Personalizing Biologic Therapy in Psoriasis: Development, Validation, and User Testing of a Precision-Dosing Dashboard.

Abstract

An increasing number of individuals receiving psoriasis biologics achieve clear/nearly clear skin (disease control). Clinical trial data indicate that some maintain disease control with lower doses, especially those with higher serum drug concentrations. This indicates the potential of model-informed precision dosing, an advanced therapeutic drug-monitoring technique, to guide dose minimization. We developed, validated, and tested a precision-dosing dashboard. We applied a model-informed precision-dosing approach that leveraged Bayesian estimation to predict individual pharmacokinetic parameters for personalized dosing recommendations. A pharmacokinetic model of the exemplar biologic risankizumab derived from phases I-III psoriasis trial data (13,123 observations from 1899 patients) was externally validated using real-world data from the United Kingdom. The Bayesian model (posterior prediction: mean absolute error = 0.89 mg/l, mean percentage error = 19.55%, root mean square error = 1.24 mg/l, R² = 0.86) had superior predictive power to the basic pharmacokinetic model (prior prediction). The model was incorporated into an interactive dashboard that enabled input of individual patient data (serum drug concentrations and model covariates). Healthcare professionals in the United Kingdom rated the dashboard as user friendly and acceptable. The mean time required to generate a dosing interval was 2 minutes. Our dashboard has the potential to incorporate other biologics and extend across disease contexts (nonresponse and other inflammatory diseases) for optimal real-world impact of precision dosing on health and cost outcomes.