A research team of the Institute of Chinese\nMedical Sciences (ICMS) of the University of Macau (UM) has developed machine\nlearning algorithms based on big data to predict and assist in the development\nof nanocrystals for nanopharmaceutical preparations. The algorithms developed\nby the team can achieve a fairly high degree of prediction accuracy for\nnanocrystals prepared with the \u2018top-down\u2019 method. The study is of great\nsignificance for rational formulation design and can potentially reduce\nresearch and development time in the industrialisation of nanocrystals. The\nrelated article has been published in the well-known Journal of Controlled Release<\/i>. \n\n<\/p>\n\n
Currently, about 40 per cent of drug candidates\nin the development pipelines are poorly water-soluble drugs. Because of this\nproperty, it is difficult for these drugs to achieve an ideal degree of\nbioavailability and treatment efficiency with traditional formulation\nstrategies. It has been reported that nanocrystals could improve the\nbioavailability of these drugs. However, the current preparation of nanocrystal\nformulations is highly dependent on the experience of experts and a process of\ntrial and error that is time- and resource-consuming with a high risk of\nfailure. In recent years, machine learning technology based on big data has\nshown great potential in many fields, such as network optimisation, information\nmanagement, biomedicine and materials science. It can quickly find the internal\nrelationship between existing data and can be applied to the prediction of\nunknown data. Based on these advantages, UM researchers extracted 910 in-house\nnanocrystal data from Dr Ying Zheng\u2019s laboratory and published articles indexed\nby the Web of Science, used machine learning algorithms to find the internal\nrules, and finally predicted the particle size and polydispersity index (PDI)\nof newly prepared nanocrystals under different preparation methods. The results\nhave confirmed that the machine learning models established by the team have a\nfairly high degree of prediction accuracy for nanocrystals prepared with the\n\u2018top-down\u2019 method. The study is of great significance for rational formulation\ndesign and can potentially reduce research and development time in the\nindustrialisation of nanocrystals.<\/p>\n\n
The Journal\nof Controlled Release<\/i> is a well-known academic journal in the field of\npharmaceutical preparations, with an impact factor of 7.901 in 2018. UM\nAssistant Professor Ouyang Defang and Full Professor Zheng Ying are the\ncorresponding authors of the research article. UM doctoral students He Yuan and\nYezhu Yifan are the co-first authors. The following individuals have also made\nimportant contributions to the study. They are UM Research Assistant Liu\nXinyang, master\u2019s students Wei Zhengjie and Qiu Fen, and Dr Li Haifeng of\nInstitute of Applied Physics and Materials Engineering. The study was supported\nby the Macao Science and Technology Development Fund and UM.<\/p>
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Source: Institute of Chinese Medical Sciences <\/p>
Media Contact Information: Communications Office, University of Macau<\/p>
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Albee Lei Tel\uff1a(853) 88228004Kelvin U Tel\uff1a(853) 88224322Email\uff1aprs.media@um.edu.moUM Website\uff1awww.um.edu.mo<\/p>
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A research team of the Institute of Chinese Medical Sciences (ICMS) of the University of Macau (UM) has developed machine learning algorithms based on big data to predict and assist…<\/p>\n","protected":false},"author":1,"featured_media":19848,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[60],"tags":[],"class_list":["post-19847","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-release"],"acf":[],"yoast_head":"\n