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Development of a DAS28-based pharmacodynamic endpoint to efficiently evaluate the dose-response relation of antirheumatic effect of JAK3 inhibitors

Other Titles
 JAK3 저해제의 항류마티스 효과의 용량-반응 관계를 효율적으로 평가하기 위한 DAS28 (Disease Active Score) 기반의 약력학적 지표의 개발 
Issue Date
Dept. of Medical Science/박사
Tasocitinib (CP-690,550), an orally available, small molecule, selective inhibitor of the Janus kinase (JAK) family, is in development as a disease modifying antirheumatic drug (DMARD) for the treatment of rheumatoid arthritis (RA). The primary clinical endpoint for the response of RA to treatment is American College of Rheumatology response criterion of 20% improvement (ACR20) by which a patient is considered a responder or non-responder. Disease Active Score measured by 28 tender and swollen joint assessments (DAS28-3(CRP), DAS28), developed as a clinical index of RA to objectively evaluate a patient’s response, has a continuous scale ranging from 0 to 9.4, reflecting the extent of underlying inflammation. Using DAS28 as a simplified notation for DAS28-3(CRP), the modeling hypothesis is that the continuous DAS28 may provide more precise dose-response curves than the binary ACR20, thus potentially improving the efficiency of dose response studies with new therapies.This study assesses the feasibility of using DAS28 as a more efficient measure than ACR20 for establishing dose-response of tasocitinib.Data were obtained from 1058 patients with RA enrolled in three phase 2 studies. Drug effects were assessed by ACR20 and DAS28 at baseline and up to 24 weeks, with ACR20 measured as a binary and DAS28 as a continuous variable. Both types of data were analyzed using NONMEM 7 within a mixed-effect model framework. For ACR20, using a logistic regression, the logit of the probability of ACR20 response was modeled as a sum of the latent variable threshold, placebo effect, and “pure” drug effect. Similarly for DAS28, the observed score was modeled as a sum of baseline, placebo effect, and pure drug effect. Model parameters were estimated sequentially in two steps as this approach estimated model parameters better than the simultaneous estimation of placebo and drug parameters. At the first step, placebo parameters were estimated from placebo group data only. Then, drug parameters were estimated from treatment group data by fixing placebo parameters at their estimates obtained at the first step. Dose-response information derived from DAS28 and ACR20 were compared using a percent change in DAS28 from placebo (DDAS) and change from placebo for ACR20 (DACR). The placebo effect was best described by a monotone increasing exponential function for ACR20 and an inverse Bateman function for DAS28. The drug effect was described as an inhibitory indirect response model to account for an inhibitory drug effect on the inflammatory RA process. For DAS28, concomitant use of MTX, dose adjustment at week 12 and baseline DAS28 were found as significant covariates. For ACR20, dose adjustment at week 12, visual scale for pain and the number of 28 swollen joint counts were identified as significant covariates. Several sample sizes for this study were simulated from the final model. For each dataset, population average percent change in DAS28 from placebo (DDAS) and change from placebo for ACR20 (DACR) were estimated for each dose group. The relationship between sample size and DDAS or DACR was explored. As a result, DDAS converged in a 50% reduced sample size compared to DACR.These findings support that a continuous DAS28 endpoint may provide more precise dose-response curve estimates than can the binary ACR20 response measure, thus offering the potential of achieving a similar level of precision in the effect size of clinically useful doses at lower sample sizes. To validate the above results, further analyses will be necessary, which will include the assessment of DAS28 for data from other studies with the same agent and other DMARDs in the same or different class.
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