Rational downstream development for adeno-associated virus full/empty capsid separation - A streamlined methodology based on high-throughput screening and mechanistic modeling

Recombinant adeno-associated virus (AAV) has emerged as one of the most promising systems for therapeutic gene delivery and has demonstrated clinical success in a wide range of genetic disorders. However, manufacturing of high-quality AAV in large amounts still remains a challenge. A significant difficulty for downstream processing is the need to remove empty capsids that are generated in all currently utilized expression systems and that represent product-related impurities that adversely affect safety and efficacy of AAV vectors. Empty and full capsids exhibit only subtle differences in surface charge and size, making chromatography-based separations highly challenging. Here, we present a rapid methodology for the systematic process development of the crucial AAV full/empty capsid separation on ion-exchange media based on high-throughput screening and mechanistic modeling. Two of the most commonly employed serotypes, AAV8 and AAV9, are used as case studies. First, high-throughput studies in filter-plate format are performed that allow the rapid and comprehensive study of binding and elution behavior of AAV on different resins, using different buffer systems, pH, salt conditions, and solution additives. Small amounts of separated empty and full AAV capsids are generated by iodixanol gradient centrifugation that allow studying the binding and elution behavior of the two vector species separately in miniaturized format. Process conditions that result in maximum differences in elution behavior between empty and full capsids are then transferred to benchtop chromatography systems that are used to generate calibration data for the estimation of steric mass-action isotherm and mass transport parameters for process simulation. The resulting column models are employed for in-silico process development that serves to enhance understanding of separation constraints and to identify optimized conditions for the removal of empty particles. Finally, optimized separation conditions are verified experimentally. The methodology presented in this work provides a systematic framework that affords mechanistic understanding of the crucial empty/full capsid separation and accelerates the development of a scalable AAV downstream process.

Understanding the effects of system differences for parameter estimation and scale-up of high throughput chromatographic data

In this paper, we evaluate how employing fraction collection and multistep gradients with RoboColumns® (Repligen, formally Atoll) affects both comparison to benchtop experimental data and column simulation parameter estimation. These operational differences arise from the RoboColumn® system (operated on an automated liquid handling device) requiring offline analysis for determination of elution profiles rather than the continuous in-line UV curves obtained with larger scale systems. In addition, multistep gradients are used to model the smooth linear gradients of larger scale systems because sequential injections are used to provide liquid flow. Comparisons of two sets of column simulations was first carried out to demonstrate that fraction collection reduced the first moments of the elution peaks by 1/2 of the fraction volumes. Additional column simulations determined that the effect of a multistep gradient approximation on retention volume was dependent upon the gradient step length. An empirical transformation was then developed to correct the first moments obtained from gradient experimental data using the RoboColumn® system. These corrected values provided a more direct comparison of the experimental data at different scales and resulted in a significant improvement in agreement with results obtained using a 20 mL benchtop column. Linear steric mass-action (SMA) parameters were then estimated using the corrected values and employed to successfully predict the performance of the benchtop system data. Finally, these parameters were demonstrated to be well suited for modeling the RoboColumn® gradient data when properly accounting for multistep gradients and fraction collection. This work continues previous investigations into understanding system differences associated with robotic liquid handling devices and proposes a methodology for properly accounting for operational differences to predict operation at larger scales using conventional chromatography systems.

Influence of the geometry of extra column volumes on band broadening in a chromatographic system. Predictions by computational fluid dynamics

A computational fluid dynamics method was used for prediction of flow behavior and band profiles of small- and macro-molecule compounds eluting in extra-column volumes (ECV) of an Äkta chromatographic system. The model compounds were: acetone, bovine serum albumin and an antibody. The construction of ECV was approximated by different types of geometries, starting from the simplest two-dimensional (2D) arrangement consisting of a straight capillary tube, and ending with a three-dimensional system (3D), which accounted for the flow path curvature of individual elements of ECV, including: injection loop capillary, multi-way valve, connecting capillary and detector cell. The accuracy of the model predictions depended on the flow path length and the eluent flowrate. 2D-geometry models reproduced pretty well the shapes of band profiles recorded at the lowest eluent flowrate used, but they failed for increased flowrates. The 3D-geometry model was found to be sufficiently accurate for all conditions investigated. It was exploited to analyze band broadening in the individual ECV elements. The simulation results revealed that the flow behavior in the injection loop capillaries strongly influenced the shape of band profiles, particularly at higher eluent velocities. This was attributed to the formation of Dean vertices triggered by centrifugal forces in curved parts of the eluent flow path.

Using multimodal chromatography for post-conjugation antibody-drug conjugate purification: A methodology from high throughput screening to in-silico process development

In this paper, a methodology for the development of a multimodal chromatography process is presented that is aimed at removal of under-conjugated antibody-drug conjugate (ADC) species. Two ADCs are used as case studies: One ADC results from site-directed conjugation to inserted cysteine residues and has a drug-to-antibody ratio (DAR) of two, the other is the product of conjugation to interchain disulfide bonds with a DAR of eight. First, filter plate screening studies are designed for the unconjugated antibody and the ADCs. Different metrics for the analysis of these data sets are presented and discussed. From this analysis, the selected process conditions are then carried out using a benchtop chromatography system to confirm the separations observed in the filter plate studies while simultaneously generating data to estimate steric mass-action isotherm and mass transport parameters for process simulation. This column model is then employed to develop separation processes in-silico for the removal of the unconjugated parent antibody and under-conjugated product variants. The optimized process conditions identified using the model are then verified experimentally. The methodology presented in this work utilizes multimodal chromatography for ADC purification and provides the framework for a streamlined systematic approach to process development.

Chromatographic parameter determination for complex biological feedstocks

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018

Development of pseudo-linear gradient elution for high-throughput resin selectivity screening in RoboColumn® Format

Rapid development of chromatographic processes relies on effective high‐throughput screening (HTS) methods. This article describes the development of pseudo‐linear gradient elution for resin selectivity screening using RoboColumns^®. It gives guidelines for the implementation of this HTS method on a Tecan Freedom EVO^® robotic platform, addressing fundamental aspects of scale down and liquid handling. The creation of a flexible script for buffer preparation and column operation plus efficient data processing provided the basis for this work. Based on the concept of discretization, linear gradient elution was transformed into multistep gradients. The impact of column size, flow rate, multistep gradient design, and fractionation scheme on separation efficiency was systematically investigated, using a ternary model protein mixture. We identified key parameters and defined optimal settings for effective column performance. For proof of concept, we examined the selectivity of several cation exchange resins using various buffer conditions. The final protocol enabled a clear differentiation of resin selectivity on miniature chromatography column (MCC) scale. Distinct differences in separation behavior of individual resins and the influence of buffer conditions could be demonstrated. Results obtained with the robotic platform were representative and consistent with data generated on a conventional chromatography system. A study on antibody monomer/high molecular weight separation comparing MCC and lab scale under higher loading conditions provided evidence of the applicability of the miniaturized approach to practically relevant feedstocks with challenging separation tasks as well as of the predictive quality for larger scale. A comparison of varying degrees of robotic method complexity with corresponding effort (analysis time and labware consumption) and output quality highlights tradeoffs to select a method appropriate for a given separation challenge or analytical constraints. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1503–1519, 2016

Systematic interpolation method predicts protein chromatographic elution from batch isotherm data without a detailed mechanistic isotherm model

Predicting protein elution for overloaded ion exchange columns requires models capable of describing protein binding over broad ranges of protein and salt concentrations. Although approximate mechanistic models are available, they do not always have the accuracy needed for precise predictions. The aim of this work is to develop a method to predict protein chromatographic behavior from batch isotherm data without relying on a mechanistic model. The method uses a systematic empirical interpolation (EI) scheme coupled with a lumped kinetic model with rate parameters determined from HETP measurements for non-binding conditions, to numerically predict the column behavior. For two experimental systems considered in this work, predictions based on the EI scheme are in excellent agreement with experimental elution profiles under highly overloaded conditions without using any adjustable parameters. A qualitative study of the sensitivity of predicting protein elution profiles to the precision, granularity, and extent of the batch adsorption data shows that the EI scheme is relatively insensitive to the properties of the dataset used, requiring only that the experimental ranges of protein and salt concentrations overlap those under which the protein actually elutes from the column and possess a ± 10% measurement precision.

Extreme scale-down approaches for rapid chromatography column design and scale-up during bioprocess development

Chromatography is a ubiquitous protein purification step owing to its unparalleled ability to recover and purify molecules from highly complex industrial feedstocks. Traditionally, column development has been driven by a combination of prior experience and empirical studies in order to make the best choices for design variables. Economic constraints now demand that companies engage with a more systematic exploration of a chromatographic design space. To deliver this capability using purely conventional laboratory columns, however, would require considerable resources to identify practical and economical operating protocols. Hence, recently there has been increased use of extremely small-scale devices that gather data quickly and with minimal feed requirements. Such information can be obtained either during early development for screening and trend-finding purposes or later for more accurate scale-up prediction. This chapter describes some of the key drivers for these small-scale studies and the different types of extreme scale-down chromatography formats that exist and illustrates their use through published case studies. Since extreme scale-down experimentation is linked to fundamental mechanistic engineering approaches as well, the utility of these in delivering process understanding is also highlighted.

How can measurement, monitoring, modeling and control advance cell culture in industrial biotechnology?

This report highlights the potential of measurement, monitoring, modeling and control (M(3) C) methodologies in animal and human cell culture technology. In particular, state-of-the-art of M(3) C technologies and their industrial relevance of existing technology are addressed. It is a summary of an expert panel discussion between biotechnologists and biochemical engineers with both academic and industrial backgrounds. The latest ascents in M(3) C are discussed from a cell culture perspective for industrial process development and production needs. The report concludes with a set of recommendations for targeting M(3) C research toward the industrial interests. These include issues of importance for biotherapeutics production, miniaturization of measurement techniques and modeling methods.

Therapeutic plasma proteins--application of proteomics in process optimization, validation, and analysis of the final product

An overview is given on the application of proteomic technology in the monitoring of different steps during the production of therapeutic proteins from human plasma. Recent advances in this technology enable the use of proteomics as an advantageous tool for the validation of already existing processes, the development and fine tuning of new production steps, the characterization and quality control of final products, the detection of both harmful impurities and modifications of the therapeutic protein and the auditing of batch-to-batch variations. Further, use of proteomics for preclinical testing of new products, which can be either recombinant or plasma-derived, is also discussed.