Fast estimation of adsorption isotherm parameters in gradient elution preparative liquid chromatography II: The competitive case

Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method

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.

Sequential purification of cannabidiol by two-dimensional liquid chromatography combined with modeling and simulation of elution profiles

Cannabidiol (CBD) has garnered significant attention for its neuroprotective properties, and research on its therapeutic effects has increased dramatically in recent years. However, the systematic purification of CBD through scalable processes has remained bottleneck due to the structural similarities of the cannabinoids. Although preparative chromatography is considered as a potential solution, it is usually time-consuming and expensive. Therefore, the development of scalable strategy via fast and accurate optimization approach is crucial. The present study aimed to develop a sequential process for the scalable purification of CBD through an eco-friendly ethanolic extraction using ultrasonic assisted extraction, decarboxylation of cannabidiolic acid optimized by response surface methodology, followed by the development of off-line two-dimensional semi-preparative chromatography, boosted with stacked injection overloading. In the first dimension, a column packed with macroporous resin allows to enrich the target substance and then, the behavior of resin column for scale-up procedure were predicted and optimized by developed mathematical model. A C18 column was used in the second dimension. The CBD purity and recovery obtained were 94.3 and 82.1 %, respectively. A robust and reliable method was employed for CBD enrichment/purification, which can be generalized to other bioactive compounds in complex matrices.

Strategies for predictive modeling of overloaded oligonucleotide elution profiles in ion-pair chromatography

Due to their potential for gene regulation, oligonucleotides have moved into focus as one of the preferred modalities modulating currently undruggable disease-associated targets. In the course of synthesis and storage of oligonucleotides a significant number of compound-related impurities can be generated. Purification protocols and analytical methods have become crucial for the therapeutic application of any oligonucleotides, be they antisense oligonucleotides (ASOs), small interfering ribonucleic acids (siRNAs) or conjugates. Ion-pair chromatography is currently the standard method for separating and analyzing therapeutic oligonucleotides. Although mathematical modeling can improve the accuracy and efficiency of ion-pair chromatography, its application remains challenging. Simple models may not be suitable to treat advanced single molecules, while complex models are still inefficient for industrial oligonucleotide optimization processes. Therefore, fundamental research to improve the accuracy and simplicity of mathematical models in ion-pair chromatography is still a necessity. In this study, we predict overloaded concentration profiles of oligonucleotides in ion-pair chromatography and compare relatively simple and more advanced predictive models. The experimental system consists of a traditional C18 column using (dibutyl)amine as the ion-pair reagent and acetonitrile as organic modifier. The models were built and tested based on three crude 16-mer oligonucleotides with varying degrees of phosphorothioation, as well as their respective n - 1 and (P = O)1 impurities. In short, the proposed models were suitable to predict the overloaded concentration profiles for different slopes of the organic modifier gradient and column load.

Estimation of Nonlinear Adsorption Isotherms in Gradient Elution RP-LC of Peptides in the Presence of an Adsorbing Additive

ABSTRACT

In electrostatic repulsive interaction chromatography, using a charged surface hybrid sorbent carrying positive charges can improve the peak shape of peptides in reversed-phase liquid chromatography (RP-LC), especially in overloaded conditions, compared with standard C18 sorbents. However, the positive surface charges can interact with anionic additives commonly used in peptide separations, e.g., trifluoroacetic acid (TFA), complicating adsorption isotherm estimation. We investigated how the competition for available adsorption sites between TFA and two peptides influenced the adsorption isotherm in gradient elution. A model accounting for the competition with TFA was compared with a model neglecting TFA adsorption. We found that the two models predicted elution profiles with the same accuracy. We also found that the adsorption isotherms were extremely similar in shape, leading to the conclusion that neglecting the competition with TFA is a valid approximation enabling faster and more robust adsorption isotherm estimation for the studied type of sorbent.

GRAPHICAL ABSTRACT

Choice of Model for Estimation of Adsorption Isotherm Parameters in Gradient Elution Preparative Liquid Chromatography

The inverse method is a numerical method for fast estimation of adsorption isotherm parameters directly from a few overloaded elution profiles and it was recently extended to adsorption isotherm acquisition in gradient elution conditions. However, the inverse method in gradient elution is cumbersome due to the complex adsorption isotherm models found in gradient elution. In this case, physicochemically correct adsorption models have very long calculation times. The aim of this study is to investigate the possibility of using a less complex adsorption isotherm model, with fewer adjustable parameters, but with preserved/acceptable predictive abilities. We found that equal or better agreement between experimental and predicted elution profiles could be achieved with less complex models. By being able to select a model with fewer adjustable parameters, the calculation times can be reduced by at least a factor of 10.

Combined effects of potassium chloride and ethanol as mobile phase modulators on hydrophobic interaction and reversed-phase chromatography of three insulin variants

The two main chromatographic modes based on hydrophobicity, hydrophobic interaction chromatography (HIC) and reversed-phase chromatography (RPC), are widely used for both analytical and preparative chromatography of proteins in the pharmaceutical industry. Despite the extensive application of these separation methods, and the vast amount of studies performed on HIC and RPC over the decades, the underlying phenomena remain elusive. As part of a systematic study of the influence of mobile phase modulators in hydrophobicity-based chromatography, we have investigated the effects of both KCl and ethanol on the retention of three insulin variants on two HIC adsorbents and two RPC adsorbents. The focus was on the linear adsorption range, separating the modulator effects from the capacity effects, but some complementary experiments at higher load were included to further investigate observed phenomena. The results show that the modulators have the same effect on the two RPC adsorbents in the linear range, indicating that the modulator concentration only affects the activity of the solute in the mobile phase, and not that of the solute-ligand complex, or that of the ligand. Unfortunately, the HIC adsorbents did not show the same behavior. However, the insulin variants displayed a strong tendency toward self-association on both HIC adsorbents; on one in particular. Since this causes peak fronting, the retention is affected, and this could probably explain the lack of congruity. This conclusion was supported by the results from the non-linear range experiments which were indicative of double-layer adsorption on the HIC adsorbents, while the RPC adsorbents gave the anticipated increased tailing at higher load.