Evidence map›Paper›PMID 40751361›Full record

Trial reportCPT: pharmacometrics & systems pharmacology2025

Pharmacometric Model-Based Sample Size Allocation for a Region of Interest in a Multi-Regional Phase 2 Trial: A Case Study of an Anti-Psoriatic Drug.

Xiao Zhang, Yubo Xiao, Junyi Wu, Scott Marshall, Xuan Zhou

Abstract readClinical Trial, Phase II
In one paragraph

Trial report in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiao ZhangGSK, Shanghai, China.ORCID 0009-0009-2033-0895
Yubo XiaoGSK, Shanghai, China.ORCID 0000-0001-8554-4899
Junyi WuGSK, Shanghai, China.ORCID 0000-0001-9017-9400
Scott MarshallGSK, Stevenage, Hertfordshire, UK.ORCID 0009-0002-5657-8584
Xuan ZhouGSK, Shanghai, China.ORCID 0000-0001-5750-1728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phase 2 trials have historically focused on characterizing the dose-exposure-response relationship in relatively homogeneous patient populations before proceeding to confirmatory trials. However, with the rise of multi-regional Phase 2 trials, it is important to strike a balance between this goal and the requirement to make sure that the optimal doses are chosen for patients from various geographic areas. This study uses a dose-ranging trial for an anti-psoriatic drug, featuring a typical design with a total sample size of N = 175, to highlight key considerations regarding sample size in multi-regional exploratory studies. The allocation of sample size to a region of interest (Region X) was evaluated using both a conventional statistical approach and a pharmacometric model-based (PMx) approach, predicated on the assumption of a minimum treatment improvement in Region X. Further evaluation was performed to assess the probability of reaching reliable conclusions regarding clinically relevant inter-regional differences in treatment response. The statistical approach, relying solely on end-of-trial observations from a single dose group, exhibited a maximum power of less than 40% in detecting treatment differences across regions when Region X accounts for 50% of the total sample size. In contrast, the PMx approach, employing data from multiple dose groups across trial duration, demonstrated that 26% of the total sample size yielded over 80% power to identify the inter-regional difference. The PMx approach has also been shown to offer a more efficient characterization of the clinical relevance of inter-regional differences, and has potential to improve decision-making in development progression by integrating prior knowledge.

Indexed as

Clinical Trials, Phase II as TopicPsoriasisDose-Response Relationship, DrugHumansResearch DesignSample Sizedose‐ranging studymodel‐informed drug developmentmulti‐regional clinical trialsample size allocation

Identifiers

PMID40751361
PMCPMC12521070

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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.