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Using artificial intelligence to discover new rapamycin-like molecules

In a recent article published in International journal of medical sciencesthe researchers described how they used artificial intelligence and machine learning tools to find molecules that inhibit mTOR [1].

mTOR is a common target for lifespan extension interventions

Mechanistic target of rapamycin (mTOR) is a well known molecule in the world of rejuvenation. Previous studies have shown that reducing mTOR activity increases the lifespan of multiple laboratory animals, including yeast, worms, flies, and mice [2].

Currently, the use of mTOR inhibitors such as rapamycin to extend human lifespan is under debate due to possible side effects [3]which include anemia, increased blood pressure, fever, headache, nausea, diarrhea, and even new-onset diabetes [4]. This team looked for different and effective inhibitors of mTOR activity, which may not have these side effects.

Using artificial intelligence to filter a thousand molecules

The researchers used machine learning tools to generate a pool of 1,000 molecules, narrowed the pool down to 132 based on their potential for targeting mTOR, then chose 29 that likely had low toxicity. The researchers then analyzed the final candidates through an ADMET profile (absorption, distribution, metabolism, excretion, toxicity). The winner of this molecular competition was TKA001.

This competition, of course, was only in a simulation, and the AI-generated molecules need to be tested in real-world models to determine if they’re actually effective. The researchers began these experiments by testing mTOR activity in human cell lines after the addition of TKA001.

mTOR binds to two different groups of proteins, thus creating the mTORC1 and mTORC2 complexes [2], each of which has its own goals and impact on downstream proteins downstream of them. When active, mTORC1 attaches a phosphate group to the S6K molecule. mTORC2, on the other hand, attaches a phosphate group to the AKT molecule.

The researchers observed a reduced number of phosphate attacks on both S6K and AKT in cells given TKA001. This suggests that TKA001 inhibited both mTORC1 and mTORC2.

TKA001 inhibits the proliferation of cancer cells

AI-based analysis predicted that TKA001 could be a potent agent in treating prostate cancer, so researchers conducted experiments to confirm its efficacy against cancer. They started with epithelial cancer cells from patients with fibrosarcoma, a type of cancer that arises from fibrous connective tissue. They also used human cervical cancer cells.

The half maximum inhibitory concentration (IC50) is a measure of the amount of a given molecule required to inhibit 50% of a biological process. In this case, it refers to the proliferation of cancer cells. Rapamycin has an IC50 of 1.8M in fibrosarcoma cells and 0.25M in cervical cancer cells, but TKA001 has 200nM and 1M, respectively, showing that it has a comparable effect on cancer.

TKA001 extends C. elegans duration

The researchers wanted to test TKA001 in full living organisms, so they chose C. elegans, a small nematode commonly used in lifespan studies. They found that different doses extended the lifespan of C. elegans when administered to adult or young adult worms. However, although the lifespan extension in C. elegans was statistically significant, appears to be quite modest.

Strong potential, but more testing needed

Overall, these results are encouraging. However, they must first be confirmed in organisms biologically closer to humans, such as mice, before this molecule is introduced into clinical trials to test safety and efficacy.

TKA001 could be an interesting alternative to rapamycin, especially since the authors’ AI-based analysis suggests that TKA001 has low toxicity. This suggests that its side effects should be limited, but it’s also something that can only be tested in clinical trials.

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[1] Vidovic T, Dakhovnik A, Hrabovskyi O, MacArthur MR, Ewald CY. AI-predicted mTOR inhibitor reduces tumor cell proliferation and extends lifespan of C. elegans. Int J Mol Sci. 2023 Apr 25;24(9):7850. doi:10.3390/ijms24097850. PMID: 37175557; PMC ID: PMC10177929.

[2] Saxton, RA and Sabatini, DM (2017). mTOR signaling in growth, metabolism and disease. Cell, 168(6), 960976. https://doi.org/10.1016/j.cell.2017.02.004

[3] Salmon AB (2015). Voltage on the metabolic side effects of rapamycin. Oncotarget, 6(5), 25852586. https://doi.org/10.18632/oncotarget.3354

[4] Johnston, O., Rose, CL, Webster, AC and Gill, JS (2008). Sirolimus is associated with new-onset diabetes in kidney transplant recipients. Journal of the American Society of Nephrology: JASN, 19(7), 14111418. https://doi.org/10.1681/ASN.2007111202

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