Abstract Increasing life expectancy is causing the prevalence of age-related diseases to rise, and there is an urgent need for new strategies to improve health at older ages. Reduced activity of insulin/insulin-like growth factor signaling (IIS) and mechanistic target of rapamycin (mTOR) nutrient-sensing signaling network can extend lifespan and improve health during aging in diverse organisms. However, the extensive feedback in this network and adverse side effects of inhibition imply that simultaneous targeting of specific effectors in the network may most effectively combat the effects of aging. We show that the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib, the mTOR complex 1 (mTORC1) inhibitor rapamycin, and the glycogen synthase kinase-3 (GSK-3) inhibitor lithium act additively to increase longevity in Drosophila. Remarkably, the triple drug combination increased lifespan by 48%. Furthermore, the combination of lithium with rapamycin cancelled the latter’s effects on lipid metabolism. In conclusion, a polypharmacology approach of combining established, prolongevity drug inhibitors of specific nodes may be the most effective way to target the nutrient-sensing network to improve late-life health.

Aging is a complex process of progressive cell, tissue, and systemic dysfunction that is involved in the etiology of age-related diseases (1). Genetic, dietary, and pharmacological interventions can ameliorate the effects of aging in laboratory animals and may lead to therapies against age-related diseases in humans (2⇓–4).

In organisms ranging from invertebrates to mammals, reducing the activity of the nutrient-sensing mechanistic target of rapamycin (mTOR) and insulin/insulin-like growth factor signaling (IIS) network can promote longevity and health during aging (2, 3). Lowering network activity can also protect against the pathology associated with genetic models of age-related diseases (1, 2). The network contains many drug targets, including mTOR, mitogen-activated protein kinase kinase (MEK), and glycogen synthase kinase-3 (GSK-3) (Fig. 1A). Down-regulation of mTOR activity by rapamycin, GSK-3 by lithium, or MEK by trametinib can each individually extend lifespan in laboratory organisms (5⇓⇓⇓⇓⇓–11), and brief inhibition of mTOR has recently been shown to increase the response of elderly people to immunization against influenza (12). In addition, both mTOR and MEK inhibitors have been shown to reduce senescent phenotypes in human cells (13), while increasing concentrations of lithium levels in drinking water correlate with reduced all-cause mortality in a Japanese population (10). An advantage of pharmacological interventions is that the timing and dose of drug administration are relatively simple to optimize, and drugs can be easily combined (4, 14⇓–16). Combination drug treatments also have the potential to counter resistance from feedback and to reduce each other’s side effects (17). Rapamycin, trametinib, and lithium each target different kinases and transcription factors to extend lifespan (5, 8, 11), and therefore their effector mechanisms are at least partially different from each other. Simultaneous inhibition of multiple targets within the nutrient-sensing network may hence be needed to optimize effector outputs and health benefits. Here, we measure the effects of combination treatments of rapamycin, lithium, and trametinib on lifespan and other traits, using Drosophila as a model organism.

Fig. 1. Lithium blocks negative side effects of mTORC1 and IIS inhibition. (A) A simplified diagram of the Drosophila nutrient-sensing network showing the target kinases of rapamycin, trametinib, and lithium. Lithium reversed the (B) hypertriglyceridemia (n = 6 replicas of 5 flies per condition, 1-way ANOVA) and (C) starvation resistance induced by rapamycin (50 µM) (n = 75). (D) Lithium treatment significantly extended lifespan of both wDah and dilp2-3,5 mutant flies. Neither (E) rapamycin (P = 0.58) nor (F) trametinib (P = 0.14) further extended lifespan of dilp2-3,5 mutant flies [log-rank test (n = 150)]. Cox Proportional Hazard analysis showed a significant genotype by treatment interaction for rapamycin (P = 0.002) and trametinib (P = 0.0018). Error bars represent SEM. ***P < 0.001 (1-way ANOVA or log-rank test).

Methods Fly Stocks, Husbandry, and Lifespan Analysis. For all experiments, a wild-type white Dahomey (wDah) stock, or, when noted, dilp2-3,5 mutant flies (wDah backcrossed), were used, and raised as previously described (20). LiCl (Sigma) in ddH 2 O, trametinib (LC laboratories) in dimethyl sulfoxide, and rapamycin (LC laboratories) in 100% ethanol were added to sugar−yeast−agar (SYA) medium to a final concentration of 1 mM, 15.6 μM, and 50 μM, respectively (5, 8, 11). Equivalent volumes and concentrations of vehicle were added to SYA medium for control treatments. Drug treatments were started 2 d posteclosion. Female flies (n = 130 to 200, 15 to 20 per vial) were sorted onto SYA medium that was replaced every 2 d to 3 d throughout life. Lifespan raw data are provided as Dataset S1. Starvation assay was performed as previously described (11). Food Intake, Fecundity, and Triglyceride Measurements. Feeding behavior (proboscis extension at 1 and 15 d of treatment) and food intake (quantified by dye-calibrated feeding) (4 to 5 flies per replicate, n = 8 to 10) were measured as previously described (23). Fecundity was quantified as number of eggs laid within 24 h (15 d), and triglyceride measurements (5 flies per replicate, n = 8) were performed as previously described (5, 11). Mass Spectrometry. Flies (n = 5, 15 flies) were treated with drugs (15 d), their digestive system was allowed to void (1 h), they were snap frozen, drugs were extracted as previously described (5), and they were resuspended in 100 µL of acetonitrile/isopropanol 70:30 for measurement with an Acquitiy UPLC I-class System/Xevo TQ-S (Waters) with MassLynx and absolute quantification.

Acknowledgments We are grateful to Prof. David Gems and Drs. Helena Cochemé, Natalie Moroz, and Filipe Cabreiro for advice and comments, and to Rachel Beltzhoover for proofreading. We thank Drs. Fiona Kerr, Anna Tillmann, and Giovanna Vinti for technical advice and assistance. We acknowledge funding from University College London Scholarships (J.I.C.-Q.), American Federation for Aging Research/Glenn Foundation for Medical Research Postdoctoral Fellowship (Grant PD18019 to J.I.C.-Q.), Max Planck Society (J.I.C.-Q., L.S.T., S.G., Y.H., and L.P.), and National Institutes of Health (Grants AG54215 and GM122610 to T.K.B.). This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 741989), European Research Council Starting Grant (Grant 311331 to I.B.), Research Into Ageing (I.B. and L.P.), Parkinson’s UK (L.L. and L.P.), Wellcome Trust Clinical Career Development Fellowship (Grant 214589/Z/18/Z to K.J.K.), Wellcome Trust Strategic Award (WT098565/Z/12/Z to L.P.), and Academy of Medical Sciences (K.J.K.).

Footnotes Author contributions: J.I.C.-Q. and L.P. designed research; J.I.C.-Q., K.J.K., L.L., S.G., Y.H., and I.B. performed research; J.I.C.-Q. and L.S.T. analyzed data; J.I.C.-Q., L.S.T., and L.P. wrote the paper; T.K.B. provided input in manuscript writing; J.I.C.-Q., L.S.T., and L.P. interpreted data; and T.K.B. and L.P. supervised experiments.

The authors declare no competing interest.

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