Email

defangouyang@um.edu.mo

Tel

(853) 8822-4514

Office

E12-1041

Consultation Hours

Tue: 9:00~10:00
Thu: 16:00~17:00

De Fang OUYANG 歐陽德方

Associate Professor

Academic Qualifications

  • PhD,  The University of Queensland, Australia (2010)
  • MSc, Shenyang Pharmaceutical University, China (2005)
  • BSc, Shenyang Pharmaceutical University, China (2000)

Teaching

HSCI4011Pharmacoinformatics
CMED7020Computational Pharmacy
CMED7035Application of Computer Technology in Pharmacy
CMED8011Advanced topics in Medicinal Administration

Research

Prof. Ouyang has a multidisciplinary background in pharmaceutics & computer modelling, with experience in academia and industry. He obtained his bachelor (2000) and master (2005) in pharmaceutics from Shenyang Pharmaceutical University, China. He completed his PhD in pharmacy at The University of Queensland, Australia, in 2010 and progressed directly to his faculty position (Lecturer in Pharmaceutics, PI) at Aston University (UK). From the end of 2014, he moved to the University of Macau.

Since 2011, he has pioneered the integration of multi-scale modeling and in silico screening techniques in the field of drug delivery – “computational pharmaceutics“. He has published 2 books, 5 book chapters and over 100 refereed SCI journal papers. He held over 10 approved patents, which had been used in medicinal products. He edited the first book (John Wiley & Sons Inc., 2015) in this research area. He serves as the associate editor of <Drug Delivery and Translational Research>, the editorial board or scientific advisor of <Asian Journal of Pharmaceutical Sciences>, <Pharmaceutical Research>, and <Journal of Pharmaceutical Sciences>. He established the first global in silico formulation platform. He successfully trained 6 PhD and over 30 master students.

His research focused on computational pharmaceutics, including:

  • In silico formulation screening: to develop novel algorithms for the prediction of pharmaceutical formulations;
  • Multi-scale modeling in drug delivery: to integrate quantum mechanics (QM), molecular dynamics (MD) and physiologically based pharmacokinetic (PBPK) modeling into drug delivery systems;
  • Pharmacoinformatics: big data analysis of pharmaceutical information from the literature, patent, clinical trial and marketed products.

Representative publications in recent 3 years

  1. Run Han, Zhuyifan Ye, Yunsen Zhang, Yaxin Cheng, Ying Zheng, and Defang Ouyang*. Predicting liposome formulations by the integrated machine learning and molecular modeling approaches. Asian Journal of Pharmaceutical Sciences, 2023 18, (3): 100811;
  2. Nannan Wang, Yunsen Zhang, Wei Wang, Zhuyifan Ye, Hongyu Chen, Guanghui Hu, and Defang Ouyang*. How can machine learning and multiscale modeling benefit ocular drug development? Advanced Drug Delivery Reviews, 2023: 114772;
  3. Wei Wang, Shuo Feng, Zhuyifan Ye, Hanlu Gao, Jinzhong Lin, Defang Ouyang*. Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm, Acta Pharmaceutica Sinica B, 2022, 12 (6), 2950-2962;
  4. Haoshi Gao, Stanislav Kan, Zhuyifan Ye, et al. Development of in silico methodology for siRNA lipid nanoparticle formulations. Chemical Engineering Journal, 2022, 442: 136310;
  5. Wang, Wei, Defang Ouyang*. Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery. Drug Discovery Today, 2022, 27(8): 2100-2120;
  6. Wei Wang, Zhuyifan Ye, Hanlu Gao, Defang Ouyang*. Computational pharmaceutics-A new paradigm of drug delivery. Journal of Controlled Release. 2021, 338, 119-136;
  7. Haoshi Gao, Haoyue Jia, Jie Dong, Xinggang Yang, Haifeng Li, Defang Ouyang*. Integrated in silico formulation design of self-emulsifying drug delivery systems. Acta Pharmaceutica Sinica B, 2021, 11(11), 3585-3594;
  8. Haoshi Gao, Yan Su, Wei Wang, Wei Xiong, Xiyang Sun, Yuanhui Ji, Hua Yu, Haifeng Li, and Defang Ouyang*. Integrated computer-aided formulation design: A case study of andrographolide/cyclodextrin ternary formulation. Asian Journal of Pharmaceutical Sciences 2021,16(4): 494-507;
  9. Yuan He, Zhuyifan Ye, Xinyang Liu, Hai-Feng Li, Ying Zheng, Defang Ouyang*. Can machine learning predict drug nanocrystals? Journal of Controlled Release, 2020, 322, 274-285; (Cover page)

  • Fellow of the Higher Education Academy (UK);
  • Associate editor of “Drug Delivery and Translational Research”;
  • Editorial board of “Asian Journal of Pharmaceutical Sciences” and “Pharmaceutical Research”;
  • Scientific Advisor of “Journal of Pharmaceutical Sciences”;
  • Grant reviewer of BBSRC (UK), French National Research Agency (ANR), Canada First Research Excellence Fund, and National Science Centre Poland;
  • Reviewer of over 20 SCI journals;

  • Computational Pharmacy Society (CPS)
  • American Chemical Society (ACS)
  • Asian Association of Schools of Pharmacy (AASP)