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

Charlotte M Thomas1, David Baudry1, Zehra Arkir2

  • 1St. John's Institute of Dermatology, King's College London, London, United Kingdom.

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Summary

Model-informed precision dosing uses Bayesian estimation to personalize biologic therapy for psoriasis, optimizing drug levels and potentially reducing doses. A developed dashboard aids healthcare professionals in making these tailored dosing recommendations efficiently.

Keywords:
Data-driven dosingImmune-mediated inflammatory diseasesModel-informed precision dosingTherapeutic drug monitoring

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Area of Science:

  • Pharmacology
  • Immunology
  • Biotechnology

Background:

  • Many patients receiving biologic therapy for psoriasis achieve disease control.
  • Some patients can maintain disease control with reduced biologic doses, particularly those with higher serum drug concentrations.
  • This suggests potential for model-informed precision dosing to guide dose minimization strategies.

Purpose of the Study:

  • To develop, validate, and user-test a precision dosing dashboard for optimizing biologic therapy in psoriasis.
  • To apply a model-informed precision dosing approach using Bayesian estimation for personalized dosing recommendations.
  • To assess the predictive power of a pharmacokinetic model and the usability of the developed dashboard.

Main Methods:

  • Developed a pharmacokinetic model for risankizumab using Phase I-III psoriasis trial data.
  • Externally validated the model using real-world UK patient data.
  • Incorporated the validated model into an interactive dashboard for personalized dosing recommendations via Bayesian estimation.
  • Conducted user testing with UK healthcare professionals.

Main Results:

  • The Bayesian pharmacokinetic model demonstrated superior predictive power compared to a basic model (posterior prediction R² = 0.86).
  • The developed dashboard was rated as user-friendly and acceptable by healthcare professionals.
  • The average time to generate a dosing interval using the dashboard was 2 minutes.

Conclusions:

  • Model-informed precision dosing, facilitated by the developed dashboard, shows promise for optimizing biologic therapy in psoriasis.
  • The dashboard enables efficient, personalized dosing recommendations, potentially improving health and cost outcomes.
  • The approach has potential for broader application across different biologics and inflammatory diseases.