Evidence map›Paper›PMID 39575837›Full record

ArticleCPT: pharmacometrics & systems pharmacology2025

Clinical study design strategies to mitigate confounding effects of time-dependent clearance on dose optimization of therapeutic antibodies.

Jeffrey R Proctor, Harvey Wong

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

2 authors.

Jeffrey R ProctorFaculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0002-2274-5607
Harvey WongFaculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0003-1550-5071

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.

Indexed as

Research DesignRituximabComputer SimulationDose-Response Relationship, DrugHumansModels, BiologicalTime FactorsRituximab

Identifiers

PMID39575837
PMCPMC11812930

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Registered trials

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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.