摘要
硝酸盐是一种机体不可或缺的营养物质,已应用于多种疾病的预防和治疗研究.但由于半衰期短,硝酸盐的临床应用受限.为了提高硝酸盐可用性,突破传统的依赖大规模高通量生物实验的药物配伍研发瓶颈,本文报道了一种基于群体学习方法的药物配伍预测系统,该系统预测维生素C为与硝酸盐配伍的首选药物.通过采用微囊化技术,筛选并优化缓释制剂工艺,以维生素C、硝酸钠及壳聚糖3000为芯材,果胶与羧甲基纤维素钠为壁材制备硝酸盐纳米颗粒,命名为耐瑞特(Nanonitrator).耐瑞特的更长循环期显著提高了硝酸盐对放射性唾液腺损伤的疗效,同时维持了安全性.相同剂量的耐瑞特比硝酸盐(包含或不包含维生素C)可显著增强PI3K-Akt信号通路转导,更好地维持细胞内稳态,提示了其广泛的临床应用潜力.此外,本文还提供了一种将无机化合物加入缓释纳米颗粒的新方法.
Inorganic nitrate is an indispensable nutrient that has been used in experimental studies for the prevention and treatment of several diseases.However,the short half-life of nitrate limits its clinical application.To increase the usability of nitrate and overcome the challenges of traditional combination drug discovery through large-scale high-throughput biological experiments,we developed a swarm learning-based combination drug prediction system that identified vitamin C as the drug of choice to be combined with nitrate.Employing microencapsulation technology,we used vitamin C,sodium nitrate,and chitosan 3000 as the core materials to prepare a nitrate nanoparticle,which we named Nanonitrator.The longcirculating delivery ability of nitrate by Nanonitrator significantly increased the efficacy and effect duration of nitrate in irradiation-induced salivary gland injury,without compromising safety.Nanonitrator at the same dose could better maintain intracellular homeostasis than nitrate(with or without vitamin C),emphasizing its potential for clinical use.More importantly,our work provides a method for incorporating inorganic compounds into sustained-release nanoparticles.
作者
潘雯
胡耿
李韶容
李国情
冯晓宇
吴志芳
张栋
秦力铮
王雪
胡亮
徐骏疾
胡磊
贾翌江
温欣
王劲松
张春梅
周建
李文斌
王晓刚
王玉记
王松灵
Wen Pan;Geng Hu;Shaorong Li;Guoqing Li;Xiaoyu Feng;Zhifang Wu;Dong Zhang;Lizheng Qin;Xue Wang;Liang Hu;Junji Xu;Lei Hu;Yijiang Jia;Xin Wen;Jinsong Wang;ChunmeZhang;Jian Zhou;Wenbin Li;Xiaogang Wang;Yuji Wang;Songlin Wang(Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction,Beijing Laboratory of Oral Health and Beijing Stomatological Hospital,Capital Medical University,Beijing 100050,China;Key Laboratory of Big Data-Based Precision Medicine,School of Engineering Medicine,Beihang University,Beijing 100191,China;Immunology Research Center for Oral and Systemic Health,Beijing Friendship Hospital,Capital Medical University,Beijing 100050,China;Department of Dental Implantology,The Affiliated Stomatological Hospital of Nanjing Medical University,Nanjing 210029,China;Department of Pediatric Dentistry,School of Stomatology,Capital Medical University,Beijing 100050,China;Beijing Key Laboratory of Tolerance Induction and Organ Protection in Transplantation,Beijing 100050,China;Department of Oral Maxillofacial Surgery,Capital Medical University School of Stomatology,Beijing 100050,China;School of Pharmaceutical Sciences,Capital Medical University,Beijing 100069,China;Beijing Area Major Laboratory of Peptide and Small Molecular Drugs,Engineering Research Center of Endogenous Prophylactic of Ministry of Education of China,Beijing 100069,China;Department of Biochemistry and Molecular Biology,School of Basic Medical Sciences,Capital Medical University,Beijing 100069,China;Laboratory for Oral and General Health Integration and Translation,Beijing Tiantan Hospital,Capital Medical University,Beijing 100070,China;Research Units of Tooth Development and Regeneration,Chinese Academy of Medical Sciences,Beijing 100069,China)
基金
supported by the Beijing Municipal Government grant(Beijing Laboratory of Oral Health,PXM2021-014226000041)
the Beijing Municipal Science and Technology Commission(Z181100001718208)
the Beijing Municipal Education Commission(119207020201)
the Innovation Research Team Project of Beijing Stomatological Hospital,Capital Medical University(CXTD202201)
the Chinese Research Unit of Tooth Development and Regeneration,Academy of Medical Sciences(2019-12M-5031)
the National Natural Science Foundation of China(92049201,82030031,81991504,and 92149301)
the Beijing Advanced Innovation Center for Big Data-based Precision Medicine(PXM2021_014226_000026)
the Beijing Municipal Government(Beijing Scholar Program,PXM2020_014226_000005 and PXM2021_014226_000020)
the Beijing Municipal Colleges and Universities High Level Talents Introduction and Cultivate Project-Beijing Great Wall Scholar Program(CIT&TCD 20180332)
the National Key Research and development Program(2022YFA1104401)。
