Development of statistical machine learning algorithms for the manufacturing of mRNA vaccines

开发用于 mRNA 疫苗制造的统计机器学习算法

• 批准号: 2744980
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2744980
• 关键词: Development; statistical; machine; learning; algorithms

mRNA vaccines have been widely used to resist covid-19 since the outbreak of the pandemic with a remarkable safety profile. A new manufacturing facility of a traditional vaccine can take years and a huge amount of money to become operational. In contrast, RNA vaccines are manufactured by a standardised procedure with minor adaptations to account for variations in RNA sequence length and composition. However, over the last few periods, companies such as Pfizer and Moderna have faced setbacks due to manufacturing disruptions. To address the challenges in vaccine manufacturing, the UK government has invested almost £215 million in the Vaccines Manufacturing and Innovation Centre since 2018 with further investment in the CPI's RNA Centre of Excellence. The manufacturing of vaccines is a complex process with a plethora of inputs and quality measures. In mRNA vaccines, the starting materials are the plasmid, the host bacteria, and the master cell bank of the recombinant microbial cells. The vaccine undergoes quality controls such as integrity of the nucleic acids, content, potency, product and process-related impurities, sterility, endotoxin, and physicochemical tests like pH and osmolality. Due to its chemistry, RNA is generally unstable, and hence the manufacturing of mRNA vaccines involves stability-indicating parameters such as RNA integrity, content and potency, supplemented by pH, appearance, and microbiological status. Finally, the vaccine undergoes stress testing that can include temperature shifts, pH shifts, photostability, humidity, or numerous freeze-thaw cycles. We hypothesise that data science methods are currently overlooked in such a complex manufacturing process. In particular, we wish to focus on understanding the temporal dynamics of processes during the manufacturing of mRNA vaccines. First, the programme concentrates on developing methods to integrate online and offline temporal data to predict process outcome and shed light on process productivity. Methodologically, we will build on the toolbox of sparse regression to discover the critical process parameters (CPPs) and identify how those parameters influence critical quality attributes (CQA). Our goal is to integrate the mathematical modelling with iterations of the modelling/manufacturing cycles. Second, the work programme builds on sparse methods developed by Steve Brunton and Nathan Kutz to identify ordinary and partial differential equations from data. Brunton and Kutz's algorithm aims to extract symbolic dynamical systems from a data stream using the toolbox of sparse regression. Their method takes a time series of a predetermined set of state-space variables supposed to describe an unknown dynamical system and identifies the coefficients of the terms on the right-hand side of the ODEs or PDEs that describe the system. Those terms are selected from a given library of candidate basis functions. To do this, the authors combine numerical methods to estimate the derivatives from the time series with a sequential thresholded least squares algorithm (SINDy) that performs variable selection on the library of basis functions. Their SINDy algorithm returns numerical coefficients for each basis function. If the dataset can be represented as a sparse dynamical system in the library of candidate basis functions, most of the coefficients are zero, and SINDy identifies the non-zero coefficients. The outcome is a description of the input dataset as a symbolic ODE or PDE expressed on the chosen state-space variables and as a function of the candidate library functions, whose numerical coefficients are found by SINDy. We will analyse datasets from bioreactors provided by the National Biologics Manufacturing Centre and its RNA Centre of Excellence, part of the Centre for Process Innovation (CPI). The work plan resides within the EPSRC Healthcare Technologies, Manufacturing the Future and Mathematical Sciences themes.

自大流行爆发以来，mRNA 疫苗已被广泛用于抵抗 covid-19，且具有显着的安全性。传统疫苗的新生产设施可能需要数年时间和巨额资金才能投入运营。相比之下，RNA 疫苗是通过标准化程序生产的，并进行了细微的调整，以适应 RNA 序列长度和组成的变化。然而，在过去的几个时期里，辉瑞（Pfizer）和摩德纳（Moderna）等公司因制造中断而面临挫折。为了应对疫苗制造方面的挑战，英国政府自 2018 年以来已向疫苗制造和创新中心投资近 2.15 亿英镑，并对 CPI 的 RNA 卓越中心进行了进一步投资。疫苗的生产是一个复杂的过程，需要大量的投入和质量措施。在mRNA疫苗中，起始材料是质粒、宿主细菌和重组微生物细胞的主细胞库。疫苗经过质量控制，例如核酸的完整性、含量、效力、产品和工艺相关的杂质、无菌、内毒素以及 pH 和渗透压等理化测试。由于其化学性质，RNA 通常不稳定，因此 mRNA 疫苗的制造涉及稳定性指示参数，例如 RNA 完整性、含量和效力，并辅以 pH、外观和微生物状态。最后，疫苗要经过压力测试，包括温度变化、pH 值变化、光稳定性、湿度或多次冻融循环。我们假设数据科学方法目前在如此复杂的制造过程中被忽视。特别是，我们希望重点了解 mRNA 疫苗制造过程中过程的时间动态。首先，该计划专注于开发集成在线和离线时间数据的方法，以预测流程结果并揭示流程生产力。在方法上，我们将建立在稀疏回归工具箱的基础上，发现关键过程参数（CPP）并确定这些参数如何影响关键质量属性（CQA）。我们的目标是将数学建模与建模/制造周期的迭代相结合。其次，该工作计划建立在 Steve Brunton 和 Nathan Kutz 开发的稀疏方法的基础上，用于从数据中识别常微分方程和偏微分方程。 Brunton 和 Kutz 的算法旨在使用稀疏回归工具箱从数据流中提取符号动力系统。他们的方法采用一组预定的状态空间变量的时间序列来描述未知的动力系统，并识别描述该系统的 ODE 或 PDE 右侧项的系数。这些项是从给定的候选基函数库中选择的。为此，作者将数值方法与顺序阈值最小二乘算法 (SINDy) 相结合，以估计时间序列的导数，该算法对基函数库执行变量选择。他们的 SINDy 算法返回每个基函数的数值系数。如果数据集可以表示为候选基函数库中的稀疏动力系统，则大多数系数为零，并且 SINDy 识别非零系数。结果是将输入数据集描述为在所选状态空间变量上表示的符号 ODE 或 PDE，以及候选库函数的函数，其数值系数由 SINDy 找到。我们将分析国家生物制剂制造中心及其 RNA 卓越中心（工艺创新中心 (CPI) 的一部分）提供的生物反应器的数据集。该工作计划属于 EPSRC 医疗保健技术、制造未来和数学科学主题。