Evidence map›Paper›PMID 41504942›Full record

ReviewEuropean journal of clinical pharmacology2026

A short review of dose-finding adaptive trial designs for clinical pharmacologists and non-statisticians in oncology.

Yoko Franchetti

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

1 author.

Yoko FranchettiGSK, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA. yoko.x.franchetti@gsk.com.ORCID http://orcid.org/0000-0001-5587-9962

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTrial designs are one of the important topics for drug developers. Technical understanding of adaptive dose-finding trial designs is beneficial to clinical pharmacologists as they can gain a new perspective for effectively using their expertise skills and making their contributions visible in a drug development process. However, there is no introductory review paper on dose-finding adaptive designs available for clinical pharmacologists since trial design publications in statistical journals often do not primarily focus on the practical considerations of clinical pharmacologists.

objectiveThis short article reviews standard dose-finding adaptive designs that are typically employed in oncology clinical trials for clinical pharmacologists and non-statisticians.

resultsStandard dose-finding adaptive designs are described without mathematical equations, and basic concepts of model-based designs vs. model-assisted designs are provided in contrast to rule-based designs. Clinical pharmacology (CP) considerations for selecting an adaptive design, regarding objectives and endpoints, safety constraints, linear assumptions, and pre-specification of unknown dose/exposure-response relationships are described. A list of potential design inputs and impact is provided from a clinical pharmacology perspective.

conclusionFamiliarizing themselves with adaptive trial designs will help clinical pharmacologists and non-statisticians to well prepare for drug development collaboration in a cross-functional team setting. Proactively getting involved in choosing a trial design will contribute to successful dose-finding in oncology drug development.

Indexed as

Antineoplastic AgentsClinical Trials as TopicPharmacology, ClinicalResearch DesignDose-Response Relationship, DrugDrug DevelopmentHumansMedical OncologyNeoplasmsAntineoplastic AgentsAdaptive trial designBOINClinical pharmacology considerationsCRMDose-findingOncology drug development

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.