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Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing
Fang J, Pieper AA, Nussinov R, Lee G, Bekris L, Leverenz JB, Cummings J, Cheng F.. Medicinal research reviews, 2020-07-13
Topic: Amyloid, tau and blood-test diagnosis
Plain-language summary
Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Fang, J., Pieper, A. A., Nussinov, R., Lee, G., Bekris, L., Leverenz, J. B., et al. (2020). Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing. Medicinal research reviews. https://doi.org/10.1002/med.21709
Vancouver
Fang J, Pieper AA, Nussinov R, Lee G, Bekris L, Leverenz JB, et al. Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing. Medicinal research reviews. 2020. doi:10.1002/med.21709. PMID: 32656864.
BibTeX
@article{fang2020-32656864,
author = {Fang, J. and Pieper, A. A. and Nussinov, R. and Lee, G. and Bekris, L. and Leverenz, J. B. and Cummings, J. and Cheng, F.},
title = {{Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing}},
journal = {Medicinal research reviews},
year = {2020},
doi = {10.1002/med.21709},
pmid = {32656864},
url = {https://doi.org/10.1002/med.21709}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases.
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Study details
- Study designcase-control study
- Evidence strengthModerate
How this study scored 52/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for sample size, effect reported, studied in people and randomized / 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 and multicenter collaboration, so those were left out: it earned 30 of the 58 points we could judge it on, which is 52 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Medicinal research reviews) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 6 / 12 | A case-control study |
| 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 | 2 / 5 | 2020 — within 10 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) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32656864) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 30 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 30 of the 58 points we could judge it on is 52 out of 100. |
| Total | 52 / 100 | — |
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