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Can machine learning predict drug nanocrystals?
He,Yuan1; Ye,Zhuyifan1; Liu,Xinyang1; Wei,Zhengjie1; Qiu,Fen1; Li,Hai Feng2; Zheng,Ying1; Ouyang,Defang1
2020-06-10
Source PublicationJournal of Controlled Release
ISSN0168-3659
Volume322Pages:274-285
Abstract

Nanocrystals have exhibited great advantage for enhancing the dissolution rate of water insoluble drugs due to the reduced size to nanoscale. However, current pharmaceutical approaches for nanocrystals formulation development highly depend on the expert experience and trial-and-error attempts which remain time and resource consuming. In this research, we utilized machine learning techniques to predict the particle size and polydispersity index (PDI) of nanocrystals. Firstly, 910 nanocrystal size data and 341 PDI data by three preparation methods (ball wet milling (BWM) method, high-pressure homogenization (HPH) method and antisolvent precipitation (ASP) method) were collected for the construction of the prediction models. The results demonstrated that light gradient boosting machine (LightGBM) exhibited well performance for the nanocrystals size and PDI prediction with BWM and HPH methods, but relatively poor predictions for ASP method. The possible reasons for the poor prediction refer to low quality of data because of the poor reproducibility and instability of nanocrystals by ASP method, which also confirm that current commercialized products were mainly manufactured by BWM and HPH approaches. Notably, the contribution of the influence factors was ranked by the LightGBM, which demonstrated that milling time, cycle index and concentration of stabilizer are crucial factors for nanocrystals prepared by BWM, HPH and ASP, respectively. Furthermore, the model generalizations and prediction accuracies of LightGBM were confirmed experimentally by the newly prepared nanocrystals. In conclusion, the machine learning techniques can be successfully utilized for the predictions of nanocrystals prepared by BWM and HPH methods. Our research also reveals a new way for nanotechnology manufacture.

KeywordMachine Learning Nanocrystals Particle Size Polydispersity Index (Pdi) Prediction
DOI10.1016/j.jconrel.2020.03.043
URLView the original
Indexed BySCIE
WOS Research AreaChemistry ; Pharmacology & Pharmacy
WOS SubjectChemistry, Multidisciplinary ; Pharmacology & Pharmacy
WOS IDWOS:000537133900005
Scopus ID2-s2.0-85082670303
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Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionInstitute of Chinese Medical Sciences
INSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING
Corresponding AuthorZheng,Ying
Affiliation1.State Key Laboratory of Quality Research in Chinese Medicine,Institute of Chinese Medical Sciences (ICMS),University of Macau,Macau,China
2.Institute of Applied Physics and Materials Engineering,University of Macau,Macau,China
First Author AffilicationInstitute of Chinese Medical Sciences
Corresponding Author AffilicationInstitute of Chinese Medical Sciences
Recommended Citation
GB/T 7714
He,Yuan,Ye,Zhuyifan,Liu,Xinyang,et al. Can machine learning predict drug nanocrystals?[J]. Journal of Controlled Release,2020,322:274-285.
APA He,Yuan,Ye,Zhuyifan,Liu,Xinyang,Wei,Zhengjie,Qiu,Fen,Li,Hai Feng,Zheng,Ying,&Ouyang,Defang.(2020).Can machine learning predict drug nanocrystals?.Journal of Controlled Release,322,274-285.
MLA He,Yuan,et al."Can machine learning predict drug nanocrystals?".Journal of Controlled Release 322(2020):274-285.
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