Evidence map›Paper›PMID 41820153›Full record

ReviewTrends in pharmacological sciences2026

Targeting metabolism to combat anticancer and antibacterial drug resistance.

Carolina H Chung, Rupa Bhowmick, Annie J Badenoch, Harkirat S Arora, Sriram Chandrasekaran

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trends in pharmacological sciences, 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

5 authors.

Carolina H ChungDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Rupa BhowmickDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Annie J BadenochCenter for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA.
Harkirat S AroraDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Sriram ChandrasekaranDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA; Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Electronic address: csriram@umich.edu.

Funding

Linking metabolic activity with drug sensitivity using metabolic influence networksR35GM137795 · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2025 to 2025
$366k
NIGMS NIH HHS R35 GM137795
6 · The paper itself

Abstract

Drug resistance is a major challenge in cancer and infectious diseases, requiring innovative solutions. Recent research suggests that bacteria and cancer cells reprogram their metabolism and manipulate their external metabolic environment to resist a diverse range of therapeutics. Emerging technologies, including single-cell and spatial omics profiling, CRISPR chemogenomics, machine learning, and metabolic network modeling, have revealed the metabolic complexities within bacterial biofilms, tuberculosis granulomas, and the tumor microenvironment. Here, we examine metabolic mechanisms that aid drug resistance across these different disease areas; this includes activation of antioxidant defenses, manipulation of the host immune response, and rewiring of energy metabolism. This analysis of shared metabolic factors across diseases may inspire repurposing of drugs, immunotherapies, and dietary interventions to overcome resistance.

Indexed as

Anti-Bacterial AgentsAntineoplastic AgentsDrug Resistance, BacterialNeoplasmsAnimalsDrug Resistance, NeoplasmHumansMetabolic ReprogrammingAnti-Bacterial AgentsAntineoplastic Agentsbacterial infectionscancercell metabolismdrug repurposingdrug resistancesystems biology

Identifiers

PMID41820153
PMCPMC13005933

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.