Lessons from Nature: Sources and Strategies for Developing AMPK Activators for Cancer Chemotherapeutics
Bibliographic record
Abstract
Adenosine Monophosphate-Activated Protein Kinase or AMPK is a highly-conserved master-regulator of numerous cellular processes, including: Maintaining cellular-energy homeostasis, modulation of cytoskeletaldynamics, directing cell growth-rates and influencing cell-death pathways. AMPK has recently emerged as a promising molecular target in cancer therapy. In fact, AMPK deficiencies have been shown to enhance cell growth and proliferation, which is consistent with enhancement of tumorigenesis by AMPK-loss. Conversely, activation of AMPK is associated with tumor growth suppression via inhibition of the Mammalian Target of Rapamycin Complex-1 (mTORC1) or the mTOR signal pathway. The scientific communities' recognition that AMPK-activating compounds possess an anti-neoplastic effect has contributed to a rush of discoveries and developments in AMPK-activating compounds as potential anticancer-drugs. One such example is the class of compounds known as Biguanides, which include Metformin and Phenformin. The current review will showcase natural compounds and their derivatives that activate the AMPK-complex and signaling pathway. In addition, the biology and history of AMPK-signaling and AMPK-activating compounds will be overviewed, their anticancer-roles and mechanisms-of-actions will be discussed, and potential strategies for the development of novel, selective AMPK-activators with enhanced efficacy and reduced toxicity will be proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".