Rapamycin: one drug, many effects
Open accessAbstract
The mammalian target of rapamycin (mTOR) signaling pathway is a master regulator of cell growth and metabolism. Deregulation of the mTOR pathway has been implicated in a number of human diseases such as cancer, diabetes, obesity, neurological diseases, and genetic disorders. Rapamycin, a specific inhibitor of mTOR, has been shown to be useful in the treatment of certain diseases. Here we discuss its mechanism of action and highlight recent findings regarding the effects and limitations of rapamycin monotherapy and the potential utility of combination therapy with rapamycin.
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Rapamycin has both beneficial and detrimental effects on metabolism.
“Rapamycin has been shown to have both beneficial and detrimental effects on metabolism, a disparity that has been largely attributed to non-specific effects depending on the time-course of administration [64, 65].”
From AMPK hyperactivation promotes dendrite retraction, synaptic loss, and neuronal dysfunction in glaucoma (2021) · cites this paper as [65]
Prolonged rapamycin treatment improves metabolic profiles, increases oxygen consumption and ketogenesis, and markedly enhances insulin sensitivity.
“Prolonged treatment improves metabolic profiles, increases oxygen consumption and ketogenesis, and markedly enhances insulin sensitivity [reviewed in Li et al. (279)].”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [279]
Rapamycin has poor solubility and pharmacokinetics, which led to the production of rapamycin analogs (rapalogs), which are currently used in some cancer therapies.
“Rapamycin, however, has poor solubility and pharmacokinetics, which led to the production of rapamycin analogs (rapalogs), which are currently used in some cancer therapies (279).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [279]
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“Rapamycin has been shown to have both beneficial and detrimental effects on metabolism, a disparity that has been largely attributed to non-specific effects depending on the timecourse of administration [64, 65].”
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- Authors
- Li, J., Kim, S. G., & Blenis, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Blenis, John
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 19
- Issue
- 3
- Pages
- 373-379
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC3972801
- OpenAlex
- W2075614768
- Cited by
- 1,287
- References
- 45
- Citations by year
- 2026: 83 · 2025: 137 · 2024: 141 · 2023: 128 · 2022: 164 · 2021: 158 · 2020: 126 · 2019: 106 · 2018: 91 · 2017: 51 · 2016: 53 · 2015: 31 · 2014: 16 · 2013: 1
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health
- Tuberous Sclerosis Alliance
- LAM Foundation
- National Cancer Institute (CA46595, R37CA046595, R01CA046595)
- National Institute of General Medical Sciences (GM51405, R01GM051405)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › PI3K/AKT/mTOR signaling in cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Health Sciences › Medicine › Epidemiology › Autophagy in Disease and Therapy
- Keywords
- rapamycin, mTOR signaling, mTOR inhibitors, cell growth regulation, metabolism, cancer, diabetes mellitus, obesity, neurological disorders, genetic disorders, combination therapy
- MeSH terms
- Aging, Drug Therapy, Combination, Humans, Longevity, Lung Diseases, Metabolic Diseases, Neoplasms, Nervous System Diseases, Signal Transduction, Sirolimus, TOR Serine-Threonine Kinases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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