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Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry

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Abstract

Background

Machine learning (ML) tools have become invaluable in potential drug candidate screening, formulation development, manufacturing, and characterization of advanced drug delivery systems. These tools are part of the Industry 4.0 revolution, which plays a vital role in microparticle and microfluidics, alongside mRNA-LNP vaccines, and stability in advanced protein therapeutics.

Area covered

This study summarizes the application of ML tools in drug discovery, formulation development, and optimization, in addition to continuous manufacturing and characterization of advanced drug delivery systems such as biopharmaceutical formulations including mRNA-LNP vaccines, microfluidics, and microparticle dosage forms. Furthermore, it includes stability concerns, and regulatory, technical, and ethical issues along with future perspectives.

Expert opinion

ML tools are essential for revolutionizing the drug development cycle, where it has been implemented to screen vast databases for drug discovery, optimize formulations, adopt Industry 4.0, and continuous manufacturing concepts, including characterizing and predicting the stability of biopharmaceuticals. However, a gap between regulatory authorities and industries is felt due to current ethical and technical issues in the drug approval process. The vast available databases can be used to train the ML models and such pre-trained ML models can address these concerns. Additionally, these pre-trained tools can predict stability, meaning that the optimization of the formulation is possible, which can save lots of time, efforts, and costs. Moreover, a multidisciplinary approach between ML tools and the drug delivery system promotes digital twin, which can lead to improved patient compliance and efficacy.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.

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Acknowledgements

This work was partially supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (NRF-2018R1A5A2023127 and NRF-2019R1A2C1083911).

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National Research Foundation of Korea (NRF), NRF-2018R1A5A2023127,Seong Hoon Jeong, NRF-2019R1A2C1083911, Ki Hyun Kim

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  1. BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University, Seoul, Gyeonggi, 10326, Republic of Korea

    Ravi Maharjan, Jae Chul Lee, Kyeong Lee, Hyo-Kyung Han, Ki Hyun Kim & Seong Hoon Jeong

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Maharjan, R., Lee, J.C., Lee, K. et al. Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry. J. Pharm. Investig. 53, 803–826 (2023). https://doi.org/10.1007/s40005-023-00637-8

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