Stochastic Simulation Uncertainty Analysis to Accelerate Flexible Biomanufacturing Process Development

Wei Xie, R. Barton, Barry L. Nelson, Keqi Wang
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引用次数: 1

Abstract

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.
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加速柔性生物制造工艺开发的随机模拟不确定性分析
基于生物制药制造的关键挑战和需求,我们提出了一个通用元模型辅助的随机仿真不确定性分析框架,以加速具有模块化设计的柔性生产过程仿真模型的开发。通常有非常有限的过程观察。因此,在系统性能估计中既存在仿真不确定性,也存在模型不确定性。在生物制药生产中,模型不确定性往往占主导地位。所提出的框架可以通过使用元模型辅助自举方法产生考虑模拟和模型不确定性的置信区间。此外,利用方差分解来估计模型不确定性和模拟不确定性的各个来源的相对贡献。该信息可用于改进系统平均性能估计。渐近分析为我们的方法提供了理论支持,而实证研究表明,它具有良好的有限样本性能。
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