报告题目：Investigation of Molecular Mechanism of Cyclodextrin Solubilization and Development
of a Predictive Model with Machine Learning Techniques
报告人：赵倩倩博士
报告时间：2018年12月17日（星期一）上午9:30-10:30
报告地点：福州大学阳光科技大厦北616
报告摘要：
Cyclodextrin (CD) complexation has become an attractive solubilizing formulation strategy for poorly water-soluble drugs in pharmaceutical industry. Although thousands of literatures have been published to investigate the drug-CD complexation system, traditional experimental methods have their own limitations to obtain detailed information about the molecular interaction between the drug candidates with CDs. Therefore, it is really difficult to identify whether the drug-CD complexation could be feasible, whether it forms drug-CD inclusion or non-inclusion complexes, whether the formulation needs to be further added the auxiliary components.
Current research combined the molecular dynamic simulation and experimental techniques to investigate the structural, energetic and hydrogen bond features to reveal the molecular mechanism of the drug-CD complexes at the molecular level. Based on the well understanding of molecular interaction in the drug-CD system, a highly soluble lutein-CD multiple-component delivery system was developed to enhance lutein’s solubility, dissolution rate and bioavailability. Furthermore, this research introduced machine learning methods to build up a highly accuracy predictive model for the complexation free energy between guest molecules and CDs based on a dataset of 3000 experimentally determined binding free energy. The integration of machine learning and molecular modeling methods could produce synergistic effect for interpreting and predicting the drug-CD system.
报告人简介：
个人教育经历：
2015-2018 博士，生物医药，澳门大学
2012-2015 硕士，药剂学，沈阳药科大学
2008-2012 学士，药物制剂，沈阳药科大学
科研简介：
主要从事难溶性药物增溶技术的研究与开发，研究剂型包括环糊精络合体系，固体分散体体系，固体口服缓控释制剂。博士期间主要致力于计算药剂学研究，结合分子模拟和实验技术评价环糊精体系主客分子相互作用类型，同时引入机器学习技术构建环糊精体系预测模型，快速筛选最优处方，缩短药物研发周期。
代表性论文:
1. Zhao, Q. Q.; Zhang, W. X.; Wang, R. M.; Wang, Y. T.; Ouyang, D. F. Research advances in molecular modeling in cyclodextrins. Curr. Pharm. Des.2017, 23, 522-531.
2. Zhang, W. X.#; Zhao, Q. Q.#; Deng, J. L.; Hu, Y. J.; Wang, Y. T.; Ouyang, D. F. Big data analysis of global advances in pharmaceutics and drug delivery 1980-2014. Drug Discov. Today 2017, 22, 1201-1208. (# Co-first authors)
3. Zhao, Q. Q.; Miriyala, N.; Su, Y.; Chen, W. J.; Gao, X. J.; Shao, L.; Yan, R.; Li, H. F.; Yao, X. J.; Cao, D. S.; Wang, Y. T.; Ouyang, D. F. Computer-aided formulation design for a highly soluble lutein-cyclodextrin multiple-component delivery system. Mol. Pharm.2018, 15, 1664-1673.
