The research team led by Ouyang Defang, associate professor in the Institute of Chinese Medical Sciences, the State Key Laboratory of Quality Research in Chinese Medicine, and the Faculty of Health Sciences at the University of Macau (UM), has developed an artificial intelligence (AI) model for virtual screening of mRNA lipid nanoparticle (LNP) delivery systems. This breakthrough is set to increase the efficiency of AI-driven mRNA drug development. The research, titled ‘Artificial intelligence-driven rational design of ionisable lipids for mRNA delivery’, has been published in the international journal Nature Communications, and an international patent has been filed.

LNPs have proven effective for mRNA delivery, as demonstrated by the COVID-19 vaccines. However, their key component, ionisable lipids, has traditionally been optimised through inefficient and costly experimental screening. Therefore, the research team developed an AI model and used virtual screening to facilitate the rational design of ionisable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionisable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, showed comparable performance to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equalled or outperformed MC3, with one exhibiting efficacy similar to a superior control lipid SM-102. Furthermore, the AI model is interpretable in terms of structure-activity relationships. This study demonstrates the immense potential of AI technology in the development of mRNA drug products, which can improve the efficiency of drug development.

The study was conducted in collaboration between UM and Fudan University. Ouyang Defang is the co-first corresponding author of the study, with UM postdoctoral fellow Wang Wei and PhD student Wu Yiyang as co-first authors. The research was supported by UM (File No: MYRGCRG2022-00008-ICMS, ICMS/RTO/EP160/2023), Science and Technology Development Fund of the Macao SAR (File No: 0071/2024/RIA1, 005/2023/SKL), Shenzhen Science and Technology Innovation Commission (File No: SGDX20210823103802016), Zhuhai Science and Technology Innovation Bureau (File No: ZH22017002210010PWC), National Natural Science Foundation of China (File No: 32301174). The research was also supported by the Shanghai Zhangjiang mRNA Innovation and Translation Center, Fudan Center for mRNA Translational Research, and Shanghai RNACure. The full text of the study is available at https://www.nature.com/articles/s41467-024-55072-6.

Source: Institute of Chinese Medical Sciences
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