首页|Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches
Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches
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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(n=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 eval-uated.Overall prediction of SVEM(＞97％)was comparably better than that of XGBoost/Bayesian opti-mization(＞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.
Vaccine manufacturingMicrofluidic deviceXGBoostBayesian optimizationSelf-validated ensemble model
Ravi Maharjan、Ki Hyun Kim、Kyeong Lee、Hyo-Kyung Han、Seong Hoon Jeong
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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
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