochemical remission at week 24, which could serve as a basis for future therapeutic decision-making algorithms.
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