Effective treatment of some types of cancer can be achieved by modulating cell lineage-specific rather than tumor-specific targets. We conducted a systematic search for novel agents selectively toxic to cells of hematopoietic origin. Chemical library screenings followed by hit-to-lead optimization identified OT-82, a small molecule with strong efficacy against hematopoietic malignancies including acute myeloblastic and lymphoblastic adult and pediatric leukemias, erythroleukemia, multiple myeloma, and Burkitt's lymphoma in vitro and in mouse xenograft models. OT-82 was also more toxic towards patients-derived leukemic cells versus healthy bone marrow-derived hematopoietic precursors. OT-82 was shown to induce cell death by inhibiting nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting enzyme in the salvage pathway of NAD synthesis. In mice, optimization of OT-82 dosing and dietary niacin further expanded the compound's therapeutic index. In toxicological studies conducted in mice and nonhuman primates, OT-82 showed no cardiac, neurological or retinal toxicities observed with other NAMPT inhibitors and had no effect on mouse aging or longevity. Hematopoietic and lymphoid organs were identified as the primary targets for dose limiting toxicity of OT-82 in both species. These results reveal strong dependence of neoplastic cells of hematopoietic origin on NAMPT and introduce OT-82 as a promising candidate for the treatment of hematological malignancies.
Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.
We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouse's health state by means of stochastic evolution of a single organism state variable, the "dynamic frailty index" (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.
N-Docosahexaenoyl dopamine exhibited antioxidant activity in the test with a stable oxygen radical galvinoxyl. This compound produced a dose-dependent protective effect on cultured granular cells from rat cerebellum under conditions of oxidative stress. N-Docosahexaenoyl dopamine decelerated the development of symptoms of Parkinson's disease in mice receiving neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine.
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