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Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

文献类型：期刊论文

作者Xiao, Fu2,3,4; Cheng, Yinxiang1,3,4; Wang, Jian-Rong3,4; Wang, Dingyan1,3,4; Zhang, Yuanyuan1,3,4; Chen, Kaixian1,2,3,4; Mei, Xuefeng1,3,4; Luo, Xiaomin1,2,3,4
刊名PHARMACEUTICS
出版日期2022-10-01
卷号14期号:10页码:19
关键词bexarotene cocrystal prediction GCN bioavailability
DOI10.3390/pharmaceutics14102198
通讯作者Mei, Xuefeng(xuefengmei@simm.ac.cn) ; Luo, Xiaomin(xmluo@simm.ac.cn)
英文摘要Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC(0-8h)) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs.
WOS关键词CRYSTAL ; DRUG ; DISCOVERY ; LYMPHOMA ; DESIGN ; TOOLS ; SALTS
资助项目National Natural Science Foundation of China[82130108] ; Natural Science Foundation of Shanghai[22ZR147390] ; Shanghai Science and Technology Innovation Action Plan[20142201800]
WOS研究方向Pharmacology & Pharmacy
语种英语
出版者MDPI
WOS记录号WOS:000873710600001
源URL[http://119.78.100.183/handle/2S10ELR8/303151]  
专题新药研究国家重点实验室
通讯作者Mei, Xuefeng; Luo, Xiaomin
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210023, Peoples R China
3.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Mat Med, Pharmaceut Analyt & Solid State Chem Res Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Fu,Cheng, Yinxiang,Wang, Jian-Rong,et al. Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement[J]. PHARMACEUTICS,2022,14(10):19.
APA Xiao, Fu.,Cheng, Yinxiang.,Wang, Jian-Rong.,Wang, Dingyan.,Zhang, Yuanyuan.,...&Luo, Xiaomin.(2022).Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement.PHARMACEUTICS,14(10),19.
MLA Xiao, Fu,et al."Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement".PHARMACEUTICS 14.10(2022):19.

入库方式： OAI收割

来源：上海药物研究所

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