Evidence map›Paper›PMID 38903817›Full record

ReviewFrontiers in medicine2024

Phenotypic drug discovery: a case for thymosin alpha-1.

Enrico Garaci, Maurizio Paci, Claudia Matteucci, Claudio Costantini, Paolo Puccetti, Luigina Romani

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Aging and Thymosin Alpha-1.International journal of molecular sciences · 2025
    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

6 authors.

Enrico GaraciSan Raffaele Sulmona, L'Aquila, Italy.
Maurizio PaciDepartment of Chemical Sciences and Technologies, University of Rome "Tor Vergata", Rome, Italy.
Claudia MatteucciDepartment of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy.
Claudio CostantiniDepartment of Medicine and Surgery, University of Perugia, Perugia, Italy.
Paolo PuccettiDepartment of Medicine and Surgery, University of Perugia, Perugia, Italy.
Luigina RomaniSan Raffaele Sulmona, L'Aquila, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

Indexed as

cancer and infection therapyimmune regulationnetwork pharmacologyphenotypic drug discoverythymosin alpha-1

Identifiers

PMID38903817
PMCPMC11187271

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.