Pharmacokinetic–pharmacodynamic modeling of fostamatinib efficacy on ACR20 to support dose selection in patients with rheumatoid arthritis (RA)

Abstract

R788 (fostamatinib) is an oral prodrug that is rapidly converted into a relatively selective spleen tyrosine kinase (SYK) inhibitor R406, evaluated for the treatment of rheumatoid arthritis (RA). This analysis aimed at developing a pharmacodynamic model for efficacy using pooled ACR20 data from two phase II studies in patients with rheumatoid arthritis (TASKi1 and TASKi2), describing the effect of fostamatinib as a function of fostamatinib exposure (dose, R406 plasma concentration) and other explanatory variables. The exposure-response relationship of fostamatinib was implemented into a continuous time Markov model describing the time course of transition probabilities between the three possible states of ACR20 non-responder, responder, and dropout at each visit. The probability of transition to the ACR20 response state was linearly (at the rate constant level) related to average R406 plasma concentrations and the onset of this drug effect was fast. Further, increases of fostamatinib dose resulted in increased dropout and subsequent loss of efficacy. This analysis provided an increased understanding of the exposure-response relationship, and provided support for fostamatinib 100 mg BID an appropriate dose regimen for further clinical evaluation.

Population Pharmacokinetics, Efficacy Exposure-response Analysis, and Model-based Meta-analysis of Fenebrutinib in Subjects with Rheumatoid Arthritis

“…The fenebrutinib effect on response was modeled as an E max function driven by fenebrutinib exposure as measured by C max , C min , or AUC, and the statistical significance of incorporating each exposure metric into the E-R model was similar in terms of p-values. Steady state AUC was chosen as the driver for efficacy, based on exploratory results, and prior E-R modeling knowledge in RA (33,34). Separate E max functions to describe the E-R relationship for each of ACR20, ACR50, and ACR70 probability were tested, but it was seen that a single E max term for all three ACR thresholds adequately captured the data.…”

Eco-Friendly UPLC–MS/MS Method for Determination of a Fostamatinib Metabolite, Tamatinib, in Plasma: Pharmacokinetic Application in Rats

“…Fostamatinib is an oral Syk inhibitor that is effective in the treatment of malignant clones through inhibition of antigen-dependent BCR signals [ 1 , 2 ]. Fostamatinib is a prodrug of the active compound, tamatinib, which is a relatively selective Syk inhibitor used to treat rheumatoid arthritis [ 3 , 4 ], especially in patients with poor therapeutic response to methotrexate [ 5 ]. It is also used to treat thrombocytopenia, a shortage of blood platelets needed for normal blood clotting [ 6 ].…”

“…This activity causes a reduction in tumor growth and was enhanced in combinations with other treatments, including bortezomib, rituximab, and dexamethasone [ 10 ]. However, dropout and subsequent loss of efficacy during fostamatinib treatment increased with the increases of its dose [ 3 ].…”

Analysis of Longitudinal‐Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models

“…Thereafter, the continuous‐time Markov model (CTMM) was introduced to accommodate data with varying time intervals between observations, where the influence of the preceding state decreases as time between observations increases. It was first proposed in 2009 to model tablet transit in gastrointestinal tract and thereafter adopted widely to characterize various types of ordered categorical data …”

“…It was first proposed in 2009 to model tablet transit in gastrointestinal tract 8 and thereafter adopted widely to characterize various types of ordered categorical data. [9][10][11][12][13][14] Some other approaches are available to analyze such data: the latent variable approach for the efficacy end points in rheumatoid arthritis and Crohn's disease, [15][16][17] the repeated time-to-categorical event approach for heartburn in gastroesophageal reflux disease, 18 and hypoglycemia in type 1 diabetes mellitus. 19 There has been an extensive discussion surrounding the Markov and the latent variable models, 4,10 which is not the focus of this article.…”