Early model-based precision-dosing at home to guide adalimumab therapy

1. Aims Underdosing of adalimumab can result in non-response and poor disease control. In this study we investigated the prediction of adalimumab levels with population pharmacokinetic model-based Bayesian forecasting early in therapy. This way underexposed non-responders can possibly be identified early to optimise disease control. 2. Methods A literature study was performed to identify adalimumab pharmacokinetic models. With data from a previous pharmacokinetic adalimumab study a model was evaluated retrospectively. In the prospective phase, a fit-for-purpose evaluation of the model was performed for rheumatologic and inflammatory bowel disease patients with peak, trough and control adalimumab samples obtained by a volumetric absorptive microsampling technique and administration data from an electronic needle container. Steady state adalimumab levels were predicted from peak and trough levels collected after the first adalimumab administration. Predictive performance was calculated with mean prediction error (MPE) and normalized root mean square error (RMSE). 3. Results An existing pharmacokinetic model was selected with external validation for the prospective phase. Thirty-six patients (22 rheumatologic and 14 IBD) were included in our study. After stratification for absence of anti-adalimumab antibodies, the calculated MPE was -2.6% and normalised RMSE 24.0%. Concordance between predicted and measured adalimumab serum levels falling within or outside the therapeutic window was 75%. Three patients (8.3%) developed detectable levels of anti-adalimumab antibodies. 4. Conclusion This prospective study demonstrates that adalimumab levels at steady state can be predicted from early samples. This concept enables early precision dosing at home to guide therapy.