Evidence map›Paper›PMID 38531888›Full record

SynthesisScientific reports2024

Background and clinical significance of biomarker-based patient enrichment in non-small-cell lung cancer drug development.

Kenji Harada, Shunsuke Ono

Abstract readComparative StudyMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2024. 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
  3. Review
  4. 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

2 authors.

Kenji HaradaLaboratory of Pharmaceutical Regulatory Science, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Shunsuke OnoLaboratory of Pharmaceutical Regulatory Science, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. shun-ono@mol.f.u-tokyo.ac.jp.

Funding

A Japanese government-based grant-in-aid from the Ministry of Education, Culture, Sports, Science and Technology, Tokyo, Japan 19K07215
6 · The paper itself

Abstract

Pharmaceutical companies have adopted biomarker-based enrichment (personalized) strategies to improve research and development productivity. We explored the background in which personalized strategies are adopted and examined whether their adoption is linked to improved efficacy of new drugs approved for non-small cell lung cancer (NSCLC) by US Food and Drug Administration (FDA). We extracted data from the first labels of drugs approved for NSCLC between May 2003 and February 2021, and performed a qualitative comparative analysis and meta-analysis. Personalized strategies were adopted in more than half of the trials (16/27) and were often used in trials aimed at obtaining first-line indications and in drugs that were not first-in-class. The meta-analysis showed that personalized trials had significantly improved progression-free survival (PFS) hazard ratio (HR) than trials without personalization but not for relative response rate ratio (RRR) or overall survival (OS) HR. Trials in which PFS HR was the primary endpoint tended to have improved PFS HR, and trials in which OS HR was the primary endpoint had worse PFS HR. The efficacy endpoints that are substantially affected by personalized strategies appear to differ, especially for new drugs with novel mechanism of action (MOA), because trial designs are employed to validate drug-specific advantages.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsBiomarkersClinical RelevanceDrug DevelopmentHumansBiomarkers

Identifiers

PMID38531888
PMCPMC10966045

What OpenQuestion holds

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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.