Li Lillian, Back Sung-In, Ma Jian, Guo Yawen, Galeandro-Diamant Thomas, Clénet Didier
Vaccine CMC Development & Supply, Sanofi, Toronto, Ontario, Canada.
Vaccine CMC Development & Supply, Sanofi, Marcy-L' Etoile, France.
PLoS One. 2025 Jun 11;20(6):e0324205. doi: 10.1371/journal.pone.0324205. eCollection 2025.
Developing vaccines with a better stability is an area of improvement to meet the global health needs of preventing infectious diseases. With the advancement of data science and artificial intelligence, innovative approaches have emerged. This manuscript highlights the applications of machine learning through two cases in which Bayesian optimization was used to develop viral vaccine formulations. The two case studies monitored the critical quality attributes of virus A in liquid form by infectious titer loss and virus B in freeze-dried form by glass transition temperature. Stepwise analysis and model optimization demonstrated progressive improvements of model quality and prediction accuracy. The cross-validation matrices of the models' predictions showed high R² and low root mean square errors, indicating their reliability. The prediction accuracy of models was further validated by using test datasets. Model analysis using prediction error plot, Shapeley Additive exPlanations, permutation importance, etc. can provide additional insights into relations between model and experimental design, the influence of features of interest, and non-linear responses. Overall, Bayesian optimization is a useful complementary tool in formulation development that can help scientists make effective data-driven decisions.
开发具有更好稳定性的疫苗是满足预防传染病全球卫生需求的一个改进领域。随着数据科学和人工智能的发展，出现了创新方法。本文通过两个使用贝叶斯优化来开发病毒疫苗配方的案例突出了机器学习的应用。这两个案例研究通过感染滴度损失监测液体形式的病毒A的关键质量属性，并通过玻璃化转变温度监测冻干形式的病毒B的关键质量属性。逐步分析和模型优化证明了模型质量和预测准确性的逐步提高。模型预测的交叉验证矩阵显示出高R²和低均方根误差，表明了它们的可靠性。通过使用测试数据集进一步验证了模型的预测准确性。使用预测误差图、夏普利加性解释、排列重要性等进行模型分析，可以提供关于模型与实验设计之间的关系、感兴趣特征的影响以及非线性响应的更多见解。总体而言，贝叶斯优化是配方开发中一种有用的补充工具，可以帮助科学家做出有效的数据驱动决策。
