机理模型是色谱过程开发和优化的强大工具。然而,疏水作用色谱(HIC)机理模型缺乏有效的逻辑参数估计方法,特别是多组分系统。在这项研究中,基于保留机制,推导了多组分系统的逐参数方法(称为mPbP-HIC),以估计HIC的Mollerup等温线的六个参数。线性参数(ks,我和Keq,i)和非线性参数(ni和qmax,i)的等温线可以通过线性回归(LR)和线性近似(LA)步骤来估计,分别。剩下的两个参数(kp,我和kkin,i)是通过逆方法(IM)获得的。利用两分量模型系统对所提出的方法进行了验证。结果表明,该模型可以准确预测10g/L的蛋白质洗脱。然而,洗脱曲线拟合对于高负载(12g/L和14g/L)不令人满意,这主要归因于LA步骤的苛刻实验条件和参数qmax的潜在较大估计误差。因此,引入逆方法进一步校准参数qmax,从而减小估计误差,改善曲线拟合。此外,简化线性近似(SLA)是通过合理的假设提出的,它提供了qmax的初始猜测,而无需求解任何复杂的矩阵,并避免了矩阵不可解的问题。在改进的mPbP-HIC方法中,qmax将由SLA初始化,最后由逆方法确定,这个策略被命名为SLA+IM。实验验证表明,改进的mPbP-HIC方法具有较好的曲线拟合效果,SLA+IM的使用降低了误差累积效应。在流程优化中,改进的mPbP-HIC方法估计的参数为模型提供了良好的预测能力和合理的外推。总之,SLA+IM策略使改进的mPbP-HIC方法更加合理,可以方便地应用于蛋白质混合物的实际分离,这将加速生物制药下游HIC的工艺开发。
Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.