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Using Real‐World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic‐Pharmacodynamic Study

Title: Using Real‐World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic‐Pharmacodynamic Study
Authors: Shan Pan; Teresa Tsakok; Nick Dand; Dagan O. Lonsdale; Floris C. Loeff; Karien Bloem; Annick deVries; David Baudry; Michael Duckworth; Satveer Mahil; Angela Pushpa‐Rajah; Alice Russell; Ali Alsharqi; Gabrielle Becher; Ruth Murphy; Shyamal Wahie; Andrew Wright; Christopher E.M. Griffiths; Nick J. Reynolds; Jonathan Barker; Richard B. Warren; A. David Burden; Theo Rispens; Joseph F. Standing; Catherine H. Smith; on behalf of the BADBIR Study Group; the BSTOP Study Group; the PSORT Consortium
Source: Clinical and Translational Science, Vol 13, Iss 2, Pp 400-409 (2020)
Publisher Information: Wiley
Publication Year: 2020
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: Therapeutics. Pharmacology; RM1-950; Public aspects of medicine; RA1-1270
Description: Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.
Document Type: article in journal/newspaper
Language: English
Relation: https://doi.org/10.1111/cts.12725; https://doaj.org/toc/1752-8054; https://doaj.org/toc/1752-8062; https://doaj.org/article/619cb5f9e3a54af0aff4b70a0b891c13
DOI: 10.1111/cts.12725
Availability: https://doi.org/10.1111/cts.12725; https://doaj.org/article/619cb5f9e3a54af0aff4b70a0b891c13
Accession Number: edsbas.BB57FFF2
Database: BASE