Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis
Abstract The aim was to analyze the relationship between peficitinib exposure and efficacy response according to American College of Rheumatology (ACR) 20 criteria and 28‐joint disease activity score based on C‐reactive protein (DAS28‐CRP) in rheumatoid arthritis (RA) patients, and to identify relev...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
|
| Series: | Pharmacology Research & Perspectives |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/prp2.744 |
| _version_ | 1818741486884225024 |
|---|---|
| author | Junko Toyoshima Atsunori Kaibara Mai Shibata Yuichiro Kaneko Hiroyuki Izutsu Tetsuya Nishimura |
| author_facet | Junko Toyoshima Atsunori Kaibara Mai Shibata Yuichiro Kaneko Hiroyuki Izutsu Tetsuya Nishimura |
| author_sort | Junko Toyoshima |
| collection | DOAJ |
| description | Abstract The aim was to analyze the relationship between peficitinib exposure and efficacy response according to American College of Rheumatology (ACR) 20 criteria and 28‐joint disease activity score based on C‐reactive protein (DAS28‐CRP) in rheumatoid arthritis (RA) patients, and to identify relevant covariates by developing exposure–response models. The analysis incorporated results from three multicenter, placebo‐controlled, double‐blind studies. As an exposure parameter, individual post hoc pharmacokinetic (PK) parameters were obtained from a previously constructed population PK model. Longitudinal ACR20 response rate and individual longitudinal DAS28‐CRP measurements were modeled by a non‐linear mixed effect model. Influential covariates were explored, and their effects on efficacy were quantitatively assessed and compared. The exposure–response models of effect of peficitinib on duration‐dependent increase in ACR20 response rate and decrease in DAS28‐CRP were adequately described by a continuous time Markov model and an indirect response model, respectively, with a sigmoidal Emax saturable of drug exposure in RA patients. The significant covariates were DAS28‐CRP and total bilirubin at baseline for the ACR20 response model, and CRP at baseline and concomitant methotrexate treatment for the DAS28–CRP model. The covariate effects were highly consistent between the two models. Our exposure–response models of peficitinib in RA patients satisfactorily described duration‐dependent improvements in ACR20 response rates and DAS28‐CRP measurements, and provided consistent covariate effects. Only the ACR20 model incorporated a patient's subjective high expectations just after the start of the treatment. Therefore, due to their similarities and differences, both models may have relevant applications in the development of RA treatment. Clinical trial registration NCT01649999 (RAJ1), NCT02308163 (RAJ3), NCT02305849 (RAJ4). |
| first_indexed | 2024-12-18T01:57:24Z |
| format | Article |
| id | doaj.art-fd008108937f4820a66711a6792b599c |
| institution | Directory Open Access Journal |
| issn | 2052-1707 |
| language | English |
| last_indexed | 2024-12-18T01:57:24Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pharmacology Research & Perspectives |
| spelling | doaj.art-fd008108937f4820a66711a6792b599c2022-12-21T21:24:53ZengWileyPharmacology Research & Perspectives2052-17072021-05-0193n/an/a10.1002/prp2.744Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritisJunko Toyoshima0Atsunori Kaibara1Mai Shibata2Yuichiro Kaneko3Hiroyuki Izutsu4Tetsuya Nishimura5Astellas Pharma Inc Tokyo JapanAstellas Pharma Inc Tokyo JapanAstellas Pharma Inc Tokyo JapanAstellas Pharma Inc Tokyo JapanAstellas Pharma Inc Tokyo JapanAstellas Pharma Inc Tokyo JapanAbstract The aim was to analyze the relationship between peficitinib exposure and efficacy response according to American College of Rheumatology (ACR) 20 criteria and 28‐joint disease activity score based on C‐reactive protein (DAS28‐CRP) in rheumatoid arthritis (RA) patients, and to identify relevant covariates by developing exposure–response models. The analysis incorporated results from three multicenter, placebo‐controlled, double‐blind studies. As an exposure parameter, individual post hoc pharmacokinetic (PK) parameters were obtained from a previously constructed population PK model. Longitudinal ACR20 response rate and individual longitudinal DAS28‐CRP measurements were modeled by a non‐linear mixed effect model. Influential covariates were explored, and their effects on efficacy were quantitatively assessed and compared. The exposure–response models of effect of peficitinib on duration‐dependent increase in ACR20 response rate and decrease in DAS28‐CRP were adequately described by a continuous time Markov model and an indirect response model, respectively, with a sigmoidal Emax saturable of drug exposure in RA patients. The significant covariates were DAS28‐CRP and total bilirubin at baseline for the ACR20 response model, and CRP at baseline and concomitant methotrexate treatment for the DAS28–CRP model. The covariate effects were highly consistent between the two models. Our exposure–response models of peficitinib in RA patients satisfactorily described duration‐dependent improvements in ACR20 response rates and DAS28‐CRP measurements, and provided consistent covariate effects. Only the ACR20 model incorporated a patient's subjective high expectations just after the start of the treatment. Therefore, due to their similarities and differences, both models may have relevant applications in the development of RA treatment. Clinical trial registration NCT01649999 (RAJ1), NCT02308163 (RAJ3), NCT02305849 (RAJ4).https://doi.org/10.1002/prp2.744modeling and simulationpharmacometricspopulation analysisrheumatoid arthritis |
| spellingShingle | Junko Toyoshima Atsunori Kaibara Mai Shibata Yuichiro Kaneko Hiroyuki Izutsu Tetsuya Nishimura Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis Pharmacology Research & Perspectives modeling and simulation pharmacometrics population analysis rheumatoid arthritis |
| title | Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| title_full | Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| title_fullStr | Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| title_full_unstemmed | Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| title_short | Exposure–response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| title_sort | exposure response modeling of peficitinib efficacy in patients with rheumatoid arthritis |
| topic | modeling and simulation pharmacometrics population analysis rheumatoid arthritis |
| url | https://doi.org/10.1002/prp2.744 |
| work_keys_str_mv | AT junkotoyoshima exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis AT atsunorikaibara exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis AT maishibata exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis AT yuichirokaneko exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis AT hiroyukiizutsu exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis AT tetsuyanishimura exposureresponsemodelingofpeficitinibefficacyinpatientswithrheumatoidarthritis |