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OC50 Improvements in pruritus after maralixibat treatment are associated with improved health-related quality of life for patients with cholestatic liver disease

2025· article· en· W4414219155 on OpenAlexaff
Richard J. Thompson, Alexander Miethke, Emmanuel Gonzalès, Binita M. Kamath, Douglas Mogul, Tiago Nunes, Jolan Terner-Rosenthal, Marshall Baek, Pamela Vig, Emmanuel Jacquemin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsQuality of life (healthcare)CholestasisPsychosocialRandomized controlled trialHealth related quality of lifeDiseaseVisual analogue scaleLiver disease

Abstract

fetched live from OpenAlex

<h3></h3> Alagille syndrome (ALGS) and Progressive Familial lntrahepatic Cholestasis (PFIC) are rare cholestatic liver diseases (CLD) associated with severe pruritus along with markedly reduced health­ related quality of life (HRQoL). Maralixibat (MRX), an oral minimally absorbed ileal bile acid transporter (IBAT) inhibitor, is approved for the treatment of cholestatic pruritus in patients with ALGS ≥2 months of age and for the treatment of PFIC ≥3 months of age in the EU, respectively. This analysis assessed whether improvements in pruritus after MRX treatment are correlated with improvements in a variety of HRQoL domains in these CLD. The study designs of the Phase 2 randomized withdrawal period (RWD) ICONIC trial and Phase 3 randomized, double-blind, placebo-controlled (PBO) MARCH trial have been previously described. Separate retrospective analyses of pruritus and HRQoL data from the ICONIC trial in ALGS (18 weeks of open-label MRX treatment) and the MARCH trial in PFIC (26 weeks of MRX or PBO treatment) were conducted. Patients in both studies had to have moderate to severe pruritus as measured using a validated caregiver-reported Itch Reported Outcome (ltchRO) severity assessment tool (0=none to 4=very severe). HRQoL was assessed using the Pediatric Quality of Life Inventory Generic Core (PedsQL; 0–100 scale, 100=best quality of life), Physical Health (PH), Psychosocial Health (PSH), and Multidimensional Fatigue (MF) scale scores, which were collected via caregiver in both studies. In MARCH, a subset of questions from the exploratory diary questionnaire (EDQ) focused on sleep disturbance were assessed for their relationship to pruritus improvement. Spearman’s (r) coefficients were determined to evaluate the relationship between pruritus improvements and HRQoL. A total of 28 patients with ALGS from ICONIC were included with a mean± SD baseline age of 5.4 ± 4.2 years, ltchRO score of 2.9 ± 0.5, PedsQL score of 60.3 ± 16.6, PH score of 64.7 ± 20.0, PSH score of 57.6 ± 16.7, and MF score of 51.2 ± 22.6. After 18 weeks of open-label MRX treatment, there was a significant positive correlation between pruritus improvement and improvements in Peds QL (r 0.50 [p=0.007]), PSH (r 0.47 [p=0.012]), and MF (r 0.71 [p=0.0002]). In MARCH, 55 patients from the AII-PFIC cohort (29 MRX, 26 PBO) were included in the analysis with a mean± SD baseline age of 4.9 ± 4.1 years, ltchRO score of 2.9 ± 0.9, PedsQL score of 55.8 ± 19.0, PH score of 61.5 ± 22.4, PSH score of 52.0 ± 20.2, MF score of 57.8 ± 20.4 and EDQ sleep disturbance score of 3.7 ± 0.8. Among MRX-treated participants after 26 weeks of treatment, there was a significant positive correlation between pruritus improvement and improvements in Peds QL (r 0.53 [p=0.003]), PH (r 0.50 [p=0.006]), PSH (r 0.47 [p=0.01]), and sleep (r 0.96 [p&lt;0.0001]). In the PBO-treated participants, improvements in pruritus were significantly correlated with improvements in PSH (r 0.41 [p=0.039]) and sleep (r 0.88 [p&lt;0.0001]). These data further illustrate the robustness of MRX’s impact on the relationship between pruritus improvement and HRQoL across multiple domains. Irrespective of the CLD studied, improvements in pruritus after MRX treatment were strongly associated with improvements in HRQoL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.410
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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