Evidence map›Paper›PMID 41710675›Full record

ArticleBMJ oncology2026

Human and financial consequences of herding in oncology drug development: clinical trials of TIGIT inhibitors.

Emily S Zhang, Leeza Osipenko, John A Hickman

Abstract read
In one paragraph

Article in BMJ oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Emily S ZhangDepartment of Health Policy, The London School of Economics and Political Science, London, UK.
Leeza OsipenkoDepartment of Health Policy, The London School of Economics and Political Science, London, UK.
John A HickmanBiological Sciences, University of Manchester - The Victoria University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-6661-8175

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To illustrate and quantitate how herding in oncology drug development (five or more biopharmaceutical companies competing to develop modulators of an identical target) amplifies the number of patients enrolled in clinical trials of experimental drugs, and to estimate the cost of trials. Methods and analysis: In the setting of globally based clinical trials in oncology drug development, two public databases (ClinicalTrials.gov and International Clinical Trials Registry Platform), registering clinical trials, were interrogated to determine patient enrolment numbers and clinical trial details for the development of inhibitors of the immune checkpoint protein TIGIT (T-cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory motif domains). Estimates were made of the costs of trials to biopharmaceutical companies and of the numbers of anonymous patients enrolled in clinical trials. Results: 21 companies are or were seeking to have 30 TIGIT inhibitors approved for clinical use in a case of herding. Nearly 49 000 patients were enrolled in 220 clinical trials of TIGIT inhibitors between May 2016 and September 2025 at an estimated cost of between US$3.1 billion and US$3.6 billion. Development of 8 of 30 (27%) TIGIT inhibitors in oncology has been terminated to date (November 2025) due to their failure to demonstrate significant clinical activity or for strategic reasons. Nearly 15 000 patients were enrolled in these terminated trials. None of the TIGIT inhibitors had received marketing authorisation as of November 2025. Conclusions: Because of the high attrition rate (95%) in the development of drugs for oncology, herding amplifies the loss of human and financial resources incurred during drug development, with large number of patients enrolled in clinical trials of experimental drugs that are unlikely to receive approval for clinical use. Herding concentrates resources at the expense of investment in a broader portfolio of drug development programmes with a spread of risk against attrition.

Indexed as

Health economicsImmunotherapy

Identifiers

PMID41710675
PMCPMC12911736

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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.