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Towards natural mimetics of metformin and rapamycin.
| Content Provider | Europe PMC |
|---|---|
| Author | Aliper, Alexander Jellen, Leslie Cortese, Franco Artemov, Artem Karpinsky-Semper, Darla Moskalev, Alexey Swick, Andrew G. Zhavoronkov, Alex |
| Copyright Year | 2017 |
| Abstract | Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals—safer, naturally-occurring compounds—that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors. |
| Journal | Aging |
| Volume Number | 9 |
| PubMed Central reference number | PMC5723685 |
| Issue Number | 11 |
| PubMed reference number | 29165314 |
| e-ISSN | 19454589 |
| DOI | 10.18632/aging.101319 |
| Language | English |
| Publisher | Impact Journals LLC |
| Publisher Date | 2017-11-01 |
| Access Restriction | Open |
| Rights License | This article is distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. Copyright: © 2017 Aliper et al. |
| Subject Keyword | geroprotector metformin rapamycin deep learning natural nutraceutical compound screening |
| Content Type | Text |
| Resource Type | Article |
| Subject | Aging Cell Biology |
