Trial reportJournal of pharmacokinetics and pharmacodynamics2017
Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients.
Trial report in Journal of pharmacokinetics and pharmacodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Pharmacokinetics of IL-23p19 Inhibitors: A Systematic Review Across Immune-Mediated Inflammatory Diseases.Clinical pharmacokinetics · 2026Pooled it
- Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis.Journal of pharmacokinetics and pharmacodynamics · 2024Trial
- Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data.The AAPS journal · 2020Trial
- Joint longitudinal model development: application to exposure-response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab.Journal of pharmacokinetics and pharmacodynamics · 2018Trial
- Continuous-Time Markov Population PK/PD Modeling of Longitudinal EASI Categorical Score in Atopic Dermatitis Treated With Rocatinlimab, an Anti-OX40 Monoclonal Antibody.CPT: pharmacometrics & systems pharmacology · 2025Article
- Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Role of Disease Progression Models in Drug Development.Pharmaceutical research · 2022Review
- Improving categorical endpoint longitudinal exposure-response modeling through the joint modeling with a related endpoint.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Impact of Pharmacokinetic and Pharmacodynamic Properties of Monoclonal Antibodies in the Management of Psoriasis.Pharmaceutics · 2022Review
- Approaches to handling missing or "problematic" pharmacology data: Pharmacokinetics.CPT: pharmacometrics & systems pharmacology · 2021Article
- Bounded Integer Modeling of Symptom Scales Specific to Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia.The AAPS journal · 2021Article
- Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data.The AAPS journal · 2020Article
- Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents.The AAPS journal · 2020Article
- Applying Beta Distribution in Analyzing Bounded Outcome Score Data.The AAPS journal · 2020Article
- Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models.CPT: pharmacometrics & systems pharmacology · 2020Article
- On the Comparison of Methods in Analyzing Bounded Outcome Score Data.The AAPS journal · 2019Review
- Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- A comprehensive evaluation of exposure-response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis.Journal of pharmacokinetics and pharmacodynamics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.