Enablers for QbD implementation: Mechanistic modeling for ion-exchange membrane chromatography

Bioseparation using membrane chromatography: Innovations, and challenges

The resin-based column continues to be the dominant incumbent in bioprocess chromatography. While alternative formats such as membrane-, monolith- and fiber-based chromatography are more visible than before, each still plays minor roles. The reasons for this are complex and some of these are explained in this paper. However, the fact remains that membrane chromatography has come a long way since its early days of development. The main advantage of membrane chromatography continues to be its convection dominant transport mechanism, the resultant benefit being fast and scalable separation. Also, resolution obtained with properly designed devices could be comparable or even better than resin-based chromatography. Significant progress has been made in new membrane development, membrane characterization, device design and novel applications development. A wider range of new membrane matrices, ligands, and ligand-matrix linking chemistries are now available. New membrane modules, formats, and process configurations have also helped improve membrane performance. However, some significant challenges still exist, and these need to be addressed if membrane chromatography is to become more mainstream in the field of bioprocessing. Also, membrane chromatography has significant potential for application in analytical separations and this space has hardly been explored. In this paper, the advances in the areas of membrane preparation, device design and process development are reviewed. A high-level cost analysis is presented and the role of process design in membrane chromatography is discussed.

Enhancing bacterial biodegradation of n-hexane by utilizing the adsorption capacity of non-degrading fungi

Biodegradation of hydrophobic volatile organic compounds (VOCs) such as n-hexane is limited by their poor accessibility. Constructing fungal-bacterial degradation alliances is an effective approach, but the role of those fungi without the capability to degrade VOCs may have been overlooked. In this study, a non-n-hexane-degrading fungus, Fusarium keratoplasticum FK, was utilized to enhance n-hexane degradation by the bacterium Mycobacterium neworleansense WCJ. It was shown that strain WCJ removed 64.84% of n-hexane (at a concentration of 648.20 mg L-1) over 3 d, and 84.04% after introducing strain FK. Microbial growth kinetic studies revealed that the growth of strain WCJ was also promoted. Through a stepwise adsorption-degradation experiment combined with qPCR technology, it was found that the strain WCJ could utilize the n-hexane pre-adsorbed by strain FK, with an increase in copy number from 108.2662 to 108.7731. Therefore, the non-degrading fungi can improved the accessibility of n-hexane by providing n-hexane adsorbed by the mycelium to the degrading bacteria. In addition, the adsorption tests and characterization of the fungal samples before and after Soxhlet extraction indicated that the adsorption of n-hexane on strain FK conformed to Lagergren's pseudo-second-order kinetics and Freundlich adsorption isotherms, and was correlated with the presence of lipids and nonpolar groups. This study emphasizes the potential role of non-degrading fungi in bioremediation and proposes a viable strategy to enhance the bacterial degradation of hydrophobic VOCs.

Application of mechanistic modelling in membrane and fiber chromatography for purification of biotherapeutics - A review

Mechanistic modelling is a simulation tool which has been effectively applied in downstream bioprocessing to model resin chromatography. Membrane and fiber chromatography are newer approaches that offer higher rates of mass transfer and consequently higher flow rates and reduced processing times. This review describes the key considerations in the development of mechanistic models for these unit operations. Mass transfer is less complex than in resin columns, but internal housing volumes can make modelling difficult, particularly for laboratory-scale devices. Flow paths are often non-linear and the dead volume is often a larger fraction of the overall volume, which may require more complex hydrodynamic models to capture residence time distributions accurately. In this respect, the combination of computational fluid dynamics with appropriate protein binding models is emerging as an ideal approach.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies

Despite the advent of many new therapies, therapeutic monoclonal antibodies remain a prominent biologics product, with a market value of billions of dollars annually. A variety of downstream processing technological advances have led to a paradigm shift in how therapeutic antibodies are developed and manufactured. A key driver of change has been the increased adoption of single-use technologies for process development and manufacturing. An early-stage developability assessment of potential lead antibodies, using both in silico and high-throughput experimental approaches, is critical to de-risk development and identify molecules amenable to manufacturing. Both statistical and mechanistic modelling approaches are being increasingly applied to downstream process development, allowing for deeper process understanding of chromatographic unit operations. Given the greater adoption of perfusion processes for antibody production, continuous and semi-continuous downstream processes are being increasingly explored as alternatives to batch processes. As part of the Quality by Design (QbD) paradigm, ever more sophisticated process analytical technologies play a key role in understanding antibody product quality in real-time. We should expect that computational prediction and modelling approaches will continue to be advanced and exploited, given the increasing sophistication and robustness of predictive methods compared to the costs, time, and resources required for experimental studies.

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.

Adsorption of a Humanized Monoclonal Antibody onto Thermoresponsive Copolymer-Grafted Sepharose Fast Flow Sorbents

The batch adsorption behavior of a humanized monoclonal antibody (hIgG2 mAb) with thermoresponsive polymer (TRP)-modified Sepharose Fast Flow sorbents with different compositions of grafted copolymers is described. At high protein loadings, the adsorption with negatively charged copolymer-modified sorbents exhibited S-shaped isotherms in most cases, indicative of unrestricted multilayer adsorption. The adsorption capacity of the negatively charged copolymer-modified sorbents increased with an increase in the applied environmental temperature due to increased protein-sorbent surface hydrophobic and electrostatic interactions. The affinity of the hIgG2 mAb for a positively charged copolymer-grafted sorbent was much lower than that found for the negatively charged copolymer-grafted sorbents at both 20 and 50 °C due to electrostatic repulsive effects. This study has documented that the molecular functionalities of the grafted copolymer can significantly affect the adsorption behavior of this humanized mAb at both 20 and 50 °C with the isothermal dependencies revealing subtle effects due to copolymer composition.

G protein-coupled receptor-in-paper, a versatile chromatographic platform to study receptor-drug interaction

High-performance affinity chromatography is limited by its high cost and high pressure. Paper is made up of porous fiber networks and has the properties of low cost, ease of fabrication, and biodegradable. Due to these advantages, herein, we immobilized beta2-adrenoceptor (β₂-AR) onto the surface of the polytetrafluoroethylene membrane, a paper-based material, and constructed a G protein-coupled receptor (GPCR)-in-paper chromatographic platform. This platform was characterized by Fourier transform infrared spectroscopy, fluorescence analysis, X-ray photoelectron spectroscopy, and chromatographic studies. These morphological and elemental analysis showed that β₂-AR was successfully immobilized on the paper surface. The specific drugs have good retentions on the GPCR-in-paper chromatographic platform. The association constants of salbutamol, terbutaline and bambuterol to β₂-AR were calculated to be 2.02 × 10⁴ M^-1, 1.15 × 10⁴ M^-1, 1.75 × 10⁴ M^-1 by adsorption energy distribution, which were in good line with the values from frontal analysis, zonal elution and previous literatures. We demonstrated that the GPCR-in-paper platform was cost-effective, easy to be modified for protein immobilization, and applicable in the receptor-drug interaction analysis. We believe such a platform sheds new light on paper chromatography for receptor-drug interaction analysis and other applications.

High-Throughput Process Development: II-Membrane Chromatography

Membrane chromatography is gradually emerging as an alternative to conventional column chromatography. It alleviates some of the major disadvantages associated with the latter, including high-pressure drop across the column bed and dependence on intraparticle diffusion for the transport of solute molecules to their binding sites within the pores of separation media. In the last decade, it has emerged as a method of choice for final polishing of biopharmaceuticals, in particular, monoclonal antibody products. The relevance of such a platform is high in view of the constraints with respect to time and resources that the biopharma industry faces today.This protocol describes the steps involved in performing HTPD of a membrane chromatography step. It describes the operation of a commercially available device (AcroPrep™ Advance filter plate with Mustang S membrane from Pall Corporation). This device is available in 96-well format with a 7 μL membrane in each well. We will discuss the challenges that one faces when performing such experiments as well as possible solutions to alleviate them. Besides describing the operation of the device, the protocol also presents an approach for statistical analysis of the data that are gathered from such a platform. A case study involving the use of the protocol for examining ion-exchange chromatography of the Granulocyte Colony Stimulating Factor (GCSF), a therapeutic product, is briefly discussed. This is intended to demonstrate the usefulness of this protocol in generating data that are representative of the data obtained at the traditional lab scale. The agreement in the data is indeed very significant (regression coefficient 0.9866). We think that this protocol will be of significant value to those involved in performing high-throughput process development of membrane chromatography.

Cross-scale quality assessment of a mechanistic cation exchange chromatography model

Cation exchange chromatography (CEX) is an essential part of most monoclonal antibody (mAb) purification platforms. Process characterization and root cause investigation of chromatographic unit operations are performed using scale down models (SDM). SDM chromatography columns typically have the identical bed height as the respective manufacturing-scale, but a significantly reduced inner diameter. While SDMs enable process development demanding less material and time, their comparability to manufacturing-scale can be affected by variability in feed composition, mobile phase and resin properties, or dispersion effects depending on the chromatography system at hand. Mechanistic models can help to close gaps between scales and reduce experimental efforts compared to experimental SDM applications. In this study, a multicomponent steric mass-action (SMA) adsorption model was applied to the scale-up of a CEX polishing step. Based on chromatograms and elution pool data ranging from laboratory- to manufacturing-scale, the proposed modeling workflow enabled early identification of differences between scales, for example, system dispersion effects or ionic capacity variability. A multistage model qualification approach was introduced to measure the model quality and to understand the model's limitations across scales. The experimental SDM and the in silico model were qualified against large-scale data using the identical state of the art equivalence testing procedure. The mechanistic chromatography model avoided limitations of the SDM by capturing effects of bed height, loading density, feed composition, and mobile phase properties. The results demonstrate the applicability of mechanistic chromatography models as a possible alternative to conventional SDM approaches.

Compartment Model of Mixing in a Bubble Trap and Its Impact on Chromatographic Separations

Chromatography equipment includes hold-up volumes that are external to the packed bed and usually not considered in the development of chromatography models. These volumes can substantially contribute to band-broadening in the system and deteriorate the predicted performance. We selected a bubble trap of a pilot scale chromatography system as an example for a hold-up volume with a non-standard mixing behavior. In a worst-case scenario, the bubble trap is not properly flushed before elution, thus causing the significant band-broadening of the elution peak. We showed that the mixing of buffers with different densities in the bubble trap device can be accurately modeled using a simple compartment model. The model was calibrated at a wide range of flow rates and salt concentrations. The simulations were performed using the open-source software CADET, and all scripts and data are published with this manuscript. The results illustrate the importance of including external holdup volumes in chromatography modeling. The band-broadening effect of tubing, pumps, valves, detectors, frits, or any other zones with non-standard mixing behavior can be considered in very similar ways.

An overview of experimental designs in HPLC method development and validation

Chemometric approaches have been increasingly viewed as precious complements to high performance liquid chromatographic practices, since a large number of variables can be simultaneously controlled to achieve the desired separations. Moreover, their applications may efficiently identify and optimize the significant factors to accomplish competent results through limited experimental trials. The present manuscript discusses usefulness of various chemometric approaches in high and ultra performance liquid chromatography for (i) methods development from dissolution studies and sample preparation to detection, considering the progressive substitution of traditional detectors with tandem mass spectrometry instruments and the importance of stability indicating assays (ii) method validation through screening and optimization designs. Choice of appropriate types of experimental designs so as to either screen the most influential factors or optimize the selected factors' combination and the mathematical models in chemometry have been briefly recalled and the advantages of chemometric approaches have been emphasized. The evolution of the design of experiments to the Quality by Design paradigm for method development has been reviewed and the Six Sigma practice as a quality indicator in chromatography has been explained. Chemometric applications and various strategies in chromatographic separations have been described.

Purification of Monoclonal Antibodies Using a Fiber Based Cation-Exchange Stationary Phase: Parameter Determination and Modeling

Monoclonal antibodies (mAb) currently dominate the market for protein therapeutics. Because chromatography unit operations are critical for the purification of therapeutic proteins, the process integration of novel chromatographic stationary phases, driven by the demand for more economic process schemes, is a field of ongoing research. Within this study it was demonstrated that the description and prediction of mAb purification on a novel fiber based cation-exchange stationary phase can be achieved using a physico-chemical model. All relevant mass-transport phenomena during a bind and elute chromatographic cycle, namely convection, axial dispersion, boundary layer mass-transfer, and the salt dependent binding behavior in the fiber bed were described. This work highlights the combination of model adaption, simulation, and experimental parameter determination through separate measurements, correlations, or geometric considerations, independent from the chromatographic cycle. The salt dependent binding behavior of a purified mAb was determined by the measurement of adsorption isotherms using batch adsorption experiments. Utilizing a combination of size exclusion and protein A chromatography as analytic techniques, this approach can be extended to a cell culture broth, describing the salt dependent binding behavior of multiple components. Model testing and validation was performed with experimental bind and elute cycles using purified mAb as well as a clarified cell culture broth. A comparison between model calculations and experimental data showed a good agreement. The influence of the model parameters is discussed in detail.