Open AccessUsing Real‐World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic‐Pharmacodynamic Study
Author(s) -
Pan Shan,
Tsakok Teresa,
Dand Nick,
Lonsdale Dagan O.,
Loeff Floris C.,
Bloem Karien,
Vries Annick,
Baudry David,
Duckworth Michael,
Mahil Satveer,
PushpaRajah Angela,
Russell Alice,
Alsharqi Ali,
Becher Gabrielle,
Murphy Ruth,
Wahie Shyamal,
Wright Andrew,
Griffiths Christopher E.M.,
Reynolds Nick J.,
Barker Jonathan,
Warren Richard B.,
David Burden A.,
Rispens Theo,
Standing Joseph F.,
Smith Catherine H.
Publication year - 2020
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/cts.12725
Subject(s) - ustekinumab , psoriasis , medicine , dosing , pharmacodynamics , psoriasis area and severity index , therapeutic drug monitoring , drug , observational study , pharmacology , pharmacokinetics , body surface area , dermatology , disease , infliximab
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 (E max ) 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.