Tocilizumab is a humanized monoclonal antibody approved for rheumatoid arthritis treatment. In clinical practice, empirical dose-tapering strategies are implemented in patients showing sustained remission or low disease activity (LDA) to avoid overtreatment and reduce costs. Since rational adaptive-dosing algorithms taking the full pharmacokinetic (PK)/pharmacodynamic (PD) characteristics into account are currently lacking, we aimed to develop novel tapering strategies and compare them with currently used empirical ones.
The overall proportion of simulated patients in remission/LDA after 1 year of the intervention was comparable between the mild empirical and the TDM-guided dose-tapering strategies, and much lower for the intense empirical dose-tapering strategy (80.3%, 78.2%, and 69.0%, respectively). Likewise, 1-year flare rates were lower for the mild empirical and TDM-guided tapering strategies. The relative dose intensity was lowest for the intense empirical dose-tapering, followed by the TDM-guided and the mild empirical dose-tapering approaches (61.2%, 71.0%, and 80.4%, respectively).
Four strategies were simulated on a virtual population. In all of them, the same initial dose was administered every 28 days for six consecutive months. Then, different strategies were considered: (1) label-dosing; (2) mild empirical dose-tapering; (3) intense empirical dose-tapering; (4) therapeutic drug monitoring (TDM)-guided dose-tapering. The different strategies were evaluated on the proportion of patients who maintain remission/LDA 1 year after the intervention. Cost-savings of direct drug costs were also estimated as relative dose intensity.
We demonstrated that the TDM-guided strategy using model-based algorithms performed similarly to mild empirical dose-tapering strategies in overall remission/LDA rates but is superior in cost-savings.
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