Pessina Daniele, Calderon De Anda Jorge, Heffernan Claire, Heng Jerry Y Y, Papathanasiou Maria M
Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
The Sargent Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
Ind Eng Chem Res. 2025 Jun 5;64(24):12025-12035. doi: 10.1021/acs.iecr.4c04517. eCollection 2025 Jun 18.
The complexity of protein crystallization and modeling challenges process intensification and the wider adoption of crystallization in biomanufacturing. For computational models to support and replace extensive experiments, they must accurately reflect experiments. However, parameter estimation can be ineffective due to the highly nonlinear model structure and inaccurate online process analytical technology, which must be addressed. In this work, an experimentally validated and model-driven parametrization methodology is presented, developed for an antisolvent batch protein crystallization system with limited offline measurements. Global sensitivity analysis is performed to assess parameter identifiability during batch operations and inform optimal measurement points. Experiments at three different initial lysozyme concentrations ( = 15, 18, 19 mg/mL) are used for estimation. Parameter uncertainty distributions are recovered through an Approximate Bayesian Computation algorithm and propagated to model outputs through Monte Carlo simulations, avoiding linearization or unnecessary assumptions on the parametric and output uncertainty distributions. The methodology was successfully validated under two new experimental conditions. The shapes of the recovered parametric and output uncertainties highlight the need for parameter estimation methodologies specifically tailored to nonlinear models.
蛋白质结晶和建模的复杂性给生物制造过程强化以及更广泛地采用结晶技术带来了挑战。对于计算模型而言，要支持并取代大量实验，就必须准确反映实验情况。然而，由于模型结构高度非线性以及在线过程分析技术不准确，参数估计可能效率低下，这些问题必须加以解决。在这项工作中，我们提出了一种经过实验验证且由模型驱动的参数化方法，该方法是针对离线测量有限的反溶剂间歇式蛋白质结晶系统开发的。进行全局敏感性分析以评估间歇操作期间的参数可识别性，并确定最佳测量点。使用三种不同初始溶菌酶浓度（= 15、18、19 mg/mL）下的实验进行估计。通过近似贝叶斯计算算法恢复参数不确定性分布，并通过蒙特卡罗模拟将其传播到模型输出，避免对参数和输出不确定性分布进行线性化或不必要的假设。该方法在两个新的实验条件下成功得到验证。恢复的参数和输出不确定性的形状凸显了针对非线性模型专门定制参数估计方法的必要性。
