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Lewis Sheiner


2017
Budapest, Hungary



2016
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Printable version

PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032

Reference:
PAGE 25 (2016) Abstr 5708 [www.page-meeting.org/?abstract=5708]


PDF poster/presentation:
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Poster: Drug/Disease modeling - Other topics


III-11 Mats Magnusson Population PKPD Analysis of CD4 and ACR Response after SC Administration of Tregalizumab to Patients with Rheumatoid Arthritis

Mats O. Magnusson (1), Elodie L. Plan (1), Angela Remy (2), Martin König (2), Silke Aigner (2), Veit Braun (2), Daniela Zipp (2), Xuefei Zhou (2), Ralf Wolter (2), Jörg Schüttrumpf (2), Andrea Wartenberg-Demand (2), Benjamin Dälken (2), Faiza Rharbaoui (2)

(1) Pharmetheus, Uppsala, Sweden; (2) Biotest, Dreieich, Germany

Objectives: Tregalizumab (BT-061) is a non-depleting anti-CD4 antibody with antagonist effect on effector T cells and agonist effect on regulatory T cells. Upon completion of the latest phase IIb trial with tregalizumab, Study 986, in rheumatoid arthritis (RA) patients with an inadequate response to methotrexate, a modeling analysis was performed. The aim was to describe the relationship between tregalizumab exposure, or dose, and the response in modulation of cell surface CD4 expression, as well as in clinical efficacy endpoints (American College of Rheumatology [ACR]).

Methods: This population PKPD analysis was based on data from 4 phase II clinical trials with tregalizumab in RA patients (studies 962, 971, 979 and 986). The PK properties of tregalizumab following single and multiple intravenous (IV) and subcutaneous (SC) administrations were first characterized using data from 4 other phase I and phase II studies in healthy volunteers and psoriasis patients (961, 967, 973 and 985). The final PK model was based on 697 samples from 159 subjects. The PK model was used to derive individual PK profiles for all subjects. The data set used in the population PKPD analysis consisted of 3848 CD4 measurements from 489 subjects, and 3726 ACR measurements from 530 subjects. The analysis was performed using non-linear mixed-effects modeling implemented in NONMEM 7.3.0.

Results: The final CD4 model was a direct effect model, where a maximum effect (Emax) function described the relationship between the tregalizumab dose and the inhibitory effect on CD4. The typical CD4 reduction was estimated to 52% in the 200 mg dose group. The final ACR model was a direct study-specific effect model between tregalizumab dose and the discrete probabilities of transition from a responder Markov state to another amongst: non-responder, ACR20, ACR50 or ACR70. In comparison to placebo effect (methotrexate only) at 12 weeks, the 200 mg dose in study 986 was predicted to lead to an 11% (absolute) reduction in the transitions from ACR20 to non-responder and an increase in the transitions from ACR20 to ACR20, ACR50 and ACR70, of 6%, 5% and 0.5%, respectively.

Conclusions: The tregalizumab dose-CD4 relationship, as well as the tregalizumab dose-ACR relationship, in studies 962, 971, 979, and 986 were well captured by the developed PKPD models. The models predicted that at the highest dose level studied, 200 mg, the maximal effect in CD4 was not reached whilst minor ACR improvements were achieved.