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.

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.

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).

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.