Analytical Tools for Process and Product Characterization

August 1, 2009

Select the best approach to determine critical quality attributes.


The Quality by Design paradigm demands an enhanced understanding of processes and products. Biotech therapeutic products, being complex molecules, require a robust analytical platform to serve as the foundation for commercialization activities such as the definition of critical quality attributes. This platform is generally a combination of orthogonal, high-resolution techniques that together provide definition to the product and the process. This article presents recent developments in four analytical applications, namely disulfide linkage analysis, glycan analysis, analytical ultracentrifugation, and flow injection protein analysis.

Prroduct and process characterization are critical components of the overall commercialization activities for protein therapeutics. Product characterization reveals the biochemical and biophysical nature of the product as well as the nature of product-related substances and impurities. Thorough product characterization is a necessary precursor to determine critical quality attributes (CQAs) and the associated analytical methods that in turn can be used as in-process controls and specifications, and for stability testing. Process characterization focuses on understanding and defining the operating and design spaces for the process to achieve a product with consistent CQAs.1,2


This article is the 18th in the Elements of Biopharmaceutical Production series and presents recent developments in four analytical applications, namely disulfide linkage analysis, glycan analysis, analytical ultracentrifugation, and flow injection protein analysis.

Anurag S. Rathore


Determining CQAs and developing methods suitable for their measurement is an intricate and evolving science. This stems from the inherent complexity of biological macromolecules. A perfectly pure biological product is in itself challenging to characterize, typically comprising a chain of hundreds of amino acids and associated glycan subunits. This chain is sometimes linked within itself or to other chains through disulfide bonds, and then folded into a discrete secondary and tertiary structure.3–6 Quaternery structures also exist, from dimerization up to potentially large numbers of noncovalently or covalently linked subunits. Furthermore, manufacturing processes never achieve 100% purity, resulting in the need to measure and control product-related variants and product- and process-related impurities.

Product-related variants closely resemble the desired product and have a potency and safety profile equivalent to those of the product itself.7 Typical examples include minor post-translational modifications such as C-terminal processing, N-terminal variants, and deamidation. Product-related impurities, however, do not resemble the desired product with respect to safety and efficacy. Examples include aggregates and highly truncated forms. Process-related impurities include host cell DNA, host cell proteins, and raw materials from the process. A complex array of analytical methods is necessary to adequately characterize both the product and process.8 These methods must be developed and then qualified and validated for their intended use.9


Regulatory agencies expect disulfide connectivity to be determined as part of product characterization.7 Contemporary methods for elucidating bonding patterns between closely-spaced cysteine residues include cyanylation-induced cleavage after limited reduction and limited reduction and alkylation followed by mass spectrometric analysis.10,11 The latter approach was successfully applied to a recombinant IgG2, revealing subspecies of disulfide variants.12 The main limitation of the reduction step inherent in both these approaches is the difficulty in finding conditions that allow for controlled reduction of specific disulfide bonds.

Recently, the analysis of mass spectral fragmentation patterns of disulfide-linked peptides has been used to elucidate disulfide linkages.13 However, the applicability of this approach has been limited to fairly simple systems, and not to the highly complex linkage patterns of the recently reported IgG2 variants.14

To overcome the above limitations, we have recently developed an innovative methodology that permits rapid determination of the connectivity between closely-spaced or adjacent cysteine residues in disulfide-linked molecules, and can be readily applied to complex monoclonal antibodies (MAbs) such as those in the IgG2 class. This methodology uses a unique combination of traditional Edman chemistry with mass spectrometric detection. Optimization of the sample processing for Edman chemistry played a crucial role and provided predictable structures for facile interpretation of mass data.

With many disulfide-linked proteins, even after proteolytic digest, there are often multiple potential disulfide connectivities that cannot be distinguished by mass alone because they merely represent different assemblies of the same peptides. However, the differences in the linkage geometries will result in characteristic "residual" and "leaving" groups after repetitive Edman cleavage of the cysteine residues from the N-terminus of each chain. An example of this scheme is presented in Figure 1, using insulin as a model substrate. The peptide presented in Figure 1 is derived from the Glu-C digestion of human insulin.

Figure 1. Differentiation of disulfide variants through sequential manual Edman cycles with characteristic residual and leaving groups

The characteristic residual and leaving species may be readily differentiated by mass spectrometry, and their identities may be confirmed by standard fragmentation analysis. If a mixture of disulfide structures for a given variant is present, it may not be possible to differentiate them from the observed leaving groups. However, the residual groups are diagnostic, and may even provide a means for a semiquantitative measure of their relative abundance, based on their relative signal in either the mass spectrometry (MS) or ultraviolet (UV) traces. Additional rounds of coupling, cleavage, and liquid chromatography–MS (LC–MS) analysis provide a further means for confirming structures to a greater level of detail, and also demonstrate the absence of scrambling during the chemical processing steps.

We have successfully applied this approach to the well-characterized insulin molecule and to a MAb where we identified new substructures of IgG2 variants, including the presence of intra-chain disulfide linkages in the hinge region.5 Details of this approach will be the focus of upcoming publications.


N-linked oligosaccharide mapping is routinely performed as an in-process test for recombinant glycoproteins derived from mammalian cell lines. Reasons include the complexities associated with processing Asparagine-linked (N-linked) oligosaccharides and the sensitivity of the enzymes involved, to even subtle changes in cell culture conditions during manufacturing. In addition, oligosaccharide mapping as a potential component of product lot release is increasingly requested from regulatory agencies for consideration.16

Standard methodologies contain several bottlenecks with respect to throughput. First, the sample preparation required for analysis is both laborious and time-consuming, requiring multiple steps of hands-on manipulation by the analyst. Second, the typical separation methods used have cycle times ranging from 90 to 180 minutes.17,18 In instances where multiple cell culture conditions are being screened or large numbers of in-process samples are being tested, the resulting analysis sequence can take days. In addition, the mobile phases typically used in the separation of oligosaccharides are not compatible with online MS analysis, requiring the collection of multiple fractions and additional sample manipulation before MS analysis, providing another bottleneck in the characterization stage.

We have implemented methodologies to address each of these throughput constraints. Through the use of robotic liquid handling, automated sample preparation, and rapid resolution reverse phase chromatography (RRRP–HPLC), we are able to completely process 30 samples per 24 hour period for oligosaccharide analysis, from the point of initial enzymatic digestion through full MS characterization of species accounting for as little as 0.1% of the oligosaccharide moiety.19,20 A traditional sample preparation scheme involves removal of deglycosylated protein by porous graphitized carbon (PGC) following PNGase F digestion, vacuum centrifugation before labeling with the fluorophore, and finally, removal of excess fluorophore and labeling reaction components using a cellulose phase matrix (S-cartridge) with subsequent vacuum centrifugation. We have implemented methodologies that replace these manually operated PGC and S-cartridges with PhyTip columns (Phynexus, Inc.) packed with Carbopack B and DPA-6S resins that are compatible with robotic platforms.

A total of six identical PNGase F digestions were performed on 500 ug aliquots of a recombinant IgG. Three of the digests were processed following traditional protocols and the remaining samples were processed following the automated method. Samples analysis was performed by traditional high pH anion exchange chromatography (HPAEC). An overlay of the resulting chromatograms is shown in Figure 2. No significant differences were observed in the chromatograms obtained by the two preparation methods. In addition to requiring limited analyst manipulation during preparation, the samples prepared following the automated protocol were ready for analysis at the end of Day 1, whereas samples prepared following the standard protocol were not ready for analysis until the start of Day 3.

Figure 2. Comparison of samples prepared by automated and standard methods and separated by high pH anion exchange chromatography

The most commonly used separation methods for oligosaccharide analysis are HPAEC and normal phase chromatography (NP–HPLC). The mobile phases used for these separations are not compatible with online MS characterization. Reverse phase (RP–HPLC) separations have been described previously, and provide superior resolution of species compared to HPAEC and NP–HPLC methods.18 However, with cycle times of three hours, they are not suitable for routine analysis. As seen in Figure 4, we have developed a method that takes advantage of new small particle-size resins available from column manufacturers. A batch of 30 samples from cell culture screening conditions can be prepared, separated, and characterized in 24 h using this approach with the chromatographic cycle time dramatically reduced relative to traditional RP–HPLC. This RRRP–HPLC method has a cycle time of 35 minutes, provides comparable resolution to standard RP–HPLC, and is compatible with online ESI–MS/MS detection with a limit of detection (LOD) of <20 fmol by fluorescence and ~1 pmol by MS. Using this approach, full characterization of the oligosaccharide map, including species accounting for as little as 0.1% of the total moiety, can be achieved in a timeframe that is competitive with basic profile fingerprinting achieved by standard capillary zone electrophoresis (CZE) methods. An example separation for an rIgG is shown in Figure 4. For this particular rIgG, a total of 36 unique species were identified from a single sample injection.

Figure 3. Comparison of flow schemes for standard and automated methodologies for oligosaccharide analysis

Through the combination of improvements in sample preparation and chromatographic cycle time, we are now able to perform complete characterization of an oligosaccharide map in a single day, providing a minimum of a five-fold reduction in process time. In addition, because of the short cycle time required by the RRRP–HPLC and compatibility with online MS detection, thorough MS analysis can be incorporated as part of routine sample analysis.

Figure 4. Typical separation by reverse-phase of an N-linked oligosaccharide pool from an rIgG


Size exclusion chromatography (SEC) is the standard method for quantifying levels of aggregation in protein products.21 However, aggregate characterization by SEC can perturb the delicate equilibrium of protein self-association because of resin–protein interactions and undesirable mobile-phase effects inherent in chromatographic-based separations.22,23 Sedimentation velocity–analytical ultracentrifugation (SV–AUC) can be applied in biopharmaceutical development as an orthogonal technique to SEC. SV–AUC is free from many limitations intrinsic to SEC but exhibits much lower precision, compromising its usefulness as a quantitative technique.24

The level of aggregation in a protein product is a CQA of the product, which must be monitored during development and controlled during manufacture. Accurate and precise quantitation of protein aggregation in biopharmaceutical products therefore meets a crucial business need. We have developed an improved SV–AUC methodology that enables enhanced precision and accuracy of protein aggregate measurements directly in product formulation buffer. The improved methodology allows successful implementation of quantitative SV–AUC in biopharmaceutical applications requiring high-precision aggregate measurements.

SV–AUC can be applied in protein biopharmaceutical development for many diverse characterization purposes.22,23,25–27 For example, SV–AUC is useful for process characterization, product structural characterization, product impurity profile determination, protein-receptor binding studies, comparability assessment, and evaluation of SEC method accuracy. Some of these applications permit qualitative assessment whereas others require quantitative results with a high degree of precision and accuracy. Unfortunately, the current precision of SV–AUC may preclude or limit its use in characterization studies that require quantitative aggregate measurements. The technique's use is particularly limited in cases of low aggregate levels (<1%), or when attempting to quantify subtle differences in aggregate levels.

The standard deviation of repeated protein aggregate measurements determined by SV–AUC is typically between 0.3 and 0.5% aggregate, but can be as high as 1% aggregate.24 Measurement precision is affected by common laboratory variations, including the instrument used, analyst technique, centerpiece quality, sample characteristics, and the data-fitting approach.24,27,28 Although the analyst has limited control over many factors, others can be deliberately controlled to improve the consistency of results. Two specific aspects of SV–AUC experiments are considered here: data analysis approaches for experiments conducted in non-ideal solutions containing excipients, and alignment of centerpieces. Proper control of these factors can dramatically improve both accuracy and precision.

Therapeutic protein formulations often contain excipients such as sugars or sugar alcohols. Typical angular velocities used during SV–AUC experiments (i.e., 40,000 to 60,000 rpm) cause excipient concentration gradients to form over experimentally relevant time scales. If excipient concentration gradients are not properly modeled during data analysis, the capability of SV–AUC to measure protein aggregation is dramatically impaired.28 For example, measured aggregate levels for a MAb declined from 3.0 to 0.7% after 10% sorbitol was added to the solution.29 Sophisticated data analysis approaches, including modeling excipient gradients, can mitigate the quantitation problems caused by the sedimentation of excipients.28,29 In this case, modeling the 10% sorbitol gradient substantially improved accuracy (2.3% aggregate measured).28 SV–AUC measurements of protein aggregate in product formulation buffer exhibit poor accuracy unless the effects of excipients are adequately taken into account during data analysis.

Poor analyst technique and inadequate experimental control can significantly increase variability in SV–AUC results.24,27 SV–AUC experimentation is complex and requires extensive analyst training compared to SEC experiments. One experimental aspect that depends on analyst technique is alignment of the sample-containing AUC cells to the center of rotation. Misalignment of the centerpieces can cause convective disturbances resulting in sample mixing and inaccurate results.24 To better control cell alignment to the center of rotation, we designed a custom alignment tool capable of controlling cell alignment within 0.25°. The standard cell alignment approach relies on visual alignment of score marks on the rotor and cell assembly. This visual alignment technique can result in up to 0.5° misalignment from the center of rotation. When AUC cells were deliberately misaligned by 0.5, 1.0, 2.0, and 4.0°, measured levels of aggregate increased linearly with increasing angle of misalignment. Slight cell misalignment (≤0.5°) outside analyst control can yield variability as high as 1% aggregate. This variability is reduced to less than 0.5% aggregate by using an alignment tool.24

The best way to improve the quantitative use of SV–AUC in biopharmaceutical development is to reduce the intrinsic variability of the technique. Many factors affect SV–AUC measurement variability; some are more difficult to control than others. For instance, inherent instrument differences (e.g., lamp intensity) and problematic sample characteristics (e.g., reversible self-association) may be unavoidable. But improved analyst technique (e.g., careful alignment of centerpieces) and advanced data analysis can dramatically increase the overall precision of aggregate measurements. Two methods to improve precision and accuracy were discussed: accounting for excipients in product formulation and alignment of cells to the center of rotation. Table 1 demonstrates how precision and accuracy can be improved by properly accounting for these effects. These methodology improvements can yield a 50% increase in precision and allow accurate measurements in product formulation.

Table 1. Accuracy and precision of aggregate measurements by sedimentation velocity–analytical ultracentrifugation for a model monoclonal antibody under different solution and experimental conditions

SV–AUC is routinely applied in many aspects of biopharmaceutical development, including process and product characterization. It is used as an orthogonal complement to SEC, with important advantages but also some limitations. SV–AUC allows analysis in product formulation buffer and separates without a stationary phase. However, SV–AUC has limited throughput and exhibits poor method performance characteristics compared to SEC. The specific examples of SV–AUC method improvements highlighted here demonstrate that measurement accuracy and precision can be substantially improved, thus enabling SV–AUC to be a quantitatively meaningful characterization technique. Further, methodology improvements are critical to ensure the continued application of SV–AUC in the biopharmaceutical industry.


Chromatography columns and ultrafiltration membranes are commonly used during the downstream purification of therapeutic proteins or MAbs. Column or membrane cleaning is typically performed to remove any protein or impurity carryover from previous runs and to restore performance between campaigns.31,32 The effectiveness of cleaning is evaluated by measuring the low protein concentration (in μg/mL range) in a mock run following cleaning using the same elution or ultrafiltration buffers and conditions.

The most commonly used methods are classic colorimetry-based assays such as Bradford, BCA (bicinchoninic acid), and Lowry methods for total protein assay. However, a variety of buffer systems with different pH levels, components, and salt strengths are used in purification processes, and many of them are not compatible with these colorimetric assays. For example, BCA assays are not compatible with reducing sugars or chelating agents, and Bradford is not compatible with detergents above certain levels. Furthermore, the high-salt strengths and buffer capacity of certain in-process pools (e.g., ion exchange elution buffer) may precipitate assay reagents or dominate the pH of an assay mixture, making these pools incompatible with the assays. Interfering substances can be removed by sample preparation steps such as buffer exchange, blocking reagents for reducing agents, or protein precipitation with organic solvents. These sample preparation steps, however, can potentially introduce assay artifacts, especially at a very low level of protein concentrations. In addition, the response factor for the same protein in different buffer systems can vary significantly because of these interfering factors, making it necessary to make protein standards in different buffers for different column pools. This in turn makes the assay more time-consuming and labor-intensive. A simple, sensitive, and robust method that does not need sample preparation steps would be beneficial for the analysis of column or membrane cleaning samples.

We have developed a flow injection protein assay (FIPA) using a regular HPLC system with a fluorescence detector. The intrinsic fluorescence of tryptophan is used to monitor and quantitate protein concentration against a standard curve obtained from the same protein of interest spiked into Dulbecco's phosphate buffered saline (DPBS) containing 0.005% polysorbate solution. Samples are directly transferred into glass HPLC vials pre-coated with Sigmacote to prevent protein loss to the glass surface. Samples are then injected onto an HPLC system with no column connected, using DPBS as the isocratic mobile phase, which effectively prevents nonspecific interaction of low concentration protein analytes with instrument surfaces and minimizes run-to-run carryover to <0.5%. The flowthrough peak is monitored by a fluorescence detector (exc 280 nm, emi 335 nm) and integrated to obtain the peak area, which is compared against a standard curve to obtain assay results. The method has been shown to be linear in the range of 1 to 16 μg/mL of protein and no interference was observed from chelating reagents, reducing sugar, or detergents because of the selectivity of fluorescence detection. The limit of detection is observed to be about 0.5–1 μg/mL (depending on the sample matrix) and is equivalent to traditional colorimetric methods. Response factors of the same protein in different commonly used inorganic buffers are relatively constant. Intermediate precision of FIPA across laboratories was determined to be around 10% at the 6 μg/mL protein level and is comparable to traditional colorimetric methods.

FIPA can be used specifically to quantify the protein of interest. It is not a total protein assay and it requires that the protein of interest have tryptophan residues in its sequence. Wavelengths that are specific to tyrosine or phenylalanine also can be used for this purpose with decreased sensitivity because of the lower molar absorptivity and quantum yield of these two amino acids.

FIPA provides a simple, sensitive, and robust protein quantitation method to demonstrate the effectiveness of column or membrane cleaning. Compared with other methods (Table 2), FIPA is quicker and has the least matrix interference. Another significant operational advantage of FIPA is that a large set of samples can be analyzed quickly by HPLC without as much analyst hands-on time as needed with the other methods.

Table 2. Comparison of the flow injection protein assay with colorimetric methods commonly used to evaluate column and membrane cleaning

Brent S. Kendrick, PhD, is scientific director, Greg Chrimes is associate scientist, Steven L. Cockrill, PhD, is principal scientist, John P. Gabrielson, PhD, is senior scientist, Kelly K. Arthur is associate scientist, Brad D. Prater is senior associate scientist, Qiang Qin, PhD, is senior scientist, and Bing Zhang is senior associate scientist of Analytical Sciences, all at Amgen, Inc., Longmont, CO.

Anurag S. Rathore, PhD, is biotech CMC consultant and a faculty member at the Indian Institute of Delhi, India, 805.744.8986, Rathore is also a member of BioPharm International's editorial advisory board.


1. Rathore AS, Winkle H. Quality by Design for pharmaceuticals: regulatory perspective and approach. Nature Biotechnol. 2009;27:26–34.

2. Rathore AS, Branning R, Cecchini D. Design space for biotech products. BioPharm Int. 2007;20(4):36–40.

3. Kozlowski S, Swann P. Considerations for biotechnology product Quality by Design. Rathore AS, Mhatre R, editors. In Quality by Design for biopharmaceuticals: Perspectives and case studies. New Jersey: Wiley Interscience; 2009. p. 9–30.

4. Swann PG, Tolnay M, Muthukkumar S, Shapiro MA, Rellahan BL, Clouse KA. Considerations for the development of therapeutic monoclonal antibodies. Curr Opin Immunol. 2008;(42):493–99.

5. Towns J, Webber K. Demonstrating comparability for well-characterized biotechnology products. BioProcess Int. 2008;2:32–43.

6. Schnerman MA, Sunday BR, Kozlowski S, Webber K, Gazzano-Santoro H, Mire-Sluis A. CMC strategy forum report: analysis and structure characterization of monoclonal antibodies. BioProcess Int. 2004;(2):42–52.

7. International Conference on Harmonization. Q6B, Specifications: test procedures and acceptance criteria for biotechnological/biological products. Geneva, Switzerland; 1999.

8. Krull IS, Swartz M. Validation in biotechnology and well-characterized biopharmaceutical product. Pharmaceutical Regulatory Guidance Book. 2006;7:18–23.

9. Apostol I, Kelner D. Managing the analytical lifecycle for biotechnology products. BioProcess Int. Part 1: 2008;9:12–19. Part 2: 2008;(10):12–19.

10. Wu J, Watson JT. Assignment of disulfide bonds in proteins by chemical cleavage and peptide mapping by mass spectrometry. Methods Mol Biol. 2002;194:1–22.

11. Yen T-Y, Yan H, Macher BA. Characterizing closely-spaced, complex disulfide bond patterns in peptides and proteins by liquid chromatography/electrospray ionization tandem mass spectrometry. J Mass Spectrom. 2002;37:15–30.

12. Martinez T, Guo A, Allen MJ, Han M, Pace D, Jones J, et al. Disulfide connectivity of human immunoglobulin G2 structural isoforms. Biochem. 2008;47:7496–508.

13. Zhang W, Marzilli LA, Rouse JC, Czupryn MJ. Complete disulfide bond assignment of a recombinant immunoglobulin G4 monoclonal antibody. Anal Biochem. 2002;311:1–9.

14. Wypych J, Li M, Guo A, Zhang Z, Martinez T, Allen MJ, et al. Human IgG2 antibodies display disulfide-mediated structural isoforms. J Biol Chem. 2008;283:16194–205.

15. Cockrill SL. Complete determination of IgG2 disulfide connectivity: defining an analytical strategy for a new structural paradigm. Invited talk. IBC 12th Annual Well Characterized Biologicals Conference; 2008 Nov 10–12; Reston, VA.

16. European Commission (Enterprise Directorate General). EMEA Guideline on Production and Quality Control of Monoclonal Antibodies and Related Substances (Draft). Brussels, Belgium; 2008 Jul. Available from

17. Anumula KR. High-sensitivity and high resolution methods for glycoprotein analysis. Anal Biochem. 2000;283:17–26.

18. Chen X, Flynn GC, Analysis of N-glycans from recombinant immunoglobulin G by on-line reversed-phase high-performance liquid chromatography/mass spectrometry. Anal Biochem. 2007;370:147–61.

19. Prater BD, Anumula KR, Hutchins JT, Automated sample preparation facilitated by PhyNexus MEA purification system for oligosaccharide mapping of glycoproteins. Anal Biochem. 2007;369:202–09.

20. Prater BD, Connelly HM, Qin Q, Cockrill SL. High-throughput immunoglobulin G N-glycan characterization using rapid resolution reverse-phase chromatography tandem mass spectrometry. Anal Biochem. 2009;385:69–79.

21. Nguyen LT, Wiencek JM, Kirsch LE. Characterization methods for the physical stability of biopharmaceuticals. PDA J Pharm Sci Technol. 2003;57:429–45.

22. Gabrielson JP, et al. Quantitation of aggregate levels in a recombinant humanized monoclonal antibody formulation by size-exclusion chromatography, asymmetrical flow field flow fractionation, and sedimentation velocity. J Pharm Sci. 2007;96(2):268–79.

23. Philo JS. Is any measurement method optimal for all aggregate sizes and types? AAPS J. 2006;8(3):E564–E571.

24. Arthur KK, et al. Detection of protein aggregates by sedimentation velocity analytical ultracentrifugation (SV-AUC): Sources of variability and their relative importance (p n/a). J Pharm Sci. 2009;doi 10.1002/jps.21654.

25. Berkowitz SA. Role of analytical ultracentrifugation in assessing the aggregation of protein biopharmaceuticals. AAPS J. 2006;8(3):E590–605.

26. Liu J, Andya JD, Shire SJ. A critical review of analytical ultracentrifugation and field flow fractionation methods for measuring protein aggregation. AAPS J. 2006;8(3):E580–89.

27. Pekar A, Sukumar M. Quantitation of aggregates in therapeutic proteins using sedimentation velocity analytical ultracentrifugation: Practical considerations that affect precision and accuracy. Anal Biochem. 2007;367:225–37.

28. Gabrielson JP, et al. Common excipients impair detection of protein aggregates during sedimentation velocity analytical ultracentrifugation. J Pharm Sci. 2009;98(1):50–62.

29. Schuck P. A model for sedimentation in inhomogeneous media. I. Dynamic density gradients from sedimenting co-solutes. Biophys Chem. 2004;108(1-3):187–200.

30. Schuck P, et al. Size-distribution analysis of proteins by analytical ultracentrifugation: Strategies and application to model systems. Biophys J. 2002;82(2):1096–1111.

31. Samavedam R, Morrison R, Kichefski T, Cote S, Rathore AS. Lifetime studies for membrane reuse: principles and case study. BioPharm Int. 2007;20(9):48–54.

32. Rathore AS, Sofer GS. Lifespan studies for chromatography and filtration media. In: Rathore AS, Sofer GS, editors. Process Validation. Marcel Dekker; 2005. p. 169–203.

Other articles from The Elements of Biopharmaceutical Production series:

1. Modeling of Microbial and Mammalian Unit Operations

2. Scaling Down Fermentation

3. Optimization, Scale-up, and Validation Issues in Filtration

4. Filter Clogging Issues in Sterile Filtration

5. Lifetime Studies for Membrane Reuse

6. Modeling of Process Chromatography Unit Operation

7. Resin Screening to Optimize Chromatographic Separations

8. Optimization and Scale-Up in Preparative Chromatography

9. Scaling Down Chromatography and Filtration

10. Qualification of a Chromatographic Column

11. Efficiency Measurements for Chromatography Columns

12. Process Validation: How Much to Do and When to Do It

13. Quality by Design for Biopharmaceuticals: Defining Design Space

14. Quality by Design for Biopharmaceuticals: Case Studies

15. Design Space for Biotech Products

16. Applying PAT to Biotech Unit Operations

17. Applications of MVDA in Biotech Processing

18. Future Technologies for Efficient Manufacturing

19. Costing Issues in the Production of Biopharmaceuticals

20. Economic Analysis as a Tool for Process Development

For the entire series of The Elements of Biopharmaceutical Production, please visit