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Ion-exchange chromatography of proteins: the inside story
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International Conference on Advances in Bioprocess Engineering and Technology 2016 (ICABET 2016)
Ion-exchange chromatography of proteins: the inside story Abraham M. Lenhoff* Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
Abstract Despite its inherent inefficiencies, chromatography remains the workhorse for protein purification in the pharmaceutical and biotechnology industries, with ion exchange the most widely used mode. Many aspects of a chromatographic process are still optimized empirically, but modeling is increasingly used to aid in scale-up and optimization. An overview is presented here of the needs that remain for greater insights that can inform truly predictive modeling of preparative protein separations by ionexchange chromatography as well as guiding rational design considerations and effective heuristics. These needs can be addressed by a more complete mechanistic understanding of the relation between adsorbent and protein structure and the values of key functional parameters describing adsorption and transport. A principal focus is on specialized experimental and modeling approaches to obtain such insights; these include molecular-level models, various modes of microscopy, and chromatographic and other methods for inferring intracolumn and intraparticle behavior. The principal findings revolve around the critical role for fundamental principles of classical chemical engineering, albeit specialized for the peculiar physicochemical properties of protein solutions. © 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016. Keywords:Biotechnology; protein separations; biologics; adsorption; intraparticle transport; diffusion.
* Corresponding author. Tel.: +1-302-831-8989; fax: +1-302-831-1048. E-mail address:[email protected] 2214-7853© 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016..
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1. Introduction Chromatography remains an essential unit operation for high-resolution purification in pharmaceutical biomanufacturing, despite its inherent inefficiencies and continuing efforts to seek alternatives [1]. Chromatographic process development in biomanufacturing retains a large element of empiricism, albeit often streamlined by the use of high-throughput screening [2-4] and heuristics. However, there is an ongoing drive to incorporate the same kinds of predictive modeling that have gradually come to dominate design of more conventional chemical engineering processes over the past half-century. These efforts rest on experimental descriptions [5] and mechanistic analyses [6] that can be traced back for the better part of a century. Mathematical descriptions that include intraparticle transport became well-established around the middle of the twentieth century [7] and form the basis for the column modeling presented in key reference texts today [8, 9]. However, the models incorporate some ambiguity regarding adsorption and transport mechanisms and the parameter values needed must be specialized for use with particular analyte molecules and chromatographic media. In many cases the mechanisms are assumed a priori and the parameter values are estimated simply by fitting model predictions to experimental data without reliable verification of accuracy and reliability. The questions left open by these approaches can be addressed by experimental, theoretical and computational methods that have been developed over recent decades. These methods span scales ranging from molecular to particle to column, and they provide a substantial but not yet complete basis for making column modeling more predictive. An important element that is included is that of accounting for the novel architectures that have been developed for chromatographic particles specialized for use with biomolecules. This has led to structure–function relations that can be applied to combinations of particles and molecules. The purpose of this paper is to summarize some of the experimental and analytical approaches that underpin the more robust, mechanistically-reliable description of biochromatographic processes. The presentation is divided into two main parts: structural characterization, primarily of the adsorbent but also of the analytes, and functional characterization and prediction. The emphasis throughout is on ion-exchange separations, which are the most widely-used in industrial bioprocessing, but many of the methods can be applied directly also for other modes of chromatography. 2. Structural characterization 2.1. Adsorbents The standard picture of a chromatographic column is of a bed packed with porous particles that provide appreciable internal surface area for adsorption. The characteristic pore size is a critical parameter in that it allows a balance to be reached between, on the one hand, the surface area available for adsorption and, on the other, transport, generally assumed to be by diffusion, between the external particle surface and the interior. That the size of proteins is typically several nanometers to more than 10 nm, with other analytes such as viruses potentially > 100 nm, means that the pore sizes needed for biochromatography are typically appreciably larger than for many other applications and can also vary extensively depending on the analytes of interest. The physical structure of the adsorbent can be visualized directly by such methods as electron microscopy, whether scanning or transmission. These methods have been routinely used in development of novel adsorbents for many years [10] and are invaluable in representing the general physical features of the particle and the pore space both qualitatively and quantitatively, and these and other microscopy methods can also be extended to in silico reconstruction of the pore space [11, 12]. However, such methods are inherently restricted to a very small fraction of the overall volume, which raises questions regarding how representative such measurements and reconstructions are. An additional challenge is the distortion that may result from drying during sample preparation for, say, electron microscopy, especially for the highly hydrophilic base matrices that are frequently used in biomolecular chromatography. For the relatively wide pores and aqueous media characteristic of biochromatographic media, a powerful tool for structural characterization is inverse size-exclusion chromatography (ISEC) [13-16]. Here each of a set of nonadsorbing polymeric probe molecules – dextran is especially widely used – is injected into and eluted from a column
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and the retention time, expressed in terms of the size-exclusion distribution coefficient Kd, is used to infer the poresize distribution. Since the number of data points is usually of order 10, the pore-size distribution is expressed using a relatively simple functional form in which a small number of parameters (1-2) is determined by fitting the Kd values. This fitting is based on an idealized model, typically of a spherical molecule in a cylindrical pore, but since the fit is sensitive primarily to the ratio of analyte size to pore size, the details of the model are less important. The value of ISEC is that the pore-size distribution, once estimated, can be used to derive additional metrics, e.g., the surface area available for adsorption, from which key functional characteristics can be estimated, as will be seen later. The emergence of particles with more specialized architectures has increased the challenge of particle characterization. In particular, there are several classes of particles in which polymer extenders are appended to the underlying base matrix [17, 18], and again both qualitative and quantitative characterization are required. ISEC can still be used but its interpretation and especially its use in functional prediction raises new questions. This is largely because the uncharged probe molecules used in ISEC can penetrate the polymer only if the probe is smaller than the polymer mesh size to some degree, whereas electrostatic attraction is able to draw much larger protein molecules into the same polymer under suitable conditions. The ideal situation is one in which both underivatized and polymer-functionalized media are available on the same base matrix, in which case direct comparison of the two sets of ISEC data can allow inference of some structural features of the attached polymer [19, 20]. Another tool that has recently been applied to characterization of chromatographic media is small-angle scattering [21], which has the advantage of sampling a relatively large sample volume and therefore providing a representative picture. The challenge in interpreting scattering data is that the results are obtained in reciprocal space, and while key characteristics can often be extracted from limiting slopes and forms in portions of the spectrum, more detailed model-building is usually needed and is not always unambiguously interpreted. 2.2. Proteins For many proteins, especially those used as model proteins in fundamental studies of protein chromatography, full three-dimensional structures are often available and can be used even for detailed molecular modeling down to the atomistic level. For biopharmaceuticals, however, such structural details are rarely available, so alternative methods are needed for estimating key biomolecular properties. Since these molecules are made by recombinant DNA methods, the gene sequence and hence the amino acid sequence is available, and apart from possible effects of post-translational modifications, this is enough to extract and estimate properties relevant to chromatographic analysis. The amino acid sequence immediately provides a measure of the protein size in terms of the molecular weight. In addition, globular proteins have partial specific volumes in the range of 0.70–0.75 cm3/g [22], which allows estimation of the molecular volume and hence of the radius of a sphere of equivalent volume. Since a globular protein has a rough surface, this radius underestimates that of the protein, and of course the globular shape is not a perfect sphere in any event. Nevertheless, this simple approach provides a very useful measure of protein size that can be juxtaposed against the length scales estimated for the pore-size distribution. The amino acid sequence also allows estimation of the titration properties of the protein, which provide the basis for assessing retention in ion-exchange chromatography. The simplest approach is just to consider the intrinsic pKa values of the different amino acids [22], but the actual protonation state will also depend on the local charge environment in the protein and on the salt concentration. More refined titration curves can be estimated using electrostatic calculations [23], although such improvements make little difference to the ability to predict protein retention. 3. Functional characterization and prediction Column behavior in protein chromatography in bind–elute mode comprises primarily the breakthrough curve during loading, followed by the elution curve, both of which reflect a synthesis of adsorption and transport properties. The position of the breakthrough front or of an elution peak is determined by the equilibrium adsorption
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properties, but the shape, especially the width, of the front or of a peak is modulated by kinetic characteristics, which for the large particles used in preparative separations are dominated by transport rates. This section is therefore divided into adsorption and transport sections, with some reference to implications for column modeling. 3.1. Adsorption Because adsorption capacity is critical for preparative separations, full adsorption isotherms must be considered in protein chromatography. Such isotherms have the hyperbolic form that is usually associated with Langmuir isotherms, but it is rarely possible to identify well-defined adsorption sites in protein chromatography, especially on ion exchangers. However, for adsorption on conventional media the isotherm can be decomposed into limiting elements that make interpretation and even prediction of adsorption behavior more tractable. The low-concentration end of the isotherm reflects adsorption of isolated molecules, i.e., protein–surface interactions. These have been modeled for many years, with the stoichiometric displacement model and its descendants [24, 25] particularly well-known and widely-referenced, albeit simplistic for proteins. With the advent of protein structural information and molecular modeling methods, such calculations have become more elaborate over the past few decades [26-31], but the quality of predictions has not necessarily improved significantly. There are several likely reasons for this. One is that although the protein structure may be known in great detail, the local topography and charge distribution of the adsorbent is not. Another is that such contributions as hydration effects remain difficult to account for accurately, especially in the presence of the protein macroion, charged ligands and free small ions due to added salt and buffer. Finally, the overall observed adsorption behavior reflects a (weighted) average over an ensemble of adsorbed states, which requires extensive configurational exploration in which the energetics are accurately captured. Therefore despite the great advances in molecular modeling, this remains a significant challenge. While protein–surface interactions are at best a grey area for predictive calculations, they can be measured trivially by simple chromatographic methods in terms of the capacity factor k', which is widely used in analytical chromatography but largely neglected in preparative work. k' can be decomposed into the product of a chemical contribution, the adsorption equilibrium constant Keq, and a physical contribution, the phase ratio φ. For a conventional adsorbent, in which the phase ratio is the specific surface area, φ can be estimated for a protein of a particular size from the pore-size distribution found from ISEC [13]. However, the phase ratio typically has a much smaller effect on k' than does Keq because of the exponential dependence of the equilibrium constant on energy. k' can be used to estimate the initial slope of the isotherm, and for protein adsorption on ion exchangers at the low salt concentrations used for column loading, the values are very large, leading to near-rectangular isotherms. Experimental measurement of k' is readily performed only at higher salt concentrations, for k' values less than about 100, but the power-law dependence of k' on ionic strength I predicted by the stoichiometric displacement model [24] is normally observed experimentally and allows extrapolation, albeit risky, to lower values of I[32], allowing the initial slope of an isotherm to be estimated [33]. In the high-concentration limit the plateau value of the isotherm is attained; here the adsorbed concentration is high enough that protein–protein interactions must be accounted for in addition to the protein–surface interactions that drive adsorption. The most important contribution to the protein–protein interactions is due to steric (excludedvolume) effects, based on which the maximum capacity would be determined simply by how many molecules can be packed into a monolayer per unit area of surface [34, 35]; this reasoning is confirmed down to the molecular level by scanning probe microscopy imaging [36-38]. The volumetric capacity is then found using the phase ratio, e.g., from ISEC measurements. These values are modulated by the possibility of non-steric interactions, especially electrostatic repulsion, which is more pronounced for larger proteins [39], as well as by the question of whether the maximum coverage would be close-packed or limited by random sequential adsorption [40, 41]; comparison with measured values indicates that these values span the range observed experimentally [42].
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Plotting the full isotherm requires connecting the low- and high-concentration trends, and at one level that can be done essentially by eye. However, more rigorously the isotherm form is determined by the relative energetics of the protein–surface and protein–protein interactions [43]. In particular, the plateau level actually attained depends on the protein–surface affinity defined by the low-concentration region, specifically the initial slope. Nevertheless, it is possible using this formulation to obtain reasonable predictions of isotherms [33]. For some materials the maximum plateau value is clearly underpredicted using ISEC-derived phase ratios. This generally happens for polymer-functionalized adsorbents, where the assumed model of monolayer adsorption fails; sorption in these systems is instead more accurately described as volumetric partitioning (Figure 1) [19, 45], and the capacity appears to correlate with the polymer volume in general, although charge effects may play a role [19].
Figure 1. Transmission electron micrographs of Q Sepharose FF (left) and XL (right) loaded to a high fraction of static capacity with protein (stained black) [44]. The FF image indicates adsorption on the bare agarose base matrix, but the dextran-functionalized XL accommodates protein more by volumetric partitioning into the charged polymer layer. Each image is approx. 1700 x 1200 nm.
3.2. Transport 3.2.1. Transport during loading The plateau of the adsorption isotherm determines the maximum adsorbed concentration attainable for a particular protein–adsorbent system under any given set of solution conditions; this represents the static or equilibrium binding capacity. A more widely referenced quantity in bioseparations practice is the dynamic binding capacity, which is the adsorbed concentration attainable during frontal loading until the breakthrough concentration reaches a specified fraction of the feed concentration. The dynamic binding capacity is always less than or equal to the static capacity, with the difference being the result of the finite rate of uptake of the protein into the adsorbent under flow conditions in a column. Although the widespread use of the dynamic capacity makes it an essential metric to consider during chromatographic loading, it is a somewhat complex measure that convolves the static capacity, an equilibrium quantity, and the transport rate, so decomposition of dynamic capacity observations into the two constituent parts can be informative. For most proteins the static capacity on an ion exchanger will decrease monotonically with increasing salt concentration. However, it is only at higher salt concentrations that the dynamic capacity closely approaches the static capacity, whereas at lower salt concentrations the dynamic capacity can be appreciably lower than the static capacity (Figure 2). This leads to the somewhat counterintuitive result that a maximum is seen in the dynamic capacity at an intermediate salt concentration (Figure 2) [20, 46, 47].
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Beyond the practical implications that this can havee for column loading, the disparate trends of the static and dynamic capacities indicate that transport is slower at lo low salt concentrations and faster at higher concentrations. These transport rates can be measured directly, by sever eral methods. Models of peak broadening in simple linear, isocratic chromatography typically include an explici licit contribution from transport rates, as do models of breakthrough concentrations in frontal loading [8, 9].. B Batch uptake measurements provide an even more direct characterization of the uptake rate, which can also be relat lated to transport parameters using suitable transport models. Measurements using these models generally confirm thee trends of transport rate as a function of salt concentration, independent of the transport model used, but the trans nsport model certainly provides a more specific basis for analyzing rates.
Figure 2. Static (solid lines) and dynamic (dotted lines) binding capacitie ities of a monoclonal antibody on SP Sepharose FF (green), SP Sepharose XL (magenta) and Capto S (blue) at a feed concentration of 2 mg/mL an and a linear velocity of 120 cm/hr[20].
In most preparative systems the particles used are la large enough that the main transport resistance is due to intraparticle diffusion, and here there are two limitingg models that are widely used [48]; both account for the coupling of diffusion with adsorption, particularly for th the high-affinity isotherms encountered in ion-exchange of proteins. The first is pore diffusion, which assumes that hat only the protein free in the pore space is able to diffuse, while any adsorbed protein is immobile. In view of thee hhigh adsorption affinities and capacities typically seen, the free protein concentration is much lower than the adsorbed bed concentration at (local) equilibrium, so the fraction of the intraparticle protein that is free to diffuse is small, lead eading to low transport rates. The other limiting model is variously referred to as homogeneous, solid, gel or su surface diffusion. Although these are not always defined completely equivalently, the key implication is that adsor sorbed protein is free to diffuse in addition to the unadsorbed protein. Although inferred homogeneous diffusivities es are appreciably lower than pore diffusivities, the high adsorbed concentrations give rise to comparably high flux luxes. In determining transport coefficients such as diffusiviti vities by fitting experimental data, a significant challenge lies in model discrimination. For instance, batch uptake data ata give rise to relatively simple monotonic curves that can often be fitted equally well by pore-diffusion and homoge geneous-diffusion models [49]. A true pore diffusivity Dp is related to the free-solution diffusivity D0 via Dp/D0 = εpψ/τ, where εp is the accessible porosity of the intraparticlepore space for the protein, ψ is a hindrancee ccoefficient for motion of the protein through a constructed pore and τ is the tortuosity [50]. One clear indication off tthe presence of homogeneous diffusion is an apparent pore
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diffusivity that exceeds D0. However, there is no such benchmark for homogeneous diffusion and other values of pore diffusivities may not allow clear discrimination. A more distinctive indicator of the transport mechanism is the intraparticle concentration profile during uptake: pore diffusion gives rise to a sharp uptake front, which in the limiting case of a rectangular isotherm leads to the shrinking-core model [51, 52], whereas homogeneous diffusion gives rise to a diffuse uptake profile. These profiles can be observed directly by confocal fluorescence laser scanning microscopy using fluorescently-labelled protein (Figure 3) [53, 54], although refractive index methods have also been exploited using unlabelled protein [55]. The gradual transition from pore to homogeneous diffusion with increasing salt concentration has been related to the decreasing strength of adsorption with increasing salt [32, 56, 57], which can be modelled using a parallel-diffusion model [56, 58].
Figure 3. Confocal micrographs of uptake of labeled lysozyme (left) and lactoferrin (right) into Capto S at 20 mM ionic strength [59]. The uptake profile for lysozyme is diffuse, consistent with homogeneous diffusion, and that for lactoferrin is sharp, consistent with pore diffusion. At such low salt concentrations uptake on conventional adsorbents is usually by pore diffusion, but on a polymer-functionalized material like Capto S homogeneous diffusion may be observed, as here for lysozyme.
While the transport behavior described above applies broadly across ion-exchange of proteins, polymerfunctionalized adsorbents appear amenable to homogeneous diffusion even at very low salt concentrations in many cases [20]. This rapid transport is an important factor – along of course with the high static capacities – giving rise to the high dynamic capacities of these adsorbents. The mechanistic basis for this homogeneous transport within the polymer layer is thought to lie in the three-dimensional distribution of charge surrounding the sorbed protein, which reduces the energetic cost to a protein molecule to diffuse [20]; this has been likened to a "bucket brigade" [60]. Although the precise transport mechanism applying in any particular system may be viewed as being of only academic interest, it can have important practical implications. The mechanism determines the uptake rate, which in turn determines the sharpness of the breakthrough front, so manipulating the uptake mechanism is one approach to manipulating the dynamic binding capacity. For more complex systems, such as binary breakthrough, invoking the wrong transport mechanism may lead to significant errors in modeling breakthrough behavior[61]. 3.2.2. Transport during elution Elution receives appreciable attention in studies of analytical chromatography because the "loading" step, i.e., sample injection, is both simple and followed immediately by elution, whether isocratic or gradient. In preparative chromatography, loading – the detailed analysis of breakthrough – is instead often the focus. This occurs despite the evident importance of elution in preparative separations: the need to have the product and the impurities elute in separate peaks that do not overlap excessively. Elution in ion-exchange separations of proteins is invariably by gradient elution, typically in salt concentration but sometimes in pH, with step gradients often preferred over linear gradients for ease of operation. The time at which a peak elutes represents the cumulative effect of migration of the peak along the column, which is determined primarily by adsorption equilibrium. Differential elution of two components requires differences in their retention strength as a function of salt concentration, with closely-related species intrinsically difficult to separate, e.g., charge variants [62, 63]. The width of an elution peak depends largely on the transport properties – how quickly the protein can exit a particle in which it is adsorbed – but where there is residual retention under the conditions used for elution, there is also some dependence on retention characteristics. This coupling
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between retention and transport is one reason for the complexity of predicting elution behavior, and although model calculations have been reported frequently, e.g., [62], general conclusions are more difficult to reach. If the problem is reduced to that of transport in the absence of retention, the analysis can be significantly simpler, but relatively little detailed analysis has been performed [64]. The most obvious mechanistic postulate is that elution occurs by very rapid desorption of protein followed by rate-limiting diffusion from the interior to the surface of the particle. Use of a simple diffusion model to fit the tails of elution curves allows estimation of diffusivities, and since adsorption is absent, these diffusivities should be simple pore diffusivities. This is confirmed to be the case in general [65], with the diffusivities especially sensitive to the fraction of the stationary-phase volume that is accessible to the protein of interest. While this result seems straightforward and predictable, it has consequences that may complicate stationaryphase selection. Specifically, polymer-functionalized stationary phases have effectively very small accessible pore volumes, making elution potentially slow, as has been observed experimentally. This presents a dilemma in that these materials typically have very high static as well as dynamic binding capacities, leading to a trade-off between performance in loading vs. elution. Another consequence of the limited pore volume of polymer-functionalized adsorbents is that the desorbed protein reaches a very high concentration in the pores immediately after desorption, increasing the likelihood of phase separation, especially at the high salt concentrations typically used [65]. Therefore the biophysical properties of the protein may have a pronounced influence on elution behavior as well. 4. Conclusions While the principles and mechanisms governing ion-exchange separations of proteins are well known conceptually, qualitative and especially quantitative understanding, particularly leading to predictive design, requires a more detailed mechanistic understanding. The entrainment of non-chromatographic methods, particularly those with spatial resolution at length scales between the colloidal and the molecular, has been shown in the overview provided here to have been instrumental in providing this new information. A complementary component is the ever-increasing repository of biophysical data, especially from structural biology, that can account for the particular properties of individual proteins of interest. Integration of this knowledge can augment long-established approaches in the synthesis of multiscale models that can lead to truly predictive tools that can play the same role in biotechnology practice that has become routine in more conventional chemical engineering design. References [1] T.M. Przybycien, N.S. Pujar, L.M. Steele, Current Opinion in Biotechnology 15 (2004) 469-478. [2] T. Bergander, K. Nilsson-Vaelimaa, K. Oberg, K.M. Lacki, Biotechnology Progress 24 (2008) 632-639. [3] B.K. Nfor, M. Noverraz, S. Chilamkurthi, P.D.E.M. Verhaert, L.A.M. van der Wielen, M. Ottens, Journal of Chromatography A 1217 (2010) 6829-6850. [4] K. Rege, M. Pepsin, B. Falcon, L. Steele, M. Heng, Biotechnology and Bioengineering 93 (2006) 618-630. [5] G.S. Bohart, E.Q. Adams, Journal of the American Chemical Society 42 (1920) 523-544. [6] D. DeVault, Journal of the American Chemical Society 65 (1943) 532-540. [7] T. Vermeulen, Industrial and Engineering Chemistry 45 (1953) 1664-1670. [8] G. Carta, A. Jungbauer, Protein Chromatography: Process Development and Scale-Up, Wiley-VCH, Weinheim, 2010. [9] G. Guiochon, A. Felinger, D.G. Shirazi, A.M. Katti, Fundamentals of Preparative and Nonlinear Chromatography, 2nd ed., ElsevierAcademic Press, San Diego, 2006. [10] A. Amsterdam, Z. Er-El, S. Shaltiel, Arch. Biochem. Biophys. 171 (1975) 673-677. [11] D. Hlushkou, S. Bruns, U. Tallarek, Journal of Chromatography A 1217 (2010) 3674-3682. [12] Y. Yao, K.J. Czymmek, R. Pazhianur, A.M. Lenhoff, Langmuir 22 (2006) 11148-11157. [13] P. DePhillips, A.M. Lenhoff, Journal of Chromatography A 883 (2000) 39-54. [14] L. Hagel, M. Östberg, T. Anderson, Journal of Chromatography A 743 (1996) 33-42 [15] J.H. Knox, H.P. Scott, Journal of Chromatography 316 (1984) 311-332 [16] Y. Yao, A.M. Lenhoff, Journal of Chromatography A 1037 (2004) 273-282. [17] A.M. Lenhoff, Journal of Chromatography A 1218 (2011) 8748-8759. [18] W. Müller, Journal of Chromatography A 510 (1990) 133-140 [19] B.D. Bowes, H. Koku, K.J. Czymmek, A.M. Lenhoff, Journal of Chromatography A 1216 (2009) 7774-7784. [20] B.D. Bowes, A.M. Lenhoff, Journal of Chromatography A 1218 (2011) 4698-4708.
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[21] S.H.S. Koshari, N.J. Wagner, A.M. Lenhoff, Journal of Chromatography A 1399 (2015) 45-52. [22] T.E. Creighton, Proteins: StructuresandMolecular Properties, 2nd ed., W.H. Freeman and Company, New York, 1993. [23] M.H.M. Olsson, C.R. Sondergaard, M. Rostkowski, J.H. Jensen, Journal of Chemical Theory and Computation 7 (2011) 525-537. [24] N.K. Boardman, S.M. Partridge, Biochem. J. 59 (1955) 543-552. [25] C.A. Brooks, S.M. Cramer, AIChE J. 38 (1992) 1969-1978. [26] D. Asthagiri, A.M. Lenhoff, Langmuir 13 (1997) 6761-6768. [27] V. Noinville, C. Vidal-Madjar, B. Sebille, J. Phys. Chem. 99 (1995) 1516-1522. [28] D.J. Roush, D.S. Gill, R.C. Willson, Biophys. J. 66 (1994) 1290-1300. [29] Y. Yao, A.M. Lenhoff, Analytical Chemistry 77 (2005) 2157-2165. [30] F.K. Insaidoo, M.A. Rauscher, S.J. Smithline, N.C. Kaarsholm, B.P. Feuston, A.D. Ortigosa, T.O. Linden, D.J. Roush, Biotechnology Progress 31 (2015) 154-164. [31] K.M.H. Lang, J. Kittelinann, C. Durr, A. Osberghaus, J. Hubbuch, Journal of Chromatography A 1381 (2015) 184-193. [32] J.F. Langford, Jr., X. Xu, Y. Yao, S.F. Maloney, A.M. Lenhoff, Journal of Chromatography A 1163 (2007) 190-202. [33] X. Xu, A.M. Lenhoff, J. Phys. Chem. B 112 (2008) 1028-1040. [34] C. Chang, A.M. Lenhoff, Journal of Chromatography A 827 (1998) 281-293. [35] M.R. Etzel, in: F. Svec, T.B. Tennikova, Z. Deyl (Eds.), Monolithic Materials - Preparation, Properties and Applications, Journal of Chromatography Library, Elsevier, Amsterdam, Vol 67, 2003, pp 213-234. [36] L. Haggerty, A.M. Lenhoff, Biophys. J. 64 (1993) 886-895. [37] L. Haggerty, B.A. Watson, M.A. Barteau, A.M. Lenhoff, Journal of Vacuum Science & Technology B 9 (1991) 1219-1222. [38] C.A. Johnson, Y. Yuan, A.M. Lenhoff, J. Coll. Interf. Sci. 223 (2000) 261-272. [39] Y. Yuan, M.R. Oberholzer, A.M. Lenhoff, Coll. Surf. A 165 (2000) 125-141. [40] J. Feder, J. Theor. Biol. 87 (1980) 237. [41] M.R. Oberholzer, J.M. Stankovich, S.L. Carnie, D.Y. Chan, A.M. Lenhoff, J. Coll. Interf. Sci. 194 (1997) 138-153. [42] Y. Yao, A.M. Lenhoff, Journal of Chromatography A 1126 (2006) 107-119. [43] M.R. Oberholzer, A.M. Lenhoff, Langmuir 15 (1999) 3905-3914. [44] H. Koku, Microstructure-Based Analysis and Simulation of Flow and Mass Transfer in Chromatographic Stationary Phases, Ph.D. thesis, University of Delaware, Newark, DE, 2011. [45] E. Muller, Chemical Engineering & Technology 28 (2005) 1295-1305. [46] S.R. Dziennik, E.B. Belcher, G.A. Barker, A.M. Lenhoff, Biotechnology and Bioengineering 91 (2005) 139-153. [47] A.M. Hardin, C. Harinarayan, G. Malmquist, A. Axen, R. van Reis, Journal of Chromatography A 1216 (2009) 4366-4371. [48] G. Carta, A.R. Ubiera, T.M. Pabst, Chemical Engineering & Technology 28 (2005) 1252-1264. [49] L.E. Weaver, G. Carta, Biotechnol. Prog. 12 (1996) 342-355. [50] W.M. Deen, AIChE J. 33 (1987) 1409-1425. [51] N.G. Pinto, E.E. Graham, Reactive Polymers 5 (1987) 49-53. [52] W.K. Teo, D.M. Ruthven, Ind. Eng. Chem. Proc. Des. Dev. 25 (1986) 17-21. [53] S.R. Dziennik, E.B. Belcher, G.A. Barker, M.J. DeBergalis, S.E. Fernandez, A.M. Lenhoff, Proc. Natl. Acad. Sci. USA 100 (2003) 420-425. [54] T. Linden, A. Ljunglöf, L. Hagel, M.R. Kula, J. Thommes, Sep. Sci. Tech. 37 (2002) 1-32. [55] M.C. Stone, G. Carta, Journal of Chromatography A 1160 (2007) 206-214. [56] A.M. Lenhoff, Langmuir 24 (2008) 5991-5995. [57] J.A. Wesselingh, J.C. Bosma, AIChE J. 47 (2001) 1571-1580. [58] H. Yoshida, M. Yoshikawa, T. Kataoka, AIChE J. 40 (1994) 2034-2044. [59] B.D. Bowes, Protein Transport and Adsorption in Polymer-Modified Ion-Exchange Media, Ph.D. thesis, University of Delaware, Newark, DE, 2011. [60] L.L. Yu, S.P. Tao, X.Y. Dong, Y. Sun, Journal of Chromatography A 1305 (2013) 76-84. [61] S.J. Traylor, X.K. Xu, A.M. Lenhoff, Journal of Chromatography A 1218 (2011) 2222-2231. [62] V. Kumar, S. Leweke, E. von Lieres, A.S. Rathore, Journal of Chromatography A 1426 (2015) 140-153. [63] T.M. Pabst, G. Carta, N. Ramasubramanyan, A.K. Hunter, P. Mensah, M.E. Gustafson, Biotechnology Progress 24 (2008) 1096-1106. [64] F.H. Arnold, H.W. Blanch, C.R. Wilke, Chemical Engineering Journal and the Biochemical Engineering Journal 30 (1985) B9-B23. [65] J.M. Angelo, A.M. Lenhoff, Journal of Chromatography A 1440 (2016) 94-104. †
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ScienceDirect Materials Today: Proceedings 3 (2016) 3559–3567
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International Conference on Advances in Bioprocess Engineering and Technology 2016 (ICABET 2016)
Ion-exchange chromatography of proteins: the inside story Abraham M. Lenhoff* Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
Abstract Despite its inherent inefficiencies, chromatography remains the workhorse for protein purification in the pharmaceutical and biotechnology industries, with ion exchange the most widely used mode. Many aspects of a chromatographic process are still optimized empirically, but modeling is increasingly used to aid in scale-up and optimization. An overview is presented here of the needs that remain for greater insights that can inform truly predictive modeling of preparative protein separations by ionexchange chromatography as well as guiding rational design considerations and effective heuristics. These needs can be addressed by a more complete mechanistic understanding of the relation between adsorbent and protein structure and the values of key functional parameters describing adsorption and transport. A principal focus is on specialized experimental and modeling approaches to obtain such insights; these include molecular-level models, various modes of microscopy, and chromatographic and other methods for inferring intracolumn and intraparticle behavior. The principal findings revolve around the critical role for fundamental principles of classical chemical engineering, albeit specialized for the peculiar physicochemical properties of protein solutions. © 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016. Keywords:Biotechnology; protein separations; biologics; adsorption; intraparticle transport; diffusion.
* Corresponding author. Tel.: +1-302-831-8989; fax: +1-302-831-1048. E-mail address:[email protected] 2214-7853© 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016..
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1. Introduction Chromatography remains an essential unit operation for high-resolution purification in pharmaceutical biomanufacturing, despite its inherent inefficiencies and continuing efforts to seek alternatives [1]. Chromatographic process development in biomanufacturing retains a large element of empiricism, albeit often streamlined by the use of high-throughput screening [2-4] and heuristics. However, there is an ongoing drive to incorporate the same kinds of predictive modeling that have gradually come to dominate design of more conventional chemical engineering processes over the past half-century. These efforts rest on experimental descriptions [5] and mechanistic analyses [6] that can be traced back for the better part of a century. Mathematical descriptions that include intraparticle transport became well-established around the middle of the twentieth century [7] and form the basis for the column modeling presented in key reference texts today [8, 9]. However, the models incorporate some ambiguity regarding adsorption and transport mechanisms and the parameter values needed must be specialized for use with particular analyte molecules and chromatographic media. In many cases the mechanisms are assumed a priori and the parameter values are estimated simply by fitting model predictions to experimental data without reliable verification of accuracy and reliability. The questions left open by these approaches can be addressed by experimental, theoretical and computational methods that have been developed over recent decades. These methods span scales ranging from molecular to particle to column, and they provide a substantial but not yet complete basis for making column modeling more predictive. An important element that is included is that of accounting for the novel architectures that have been developed for chromatographic particles specialized for use with biomolecules. This has led to structure–function relations that can be applied to combinations of particles and molecules. The purpose of this paper is to summarize some of the experimental and analytical approaches that underpin the more robust, mechanistically-reliable description of biochromatographic processes. The presentation is divided into two main parts: structural characterization, primarily of the adsorbent but also of the analytes, and functional characterization and prediction. The emphasis throughout is on ion-exchange separations, which are the most widely-used in industrial bioprocessing, but many of the methods can be applied directly also for other modes of chromatography. 2. Structural characterization 2.1. Adsorbents The standard picture of a chromatographic column is of a bed packed with porous particles that provide appreciable internal surface area for adsorption. The characteristic pore size is a critical parameter in that it allows a balance to be reached between, on the one hand, the surface area available for adsorption and, on the other, transport, generally assumed to be by diffusion, between the external particle surface and the interior. That the size of proteins is typically several nanometers to more than 10 nm, with other analytes such as viruses potentially > 100 nm, means that the pore sizes needed for biochromatography are typically appreciably larger than for many other applications and can also vary extensively depending on the analytes of interest. The physical structure of the adsorbent can be visualized directly by such methods as electron microscopy, whether scanning or transmission. These methods have been routinely used in development of novel adsorbents for many years [10] and are invaluable in representing the general physical features of the particle and the pore space both qualitatively and quantitatively, and these and other microscopy methods can also be extended to in silico reconstruction of the pore space [11, 12]. However, such methods are inherently restricted to a very small fraction of the overall volume, which raises questions regarding how representative such measurements and reconstructions are. An additional challenge is the distortion that may result from drying during sample preparation for, say, electron microscopy, especially for the highly hydrophilic base matrices that are frequently used in biomolecular chromatography. For the relatively wide pores and aqueous media characteristic of biochromatographic media, a powerful tool for structural characterization is inverse size-exclusion chromatography (ISEC) [13-16]. Here each of a set of nonadsorbing polymeric probe molecules – dextran is especially widely used – is injected into and eluted from a column
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and the retention time, expressed in terms of the size-exclusion distribution coefficient Kd, is used to infer the poresize distribution. Since the number of data points is usually of order 10, the pore-size distribution is expressed using a relatively simple functional form in which a small number of parameters (1-2) is determined by fitting the Kd values. This fitting is based on an idealized model, typically of a spherical molecule in a cylindrical pore, but since the fit is sensitive primarily to the ratio of analyte size to pore size, the details of the model are less important. The value of ISEC is that the pore-size distribution, once estimated, can be used to derive additional metrics, e.g., the surface area available for adsorption, from which key functional characteristics can be estimated, as will be seen later. The emergence of particles with more specialized architectures has increased the challenge of particle characterization. In particular, there are several classes of particles in which polymer extenders are appended to the underlying base matrix [17, 18], and again both qualitative and quantitative characterization are required. ISEC can still be used but its interpretation and especially its use in functional prediction raises new questions. This is largely because the uncharged probe molecules used in ISEC can penetrate the polymer only if the probe is smaller than the polymer mesh size to some degree, whereas electrostatic attraction is able to draw much larger protein molecules into the same polymer under suitable conditions. The ideal situation is one in which both underivatized and polymer-functionalized media are available on the same base matrix, in which case direct comparison of the two sets of ISEC data can allow inference of some structural features of the attached polymer [19, 20]. Another tool that has recently been applied to characterization of chromatographic media is small-angle scattering [21], which has the advantage of sampling a relatively large sample volume and therefore providing a representative picture. The challenge in interpreting scattering data is that the results are obtained in reciprocal space, and while key characteristics can often be extracted from limiting slopes and forms in portions of the spectrum, more detailed model-building is usually needed and is not always unambiguously interpreted. 2.2. Proteins For many proteins, especially those used as model proteins in fundamental studies of protein chromatography, full three-dimensional structures are often available and can be used even for detailed molecular modeling down to the atomistic level. For biopharmaceuticals, however, such structural details are rarely available, so alternative methods are needed for estimating key biomolecular properties. Since these molecules are made by recombinant DNA methods, the gene sequence and hence the amino acid sequence is available, and apart from possible effects of post-translational modifications, this is enough to extract and estimate properties relevant to chromatographic analysis. The amino acid sequence immediately provides a measure of the protein size in terms of the molecular weight. In addition, globular proteins have partial specific volumes in the range of 0.70–0.75 cm3/g [22], which allows estimation of the molecular volume and hence of the radius of a sphere of equivalent volume. Since a globular protein has a rough surface, this radius underestimates that of the protein, and of course the globular shape is not a perfect sphere in any event. Nevertheless, this simple approach provides a very useful measure of protein size that can be juxtaposed against the length scales estimated for the pore-size distribution. The amino acid sequence also allows estimation of the titration properties of the protein, which provide the basis for assessing retention in ion-exchange chromatography. The simplest approach is just to consider the intrinsic pKa values of the different amino acids [22], but the actual protonation state will also depend on the local charge environment in the protein and on the salt concentration. More refined titration curves can be estimated using electrostatic calculations [23], although such improvements make little difference to the ability to predict protein retention. 3. Functional characterization and prediction Column behavior in protein chromatography in bind–elute mode comprises primarily the breakthrough curve during loading, followed by the elution curve, both of which reflect a synthesis of adsorption and transport properties. The position of the breakthrough front or of an elution peak is determined by the equilibrium adsorption
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properties, but the shape, especially the width, of the front or of a peak is modulated by kinetic characteristics, which for the large particles used in preparative separations are dominated by transport rates. This section is therefore divided into adsorption and transport sections, with some reference to implications for column modeling. 3.1. Adsorption Because adsorption capacity is critical for preparative separations, full adsorption isotherms must be considered in protein chromatography. Such isotherms have the hyperbolic form that is usually associated with Langmuir isotherms, but it is rarely possible to identify well-defined adsorption sites in protein chromatography, especially on ion exchangers. However, for adsorption on conventional media the isotherm can be decomposed into limiting elements that make interpretation and even prediction of adsorption behavior more tractable. The low-concentration end of the isotherm reflects adsorption of isolated molecules, i.e., protein–surface interactions. These have been modeled for many years, with the stoichiometric displacement model and its descendants [24, 25] particularly well-known and widely-referenced, albeit simplistic for proteins. With the advent of protein structural information and molecular modeling methods, such calculations have become more elaborate over the past few decades [26-31], but the quality of predictions has not necessarily improved significantly. There are several likely reasons for this. One is that although the protein structure may be known in great detail, the local topography and charge distribution of the adsorbent is not. Another is that such contributions as hydration effects remain difficult to account for accurately, especially in the presence of the protein macroion, charged ligands and free small ions due to added salt and buffer. Finally, the overall observed adsorption behavior reflects a (weighted) average over an ensemble of adsorbed states, which requires extensive configurational exploration in which the energetics are accurately captured. Therefore despite the great advances in molecular modeling, this remains a significant challenge. While protein–surface interactions are at best a grey area for predictive calculations, they can be measured trivially by simple chromatographic methods in terms of the capacity factor k', which is widely used in analytical chromatography but largely neglected in preparative work. k' can be decomposed into the product of a chemical contribution, the adsorption equilibrium constant Keq, and a physical contribution, the phase ratio φ. For a conventional adsorbent, in which the phase ratio is the specific surface area, φ can be estimated for a protein of a particular size from the pore-size distribution found from ISEC [13]. However, the phase ratio typically has a much smaller effect on k' than does Keq because of the exponential dependence of the equilibrium constant on energy. k' can be used to estimate the initial slope of the isotherm, and for protein adsorption on ion exchangers at the low salt concentrations used for column loading, the values are very large, leading to near-rectangular isotherms. Experimental measurement of k' is readily performed only at higher salt concentrations, for k' values less than about 100, but the power-law dependence of k' on ionic strength I predicted by the stoichiometric displacement model [24] is normally observed experimentally and allows extrapolation, albeit risky, to lower values of I[32], allowing the initial slope of an isotherm to be estimated [33]. In the high-concentration limit the plateau value of the isotherm is attained; here the adsorbed concentration is high enough that protein–protein interactions must be accounted for in addition to the protein–surface interactions that drive adsorption. The most important contribution to the protein–protein interactions is due to steric (excludedvolume) effects, based on which the maximum capacity would be determined simply by how many molecules can be packed into a monolayer per unit area of surface [34, 35]; this reasoning is confirmed down to the molecular level by scanning probe microscopy imaging [36-38]. The volumetric capacity is then found using the phase ratio, e.g., from ISEC measurements. These values are modulated by the possibility of non-steric interactions, especially electrostatic repulsion, which is more pronounced for larger proteins [39], as well as by the question of whether the maximum coverage would be close-packed or limited by random sequential adsorption [40, 41]; comparison with measured values indicates that these values span the range observed experimentally [42].
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Plotting the full isotherm requires connecting the low- and high-concentration trends, and at one level that can be done essentially by eye. However, more rigorously the isotherm form is determined by the relative energetics of the protein–surface and protein–protein interactions [43]. In particular, the plateau level actually attained depends on the protein–surface affinity defined by the low-concentration region, specifically the initial slope. Nevertheless, it is possible using this formulation to obtain reasonable predictions of isotherms [33]. For some materials the maximum plateau value is clearly underpredicted using ISEC-derived phase ratios. This generally happens for polymer-functionalized adsorbents, where the assumed model of monolayer adsorption fails; sorption in these systems is instead more accurately described as volumetric partitioning (Figure 1) [19, 45], and the capacity appears to correlate with the polymer volume in general, although charge effects may play a role [19].
Figure 1. Transmission electron micrographs of Q Sepharose FF (left) and XL (right) loaded to a high fraction of static capacity with protein (stained black) [44]. The FF image indicates adsorption on the bare agarose base matrix, but the dextran-functionalized XL accommodates protein more by volumetric partitioning into the charged polymer layer. Each image is approx. 1700 x 1200 nm.
3.2. Transport 3.2.1. Transport during loading The plateau of the adsorption isotherm determines the maximum adsorbed concentration attainable for a particular protein–adsorbent system under any given set of solution conditions; this represents the static or equilibrium binding capacity. A more widely referenced quantity in bioseparations practice is the dynamic binding capacity, which is the adsorbed concentration attainable during frontal loading until the breakthrough concentration reaches a specified fraction of the feed concentration. The dynamic binding capacity is always less than or equal to the static capacity, with the difference being the result of the finite rate of uptake of the protein into the adsorbent under flow conditions in a column. Although the widespread use of the dynamic capacity makes it an essential metric to consider during chromatographic loading, it is a somewhat complex measure that convolves the static capacity, an equilibrium quantity, and the transport rate, so decomposition of dynamic capacity observations into the two constituent parts can be informative. For most proteins the static capacity on an ion exchanger will decrease monotonically with increasing salt concentration. However, it is only at higher salt concentrations that the dynamic capacity closely approaches the static capacity, whereas at lower salt concentrations the dynamic capacity can be appreciably lower than the static capacity (Figure 2). This leads to the somewhat counterintuitive result that a maximum is seen in the dynamic capacity at an intermediate salt concentration (Figure 2) [20, 46, 47].
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Beyond the practical implications that this can havee for column loading, the disparate trends of the static and dynamic capacities indicate that transport is slower at lo low salt concentrations and faster at higher concentrations. These transport rates can be measured directly, by sever eral methods. Models of peak broadening in simple linear, isocratic chromatography typically include an explici licit contribution from transport rates, as do models of breakthrough concentrations in frontal loading [8, 9].. B Batch uptake measurements provide an even more direct characterization of the uptake rate, which can also be relat lated to transport parameters using suitable transport models. Measurements using these models generally confirm thee trends of transport rate as a function of salt concentration, independent of the transport model used, but the trans nsport model certainly provides a more specific basis for analyzing rates.
Figure 2. Static (solid lines) and dynamic (dotted lines) binding capacitie ities of a monoclonal antibody on SP Sepharose FF (green), SP Sepharose XL (magenta) and Capto S (blue) at a feed concentration of 2 mg/mL an and a linear velocity of 120 cm/hr[20].
In most preparative systems the particles used are la large enough that the main transport resistance is due to intraparticle diffusion, and here there are two limitingg models that are widely used [48]; both account for the coupling of diffusion with adsorption, particularly for th the high-affinity isotherms encountered in ion-exchange of proteins. The first is pore diffusion, which assumes that hat only the protein free in the pore space is able to diffuse, while any adsorbed protein is immobile. In view of thee hhigh adsorption affinities and capacities typically seen, the free protein concentration is much lower than the adsorbed bed concentration at (local) equilibrium, so the fraction of the intraparticle protein that is free to diffuse is small, lead eading to low transport rates. The other limiting model is variously referred to as homogeneous, solid, gel or su surface diffusion. Although these are not always defined completely equivalently, the key implication is that adsor sorbed protein is free to diffuse in addition to the unadsorbed protein. Although inferred homogeneous diffusivities es are appreciably lower than pore diffusivities, the high adsorbed concentrations give rise to comparably high flux luxes. In determining transport coefficients such as diffusiviti vities by fitting experimental data, a significant challenge lies in model discrimination. For instance, batch uptake data ata give rise to relatively simple monotonic curves that can often be fitted equally well by pore-diffusion and homoge geneous-diffusion models [49]. A true pore diffusivity Dp is related to the free-solution diffusivity D0 via Dp/D0 = εpψ/τ, where εp is the accessible porosity of the intraparticlepore space for the protein, ψ is a hindrancee ccoefficient for motion of the protein through a constructed pore and τ is the tortuosity [50]. One clear indication off tthe presence of homogeneous diffusion is an apparent pore
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diffusivity that exceeds D0. However, there is no such benchmark for homogeneous diffusion and other values of pore diffusivities may not allow clear discrimination. A more distinctive indicator of the transport mechanism is the intraparticle concentration profile during uptake: pore diffusion gives rise to a sharp uptake front, which in the limiting case of a rectangular isotherm leads to the shrinking-core model [51, 52], whereas homogeneous diffusion gives rise to a diffuse uptake profile. These profiles can be observed directly by confocal fluorescence laser scanning microscopy using fluorescently-labelled protein (Figure 3) [53, 54], although refractive index methods have also been exploited using unlabelled protein [55]. The gradual transition from pore to homogeneous diffusion with increasing salt concentration has been related to the decreasing strength of adsorption with increasing salt [32, 56, 57], which can be modelled using a parallel-diffusion model [56, 58].
Figure 3. Confocal micrographs of uptake of labeled lysozyme (left) and lactoferrin (right) into Capto S at 20 mM ionic strength [59]. The uptake profile for lysozyme is diffuse, consistent with homogeneous diffusion, and that for lactoferrin is sharp, consistent with pore diffusion. At such low salt concentrations uptake on conventional adsorbents is usually by pore diffusion, but on a polymer-functionalized material like Capto S homogeneous diffusion may be observed, as here for lysozyme.
While the transport behavior described above applies broadly across ion-exchange of proteins, polymerfunctionalized adsorbents appear amenable to homogeneous diffusion even at very low salt concentrations in many cases [20]. This rapid transport is an important factor – along of course with the high static capacities – giving rise to the high dynamic capacities of these adsorbents. The mechanistic basis for this homogeneous transport within the polymer layer is thought to lie in the three-dimensional distribution of charge surrounding the sorbed protein, which reduces the energetic cost to a protein molecule to diffuse [20]; this has been likened to a "bucket brigade" [60]. Although the precise transport mechanism applying in any particular system may be viewed as being of only academic interest, it can have important practical implications. The mechanism determines the uptake rate, which in turn determines the sharpness of the breakthrough front, so manipulating the uptake mechanism is one approach to manipulating the dynamic binding capacity. For more complex systems, such as binary breakthrough, invoking the wrong transport mechanism may lead to significant errors in modeling breakthrough behavior[61]. 3.2.2. Transport during elution Elution receives appreciable attention in studies of analytical chromatography because the "loading" step, i.e., sample injection, is both simple and followed immediately by elution, whether isocratic or gradient. In preparative chromatography, loading – the detailed analysis of breakthrough – is instead often the focus. This occurs despite the evident importance of elution in preparative separations: the need to have the product and the impurities elute in separate peaks that do not overlap excessively. Elution in ion-exchange separations of proteins is invariably by gradient elution, typically in salt concentration but sometimes in pH, with step gradients often preferred over linear gradients for ease of operation. The time at which a peak elutes represents the cumulative effect of migration of the peak along the column, which is determined primarily by adsorption equilibrium. Differential elution of two components requires differences in their retention strength as a function of salt concentration, with closely-related species intrinsically difficult to separate, e.g., charge variants [62, 63]. The width of an elution peak depends largely on the transport properties – how quickly the protein can exit a particle in which it is adsorbed – but where there is residual retention under the conditions used for elution, there is also some dependence on retention characteristics. This coupling
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between retention and transport is one reason for the complexity of predicting elution behavior, and although model calculations have been reported frequently, e.g., [62], general conclusions are more difficult to reach. If the problem is reduced to that of transport in the absence of retention, the analysis can be significantly simpler, but relatively little detailed analysis has been performed [64]. The most obvious mechanistic postulate is that elution occurs by very rapid desorption of protein followed by rate-limiting diffusion from the interior to the surface of the particle. Use of a simple diffusion model to fit the tails of elution curves allows estimation of diffusivities, and since adsorption is absent, these diffusivities should be simple pore diffusivities. This is confirmed to be the case in general [65], with the diffusivities especially sensitive to the fraction of the stationary-phase volume that is accessible to the protein of interest. While this result seems straightforward and predictable, it has consequences that may complicate stationaryphase selection. Specifically, polymer-functionalized stationary phases have effectively very small accessible pore volumes, making elution potentially slow, as has been observed experimentally. This presents a dilemma in that these materials typically have very high static as well as dynamic binding capacities, leading to a trade-off between performance in loading vs. elution. Another consequence of the limited pore volume of polymer-functionalized adsorbents is that the desorbed protein reaches a very high concentration in the pores immediately after desorption, increasing the likelihood of phase separation, especially at the high salt concentrations typically used [65]. Therefore the biophysical properties of the protein may have a pronounced influence on elution behavior as well. 4. Conclusions While the principles and mechanisms governing ion-exchange separations of proteins are well known conceptually, qualitative and especially quantitative understanding, particularly leading to predictive design, requires a more detailed mechanistic understanding. The entrainment of non-chromatographic methods, particularly those with spatial resolution at length scales between the colloidal and the molecular, has been shown in the overview provided here to have been instrumental in providing this new information. A complementary component is the ever-increasing repository of biophysical data, especially from structural biology, that can account for the particular properties of individual proteins of interest. Integration of this knowledge can augment long-established approaches in the synthesis of multiscale models that can lead to truly predictive tools that can play the same role in biotechnology practice that has become routine in more conventional chemical engineering design. References [1] T.M. Przybycien, N.S. Pujar, L.M. Steele, Current Opinion in Biotechnology 15 (2004) 469-478. [2] T. Bergander, K. Nilsson-Vaelimaa, K. Oberg, K.M. Lacki, Biotechnology Progress 24 (2008) 632-639. [3] B.K. Nfor, M. Noverraz, S. Chilamkurthi, P.D.E.M. Verhaert, L.A.M. van der Wielen, M. Ottens, Journal of Chromatography A 1217 (2010) 6829-6850. [4] K. Rege, M. Pepsin, B. Falcon, L. Steele, M. Heng, Biotechnology and Bioengineering 93 (2006) 618-630. [5] G.S. Bohart, E.Q. Adams, Journal of the American Chemical Society 42 (1920) 523-544. [6] D. DeVault, Journal of the American Chemical Society 65 (1943) 532-540. [7] T. Vermeulen, Industrial and Engineering Chemistry 45 (1953) 1664-1670. [8] G. Carta, A. Jungbauer, Protein Chromatography: Process Development and Scale-Up, Wiley-VCH, Weinheim, 2010. [9] G. Guiochon, A. Felinger, D.G. Shirazi, A.M. Katti, Fundamentals of Preparative and Nonlinear Chromatography, 2nd ed., ElsevierAcademic Press, San Diego, 2006. [10] A. Amsterdam, Z. Er-El, S. Shaltiel, Arch. Biochem. Biophys. 171 (1975) 673-677. [11] D. Hlushkou, S. Bruns, U. Tallarek, Journal of Chromatography A 1217 (2010) 3674-3682. [12] Y. Yao, K.J. Czymmek, R. Pazhianur, A.M. Lenhoff, Langmuir 22 (2006) 11148-11157. [13] P. DePhillips, A.M. Lenhoff, Journal of Chromatography A 883 (2000) 39-54. [14] L. Hagel, M. Östberg, T. Anderson, Journal of Chromatography A 743 (1996) 33-42 [15] J.H. Knox, H.P. Scott, Journal of Chromatography 316 (1984) 311-332 [16] Y. Yao, A.M. Lenhoff, Journal of Chromatography A 1037 (2004) 273-282. [17] A.M. Lenhoff, Journal of Chromatography A 1218 (2011) 8748-8759. [18] W. Müller, Journal of Chromatography A 510 (1990) 133-140 [19] B.D. Bowes, H. Koku, K.J. Czymmek, A.M. Lenhoff, Journal of Chromatography A 1216 (2009) 7774-7784. [20] B.D. Bowes, A.M. Lenhoff, Journal of Chromatography A 1218 (2011) 4698-4708.
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