一种用于基于滤板的色谱分配系数高通量筛选中不确定性量化的建模框架
A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the chromatography partition coefficient.
摘要(中文译文)
高通通量筛选（HTS）测定色谱分配系数（K）广泛应用于蛋白质纯化工艺开发，涵盖精度要求显著不同的多种应用。尽管应用广泛，但关于实验不确定度如何通过基于滤板的K测定方法传播并影响关键设计决策的定量指导一直缺乏。我们提出了一种经实验校准的蒙特卡罗（MC）框架，用于基于滤板的K高通量筛选中的不确定度传播分析，该框架可量化总体不确定度，并识别在纯化相关平衡区域内占主导地位的实验误差来源。该模型通过对POROS XS阳离子交换介质上单克隆抗体（mAb）单体案例研究中主要实验误差来源的表征进行参数化，能够准确再现所观察到的误差幅度、异方差性及板间变异性。方差分解表明，树脂浆液分配和上清液浓度测量的不确定度是主要贡献因素，在研究范围内的理想分配系数（K）下，约占K总方差的97%。该框架进一步支持对设计选择进行定量评估：对液固相比（β）的分析揭示了测量精度与蛋白质材料需求之间的明显权衡，从而能够推导出针对具体应用的相比建议。总体而言，这项工作为具有不确定度意识的高通量筛选设计提供了定量基础，并为纯化工艺开发决策和建模提供了更可靠的K数据。
Abstract
High‑throughput screening (HTS) of chromatography partition coefficients (K) is widely used in protein purification process development, spanning applications with markedly different precision requirements. Despite this widespread use, quantitative guidance on how experimental uncertainty propagates through filter-plate-based K assays and informs key design decisions has been lacking. We present an experimentally calibrated Monte Carlo (MC) framework for uncertainty propagation in filter-plate-based K HTS, which quantifies overall uncertainty and identifies the dominant experimental error sources across purification‑relevant equilibrium regimes. The model is parameterized by characterization of the major sources of experimental errors in a monoclonal antibody (mAb) monomer case study on POROS XS cation‑exchange media and accurately reproduces the magnitude, heteroscedasticity, and plate‑to‑plate variability observed. Variance decomposition identifies uncertainty in resin slurry distribution and supernatant concentration measurements as the dominant contributors, accounting for ≈ 97 % of total K variance over the investigated range of ideal partition coefficients (K). The framework further enables quantitative evaluation of design choices: analysis of the liquid‑to‑solid phase ratio (β) reveals a clear trade‑off between measurement precision and protein material demand, allowing derivation of application‑specific phase ratio recommendations. Overall, this work provides a quantitative basis for uncertainty‑aware HTS design and more reliable K data for purification process development decisions and modeling.
如何引用
AMA
Jan Faessler, Emmy Schiess, Rudger Hess, Till Briskot, Sebastian Andris, Melanie Maier, et al. A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the chromatography partition coefficient.. Journal of chromatography. A. 2026; doi:10.1016/j.chroma.2026.467325.
APA
Jan Faessler, Emmy Schiess, Rudger Hess, Till Briskot, Sebastian Andris, Melanie Maier, et al (2026). A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the chromatography partition coefficient.. Journal of chromatography. A. https://doi.org/10.1016/j.chroma.2026.467325
GB/T 7714
Jan Faessler, Emmy Schiess, Rudger Hess, Till Briskot, Sebastian Andris, Melanie Maier, et al. A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the chromatography partition coefficient.[J]. Journal of chromatography. A, 2026 doi:10.1016/j.chroma.2026.467325.
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