High-throughput technologies for bioseparation process development

High-Throughput Technologies

for

Bioseparation Process Development

High-Throughput Technologies

for

Bioseparation Process Development

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema voorzitter van het Hoofdleften College voor Promoties,

in het openbaar te verdedigen

op donderdag 09 oktober 2008 om 15:00 uur

door

Tangir Ahamed

Technologisch Ontwerper in Bioproces Technologie

Prof. dr. ir. L.A.M. van der Wielen

Copromotor Dr. ir. M. Ottens

Samenstelling promotiecommissie:

Rector Magnificus Voorzitter

Prof. dr. ir. L.A.M. van der Wielen Delft University of Technology, promotor

Dr. ir. M. Ottens Delft University of Technology, copromotor

Prof. dr. P.D.E.M. Verhaert Delft University of Technology

Prof. dr. ir. R.M. Boom Wageningen University and Research Centre

Prof. dr. J. Hubbuch University of Karlsruhe, Germany

Prof. dr. A.M. Lenhoff University of Delaware, USA

Dr. ir. M.H.M. Eppink Schering-Plough Corporation, adviseur

The research described in this thesis was performed at the Department of Biotechnology, Delft University of Technology, The Netherlands.

This project is financially supported by the Netherlands Ministry of Economic Affairs and the B-Basic partner organizations (www.b-basic.nl) through B-Basic, a public private NWO-ACTS program (NWO-ACTS = Advanced Chemical Technologies for Sustainability).

Nomenclature iv

Chapter 1 1

Introduction

Chapter 2 11

A generalized approach to thermodynamic properties of biomolecules for use in bioseparation process design

Chapter 3 37

Design of self-interaction chromatography as an analytical tool of predicting protein phase behavior

Chapter 4 63

Phase behavior of an intact monoclonal antibody

Chapter 5 81

pH-gradient ion-exchange chromatography: An analytical tool for design and optimization of protein separations

Chapter 6 97

Optimization of pH-related parameters in ion-exchange chromatography using pH-gradient operations

Chapter 7 111

Towards establishment of database for bioseparation process development parameters Chapter 8 129 Outlook Summary 132 Samenvatting 135 Curriculum Vitae 138 Publications 139 Acknowledgements 140

2D Two-dimensional

AC Affinity chromatography

AEX Anion-exchange chromatography ATPS Aqueous-two phase separation

BCA Bicinchoninic acid

BHS Hard sphere/excluded volume contribution to Bmm Bicine N,N-Bis(2-hydroxyethyl)glycine

Bmm Osmotic second virial coefficient BSA Bovine serum albumin

CA Carbonic anhydrase

CCS Cell culture supernatant

CEX Cation-exchange chromatography CHES 2-(Cyclohexylamino)ethanesulfonic acid

FF Fast flow

GE Gel electrophoresis

HIC Hydrophobic-interaction chromatography HTE High-throughput experimentation

HTS High-throughput screening IEX Ion-exchange chromatography IEF Isoelectric focusing

LALLS Low-angle laser-light scattering

MAb Monoclonal antibody

MINLP Mixed integer nonlinear programming

MO Membrane osmometry

MS Mass spectrometry

NHS N-hydroxysuccinimide

NMWCO Nominal molecular weight cut-off PAGE Polyacrylamide gel electrophoresis

PAN Primary access number in UniProtKB/Swiss-Prot database PEG Polyethylene glycol

RPC Reversed-phase chromatography SANS Small-angle neutron scattering SAXS Small-angle X-ray scattering

SELDI Surface-enhanced laser desorption/ionization SEC Size-exclusion chromatography

SIC Self-interaction chromatography SLS Static light scattering

1

Introduction

1.1. Bioseparation process development: Challenges and the need for acceleration

The biopharmaceutical industry is one of the fastest growing sectors in global economy [1]. Biological macromolecules, such as proteins, constitute an important class of biopharmaceutical products, which also have food and biotechnology applications [2]. Over the last 25 years, advances in recombinant DNA, metabolic engineering, and hybridoma technologies have permitted the large-scale production of virtually any biomolecule at increased titer through a fermentation route [3], thereby shifting the overall production process bottleneck to the downstream processing [4,5]. Regardless of their route of production protein products are typically produced in a multi-component environment consisting of numerous non-protein impurities and co-produced contaminant proteins, some of which having properties very similar to the product itself. The concentration of products in the produced biological feed streams is typically low and the product stability window is often narrow. In addition, little information is available on the physicochemical and thermodynamic properties of impurities present in the crude mixture containing the product. Nevertheless, for certain classes of bioproducts regulatory authorities require stringent product quality. For example, biopharmaceuticals destined for administration by injection requires no detectable impurities in the product [6]. Also in products requiring less overall purity, certain impurities must be removed completely. The downstream processing of biologics is, therefore, widely recognized to be technically and economically challenging, accounting for a substantial fraction of the total manufacturing cost [7].

Against this backdrop, the biopharmaceutical industry is currently challenged to develop numerous high quality products at lower cost [8]. A safe and optimal downstream process must, therefore, be found quickly somewhere in an extremely large design space consisting of a high number of potential process parameters [7]. Bioseparation process development is traditionally carried out empirically, relying heavily on heuristics-based expert knowledge and on numerous trial-and-error experiments, often resulting in overall suboptimal processes with inefficient feedstock and auxiliary materials utilization along with a slow developmental process. The necessity of being ‘first on the market’ [9] paired with

lack of competent process development tools highlights the importance for fast process development strategies and techniques.

In the chemical engineering domain, separation process design tools and methods developed over the last decades have reached a level of maturity that has provided advantages to industrial practitioners as well as in academic process design education [10]. There is an extensive list of literature on chemical process synthesis and design, which has been comprehensively reviewed [10-12]. In contrast, there is still lack of a suitable general purification process development strategy for biologically derived macromolecules, especially proteins [13].

1.2. Trends in bioseparation process development

At the onset of purification process development, little or nothing is known about the properties of product and impurities present in the complex crude mixture from biological sources. Consequently, purification process development is mostly carried out by an empirical approach, known as heuristics. The currently practiced heuristics or knowledge based process development methods rely on a set of rules based on a combination of experience, insight, available knowledge, and numerous trial-and-error experiments. The most common heuristics for downstream processing have been summarized by Asenjo et al [14,15]. The obvious way to speed up and systematize bioseparation process development is to complement the current process development paradigm by new and emerging technologies that have paved the way for more systematic and rational strategies. Examples of such design tools include computer aided process design and high-throughput experimentation (HTE) using a robotic workstation.

In computer aided bioseparation process development, attempts have first been made to capture heuristics on computer in the form of expert systems [15,16]. However, heuristics can neither guarantee an inclusion of all promising process alternatives within the design search space nor justify the fact that the selected process is the best among the evaluated process options. Therefore, more rigorous algorithmic methods based on optimization using advanced mathematical programming are developed to determine the best process option.

Samsatli and Shah [17] proposed a two-stage optimization based approach in which the process conditions, rates, and capacities are determined in the first stage through dynamic optimization. The second stage deals with detailed scheduling and design adjustment for sequencing and timing. Titchener-Hooker et al. [18-20] proposed an alternative optimization approach based on a graphical representation of the feasible windows of operation for integrated protein recovery bioprocesses and the Pareto method for locating the optimal operating points within the feasible regions [21,22]. Asenjo et al. [23] have attempted the use of a mixed integer nonlinear programming (MINLP) formulation for developing strategies for simultaneous optimization of the structure and process variables of an integrated protein production plant. Bogle et al. [24] presented a synthesis methodology for integrated biochemical processes in which downstream processing unit operations were first screened

using physicochemical properties information, followed by a systematic evaluation of the generated superstructure using an implicit enumeration synthesis tool in which the MINLP problem is converted into a graph generation and search problem. Mixed integer linear programming formulations have also been reported for an optimal synthesis of chromatographic protein purification processes [25]. All of these algorithmic methods require detailed understanding of the overall process and rely heavily on the availability of reliable unit operation models, solute properties, and auxiliary materials properties, without which this approach is impractical.

The latest trend in speeding-up bioseparation process development is to complement the screening and trial-and-error experiments by high-throughput screening (HTS) technologies and design of experiments [26]. The most widely used HTS approach is simply the miniaturization of conventional batch screening system to microtitre plate format and automation of operations in robotic workstations. Crude protein mixtures containing the product are loaded on aliquots of different chromatographic media at different solution conditions in microtiter plates and eluted either serially or at once. Identification of a successful chromatography step and operating conditions are usually based on the batch-binding results. The batch-batch-binding method can be compared favorably to chromatographic column separation steps for cGMP protein purification processes [27]. Microtiter plate based screening was reported to identify an appropriate sequence of chromatographic steps and corresponding process conditions that resulted in high bioproduct purity [28]. Claims were also made on determination of optimum parameters (ligand, resin, operational pH, and ionic strength) for chromatographic purification of biomolecules based on microtitre plate based batch screening [27,29-34]. Alternative approaches of batch mode HTS of chromatography process conditions are also recently investigated. Examples of such approaches include the use of ProteinChip Arrays carrying the same functional groups as the resin for the prediction of chromatography conditions [26,35-38], and the use of different adsorber membrane stacks in spin column format arranged in microtitre plate [39]. The ProteinChip technology provides an advantage of coupling possibility with mass spectrometry in order to cope with the analytical bottleneck in HTS.

The data obtained from batch mode HTS can be applied to narrow down the design space for subsequent process development and optimization by dynamic screening mode [26]. In dynamic binding mode, process sequence and operating conditions are usually developed by conducting medium-throughput screening experiments in packed mini-columns [40,41]. A more advanced HTS approach for bioseparation process development was undertaken in dynamic binding mode by conducting experiments in micro-column format using robotic workstation with mass spectrometric detection [26,42]. In addition to increasing screening throughput, HTS reduces the resin and sample consumption by at least one order of magnitude. The overall HTE approach, however, does not reduce the number of experiments, but increases the throughput by miniaturization and parallelization.

1.3. Objective and research methodology

The objective of this research is to develop novel tools and technologies for speeding up bioseparation process development. In pursuing this ambitious goal, a hybrid approach is undertaken (Fig. 1.1) to overcome the limitations of conventional empirical screening, HTS, and computer aided process development. The proposed hybrid approach makes use of a combination of high-throughput and/or multidimensional experimentation techniques for critical data acquisition and state-of-the-art bioseparation modeling tools for rationally synthesizing and designing bioseparation processes [13]. The Food and Drug Administration through its Process Analysis Technology initiative, now encourages the use of this kind of novel design tools and platform technologies as long as thorough understanding of the critical process parameters that have the strongest impact on the quality attributes of the manufactured product is clearly demonstrated [43]. It is also essential to incorporate new design tools very early on in the process development because changes in the production process may impose both technological and regulatory risks [9].

Obtaining physicochemical and thermodynamic properties

Optimization of each feasible separation method

Generation of process alternatives

Modeling of different process options

Thermodynamic Interrelation HTE

Selection of the best process Modeling

Process synthesis methodology

Fig. 1.1. An integrated (hybrid) approach of bioseparation process development.

1.3.1. An integrated methodology for bioseparation process development

The undertaken hybrid approach of bioseparation process development (Fig. 1.1) is largely based on modeling. State-of-the-art models of different bioseparation techniques are shown in the literature, which can be simulated using advanced mathematical programming, such as MATLAB. Simulation of each bioseparation unit was shown to provide a complete performance of the unit, including economics. However, for successful representation of an experimental output, modeling requires physicochemical properties of the product and of all major impurity components present in the product containing stream. It must be noted that the critical physicochemical properties are usually a function of operating conditions (such as

pH, ionic strength, type of resin). Therefore, the performance of most bioseparation techniques is resin conditions specific, which may consequently require an a priori resin selection of each feasible unit operation. Critical input data required for the modeling of each feasible bioseparation unit are then acquired at the optimized conditions by high-throughput and/or multidimensional experimentation techniques.

Once all feasible separation techniques are optimized and all required critical parameters are acquired, different process alternatives can be generated within the boundary of overall process specifications. One can also strike out the most obvious non-promising process alternatives at this stage. Each promising process option can then be simulated by a cascade of unit operation models in advanced mathematical programming. In this approach, the outlet stream of a unit would be used as an inlet stream of the next unit. The best process option can finally be selected based on the obtained simulation result with respect to the desired objective functions, such as end product quality, economic benefits, and regulatory threat.

In addition to accelerating bioseparation process development, an advantage of this modeling and optimization based approach is that it enables in-silico study and comparison of various process alternatives in a way that mimics the actual industrial process. Therefore, the scale-up issues are simultaneously addressed in this way. The integrated approach also involves parallel development of experimentation tools for optimization of process operating conditions and rapid acquisition of process modeling data from the crude mixture. The obtained data can be utilized for developing the process of a different product produced from the same host system.

1.3.2. Critical bioseparation separation process development parameters

The accuracy of a bioseparation process modeling output is highly dictated by the availability of correct input data. The range of input data requirement includes physical properties of the input stream, physicochemical properties of the auxiliary stream or phase, and physicochemical and thermodynamic properties of the product and impurity components present in the crude sample. The critical requirement of physical properties of the input stream include density, viscosity, and existence of more than one phase, all of which can be visualized or easily determine by simple equipments. However, the critical physicochemical properties requirement of auxiliary stream or phase and components to be separated is mostly unit operation specific.

Downstream processing steps of biologics are usually divided into two main categories, firstly recovery and then purification. The main objective of recovery steps is to separate particulate matter from the dissolved species, whereas purification steps isolate the product from a similar class of impurities. A description of various unit operations applied for downstream processing of biologics and their implications have recently been reviewed comprehensively [44]. Since recovery operations are rather straightforward, this study is focused on high-resolution purification operations of biologics.

Commercially implemented techniques for purification of biological macromolecules include ion-exchange chromatography (IEX), hydrophobic-interaction chromatography (HIC), affinity chromatography (AC), size-exclusion chromatography (SEC), precipitation, and crystallization [13]. Successful modeling of chromatography operations requires critical stationary phase properties of the column and isotherm parameters of all major components present in the crude mixture (Table 1.1). The stationary phase parameters are actually constant for a particular column and are usually provided by the column supplier. On the other hand, different chromatography operations require concentration, diffusivity, and respective class of isotherm parameters of the product and impurity components (Table 1.1). For example, the steric mass action model of IEX requires effective charge, equilibrium constant, and steric factor as isotherm parameters [45]. Both HIC and AC can be modeled through a competitive equilibrium isotherm framework [46], which requires hydrophobicity or affinity and the maximum binding capacity as input parameters. The hydrophobicity in this approach can be described as an elution-salt concentration from a HIC column, because only surface hydrophobic groups of a molecule take part in HIC. Modeling of SEC is relatively straightforward using molecular size and gel fiber diameter [47].

Table 1.1. Critical bioseparation process modeling parameters.

Separation method Critical process modeling parameters

Column

paramet

ers

All chromatography Column length, column diameter, particle diameter, pore diameter, bed porosity, particle porosity, flow rate, and maximum pressure drop limit

IEX Ionic capacity SEC Diameter of gel fiber

Isotherm _paramet

ers

(of all major components)

All chromatography Concentration and diffusivity

IEX Effective charge, equilibrium constant, and steric factor HIC Hydrophobicity and maximum binding capacity

AC Affinity constant and maximum binding capacity SEC Molecular size

Crystallization Solubility and osmotic second virial coefficient Precipitation Solubility

In contrast to various chromatography systems, the prediction of a precipitation process is rather simple when solubility is available. However, successful prediction of crystallization requires solubility and optimized process conditions, which can be translated into osmotic second virial coefficient [48]. Estimation of all isotherm parameters listed in Table 1.1 requires experimentation, although there is a possibility of interrelating different parameters through simple equations. For example, diffusivity of a component can be estimated from the correlation with molecular radius or molecular mass [49].

1.3.3. Objective of the research

The objective of this study is to develop experimentation techniques for fast acquisition of critical bioseparation process development parameters (Table 1.1) from complex biological mixtures. Since the properties of auxiliary materials or phases are easily obtainable from the supplier, this study is focused on developing HTE tools for process condition optimization, and estimation of chromatographic isotherm parameters and phase separation properties.

1.4. High-throughput technologies

The number of critical parameters, to be determined experimentally, is first minimized in Chapter 2 by exploiting thermodynamic interrelation among different parameters. A set of novel HTE technologies is then developed for fractionation and characterization of crude biological mixtures and to acquire critical bioseparation process development parameters. The technologies developed to achieve this ambitious goal include a miniaturized self-interaction chromatography (SIC) technique, a novel pH-gradient IEX, and a multi-dimensional fractionation and characterization system (Fig. 1.2). The number of critical parameters obtained in this framework and their accuracy are good enough to implement in state-of-the-art models of different bioseparation methods.

Fig. 1.2. High-throughput technologies for estimating critical process development parameters.

As one of the high-throughput technologies, SIC is designed for rapid measurement of osmotic second virial coefficient (Bmm) of proteins. In Chapter 3, a miniaturized SIC technology is designed in details with respect to theoretical framework, experimental methodology, data analysis, troubleshooting, and application in different kinds of proteins. The approach requires retention data from an immobilized protein column as well as from a protein free column for determination of a Bmm value. The novelty in this approach, unlike other SIC techniques, is the freedom to use non-identically packed columns in terms of column volume and packing integrity. The developed methodology is then successfully applied in Chapter 4 to predict the phase behavior of an intact monoclonal antibody.

The next developed high-throughput technology is a novel pH-gradient IEX technique to rationally select the optimum IEX operating conditions, thereby eliminating need for

traditional empirical screening. In Chapter 5, the pH-gradient IEX technology explains the previously non-understood behavior of neutral proteins in IEX and dismisses the use of pI as the key parameter for selection of optimum IEX operational conditions, in contrast to traditional assumptions or rules-of-thumb. The pH-gradient IEX is eventually applied in

Chapter 6 for the optimization of IEX operational parameters in a case study on the capture

of a monoclonal antibody. This fast and rational approach of optimizing pH-related parameters in an IEX, can be identified as a key HTE technique for replacing presently existing pH-scouting and robotic screening.

The final high-throughput technology developed is a multi-dimensional fractionation and characterization system for fast acquisition of chromatographic isotherm parameters from crude biological mixtures. Chapter 7 shows that IEX parameters (effective charge, equilibrium constant, and steric factor) of the product and all major impurities can be obtained by model fitting from two salt-gradient IEX runs at the optimized pH. The collected IEX fractions are further analyzed by HIC, gel electrophoresis (GE), and other techniques (if required) in parallel to estimate HIC parameters, size, and other parameters (such as affinity), respectively. Finally, the GE bands are analyzed by mass spectrometry to reveal the identity of the impurities. This multi-dimensional fractionation and characterization system is applied to acquire bioseparation process development data from an Escherichia coli lysate and a hybridoma cell culture supernatant. All the parameters required for modeling and rational process synthesis can be obtained within an experimental period of maximum two weeks.

The data obtained through this fractionation and characterization system are mostly not case specific and readily usable for other products produced from the same host organism. For example, the profile of impurities and their properties are the same for any protein overexpressed in the same E. coli strain. The same E. coli lysate impurities database can eventually be used for developing separation process of any protein produced in E. coli. This work, thus, emphasizes the need and importance for a ‘database for bioseparation processes’ for common host cells. The foundation of such a database, which does not currently exist, is provided through this research.

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2

A generalized approach to thermodynamic

properties of biomolecules for use in

bioseparation process design

Abstract

Bioseparation techniques exploit the differences of physicochemical or thermodynamic properties between the product and the contaminants. Rapid development of a downstream process, therefore, requires physicochemical and thermodynamic characterization of the components to be separated. In this paper, we investigate that a generalized thermodynamic interrelation exists among different parameters. For instance activity coefficient, osmotic virial coefficients and the solubility of macromolecule are interrelated among each other. Experimental determination of any one of these parameters can be translated across the boundaries of different separation techniques. A number of downstream separation processes, including size-exclusion chromatography, hydrophobic-interaction chromatography, reversed-phase chromatography, aqueous-two phase separation, crystallization and precipitation, are found to be explained and designed using this generalized thermodynamics. This generalization of thermodynamic properties together with high-throughput experimentation provides a systematic and high-speed approach to bioseparation process development and optimization. The applicability of this approach for the bioseparation process design was investigated by a case study on nystatin, a medium-sized biomolecules. The distribution coefficients of nystatin in reversed-phase chromatography showed straightforward relationship with the solubilities at various solvent compositions and the experimental data supported the trend of the relationship.

Keywords: Activity coefficient; Osmotic virial coefficient; Bioseparation; Biomolecule;

Bioprocess Design

___________________________________________________________________________

Published as T. Ahamed, M. Ottens, B.K. Nfor, G.W.K. van Dedem & L.A.M. van der Wielen. Fluid

2.1. Introduction

Purification processes of biomolecules are never straightforward and usually composed of a sequence of operations. Recent advances in various separation methods provide the possibility of generating a number of process options that may satisfy the process and end product constraints (i.e., yield, purity, activity, sterility). A quantitative comparison of the process options is required in order to facilitate the selection of the best process alternative.

Separation methods, in principle, exploit the differences in physicochemical, thermodynamic or molecular properties between the target compound and its contaminants. Therefore, the first step in developing a downstream process is to acquire sufficient knowledge on the properties of the target compound and its contaminants. These properties are then used as input data for designing separation methods and generating process options in a rational manner [1]. In order to quantify molecular or thermodynamic properties and operating condition, process designers usually use intuitive qualitative concepts, based on substantial experience. Generalizing quantitative principles in the determination of thermodynamic properties can assist in translating this valuable knowledge into quantitative tools, thereby making process design systematic and high-speed.

Physicochemical properties of biomolecules important in chromatographic separation processes are molecular size, charge, pI, hydrophobicity and affinity [2]. In addition to these constant physicochemical properties, interactions of biomolecules among themselves and with the environment are the key parameters in partitioning of biomolecules in different phases. For instance, the osmotic second virial coefficient (Bmm) is a thermodynamic property of dilute protein solution, which characterizes pair-wise protein self-interactions including contributions from excluded volume, electrostatic interactions and short-range interactions [3]. This Bmm has been used to model and/or thermodynamically explain a number of separation techniques, including crystallization [4], precipitation [5], aqueous two-phase separation (ATPS) [6], folding/refolding and aggregation [7]. On the other hand, some separation methods are complex to model and require many physicochemical and thermodynamic parameters as input. For example, detailed modeling of partitioning behavior in ATPS process fundamentally requires knowledge of molecular size, charge, hydrophobicity and Bmm [8]. The question arises whether any general relationship does exist among these properties. A previous study was done in this regard and a general relationship was established to correlate limiting thermodynamic properties, which are in use in a wide variety of separation processes [9]. The parameters in this general methodology can be obtained from a limited number of experiments, translated across the boundaries of different separation techniques and can be predicted from data that are commonly found in the characteristics of the final product. However, the study was restricted to small sized peptides, for which conformational changes may not be significant. Larger molecules exhibit separation behavior that is strongly affected by conformational changes that could also be induced by the environment. This complicates any generalized approach for biological

macromolecules. Nevertheless, the general thermodynamic framework may, in principle, be applied to macromolecules.

A generalization of thermodynamic parameters was shown previously in a number of cases, although not for the purpose of process design. For instance, the solubility of proteins may be interrelated to Bmm [10-12]. In this chapter, the physicochemical and thermodynamic parameters of biomolecules and its aqueous solution, needed for separation process design, and the existing high-throughput experimentation (HTE) techniques of obtaining the properties are reviewed. The second part of this chapter shows the interrelationship among different thermodynamic properties and how a limited number of variables can be used to model and predict almost all of the separation methods applied in biotechnology industries today. In order to realize the practical use of the thermodynamic generalization approach for bioseparation process design, a case study on the relationship between solubility and chromatographic distribution coefficient of an intermediately large biomolecule, nystatin, is shown at the last part of this chapter.

2.2. Properties of biomolecules needed in purification process design

Selection and design of downstream processing operations for biomolecules have been virtually impossible in a systematic manner due to a lack of fundamental knowledge on the thermodynamic properties of the components to be separated. In order to approach the issue more systematically, it is important to summarize first the separation techniques usually applied in the biotechnology and biopharmaceutical industries and the input thermodynamic data required for designing each of these methods.

Industrial scale downstream processes usually consist of recovery steps followed by purification steps; the recovery steps are rather straightforward and impose fewer complications in design and selection [13]. On the other hand, a wide range of techniques is available for high-resolution purification and many combinations of them may achieve the desired level of purity [14]. We, therefore, focus only on the high-resolution separation techniques and list out the important parameters governing each of these techniques.

2.2.1. Chromatography

Chromatography is undoubtedly ubiquitous for separation of biomolecules because of its industrial maturity and very high-resolution [15]. A number of chromatography methods are being used nowadays, for example ion exchange, size-exclusion, hydrophobic-interaction, reversed-phase, and affinity chromatography, all of which differ from each other in their separation principles. Regardless of the type of chromatographic operation, the partitioning behavior of the product and contaminant molecules, in terms of distribution coefficients, is the parameter required for selecting and designing the chromatographic separation processes. Asenjo and co-workers listed out the physicochemical parameters governing different chromatographic methods [2,13,16-21]. In order to identify each of the parameters distinguishably, we elaborate this issue to some extent.

Separation in ion exchange chromatography occurs due to charge differences between product and contaminant molecules. Since the net charge of a biomolecule varies with the pH of the solution, the elution profile depends solely on operational pH and ionic strength. For a given operational condition, charge density, rather than the net charge or surface charge, is the parameter affecting protein-partitioning behavior in ion exchange chromatography [20]. Charge density, defined as net charge divided by molecular weight, as a function of pH can therefore be used to design ion-exchange chromatography processes.

The separation principle in size-exclusion chromatography (SEC) is simply based on molecular size. However, concentration dependence of retention appears to be a non-negligible parameter in SEC when chromatographic operation is done at high concentration of biomolecules [22-24]. Molecular size is usually approximated simply from the molecular weight.

Two other chromatography techniques, hydrophobic-interaction and reversed-phase, exploit the variable hydrophobic nature of biomolecules. Hydrophobic-interaction chromatography (HIC) is based on the reversible interaction between the hydrophobic patches on the biomolecules and the mildly hydrophobic stationary phase at high salt concentration [25]. Retentions of biomolecules in HIC systems largely depend on the environment, for instance type and concentration of salt [26,27], density and type of hydrophobic ligand in the stationary phase [28]. The type of matrix and salt effect do not, as a rule, alter the elution order of proteins despite the hydrophobic moieties in different matrices interact differently with proteins [29]. For a defined system, separation occurs due to differences in hydrophobicity of biomolecules. The term hydrophobicity covers average surface hydrophobicity as well as location and size of hydrophobic patches on the biomolecule surface, called surface hydrophobicity distribution [30]. The basic retention process in reversed-phase chromatography (RPC) is principally the same as in HIC. RPC matrices are generally more hydrophobic than HIC matrices. The hydrophobic-interaction in RPC is therefore so strong that the elution is accomplished by organic solvents rather than aqueous electrolyte solutions [31].

One more chromatography method, used largely in pharmaceutical protein purification, is based on the biospecific affinity between the ligand attached on the stationary phase and biomolecules in the liquid phase. Modeling of affinity chromatography requires specific information on the ligand-biomolecule binding affinity, in term of association/dissociation constant or adsorption isotherm.

2.2.2. Liquid-liquid extraction

Liquid-liquid extraction in aqueous two-phase systems has been widely used for separation of proteins and removal of contaminants from fermentations as an initial separation. Factors and mechanisms that cause the distribution of biomolecules over the different phases are poorly understood. The value of the overall distribution coefficient depends on the molecular size [32], charge [33-36], hydrophobicity [37-39], solubility and

affinity [40]. However, not all these parameters are equally important as this depends on the chosen system.

Beyond ATPS, precipitated material is accumulated at the interface between an organic liquid phase and an aqueous solution of a salting out salt to form a third interphase. This three-phase partitioning method has been used recently for the purification of some proteins [41-43] and alginate [44]. The parameters important for designing three-phase partitioning system are essentially the same as for ATPS.

2.2.3. Crystallization and precipitation

Most biological macromolecules at ambient conditions are solids when pure. Solid-liquid phase separation occurs, as a consequence, when the biomolecule concentration is high enough to exceed the solubility limit. Formation of solids may occur in the form of either crystals or precipitates, depending on the Bmm value of the biomolecule at that solution conditions chosen [4]. Therefore, Bmm and solubility are two important thermodynamic parameters governing crystallization/precipitation processes.

Commonly used high-resolution purification techniques and their respective physicochemical or thermodynamic parameters are listed in Table 2.1. In addition to these properties, the stability of biomolecules is also of the utmost importance and may dictate conditions to be used and the viability of given separation steps. Design of a bioseparation process requires the availability of these physicochemical and thermodynamic properties.

Table 2.1. Most commonly used purification methods and respective properties of biomolecules on which design of these methods are based.

Separation method Parameter by principle Process design parameter

Chromatography Partition coefficient Ion-exchange Charge density as a function of pH

Size-exclusion Molecular size Hydrophobic-interaction Surface hydrophobicity Reversed-phase Hydrophilic and hydrophobic

interactions

Affinity Specific binding affinity Liquid-liquid extraction Molecular charge, size and

conformation, hydrophobicity, affinity, solubility, Bmm

Partition coefficient Aqueous two-phase separation

Triple-phase partitioning

Precipitation/crystallization Solubility, Bmm Solubility, Bmm

Centrifugation Molecular density Sedimentation coefficient Membrane separation Molecular size Permeability

2.3. Models of generalization of thermodynamic properties

2.3.1. Activity coefficient and virial coefficients of aqueous solution of macromolecule

Addition of a biological macromolecule to a solvent gives rise to thermodynamic non-ideality of the solution. The thermodynamic property of such a macromolecular solution is

explained by the classical virial expansion of the osmotic pressure, π , in terms of molar concentration scale [45], namely

(

)

1

m m mm m mmm

RTc c B c B

π = + + +" (2.1)

where R is the universal gas constant, T is the temperature in Kelvin, cm is the concentration of macromolecule in molar scale, and Bmm, Bmmm, are the McMillan-Mayer’s osmotic virial coefficients [46]. For a binary solution of macromolecule and solvent, deviations from thermodynamic ideality are expressed conventionally, in terms of the chemical potential of the macromolecule in the liquid phase (

" L m μ ) as ln L L m m L m RT a μ = ° +μ (2.2)

where μ° is the standard state chemical potential, is the thermodynamic activity of the macromolecule in solution. The choice of the standard state of the macromolecule depends primarily on the pressure and the chemical potential of the solvent (

L m a

s

μ ) as well as on the choice of the concentration scale (mole fraction, molar or molal) of the macromolecule. According to the McMillan-Mayer solution theory [46], the thermodynamic properties of a multicomponent system are expressed as a power series of solute concentration in molarity scale. In order to comply with the McMillan-Mayer theory, the μ_s must be held constant even as the concentration of biomolecules changes or the expression of L

m

μ must be converted from a state at constant μs to a state at constant pressure [47]. Under the usual laboratory

constraints of constant pressure and temperature, μ_s does not remain constant with changes of macromolecule concentration. Under these conditions, is most conveniently expressed as the product of an activity coefficient,

L m a L

m

γ , and a concentration, , in molal (moles of macromolecule per kilogram of solvent) dimension. Therefore

m m ln ln L L L m m RT m RT m μ = ° +μ γ + m (2.3)

The chemical potential of the solvent, μ_s, due to the addition of the macromolecule can be expressed as

(

)

1

s s RTM ms m B mmm m Bmmmmm

μ = ° −μ +  +  +" (2.4)

where μ° is the standard state chemical potential of the solvent, _s M is the molecular mass _s

of solvent, and B_mm, B_mmm, are Hill’s osmotic virial coefficients, accounting for interaction among macromolecules [48]. Differentiation of Eq. (2.4) yields

"

(

)

Using the appropriate form of the Gibbs-Dühem equation at constant pressure and temperature, it is straightforward to show that

1 L m m s m m M m m s m μ μ ∂ _{= −} ∂ ∂ ∂ (2.6)

(

)

Integration of Eq. (2.7) gives the relation

2 3 ln 2 2 L L m m RT mm B mmm m Bmmmmm μ − ° =μ ⎛_⎜ + + + ⎝   "⎞⎟ (2.8) ⎠

We have now two equations expressing non-ideal behavior, Eq. (2.3) in terms of an activity coefficient and Eq. (2.8) in terms of virial coefficients. Combining Eq. (2.3) and (2.8) yields a relationship between an activity coefficient of the macromolecule and the osmotic virial coefficients, for a particular solution condition at a particular macromolecule concentration, as 2 3 ln 2 2 L m B mmm m Bmmmmm γ =  +  + (2.9) "

Since the solvent chemical potential is not kept constant during the virial coefficient measurement experimentation, Hill’s virial coefficients are the ones often accessible experimentally [49]. There is also a simple and rigorous thermodynamic relationship between Hill’s virial coefficient, B , and McMillan-Mayer’s virial coefficient, B. If both solvent and macromolecule are assumed to be incompressible, which is typically the case for aqueous solution of macromolecules, the relationship is then [47,50]

(

)

( )

{

( )

}

where ρ_s0 is the molar density of pure solvent (mol/ml) and ν_m0 is the partial molar volume of the macromolecule (ml/mol).

It has been shown in Eq. (2.9) to (2.11) that activity coefficient, McMillan-Mayer’s virial coefficients and Hill’s virial coefficients are interrelated to each other. Experimental determination of any one of them can directly be used to access the others. For the description of a dilute solution of macromolecule, three-body or higher order interactions may be neglected from Eq. (2.9), whereas two-body interactions (B_mm) can easily be measured experimentally by traditional light scattering technique [48]. Several other analytical techniques, including membrane osmometry [51-53], sedimentation equilibrium [54], self-interaction chromatography (SIC) [55-57] and SEC [24], have been used over past decade for the measurement of second virial coefficient. Except light scattering [48,49,58], no other technique was ever investigated to show how close is the experimentally obtained second virial coefficient to the McMillan-Mayers’s B_mm. Indeed all of these techniques have significant inherent inaccuracy in measuring Bmm, whereas the inherent error limit is minimum in SIC [57].

2.3.2. Application of thermodynamic models in bioseparation processes

Separation of biomolecules is usually based on the difference in thermodynamic properties of different components. Therefore, a number of downstream separation methods,

for instance crystallization/precipitation, ATPS, chromatography, can be described with thermodynamic parameters shown in section 2.3.1 and consequently be predicted, modeled and designed.

2.3.2.1. Size-exclusion chromatography process

In SEC, partitioning of a biomolecules species between mobile phase and stationary gel phase can be approximated simply based on their molecular size and assuming thermodynamic ideal behavior of solution [59,60]. At higher concentration of biomolecules, thermodynamic non-ideality becomes a non-negligible term. In order to explain this phenomenon, we assume the equilibrium condition, i.e. the chemical potential of the macromolecule in mobile phase (μ_mL) is equal to the chemical potential of the macromolecule in the gel phase (μ_mG), μ_mL =μ_mG. Using Eq. (2.3) we have

ln ln ln L G G G L m m m m m L m m RT m μ° − °μ _{= γ − γ} ⎛ ⎞ + ⎜ ⎝ ⎠⎟ (2.12) where γmG and L m

γ are the activity coefficients of the macromolecule in gel phase and mobile phase, respectively, and and are the macromolecule concentration in the gel phase and the mobile phase, respectively. Here, the concentration in the gel phase is defined as the amount of injected biomolecule divided by the accessible volume. The term / is described as the distribution coefficient and denoted as KSEC.

G m m m_mL G m m m_mL L m γ in Eq. (2.12) can be described in virial expansion terms as shown in Eq. (2.9). However, virial expansion of γ_mG considers interaction of macromolecules with the matrices as well as self-interactions. Neglecting the interactions with matrices, which is typically the case for SEC systems, Eq. (2.9) can be written as

( )

( )

Combining Eq. (2.13) and (2.14) yields

(

)

( ) (

)

SEC mm m SEC mmm m SEC

K B m K B m K

RT

μ° − °μ ⎧ ⎫

= +_⎨ − + − _⎬

⎩   +"⎭ (2.15)

Eq. (2.15) describes the concentration dependent retention behavior of macromolecules in the SEC column in term of virial coefficients. The first term on the right hand side of Eq. (2.15) is a constant, independent of concentration, which denotes distribution coefficient of the macromolecule in the limit of infinite dilution [22].

2.3.2.2. Hydrophobic-interaction chromatography process

Retentions in HIC are, in principle, based on the surface hydrophobicity of the macromolecule. Unfortunately, surface hydrophobicity still lacks an absolute definition. A

number of hydrophobicity scales were used in literature for computing protein hydrophobicity. For HIC modeling, Asenjo and co-workers [61] estimated the average surface hydrophobicity of proteins from the three-dimensional structure data by calculating the hydrophobic contribution of the exposed amino acid residues as a weighted average. Based on this average surface hydrophobicity, they also proposed a model for predicting retention in HIC [61,62]. However, the model does not consider the possibility of a heterogeneous distribution of hydrophobic patches throughout the biomolecular surface, which may largely affect on protein retention in HIC [30]. A better prediction may be obtained, if the areas and location of the hydrophobic patches that react with HIC resin is known [29]. In addition, it is not at all clear how this approach can be applied for a molecule whose three-dimensional structure has not yet been elucidated. Furthermore, the approach does not account for the possibility of specific interactions between proteins and matrix or for the conformational changes of proteins during the chromatography process [63]. A detailed thermodynamic modeling of retention in HIC was reviewed earlier [64]. However, we approach the issue differently, based on the activity coefficients of macromolecules in different phases.

The magnitude of solute retention in linear elution chromatography is measured under isocratic conditions in terms of the retention factor or the capacity factor, k , which is related to the equilibrium constant, KHIC , for the distribution of solute between the bulk mobile

phase and stationary phase as

′

(

)

where tm and tu are the retention time of solute and an unretained tracer, respectively. φ′ is the phase ratio of the column, i.e. ratio of the volume of the stationary phase to that of the mobile phase. Applying Eq. (2.3) at zero ionic strength (superscript (0)) and ionic strength I (superscript (I)) we have

(2.17) ( ) ( ) ( ) ln ln L I L L I L I m m RT m RT m μ = ° +μ γ + m L m (2.18) (0) (0) (0) ln ln L L L m m RT m RT m μ = ° +μ γ +

Now the chemical potential of the macromolecule in the liquid phase at ionic strength I (μ_mL I( )) can be written as ( ) ( ) ( ) (0) (0) (0) ln ln L I L I L I L m m m L m m m RT RT m γ μ μ γ ⎛ ⎞ ⎛ = + _{⎜ ⎟}+ _⎜ ⎝ ⎠ ⎝ m L ⎞ ⎟ ⎠ (2.19)

Since the same column load is applied in all the experiments, it is assumed that the activity coefficients are a function of ionic strength (I) only. At equilibrium the chemical potential of the macromolecule in the mobile phase is equal to the chemical potential of solute on the stationary gel phase, i.e. and . Using Eq. (2.19) for both phases at equilibrium ( ) ( ) L I G I m m μ =μ L(0) G(0) m m μ =μ ( ) ( ) ( ) ( ) (0) (0) (0) (0) ln ln ln ln G I L I L I G I m m m G L L m m m m m m m γ γ γ γ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ − = − ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ m G m ⎞ ⎟ ⎠ (2.20)

The concentrations in the gel phase in Eq. (2.20) are defined as the amount of biomolecule bound to the solid phase divided by the volume of stationary phase. Manipulating Eq. (2.20) and using the definition of distribution coefficient, K_HIC =mG_I /m_IL

(0) ( ) ( ) (0) (0) (0) ln ln ln ln G L I m m HIC L L m m m K m γ γ γ γ ⎛ ⎞ ⎛ ⎞ ⎛ = _{⎜ ⎟}+ _{⎜ ⎟}− _⎜ ⎝ ⎠ ⎝ ⎠ ⎝ G I m G m ⎞ ⎟ ⎠ 2 Φ (2.21)

Eq. (2.21) can be used for modeling distribution coefficient in the HIC system. The first term on the right hand side of the Eq. (2.21) is a constant, describing the distribution coefficient at zero ionic strength. The ratio of activity coefficients in the mobile phase (the middle term on the right hand side of Eq. (2.21) can be interrelated with osmotic virial coefficients as shown in Eq. (2.9), consequently can be obtained experimentally. Alternatively, this term can also be modeled by a Debye-Hückel term plus a linear term [65]. The last term on the right hand side of Eq. (2.21) is the ratio of activity coefficients on the stationary phase, consequently most difficult to access because it is virtually impossible to directly measure the activity coefficients of macromolecules in the stationary phase. Some researchers believe that the activity coefficient of macromolecule in the stationary phase remains practically constant for a particular resin/solvent combination while varying the mobile phase composition [66]. In that case, the last term on in Eq. (2.21) can be neglected.

2.3.2.3. Aqueous two-phase separation process

Several theories have been proposed for correlating and predicting distribution of solutes between two aqueous continuous phases. A detailed review on different theories has been presented earlier [47,67-69]. Here we reproduce the osmotic virial expansion based thermodynamic model for ATPS to show that osmotic virial coefficients are also important parameters for designing ATPS processes. The osmotic virial expansion that is commonly used for ATPS was first proposed by Edmond and Ogston [70,71]. On this basis, Prausnitz and co-workers established a simple theoretical framework to predict phase separation of aqueous polymer solutions [72] and biomolecule partitioning in these phases [73]. When a macromolecule is placed in an aqueous two-phase system, the electrochemical potential of a macromolecule in a four-component system is expressed as

(2.22) 2 1 1 2 2 1 1 2 11 1 12 1 2 2 2 22 2 (ln ) m m m mm m m m mmm m mm m m m mm m m m RT m b m b m b m b m b m m b m b m m b m m b m z F μ − ° =μ + + + + + + + + + +" +

where is the charge of macromolecule, F is the Faraday constant, and is the purely electric potential. Subscripts 1 and 2 represent two different polymers. Interaction parameters

b in Eq. (2.22) are directly related to osmotic virial coefficients as [73] m z Φ

(

)

(

)

where M1 and M2 are molecular weight of polymer 1 and 2, respectively. In thermodynamic equilibrium, the electrochemical potential of each component must be the same in the two phases. Calculation of phase diagram using osmotic virial expansion usually truncated after the second virial coefficient. From this basic thermodynamics, King et al. [6] showed that for a dilute macromolecule solution, the distribution coefficient of macromolecule ( ) into top (T) and bottom (B) phases could be well described by

ATPS K

(

)

(

)

(

)

where the superscripts T and B represents top and bottom phases, respectively. Although Eq. (2.25) was derived for the description of a dilute macromolecule solutions, it can be applied to concentrated solution as long as the distribution coefficient is independent of the macromolecule’s own concentration, which is approximately 30 wt. % [74].

2.3.2.4. Crystallization and precipitation

Solid-liquid phase separation in macromolecule solutions occurs when macromolecule concentration exceeds the solubility limit. Solid phase can be developed in the form of either crystal or precipitate. George and Wilson [4] observed that solution conditions under which proteins have a tendency to crystallize correspond to a slightly negative Bmm. They correlated protein crystallizations with Bmm values in the form of so called “crystallization slot” [75]. When the Bmm value is more negative than the crystallization slot, the interactions between molecules are so high that amorphous precipitation is more likely to occur, rather than highly organized crystal structure. A positive Bmm value does not completely exclude the possibility of crystallization or precipitation, but typically requires impractically high concentration of macromolecule in order to bring about any kind of phase separation. A thermodynamic understanding why a particular range of Bmm values promotes crystallization was described in the literature [3,4,76-78]. It was later investigated that the

Bmm value within the crystallization slot is an essential prerequisite of crystallization, but does not guarantee successful crystal growth [57]. In addition to the Bmm, solubility of the macromolecule is also an important parameter. Indeed, a relationship exists between solubility and Bmm.

Both the solubility and the Bmm of biological macromolecules are determined by the interaction between the protein molecules. In dilute aqueous solutions, both depend mainly on two important solution parameters, pH and ionic strength. Solubility depends on the binding energy between molecules at a short-distance from one another for their very specific orientations. On the other hand, Bmm is a statistical average of the interaction forces over all distances and orientations of two molecules in the liquid phase. Nevertheless, the similarity in the qualitative nature of Bmm trend and solubility trend is obvious from literature [10,12,76]. In order to simply realize the relationship, we extend the activity coefficient model to supersaturated solutions.

The criterion for thermodynamic equilibrium in a solid-liquid phase separation process is that the chemical potential of the macromolecule in the liquid phase (μ_mL) is equal to the chemical potential of the macromolecule in the solid (crystal/precipitate) phase (μ_mS). Therefore, for a saturated macromolecular solution

ln ln S sat s m m m m m at RT RT μ =μ = ° +μ γ + m (2.26)

where m_m of Eq. (2.3) has been replaced by the solubility of macromolecule, m_msat andμ_msat represents the activity coefficient of the macromolecule in the liquid phase at saturation. Solubility can be correlated with virial coefficients by evaluating Eq. (2.24) with Eq. (2.9) as

( )

3

ln 2

2

S

sat sat sat

m m m mm m mmm m m B m B m RT μ − °μ _{− =  +  +} " (2.27)

Eq. (2.27) shows that a straightforward relationship exists between virial coefficients and solubility. When the solubility of the macromolecule is low enough to neglect higher-body interactions, Bmm alone can predict the solubility. However, the solubility of macromolecules is very high in some solution conditions, where Bmmm cannot be neglected. This phenomenon is evident from the observation of Ruppert and co-workers [12] that Bmm does not correlate strongly with solubility data at protein concentrations >30 mg/ml. Eq. (2.27) cannot be used readily to measure solubility from Bmm, because μ_mS is unknown and impossible to measure experimentally. One way to obtain solubility from Bmm is to assume that the solid phase thermodynamic properties, i.e. μ_mS, are independent of solution parameters. Then experimental measurement of solubility in only one solution condition will be enough to access first term on the left hand side of Eq. (2.27). This could be the case for a particular morphology of crystal or precipitate. However, different solution conditions produce different types of crystals, which may not have the same thermodynamic properties. Similarly, precipitates may also occur in different forms, for instance concentrated liquid, aggregated solids, or disordered precipitates. Different forms of the solid phase are therefore expected to have different chemical potentials. Another way to obtain solubility from Bmm is to fit Bmm data to the UNIQUAC activity coefficient model. Solubility can then be predicted from this model [79,80]. In order to predict solubility from Bmm without any other variable, Haas and co-workers [11] combined a square-well potential model to Bmm data with Gibbs free energy models for the crystalline and solution phases.

2.4. Generalized approach to bioseparation process design

In designing a bioseparation process, characterization of the starting material and defining the specifications of final products are required first. If the design program starts from the fermentation broth, the separation process can be divided into two main categories, firstly protein recovery and then protein purification. Since recovery operations are relatively straightforward, we focus only on purification operations in this paper.

Obtaining physicochem ical and therm odynam ic properties

M odeling of separation m ethods

G eneration of process alternatives

Selection of the best process/es Expert System

Therm odynam ic generalization

HTE

O ptim ization of the process

D etailed design of the process HTE

D esign of experim ents

Fig. 2.1. An integrated way to bioseparation process design.

2.4.1. Process synthesis, optimization and design

The first step in designing a purification process is to make decision on whether a particular unit is applicable for separation of the target component from its contaminants. The feasibility of a separation method can be studied by modeling. As it is shown in section 2.3.2, modeling of a separation method requires information on constant physicochemical properties as well as solution condition dependent thermodynamic properties of the components present in the mixture. Once modeling is done, process options can be generated based on the standard rules [18] as well as on the end product requirements. An Expert system may also be of significant help for choosing the sequence of operations [1,81-84]. Block diagrams of different process alternatives with some knowledge on the prospective yield and purity can be obtained in this way. The best process option may also be selected based on this primary knowledge and purity. However, optimization of each separation unit is further required for the detailed design of the process. For instance, we know from an Expert system that the first unit operation of a process is a HIC. We still have a number of variables to be optimized in the HIC unit, including the resin to be used (which supplier, which resin), eluent composition (which salt in which concentration), pH and mode of elution (stepwise or gradient), and sensitivity. The basic model based on thermodynamic and physicochemical properties may not provide the precise data required for optimization. This is also due to the fact that all resin specifications and its behavior in different eluents is not readily provided by