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Machine learning in accelerating microsphere formulation development

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Abstract

Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R2 = 0.880 vs. R2 = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.

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Acknowledgements

Molecular modeling was performed at the High-Performance Computing Cluster (HPCC) which is supported by Information and Communication Technology Office (ICTO) of the University of Macau.

Funding

Current research is financially supported by the Zhuhai-HongKong-Macau Collaboration Project (ZH22017002210010PWC) and the Macau FDCT research grant (0108/2021/A).

Author information

Author notes

  1. Jiayin Deng, Zhuyifan Ye, Wenwen Zheng, and Jian Chen equally contributed to this paper.

Authors and Affiliations

  1. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China

    Jiayin Deng, Zhuyifan Ye, Haoshi Gao, Zheng Wu, Ging Chan, Simon Ming-Yuen Lee & Defang Ouyang

  2. Department of Clinical Laboratory, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Wenwen Zheng

  3. Zhuhai Livzon Microsphere Technology Co., Ltd, Zhuhai, China

    Jian Chen & Yanqing Wang

  4. Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China

    Haoshi Gao

  5. Faculty of Health Sciences, University of Macau, Macau, China

    Ging Chan, Simon Ming-Yuen Lee & Defang Ouyang

  6. Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, China

    Yongjun Wang

  7. Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China

    Dongsheng Cao

Authors
  1. Jiayin Deng
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Contributions

Jiayin Deng: data collection, molecular dynamic simulation, and writing—original draft preparation; Zhuyifan Ye: machine learning modeling and writing—reviewing and editing; Wenwen Zheng: study design and revision; Jian Chen: experimental work; Haoshi Gao: data analysis; Zheng Wu: platform construction; Ging Chan: supervision; Yongjun Wang: supervision; Dongsheng Cao: supervision; Yanqing Wang: study conception and design; Simon Ming-Yuen Lee: study conception and design; Defang Ouyang: study conception and design and writing—reviewing and editing.

Corresponding authors

Correspondence to Yanqing Wang, Simon Ming-Yuen Lee or Defang Ouyang.

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Deng, J., Ye, Z., Zheng, W. et al. Machine learning in accelerating microsphere formulation development. Drug Deliv. and Transl. Res. 13, 966–982 (2023). https://doi.org/10.1007/s13346-022-01253-z

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