Prediction uncertainty assessment of chromatography models using Bayesian inference
Prediction uncertainty assessment of chromatography models using Bayesian inference
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DOI：
10.1016/j.chroma.2018.11.076
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发表时间：
2019-02-22
影响因子：
4.1
通讯作者：
Hubbuch, Juergen
中科院分区：
文献类型：
--
作者：
Briskot, Till;Stueckler, Ferdinand;Hubbuch, Juergen
色谱的机械模型在学术界已经存在了数十年，并且近年来在制药公司中获得了越来越多的支持。尽管有大量已发表的成功应用，但当今制药行业的工艺开发仍然没有完全受益于基于系统机械模型的方法。工业界对系统地应用机械模型的犹豫通常可以归因于缺乏确定模型是否有资格支持工艺开发决策的通用方法。在这项工作中，引入了用于机械色谱模型校准和质量评估的贝叶斯框架。贝叶斯马尔可夫链蒙特卡罗用于通过从参数后验分布生成样本来评估参数不确定性。一旦估计了参数后验分布，就可以将其用于将参数不确定性传播到模型预测，从而允许对模型进行基于预测的不确定性评估。使用描述强阳离子交换剂上抗体与其杂质分离的机械模型的示例证明了这种不确定性评估的好处。机械模型在中等柱负载密度下进行校准，并用于在高负载条件下进行外推。使用贝叶斯框架，可以证明，尽管参数存在很大的不确定性，该模型仍可以高精度地推断超出观察到的过程条件，并且有资格支持过程开发。 (C) 2018 Elsevier B.V. 保留所有权利。
Mechanistic modeling of chromatography has been around in academia for decades and has gained increased support in pharmaceutical companies in recent years. Despite the large number of published successful applications, process development in the pharmaceutical industry today still does not fully benefit from a systematic mechanistic model-based approach. The hesitation on the part of industry to systematically apply mechanistic models can often be attributed to the absence of a general approach for determining if a model is qualified to support decision making in process development.In this work a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development. (C) 2018 Elsevier B.V. All rights reserved.