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相关β剂量优化方法:使用数学建模和适应性试验设计的最佳疫苗剂量确定

The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design.

作者信息

Benest John, Rhodes Sophie, Evans Thomas G, White Richard G

机构信息

Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.

Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK.

出版信息

Vaccines (Basel). 2022 Oct 30;10(11):1838. doi: 10.3390/vaccines10111838.

Abstract

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.

摘要

数学建模方法和适应性试验设计可能对优化疫苗剂量有效,但尚未普遍使用。这可能是由于在剂量效力或剂量毒性的参数模型正确选择方面存在不确定性。以前有人建议非参数模型在这种情况下可能有用。我们提出了一种基于非参数连续相关贝塔过程模型和适应性试验设计来确定最佳疫苗剂量的新方法。我们将此称为“相关贝塔”或“CoBe”剂量优化方法。我们通过模拟研究评估了CoBe剂量优化方法与其他疫苗剂量优化方法的比较。尽管使用的假设比其他基于建模的方法更简单,但我们发现CoBe剂量优化方法能够有效地找到单剂量和初免/加强接种疫苗的最大效力剂量。CoBe剂量优化方法在找到使疫苗效力最大化和疫苗相关毒性最小化的剂量方面也很有效。此外,我们发现这些建模方法可以受益于纳入专家知识,而这对以前的参数建模方法来说是困难的。这项工作进一步表明,使用数学建模和适应性试验设计可能有利于确定最佳疫苗剂量,确保最大的疫苗效益和减轻疾病负担,最终拯救生命。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f68/9693615/c3c679d62f93/vaccines-10-01838-g001.jpg

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