Aim The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests. Methods A total of 3657 community‐dwelling adults aged ≥60 years from Nanchang city were recruited in this study. A 3‐year follow‐up test was carried out for all the participants to determine whether they turn to non‐SA. Developed questionnaires and physical fitness tests were used to obtain overall health condition, balance, agility, speed, reactions and gait. Four machine learning models (logistic regression, deep learning, random forest and gradient boosting decision tree) were applied to develop the prediction models, the analyzed sample was 890. Results The baseline prevalence of successful aging was 26.99%, The average annual incidence rate of SA to non‐SA was 11.04%. There were significant differences between the SA and non‐SA groups for all physical fitness tests at baseline. The accuracy and area under the curve of all four machine learning models was >85%, the positive predictive value and sensitivity was >75%, and the specificity was >86% on the average. The deep learning model outperformed the other model, with area under the curve 90.00%, accuracy 89.3%, positive predictive value 85.8% and specificity 93.1%, respectively. Compared with other models, the logistic regression model performed best in sensitivity. Age, arm curl, 30‐s sit‐to‐stand and reaction time were important predictors in all models. Conclusion The deep learning model is ideal in the prediction of SA maintenance, and the corresponding physical fitness interventions are essential to ensuring SA. Geriatr Gerontol Int 2020; ••: ••–••.
Both vascular endothelial growth factor receptor (VEGFR) and fibroblast growth factor receptor (FGFR) singling pathways can mediate tumor angiogenesis. Colony stimulating factor 1 receptor (CSF1R) plays an important role on functions of macrophages. Recently the roles of the VEGFR, FGFR and CSF1R in regulation of T cells, tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells, thereby increasing tumor immune evasion, have been demonstrated[1-3]. Therefore, blockade of tumor angiogenesis and tumor immune evasion by simultaneously targeting VEGFR, FGFR and CSF1R kinases may represent a promising approach for anti-cancer therapy. We report here the preclinical studies for sulfatinib (HMPL-012), a potent and highly selective small molecule tyrosine kinase inhibitor against VEGFR, FGFR1 and CSF1R. Sulfatinib inhibited VEGFR1, 2, and 3, FGFR1 and CSF1R kinases with IC50s in a range of 1~24 nM, and it strongly blocked VEGF induced VEGFR2 phosphorylation in HEK293KDR cells and CSF1 stimulated CSF1R phosphorylation in RAW264.7 cells with IC50 of 2 and 79 nM, respectively. Sulfatinib also attenuated VEGF or FGF stimulated HUVEC cells proliferation with IC50 < 50 nM. In animal studies, a single oral dosing of sulfatinib inhibited VEGF stimulated VEGFR2 phosphorylation in lung tissues of nude mice in an exposure-dependent manner. Furthermore, elevation of FGF23 levels in plasma 24 hours post dosing suggested suppression of FGFR signaling. Sulfatinib demonstrated potent tumor growth inhibition in multiple human xenograft models and decreased CD31 expression remarkably, suggesting strong inhibition on angiogenesis through VEGFR and FGFR signaling. In a syngeneic murine colon cancer model CT-26, sulfatinib demonstrated moderate tumor growth inhibition after single agent treatment. Flow cytometry and immunohistochemistry analysis revealed an increase of CD8+ T cells and a significant reduction in TAMs, (CD163+ or F4/80+CD11b+CD45+) and CSF1R+ TAMs in tumor tissue indicating strong effect on CSF1R. Interestingly, combination of sulfatinib with a PD-L1 antibody resulted in enhanced anti-tumor effect. These results suggested that sulfatinib has a strong effect in modulating angiogenesis and cancer immunity. In summary, sulfatinib is a novel angio-immuno kinase inhibitor targeting VEGFR, FGFR1 and CSF1R kinases that could simultaneously block tumor angiogenesis and immune evasion. This unique feature seems to support sulfatinib as an attractive candidate for exploration of possible combinations with checkpoint inhibitors against various cancers. Sulfatinib is currently in multiple clinical trials including two Phase III trials against neuroendocrine tumors. Reference: 1. Voron T, et al. J Exp Med. 2015; 212(2):139-48. 2. Ries CH, et al. Cancer Cell. 2014; 25(6):846-59. 3. Katoh M, et al. Int. J. of Mol Med. 2016; 38: 3-15. Citation Format: jinghong Zhou, Jun Ni, Min Cheng, Na Yang, Junqing Liang, Liang Ge, Wei Zhang, Jianxing Tang, qiaoling Sun, Fu Li, Jia Hu, Dongxia Shi, Hongbo Chen, Jingwen Long, Junen Sun, Fang Yin, Xuelei Ge, Hong Jia, Feng Zhou, Yongxin ren, Weiguo Qing, Weiguo Su. Preclinical evaluation of sulfatinib, a novel angio-immuno kinase inhibitor targeting VEGFR, FGFR1 and CSF1R kinases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4187. doi:10.1158/1538-7445.AM2017-4187
and sulfatinib are both being evaluated in Phase III clinical trials for various cancers. Fruquintinib is designed to be a highly selective and potent oral inhibitor of vascular endothelial growth factor receptors ("VEGFR") with a tolerability profile that enables rational combination with other cancer therapies. A new drug application ("NDA") for fruquintinib to the China Food and Drug Administration ("CFDA") is expected to be filed in mid-2017. It is currently under the joint development in China by ChiMed and its partner Eli Lilly and Company ("Lilly"). Sulfatinib is an oral, novel angio-immunokinase inhibitor that selectively targets VEGFR, fibroblast growth factor receptor ("FGFR") and colony-stimulating factor-1 receptor ("CSF-1R"), three key tyrosine kinase receptors involved in tumor angiogenesis and immune evasion. Two Phase III trials are underway in neuroendocrine tumor ("NET") patients in China. The presentations were as follows:
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