Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease
Jiansong Fang, Pengyue Zhang, Quan Wang, Chien-Wei Chiang, Yadi Zhou, Yuan Hou, Jielin Xu, Rui Chen et al.. Alzheimer's research & therapy, 2022-01-10
Topic: Genetics and APOE
Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.
Key findings
- Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD.
- Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations.
- Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation.
- Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes.
- In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.
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Study details
- Study designcohort study
- Effect sizeHR = 0.921
- Evidence strengthModerate
How this study scored 63/100
Published in a peer-reviewed journal, and it is a cohort study following people over time. Scored zero for sample size, 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 35 of the 56 points we could judge it on, which is 63 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A cohort study following people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 35012639) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 35 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 35 of the 56 points we could judge it on is 63 out of 100. |
| Total | 63 / 100 | — |
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APA 7th
Fang, J., Zhang, P., Wang, Q., Chiang, C.-W., Zhou, Y., Hou, Y., et al. (2022). Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-021-00951-z
Vancouver
Fang J, Zhang P, Wang Q, Chiang C.-W, Zhou Y, Hou Y, et al. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Alzheimer's research & therapy. 2022. doi:10.1186/s13195-021-00951-z. PMID: 35012639.
BibTeX
@article{fang2022-35012639,
author = {Fang, Jiansong and Zhang, Pengyue and Wang, Quan and Chiang, Chien-Wei and Zhou, Yadi and Hou, Yuan and Xu, Jielin and Chen, Rui and others},
title = {{Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease}},
journal = {Alzheimer's research \& therapy},
year = {2022},
doi = {10.1186/s13195-021-00951-z},
pmid = {35012639},
url = {https://doi.org/10.1186/s13195-021-00951-z}
}