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A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease
James A Timmons, Andrew Anighoro, Robert J Brogan, Jack Stahl, Claes Wahlestedt, David Gordon Farquhar, Jake Taylor-King, Claude-Henry Volmar et al.. eLife, 2022-01-17
Topic: Genetics and APOE
Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.
Key findings
- Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease.
- We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies).
- The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways.
- Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology.
- Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.
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Study details
- Evidence strengthLow
How this study scored 45/100
Published in a leading specialty journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 25 of the 56 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (eLife) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35037854) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 25 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 25 of the 56 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Timmons, J. A., Anighoro, A., Brogan, R. J., Stahl, J., Wahlestedt, C., Farquhar, D. G., et al. (2022). A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease. eLife. https://doi.org/10.7554/elife.68832
Vancouver
Timmons JA, Anighoro A, Brogan RJ, Stahl J, Wahlestedt C, Farquhar DG, et al. A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease. eLife. 2022. doi:10.7554/elife.68832. PMID: 35037854.
BibTeX
@article{timmons2022-35037854,
author = {Timmons, James A and Anighoro, Andrew and Brogan, Robert J and Stahl, Jack and Wahlestedt, Claes and Farquhar, David Gordon and Taylor-King, Jake and Volmar, Claude-Henry and others},
title = {{A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease}},
journal = {eLife},
year = {2022},
doi = {10.7554/elife.68832},
pmid = {35037854},
url = {https://doi.org/10.7554/elife.68832}
}