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基于XGBoost/贝叶斯方法和集成模型方法的机器学习驱动的mRNA-脂质纳米颗粒疫苗质量优化

Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches.

作者信息

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

机构信息

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

College of Pharmacy, Mokpo National University, Jeonnam, 58554, Republic of Korea.

出版信息

J Pharm Anal. 2024 Nov;14(11):100996. doi: 10.1016/j.jpha.2024.100996. Epub 2024 May 8.

DOI:10.1016/j.jpha.2024.100996
PMID:39759971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11696778/
Abstract

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA-lipid nanoparticles (mRNA-LNP). Different mRNA-LNP formulations ( = 24) were developed using an I-optimal design, where machine learning tools (XGBoost/Bayesian optimization and self-validated ensemble (SVEM)) were used to optimize the process and predict lipid mix ratio. The investigation included material attributes, their respective ratios, and process attributes. The critical responses like particle size (PS), polydispersity index (PDI), Zeta potential, pKa, heat trend cycle, encapsulation efficiency (EE), recovery ratio, and encapsulated mRNA were evaluated. Overall prediction of SVEM (>97%) was comparably better than that of XGBoost/Bayesian optimization (>94%). Moreover, in actual experimental outcomes, SVEM prediction is close to the actual data as confirmed by the experimental PS (94-96 nm) is close to the predicted one (95-97 nm). The other parameters including PDI and EE were also close to the actual experimental data.

摘要

为提高疫苗生产效率，本研究聚焦于优化信使核糖核酸-脂质纳米颗粒（mRNA-LNP）的微流控条件和脂质混合比例。使用I-最优设计开发了不同的mRNA-LNP配方（n = 24），其中利用机器学习工具（XGBoost/贝叶斯优化和自验证集成（SVEM））来优化工艺并预测脂质混合比例。该研究涵盖材料属性、它们各自的比例以及工艺属性。对诸如粒径（PS）、多分散指数（PDI）、zeta电位、pKa、热趋势循环、包封效率（EE）、回收率和包封的mRNA等关键响应进行了评估。SVEM的总体预测（>97%）比XGBoost/贝叶斯优化（>94%）要好。此外，在实际实验结果中，SVEM预测与实际数据接近，如实验测得的PS（94 - 96纳米）接近预测值（95 - 97纳米）所证实。包括PDI和EE在内的其他参数也与实际实验数据接近。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/9f7156050e19/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/cfa878103ab9/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/b800223066ba/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/ca55f917f488/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/a38a6a705ec7/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/b587c8021383/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/0e529fb61d0e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/40409e5c8838/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/138a28907d81/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/a72f453cb2f1/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/afce048277af/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/9f7156050e19/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/cfa878103ab9/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/b800223066ba/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/ca55f917f488/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/a38a6a705ec7/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/b587c8021383/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/0e529fb61d0e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/40409e5c8838/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/138a28907d81/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/a72f453cb2f1/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/afce048277af/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee7/11696778/9f7156050e19/gr10.jpg

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[5]
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[6]
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本文引用的文献

[1]
AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.

Nat Commun. 2024-7-26

[2]
A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM).

J Vis Exp. 2023-8-18

[3]
Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms.

Sci Rep. 2023-7-26

[4]
Evaluation of a DoE based approach for comprehensive modelling of the effect of lipid nanoparticle composition on nucleic acid delivery.

Biomaterials. 2023-8

[5]
Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach.

Int J Pharm. 2023-6-10

[6]
Successful batch and continuous lyophilization of mRNA LNP formulations depend on cryoprotectants and ionizable lipids.

Biomater Sci. 2023-6-13

[7]
Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Nano Today. 2022-12

[8]
Nucleic acid nanoassembly-enhanced RNA therapeutics and diagnosis.

Acta Pharm Sin B. 2023-3

[9]
Effect of Cholesterol Content of Lipid Composition in mRNA-LNPs on the Protein Expression in the Injected Site and Liver After Local Administration in Mice.

J Pharm Sci. 2023-5

[10]
Lipid Microparticles Show Similar Efficacy With Lipid Nanoparticles in Delivering mRNA and Preventing Cancer.

Pharm Res. 2023-1

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