GE Healthcare Life Sciences · 2012-08-01 · GE Healthcare Life Sciences Extended Reports from the 1st International Conference on ... chromatography ... linear or planar array facilitates

GE HealthcareLife Sciences

Extended Reports from the 1st International Conference on High-Throughput Process DevelopmentKraków, Poland Oct 4-7, 2010

HTPD 2010 | Extended reports 3

5 From the chairman

Oral presentations

6 High-throughput techniques for small molecule drug discovery and early development: lessons learned

7 Studying host cell protein interactions using high-throughput Protein A affinity chromatography

8 High-throughput screening of excipients intended to prevent antigen aggregation at air-water interface

10 Process characterization to establish a control strategy for management of chromatography resin lot-to-lot variability

12 Using robotics and high-throughput screening in early stage process development

14 An analytical approach to batch uptake characterization of chromatography media

16 Selective protein quantification using multivariate calibration – An analytical tool for high-throughput experimenting and inline monitoring of chromatographic separations

19 Holistic approaches to improving the throughput of analytical methods to support therapeutic monoclonal antibody process development

21 Strategic assay deployment in high-throughput process development

24 Calibration of mathematical models of preparative chromatography: removal of antibody aggregates

26 Achieving comparability between batch binding and packed column methods

28 Combination of two modeling approaches for a rapid and highly predictive optimization of an ion exchange chromatography step based on HTE data

29 The next decade of HTPD: evolution or revolution?

32 Development of a capture chromatographic step for purification of r-pro-insulin expressed in E. coli

35 Review and outlook on automated, small-scale parallelized biochromatography

38 High-throughput, downstream screening system for protein purification using membrane adsorbers

41 List of posters presented

43 Author index

In this issue

4 HTPD 2010 | Extended reports


The idea of organizing a conference exclusively devoted to High-Throughput Process Development (HTPD) emerged for the first time in late 2008 during scientific discussion between GE Healthcare Life Sciences, Sweden and Genentech Inc. USA. It was recognized that there was a need for a scientific conference with the goal of establishing a forum for discussions and exchange of ideas surrounding the challenges and benefits of employing high-throughput techniques in the development of production processes for biological products. The outcome became HTPD 2010, held in Kraków, Poland.

The conference program for HTPD 2010 included a keynote lecture, two case study sessions, one session devoted to Design of Experiments and data mining, one session focusing on eliminating analytical challenges, a session discussing scale-up and scale-down aspects of HTPD, and one session where the future of the HTPD field was discussed. In total 29 oral presentations were delivered and 25 posters were presented; additionally, there was an opportunity to discuss common challenges encountered in executing HTPD experiments during a morning panel session.

This extended abstract book captures some of the presentations from this very exciting conference. We hope that it will serve as a resource and summary of the excellent talks and discussions, as well as encourage you to participate in the next meeting and develop the HTPD conference series as a leading forum within its field.

Our thanks go the session chairs for their efforts in putting together great sessions, the presenters for their contributions, and the participants for making this a truly valuable and enjoyable event.

Looking forward to seeing you at HTPD 2012.

Philip Lester Genentech

Catharina Hemström Nilsson GE Healthcare Life Sciences

Karol Łącki GE Healthcare Life Sciences

From the conference chairs


High-throughput techniques for small molecule drug discovery and early development: lessons learnedChristof FattingerF. Hoffmann-La Roche Ltd, Pharma Research and Early Development, Discovery Technologies, CH-4070 Basel, Switzerland

e-mail: [email protected]

The functions and the functional elements of four, rather different high-throughput methods have been developed and implemented at Roche in the last decade. The methods are used in small molecule drug discovery and early development and include:

1. Identification and characterization of lead compounds for a drug target by high-throughput screening and high-throughput compound profiling in drug discovery.

2. Exploration and characterization of the salts and polymorphs of an active pharmaceutical ingredient by high-throughput solid form screening in chemical process and formulation development.

3. A combination of separation by size-exclusion chromatography (SEC) with reverse phase protein arrays (RPA) for multidimensional profiling of proteins and characterization of their functions and interactions with a small molecule drug candidate.

4. Automation of the Ames Test for high-throughput mutagenicity profiling of new, small molecule drug candidates.

Some of the common challenges of high-throughput methods and the rules that lead to their solution are discussed below.

Carrying out a high-throughput assay often requires relatively long time spans (some minutes, hours, or even days) for incubation and conditioning of the samples prior to read-out of the assay results. Most assays require comparative measurements at defined time points involving blank, standard, and control samples for calibration of the assay and also for quality control. Arrangement of the samples in a linear or planar array facilitates ‘parallel processing and conditioning’ of the samples prior to read-out of the assay results. The integration of the samples to be analyzed in an array provides for parallel execution of many process steps in the assay, such as allocation of the samples, sample preparation, incubation and conditioning, separation and washing steps, and parallel readout of the analytical results.

Breaking down the different process steps in the assay into optimized subprocesses often leads to a significant improvement of the overall throughput and performance. In order to work efficiently, each subprocess relies on different strategies for parallel execution of process steps in the assay, while keeping the involved subprocesses and the main process fairly simple. The essence of an efficient high-throughput process is “intelligent nesting of subprocesses”.

During the development and implementation phases of a new high-throughput process on an automated system, the seamless interplay between biology and technical sciences is crucial.

The initiative Standardization in Lab Automation (SiLA) focuses on rapid integration of newly configured high-throughput automation systems (www.sila.coop). Short set-up time for a newly developed assay on an automated system is at least as important as high-throughput per se. We need to have the ability to integrate new components and new functions in automated systems in order to cope with changing needs of researchers in drug discovery.

The objectives and advantages of the SiLA initiative are:

• Easier to cope with changing needs of researchers

• Faster integrations

• Easier to understand the behavior of instruments (state machine, error handling)

• Lower cost for integrated systems

• Faster access to new devices, instruments, technologies

• More labs, assays, processes amendable to automation

• More modular and flexible automated systems possible

• Larger selection of Process Management Software (driver independent)


Studying host cell protein interactions using high-throughput Protein A affinity chromatographyVikram N. Sisodiya, Paul J. McDonald, Kathlyn Lazzareschi, Maricel Rodriguez, and Robert Fahrner Purification Development, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA


Protein A affinity chromatography is typically used as the primary capture step in the purification of monoclonal antibodies. Although exploiting an affinity interaction for purification, levels of host cell proteins (HCP) in the protein A purified pool are highly variable. Historical data for 21 antibodies showed no correlation between the levels of HCP in the load and elution pool. Some of the contributors to variability in HCP levels between antibodies include different cell lines and cell culture conditions contributing to different levels as well as HCP species in the load, differences in protein A operating conditions including the use of different protein A resins as well as antibody characteristics such as isoelectric points and hydrophobicity.

Using high-throughput protein A purification, we studied the effect of resin type, load density, load titer, and load HCP on HCP clearance across the protein A step. We used a library of purified antibodies in conjunction with a common starting material, harvested cell culture fluid (HCCF) from a non-producing cell line, in order to normalize the starting level and species of HCP. The purified antibodies were added to the null HCCF, generating feedstocks with identical levels and species of HCP. When comparing MabSelect SuRe™ and Prosep vA, similar levels of HCP were observed in the elution pool with the same antibody. However, levels of HCP in the protein A elution pool varied between antibodies, indicating that antibody-HCP interactions predominantly contributed to the HCP levels in the elution pool (Fig 1). Sixteen of the 21 antibodies showed HCP levels of less than 2000 ppm in the elution pool whereas the remaining five antibodies (antibody 8, 10, 14, 16, and 21) showed HCP levels ranging from 4000 to 16 000 ppm. Similar trends were observed for these five antibodies across a range of load densities from 7 to 30 g of antibody/L of resin.

We also found that certain additives could disrupt the antibody-HCP interaction, suggesting a possible mechanism for this interaction. When spiked into HCCF prior to loading onto Mabselect SuRe and Prosep vA, guanidine and sodium chloride partially disrupted antibody-HCP interactions. The reduction in HCP with the addition of these additives varied between antibodies. This suggests that both hydrophobic and ionic interactions are responsible for the antibody-HCP interaction; however the nature and strength of the interaction varies between antibodies. Future work will include studying antibody characteristics such as hydrophobicity and other sequence motifs which could help predict HCP clearance over protein A.

Fig 1. Comparison of elution pool CHOP for 21 antibodies spiked into null HCCF and purified over MabSelect SuRe (N=7) and Prosep vA (N=8) resins at a load density of 14 g/L using a typical protein A purification process with a 0.4M potassium phosphate pH 7 wash phase. The error bars represent the standard deviation between replicates. Different antibodies contribute to different levels of CHOP on both protein A resins when the levels and species of CHOP are normalized. For individual antibodies, similar levels of CHOP are observed on both resins suggesting that the properties of the antibody and not properties of the protein A resin are responsible for the varying levels of CHOP in the protein A purified pools.

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High-throughput screening of excipients intended to prevent antigen aggregation at air-water interfaceSébastien Dasnoy1, Nancy Dezutter2, Dominique Lemoine2, Vivien Le Bras2, and Véronique Préat1

1 Université catholique de Louvain, Louvain Drug Research Institute, Unité de Pharmacie Galénique, Avenue Emmanuel Mounier 73, 1200 Brussels, Belgium

2 GlaxoSmithKline Biologicals s.a., Research & Development, Rue de l’Institut 89, 1330 Rixensart, Belgium


From manufacturing to patient administration, vaccines undergo various stresses such as exposure to light, temperature and shaking. Under these conditions, antigen integrity and therefore vaccine efficacy may be impacted. Among these stresses, hydrophobic air-liquid interfaces, which are present at different steps of the formulation process as well as in the final container, are a common cause of protein aggregation (1). Ensuring antigen integrity is a critical element in the development of new vaccine candidates.

A stress test in microplate was developed for studying the sensitivity to aggregation at air-liquid interface of an experimental recombinant protein antigen called Antigen 18A. Briefly, air bubbles were blown in microplate wells, while avoiding extensive foaming. Analysis of the aggregates by size-exclusion chromatography (Fig 1A) was performed and a correlation with changes in tryptophan fluorescence emission was observed (Fig 1B). This label-free method was used to trace the prevention of aggregation of Antigen 18A at air-water interface by a series of excipients.

The effectiveness of 44 excipients (amino acids, sugars, polyols, polymers and surfactants) against protein aggregation at air-water interface was evaluated in a high-throughput screening (HTS) mode. All conditions were randomized on 12 plates prepared with an automated liquid handler. Automated data management revealed preservation (on a statistical basis) of protein integrity by five surfactants and two cyclodextrins (Fig 2).

In order to evaluate the feasibility of identifying hit excipients by this HTS method, we calculated the z’-value post-screening. This adimensional statistical parameter gives an idea of the width of the screening window (2). The calculated z’-value was 0.57, making the identification of excipient hits by this HTS method feasible (Fig 3). A set of biophysical and biological techniques was subsequently used to confirm the effectiveness of these excipients against protein aggregation at a classical formulation scale, in a vial shaking model (Fig 4).

Fig 1. Influence of air-liquid interface on Antigen 18A aggregation profile by size-exclusion chromatography with UV detection and tryptophan fluorescence spectroscopy. Air-liquid interface was created by air bubbling in microplate. (A) Evolution of monomer and soluble aggregate species upon air bubbling. Error bars represent the standard deviation from three independent experiments. (B) Comparison of size-exclusion chromatography and tryptophan fluorescence spectroscopy for measuring Antigen 18A stability.


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The high-throughput platform, based on limited material per testing condition and rapid analytical read-outs, allows screening of a larger area of the formulation space in a limited time period and enables rapid identification of candidate excipients for further formulation development. More details on the present study can be found in (3).

Fig 2. High-throughput screening of excipients intended to prevent Antigen 18A aggregation at air-liquid interface, followed by tryptophan emission at 336 nm. A total of 100 air bubbles was used for creating air-liquid interface in microplate wells. Closed symbols represent conditions where statistically significant protection (blue) or destabilization (red) was noticed (α = 0.01). Error bars represent the standard deviation from three replicates randomly located on different plates.

Fig 3. Post-screening validation of the high-throughput screening assay. Controls were selected based on the HTS results, where significant protection (poloxamer 188 0.125%) or destabilization (polyvinylpyrrolidone K17 0.25%) of Antigen 18A against aggregation at air-liquid interface was noticed. Results were obtained from a single microplate.

Fig 4. Confirmation of the performance of excipient candidates in a shaken vial model. Agitation was performed for 90 min. (A) Monomer recovery by size-exclusion chromatography with UV detection. (B) Integrity of Antigen 18A epitopes by enzyme-linked immunosorbent assay. Cyclodextrins and surfactants were added at a concentration of 10% and 0.015%, respectively. Histogram bars represent geometric means and their confidence interval (α = 0.05) from five independent measurements. Star symbols indicate significant difference with the control.

References1. Wang, W. Protein aggregation and its inhibition in

biopharmaceutics. Int J Pharm. 289, 1–30 (2005).

2. Zhang, J-H, et al. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen. 4, 67–73 (1999).

3. Dasnoy S., et al. High-throughput screening of excipients intended to prevent antigen aggregation at air-liquid interface. Pharm Res., DOI 10.1007/s11095-011-0393-x.


Process characterization to establish a control strategy for management of chromatography resin lot-to-lot variabilityTryggve Bergander, Mattias Ahnfelt, Gunnar Malmquist, Eggert Brekkan, Catharina Hemström Nilsson, and Karol ŁąckiGE Healthcare Bio-Sciences AB, Björkgatan 30, SE-751 84 Uppsala, Sweden


Quality by Design highlights the need for better process understanding, both regarding Critical Process Parameters and Critical Raw Material Attributes (CMA). Chromatographic resins can be described by several parameters, some of which can be considered as potential CMA’s. One of these could be ligand density as in some processes the ligand density can affect resin selectivity (i.e., the purification) and thus the product’s Critical Quality Attributes (CQA). Testing the effect of ligand density in a typical process development workflow could be compared to resin screening where different resins are compared, but in this case all ligand density variants to be tested would represent variation in ligand density typical for a given resin.

In this work, batch uptake experiments were used to establish the effect of ligand density and mobile phase conditions on CQA. The study included process parameter adaptation by looking for parameter interactions that allowed the development of a control strategy where the effect of ligand density variations could be counteracted by changing other factors (e.g., mobile phase conditions). In particular, the effect of ligand density of Capto™ adhere on the reduction of aggregate content from a monoclonal antibody pool after capture on MabSelect SuRe™ was investigated. The Capto adhere step was designed to be operated in the flowthrough mode and the sample pool contained a high concentration of aggregates (29%). The process parameters investigated included pH and NaCl concentration during the loading step. The study was performed in 96-well filter plates with Capto adhere ligand density variants, specifically produced for this study, that covered the entire ligand density specification range (90 to 120 µmol/mL). The study was a DoE study where the factors were ligand density (96 to 123 µmol/mL), load pH (6 to 7.8), load NaCl (0 to 150 mM), and the responses used included purity (expressed through monomer content) and yield.

Static monomer and aggregate capacities (Q) were calculated using Equation 1. The predicted purity and yield for the flowthrough step in the column format were calculated using Equations 2 and 3, respectively, assuming that Vload of 100 g/L was applied to a column and that dynamic binding capacities (DBC) for monomer and aggregates were equal to the respective static capacities (Q).

[1]

[2]

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The DoE model obtained coefficient plots (Fig 1) show the significant terms identified in the study and how they affected the predicted purity and yield. The result showed that a low pH should be used as pH was negatively correlated with both responses. Load NaCl was negatively correlated with purity and positively correlated with yield, which leads to a compromise. The response surface plots (Fig 2) showed the effect of load NaCl and ligand density on the responses. The pH had been locked at a low level, pH 6. For maximal purity, the ligand density should be low as should load NaCl, while for maximal yield, the ligand density should be low and load NaCl high.

The results can be used to develop a control strategy for management of the effect of ligand density. By setting criteria for purity and yield (e.g., purity > 99% and yield > 90%), a sweet spot plot can be constructed (Fig 3) within which both criteria are fulfilled. Within the sweet spot, different control strategies can be


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envisioned and two possible strategies are presented below. For the first control strategy, the process parameter load NaCl is kept constant at ~ 55 mM (Fig 3). This approach makes the ligand density a non-critical raw material attribute as the set criteria for the process are fulfilled over the entire ligand density specification range. For the second control strategy (Fig 4), the yield is maximized without sacrificing the 99% purity criterion. This is achieved by allowing adjustment of the load NaCl concentration, depending on the ligand density. Yield will then vary from 92% to 100%, while the purity is maintained at 99%.

By explicitly investigating a potential CMA, the proposed approach outperforms conventional testing of a few resin lots from normal production. Eliminating the need for resin lot picking leads to a more robust process and increased security of supply. The study also shows that rapid, high-throughput formats (i.e., microtiter plate formats) are an excellent tool for this purpose.

Fig 1. Coefficient plots for predicted purity and yield. pH is negatively correlated with both responses suggesting that a low pH should be used. NaCl is negatively correlated with purity, but positively correlated with yield, suggesting a compromise is needed for optimization of both responses. For both responses, an interaction effect between ligand density and NaCl is observed, as is an interaction effect between pH and NaCl.

Fig 2. Response surface plots for predicted monomer purity and yield. pH has been set to a low value, 6, as suggested by the coefficient plots (Fig 1). Low ligand density is good for both purity and yield, while load NaCl has opposite effect on the two responses. Due to the interaction effect between ligand density and NaCl, the estimated effect from variations in ligand density on the two responses is less pronounced at higher NaCl levels.

Fig 3. Sweet spot plot created from response surface plots in Fig 2. Green areas are where purity (> 99%) and yield (> 90%) criteria are fulfilled. At 55 mM load NaCl concentration (red dotted line), the ligand density becomes a non-critical raw material attribute as the CQA (purity) is reached independent of ligand density.

Fig 4. Response surface plot for predicted monomer yield. The dotted red line corresponds to the 99% purity level from the response surface plot for predicted monomer purity in Fig 3. By varying the NaCl concentration (depending on ligand density) to keep constant purity (99%), the yield can vary between 92% and 100%. This control strategy maximizes the yield while targeting a purity of 99% for all ligand densities in the investigated range.


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Using robotics and high-throughput screening in early stage process developmentPaul McDonald, Joseph Cheng, Marc Wong, Nick Cai, Ivy Lin, Jennifer Hopp, Tony Cano, Philip Lester, and Brian KelleyGenentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA


Monoclonal antibodies are typically purified using a platform purification process. At Genentech, a platform was designed to accommodate a wide range of antibody characteristics. The platform consists of three chromatography steps exploiting different modes of separation: protein A affinity chromatography followed by two ion exchange chromatography steps. The anion exchange step is typically run in flowthrough mode while the cation exchange step is operated in bind and elute mode. We have developed high-throughput screens to evaluate the fit of antibodies to our chromatography processes and to guide the development of antibodies with poor fit. The screens are performed as part of the molecular assessment of new antibodies. Fit within the platform can be assessed with approximately 100 mg of antibody allowing us to gauge the resources and timelines for new antibodies. In addition, secondary screens quickly identify alternate conditions and help to normalize the development timelines for antibodies with off-platform behavior.

The screens map partition coefficients (Kp) as a function of pH and counterion concentration on the chromatography resins used in the purification platform. The method for determining Kp is operated in batch binding mode on 96-well filter plates and has been fully automated using a Tecan Freedom Evo robot. Kp is calculated implicitly from the protein remaining in solution after incubation with the chromatography resins. A response surface model is applied to the experimental data as a function of pH and counterion. In order to accurately model the data, it is important to have sufficient points in the transition zone from high log Kp to low log Kp. In addition, conditions with a log Kp > 2 are excluded due to the error associated with measuring very low antibody concentrations.

Using these screens we built a data library with approximately thirty antibodies that we correlated to

antibody behavior on chromatography columns. These correlations include the prediction of flowthrough behavior on anion exchange resins and the prediction of elution conditions for cation exchange resins. Figure 1 shows the contour plots of log Kp for six antibodies for the anion exchange resin used in the platform purification process. The majority of antibodies fit to platform and have similar profiles to MAb A and MAb B. However, with the entire library of antibodies we see wide variations in binding to the anion exchange resin, which can make setting platform operating conditions a challenge. With the six antibodies in Figure 1, we see increasing interaction with the anion exchange resin from MAb A to MAb F. Looking at their isoelectric points, we see that isoelectric point does not adequately predict performance on the anion exchange resin. Both MAb B and Mab F have similar isoelectric points of 8.1−8.2, but MAb B has a low log Kp (< -0.1) under the majority of conditions, while MAb F has a high log Kp (> 1.1) under the majority of conditions tested. MAb B can be operated in flowthrough mode, but flowthrough conditions that met process requirements could not be identified for Mab F.

Fig 1. Contour plots of log Kp show the diversity of behavior on an anion exchange chromatography resin. The white circle represents the target operating conditions for the purification platform.


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Using the response surface modeled data, we identified the log Kp for the six antibodies at the platform target conditions and compared the log Kp values to chromatographic performance. With increasing log Kp, we see increased interaction with the resin in dynamic mode as indicated by a delay in flowthrough and tailing during the wash (Fig 2). MAb A, with a log Kp of -0.3, has a box-shaped chromatogram associated with a flowthrough step. By contrast, MAb F, with a log Kp of 1.4, initially binds to the resin, begins to break through five column volumes into the load, and continues to elute during the wash phase. By building these correlations for our existing antibodies, we can correlate Kp to flowthrough performance and quickly map the behavior of new antibodies as they enter development. Antibodies such as MAbs D−F that do not fit the platform trigger secondary screens and database comparisons. Secondary screens typically evaluate bind and elution conditions on the anion exchange resin as well as alternative chromatography resins. These secondary screens, combined with our database of antibody behavior, help us to quickly identify alternate process conditions and normalize the development timelines for antibodies with off-platform behavior.

Fig 4. Contour plots of log Kp can be used to identify elution conditions from cation exchange chromatography. The white bar represents gradient elution conditions for the purification platform.

Fig 2. Correlating log Kp to behavior on an anion exchange chromatography column.

In addition to identifying flowthrough conditions, the Kp screens can also be used to identify bind and elution conditions. The cation exchange purification process uses a gradient elution to accommodate a large variety of antibody behavior. Figure 3 shows the

We have observed that antibody binding to anion and cation exchangers is diverse and has significant processing implications. Using our Kp screens, we can characterize this binding behavior across a wide operating space of pH and counterion concentration allowing us to rapidly assess the fit of antibodies to our platform purification process and normalize the development of off-platform antibodies. The Kp screens are performed as part of the molecular assessment of new antibodies allowing us to gauge the resources and timelines for new antibodies entering development.

Fig 3. Gradient elution profile for three antibodies from a cation exchange chromatography column.

overlay of elution profiles for an early-, mid- and late-eluting antibody within a salt gradient when loaded to the same load density. Looking at the contour plots for the three antibodies, we see that the counterion concentration at which transition from high log Kp (≥ 2.00) to low log Kp (≤ 0.25) occurs correlates to elution counterion concentration within the gradient (Fig 4). In addition, antibodies with a broader transition zone have an increased elution peak width.


An analytical approach to batch uptake characterization of chromatography mediaPeter Sandblad, Anneli Florén, Ann-Katrin Hellman, and Tryggve BerganderGE Healthcare Bio-Sciences AB, Björkgatan 30, 751 84 Uppsala, Sweden


Chromatographic methods in packed bed format are widely used in R&D, process development, and quality control for resin characterization. Chromatographic experiments are often time-consuming and require large sample and test substance amounts. The use of high-throughput methods such as parallel batch uptake experiments in microtiter plate format has the potential to substantially reduce both analysis time and material costs. Batch uptake assays have long been used in screening studies, but this study shows that analytical batch uptake methods can be used for determination of dynamic and total binding capacity for chromatographic resins.

There are some fundamental differences between chromatographic and batch uptake methods, but both analysis techniques can measure the same mass transport properties of the resin. In this study, batch uptake capacity determinations were performed by measuring the unbound fraction of a pure test substance during the adsorption phase. The capacity in a time-dependent batch uptake experiment is determined through the equation:

For this study we have used prototypes from a new affinity chromatography resin. The prototypes were synthesized according to a four factor DoE with factors chosen that have impact on the capacity. In total 16 corner points plus three center points were synthesized. All 19 prototypes were characterized in packed bed columns for dynamic capacity (10% and 80% breakthrough) at two different residence times (2.4 and 6 min). Prototypes were also filled in 96-well filter plates using a high precision dispensing method and were characterized at four different initial concentrations (0.5 to 4 mg/mL) and several different incubation times (2 to 150 min).

A multivariate evaluation of the measured responses shows that several of the column and batch uptake capacities can be used to build a model of the DoE prototypes with high correlation and predictability. A comparison of the DoE coefficient plots for column and batch uptake responses shows that there is a clear correlation between the two techniques. Figure 1 shows the coefficient plots for 10% breakthrough capacity at the two different investigated residence times and the batch uptake incubation capacities that show the most similar plots. The column chromatography plots show that the four design factors have different effects on the capacity depending of the loading velocity - in both cases there are batch uptake responses that describe the system in a very similar way. This study further shows that a batch uptake incubation capacity experiment with 4 mg/mL initial concentration and 15 min incubation time can be used to describe the resin’s mass transport properties (just as a traditional breakthrough capacity experiment with 1 mg/mL sample concentration and 2.4 min residence time can). In the same way, a 28 min batch uptake incubation capacity can describe similar binding properties as obtained with chromatography column at 6 min residence time. Models for batch uptake experiments at long incubation times (> 2 h) also match models for saturation capacities (Q

B80%) in column format (results not shown).

whereq = capacityVliq = liquid phase volumeVres = resin volumec = free test substance concentration

This study focuses on improving the precision in a capacity determination method. Experiments were typically performed in 96-well filter plates dispensed with 6 µL resin and 200 µL sample. When working with such small sample volumes it is challenging to achieve an analytical characterization method. Chemical and physical conditions have been investigated in steps from resin slurry preparation and plate dispensing to incubation and detection in order to fully optimize the batch uptake technique to an analytical characterization method.


-6

-4

-2

0

2

A B C D

B*D

C*D

Column QB10% 2.4 min

R2 = 0.969 RSD = 1.477 Q2 = 0.928 Conf = 0.95

-4

-2

0

2

A B C D

B*D

C*D

Batch uptake 15 min

R2 = 0.972 RSD = 0.8086 Q2 = 0.935 Conf = 0.95

-8

0

8

A B C D

A*D

B*D

C*D

Column QB10% 6 min

R2 = 0.941 RSD = 2.076 Q2 = 0.852 Conf = 0.95

-4

0

4

A B C D

A*D

B*D

C*D

Batch uptake 28 min

R2 = 0.929 RSD = 1.253 Q2 = 0.858 Conf = 0.95

y = 0,56x + 29,33

R2 = 0,90

0

20

40

60

80

20 40 60 80 20 40 60 10080

20 40 60 1008020 40 60 80

QB10% 2.4 min

Batc

h up

take

cap

acity

15

min

y = 0,85x + 12,37

R2 = 0,93

0

20

40

60

80

100

QB80% 2.4 min

Batc

h up

take

cap

acity

150

min

y = 0,69x + 19,01

R2 = 0,93

0

20

40

60

80

100

QB80% 6 min

Batc

h up

take

cap

acity

150

min

y = 0,53x + 29,10

R2 = 0,85

0

20

40

60

80

QB10% 6 min

Batc

h up

take

cap

acity

32

min

least favourable way leads to a 3% shift in measured capacity. Method validation further shows that the precision is very good. A method with 16 replicates has a repeatability of about 0.5% and reproducibility of 2% for dynamic binding capacity and below 1% for total capacity. Table 1 shows a comparison of the method precision parameters for a different number of replicates and a comparison with a corresponding column-based dynamic binding capacity method.

The use of high-throughput methods in resin characterization has several benefits over traditional frontal analysis in packed-bed columns. The higher analysis throughput gives increased productivity, which can be used to increase design space and decrease lead time in development projects, process development, and product quality control. The batch uptake technique can reduce resin and test substance cost by > 95% and reduce manual labor in resin characterization by > 80%. The batch uptake technique will also enable a full characterization of systems where material availability is limited. The high method precision presented in this study shows that the batch uptake technique no longer is limited to screening usage, but can be used for analytical capacity determinations in both research and quality control.

Fig 1. Coefficient plots for capacities determined using column and batch uptake techniques. Times represent residence times in column experiments and incubation times in batch uptake experiments.

Scatter plots of all column responses and their best corresponding batch uptake response are shown in Figure 2. The correlation coefficients between the two techniques are high (R2 > 0.87) for all four responses, showing that batch uptake capacity determinations have a direct correlation to breakthrough capacities measured in column format. It is also worth mentioning that absolute capacities seem to vary between the different techniques. This is mostly a consequence of the different resin volume definitions. In batch uptake, capacity is determined in mg test substance per mL drained resin, whereas column volumes are measured per mL packed resin.

In order to validate the observed correlations, a full robustness study and method validation was performed on the batch uptake capacity method. Sample concentration, sample volume, incubation time, and resin volume were varied independently in a DoE setup. Data were sufficient to create a valid model with R2 and Q2 above 0.95. Results show that the method is rather robust – a 1% variation of all factors in the

Fig 2. A direct comparison of batch uptake capacity versus column-based QB10% and QB80% shows a high correlation (R2 ≈ 0.9).

Table 1. Method precision data for a batch uptake capacity method

Validation parameter30-35 min

incubation capacity120 min

incubation capacityQB10%

Breakthrough capacity

# of replicates 8 16 8 16 2

RSDr [%] 0.9 0.6 0.8 0.5 ≈1

RSDR [%] 2.1 2.0 0.9 0.7 ≈3


Selective protein quantification using multivariate calibration – An analytical tool for high-throughput experimenting and inline monitoring of chromatographic separationsSigrid Hansen1, Erik Skibsted2, Arne Staby2, Jürgen Hubbuch1

1 Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, D76131 Karlsruhe, Germany

2 Novo Nordisk, Copenhagen, Denmark


IntroductionOver the past few years, the use of high-throughput techniques for development of protein purification processes has increased rapidly. The main drivers of this shift towards high-throughput process development (HTPD) have been (and still are) the need to speed up both time to clinic and time to market.

Alongside the development of high-throughput experimentation (HTE) techniques, there has also been a trend towards replacing the traditional “one factor at the time” screenings with more sophisticated approaches such as factorial designs, intelligent search algorithms, and mechanistic modeling. These approaches make process development more efficient and can result in a deeper understanding of the developed processes. However, regardless of how sophisticated a search algorithm or how good a design is, a high number of samples has to be analyzed when performing state of the art HTPD. In order not to compromise the throughput and level of automation, univariate spectroscopic measurements performed in plate readers is the most attractive analytical solution. If the nature of the experiments is more complex and requires selective or specific protein quantification on the analytical side, mass spectrometry, analytical chromatography, electrophoresis, and enzyme-linked immunosorption assays can be applied. However, all methods have drawbacks related to throughput, sample preparation, and/or automated integration. Hence, the enhanced experimental throughput has created an analytical bottleneck where the experimenter is forced to make decisions on the basis of a tradeoff between analysis time and analytical information.

The motivation behind the work presented here was to find a way to limit the analytical trade-off without compromising the level of throughput and automation of the experimental side. The idea was to look deeper into the mid-UV absorption characteristics of proteins and examine to what extent it is possible to correlate protein absorption characteristics to selective protein concentrations.

Model calibraitonAs can be seen in Figure 1, the absorption spectra of the aromatic amino acids differ significantly, not only in intensity but also in shape. This fact will cause the absorption spectra of different proteins to change depending on the ratios of the aromatic residues present in the proteins. Hence, by correlating spectral data with protein concentrations, selective protein quantification should be possible.

The selective protein concentrations in a three component system containing cytochrome C (cytC), ribonuclease A (ribA) and lysozyme (lys) were calibrated to the corresponding absorption spectra in the range from 240-300 nm. Figure 2 gives an overview of the cross-validations performed in the model building. The results showed that it was indeed possible to correlate the absorption spectra to the protein concentrations, (i.e., a calibration model with a high predictive power was obtained). The high predictive power is reflected in the high coefficients of determination, which were 0.9989 or higher. Above a concentration of 0.1 g/L the mean relative error was approximately 1.5% (ribA 1.59%, cytC 1.49%, and lys 1.56%). At lower concentrations the accuracy varied among the calibrated proteins.


The lowest concentration of 0.05 g/L of a single component resulted in an error of 22% for ribA, 2.5% for cytC, and 3.9% for lys. In general, the relative error decreases as a function of nominal concentration for all three proteins. This is consistent with the fact that measurement noise and pipetting error has a greater influence on low concentrations. The fact that the relative error of the lowest concentrations is smaller for lys compared to the two other proteins is most likely ascribable to the fact that lys has the highest absorption intensity (see Fig 1).

In addition to the aromatic residues, a heme group also contributes to the absorption spectrum of cytC in the applied range. Hence, the protein mixture applied here (to demonstrate the possibility to perform selective quantification based on absorption spectra) does not directly demonstrate applicability to protein mixtures where the absorption spectra are ascribable to aromatic residues only. However, experiments with proteins not containing heme groups (data not shown

here) showed that it is not necessary for one of the proteins to contain a heme group in order to achieve a calibration model of high predictive power for a mixture of three proteins.

Adsorber screeningTo prove the value of using the absorption spectra for selective protein quantification, an adsorber screening was performed with automated RoboColumns. The same three proteins were separated using four different cation exchange resins packed in RoboColumns and the calibration model described above was used to determine the selective protein concentrations in each of the elution fractions. The four resins resulted in four different elution scenarios: 1) the first two components coeluting, 2) the last two components coeluting, 3) all components coeluting, and 4) separation of all three components. The chromatograms of complete coelution and coelution of the last two components are shown in Figure 3.

Fig 1. Absorption spectra of the three proteinogenic aromatic amino acids and three proteins (ribA, cytC, and lys) were recorded in aqueous solutions. Tyrosine and Tryptophan were measured at concentration of 0.1 g/L. Due to the relatively low absorption of phenyl alanine, the spectra were recorded at both 0.1 and 1.0 g/L. The spectra of all three proteins were recorded at a concentration of 1 g/L. All recordings were performed with a path length of 10 mm.

Fig 2. The results of the cross-validation for each protein is displayed with the calculated relative error for each calibration sample. To ease the visual comparison, the same scales were used for all proteins.


Automated evaluation based on the total absorption signal alone would have been difficult (if not impossible). However, using the spectral-based method for selective quantification, detailed information can be derived from the performed experiments. For comparison, the protein concentrations in the elution fractions were quantified using analytical chromatography. The chromatograms as well as the mass balances were very similar (90% to 110%) for both analytical methods. Comparing the time necessary for spectral analysis (which is counted in minutes) with the time needed for chromatographic analysis (which is counted in days), the advantage of a spectral-based analytical assay becomes very clear.

Online monitoringThe ability to perform selective protein quantification online would be a valuable tool for PAT and QbD. Column performance and product purity could be monitored inline and real time release to the next process step could be realized. As a proof of principle for an online application, the method was applied offline to the spectral data recorded inline with a diode array detector during a separation of the three proteins using a standard chromatography system. This resulted in a clear visualization of the separation, however with an imprecise baseline. Further work will look into this matter.

Fig 3. Separation of ribA, cytC, and lys using two different cation exchanger resins packed in RoboColunms. The total absorption at 280 nm and the protein concentrations calculated with the PLS model are displayed.


Scale injection volume (IV) to cross section (simple case or L constant)

Target flow rate keeping linear velocity constant (if particle size is constant)

4.6 × 300 mm

1.0 × 300 mm

0.9 µL

20 µL

Injectionvolume

300 µm × 300 mm

20 nL

0.35 mL/min

16.5 µL/min

Flow rate

10 µlLmin

Detectionranges

[5–20 µg]

[236–945 ng]

[5–85 ng]

[~ 10 µL]

[~ 0.45 µL]

[~ 0.01 µL]

Detectioncell volume

0 5 10 15 20 24

0

100

200

300

400

500

600

mAU

Time (min)

HM

WS

MO

NO

MER

WVL: 214 nm

HM

WS M

ON

OM

ER

20 ng injection!

HMWS(% Rel. Area)

MONOMER(% Rel. Area)

1 mg/mL 0.34 99.66

Holistic approaches to improving the throughput of analytical methods to support therapeutic monoclonal antibody process developmentDell Farnan, Tony Moreno, Jennifer Rea, and John T. StultsProtein Analytical Chemistry, Genentech Inc., South San Francisco, CA 94080, USA


Automation of experiments in cell culture and recovery process development has created the opportunity to generate a wealth of knowledge. However, the analytics are often a significant bottleneck in realizing this process knowledge. We have developed and demonstrated multi-product size- and charge-heterogeneity assessment tools that have much lower sample requirements, are more robust with respect to sample matrix, and significantly increase the throughput of the analytical methods. Each of these methods is capable of analyzing a couple of hundred MAb analyses per day, per HPLC.

Interlacing size exclusion chromatography (SEC) almost doubles the throughput of any given SEC method without loss of resolution (1). The singular circumstance that the separation in SEC occurs in less than a single column volume allows multiple samples to pass through the column at the same time. With gated detection time frames, data for each sample can be obtained separately. With minor configuration changes to the equipment, existing methods can increase the throughput by 75% to 100%. Looking forward, UPLC columns are becoming available that can offer separations an order of magnitude faster than conventional particle size columns (e.g., 3 min rather than 30 min). UPLC can offer a 5-fold improvement over even interlaced workflows (although, column choices are currently limited).

Capillary-scale systems allow the possibility to generate an analytical assessment from as little as 1 µg of product in several mL of cell culture fluid. Amounts required for sample loading on to the capillary column are in the range of 50 to 100 ng. Sample preparation is the more challenging part of the procedure to miniaturize. Figure 1 contains a concise summary of the volumes and amounts as a function of column diameter. To get to the 1 µL target amount, 300 micron

inner diameter columns are required. Data using a modified, commercially available instrument and custom-packed column data (see Fig 2) are comparable to those obtained on a conventional scale column.

Fig 1. Similitude of experimental parameters of size exclusion chromatography as a function of the column inner diameter.

Fig 2. Size exclusion chromatography elution profile for a monoclonal antibody. Twenty nano-grams of monoclonal was loaded onto a 300 micron inner diameter column. Pico-gram level quantification limits are observed.


Time (min)

Time (min)

0 10 20 30

0

10

20

30

40

main peak to 2.0 OD

main peak to 1.5 OD

peak + tail

tail

1M salt regen

mAU

mAU

Solvent A: 20 mM sodium acetate, pH 5.71Solvent B: 0.1 M sodium sulfate in A.Sample load: 20 µl (20 µg) Column: ProPac WCX-10Column temp.: 30°CLinear gradient: 40%-62%BFlow rate: 2 ml/min-1

Detection: 280 nm

10 11 12 13 14 15 16

0

20

40

60

pool(200 mM Acetate)

peak tail(350 mM Acetate)

salt regen ProA clean up

(< 100 mM acetate)

ProA pool(100 mM Acetic Acid)

(200 mM acetate)

pH Gradient

Ionic strength gradient

Relative precision of charge methods

Six sigmarange for

main peak relative

area

pH-IECicIEFIEC

Minimum significant detectable change is directly proportional to the six sigma system suitability range

MAb #

21 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

3

4

5

6

7

8

9

10

Fig 3. Robustness of MAb elution profiles by pH gradient IEC for a high salt, low pH sample matrix. Salt-based ion-exchange mechanisms are not so robust, as shown in the upper panel.

Ion exchange chromatography for a wide variety of MAbs is possible with a common pH gradient elution program (2). Conventional ionic strength gradients are product specific and can require significant development. A useful capability of the pH gradient separation is the robustness of the separation with respect to sample matrix. In Figure 3 a comparison for a series of cation exchange pool samples is used to demonstrate the applicability of the pH gradient for testing in-process samples without the need for additional sample preparation prior to the chromatography. It is also shown (Fig 4) that as well as being capable of faster cycle time (< 15 min) there is also increased precision relative to other charge heterogeneity methods (3).

Subsequent analysis of the large volume of data generated by these measurements has been streamlined for efficient interpretation. By taking a holistic approach from sample-in to data-out, we have adopted methods that minimize the total time and material required to generate results (4).

References1. Farnan, D., et al. Interlaced size exclusion liquid

chromatography of monoclonal antibodies. J. Chromatogr. A 1216, 8904–8909 (2009).

2. Farnan, D., and Moreno, T.G. Multi-product high resolution monoclonal antibody charge variant separations by pH gradient ion-exchange chromatography. Analytical Chemistry 81, 8846–8857 (2009).

3. Rea, J.C. et al. Validation of a pH gradient-based ion-exchange chromatography method for high-resolution monoclonal antibody charge variant separations, J. Phar. Biomed. Anal. 54, 317–323 (2010).

4. Rea, J.C., et al. High-throughput multi-product liquid chromatography for characterization of monoclonal antibodies. BioPharm International 23, 44–51 (2010).

Fig 4. Comparison of method precision for a series of MAbs as observed for the major charge heterogeneity methods. In three cases the MAbs have been evaluated on more than one method. The improvement in the precision by using pH gradients is clear.


Strategic assay deployment in high-throughput process developmentSpyridon Konstantinidis1, Eva Heldin2, Sunil Chhatre1, and Nigel Titchener-Hooker1

1 The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London, United Kingdom, WC1E 7JE

2GE Healthcare Bio-Sciences AB, Björkgatan 30, Uppsala 751 84, Sweden


IntroductionA High-Throughput Process Development (HTPD) approach to process development is usually accompanied by the generation of large sample sets. Analytical methods are selected to assay for the analytes of interest in order to associate process inputs to process outputs and here the quality of the measurements provided by the analytics is a prerequisite for the correct characterization of an experimental space. This quality is often proportional to the sophistication and duration of the assay. As a result slow, but high quality assays may be preferred over fast, but lower quality assays to measure a given analyte. When large sample sets are to be tested for multiple analytes, this will pose a potential bottleneck on the workflow of HTPD.

We propose a method for the strategic deployment of assays that leads to an efficient use of analytics in high-throughput studies. The method has at its core a Ranking Procedure, which ranks assays depending on their predicted performance on predefined criteria, and a subsequent Assay Evaluation Procedure, which aims to identify which assays can be used in the particular study and how. We term the iterative combination of the two procedures as Strategic Assay Deployment (SAD). To illustrate the SAD methodology we consider investigating the binding conditions of Green Fluorescent Protein (GFP) to an anion exchange resin CaptoTM DEAE.

Creation of a Strategic Assay Deployment frameworkIn Strategic Assay Deployment a preparative step defines the inputs of the methodology -namely the analytes that are to be assayed and the analytics considered for deployment. The critical attributes of the latter, such as sample preparation time, throughput, analysis time etc., are tabulated and substituted in mathematical functions that reflect how their deployment relates to practical aspects of their use. Three criteria are considered: ExperimentationTime, SampleConsumption and Specificity, and are defined by Equations 1, 2 and 3 respectively.

The Assay Evaluation Procedure compares high quality, standard methods “Reference Assays”, against alternative methods “Test Assays”. The evaluation procedure has three possible outcomes for each Test Assay; (a) Best Case Scenario (BCS): The Test Assay replaces the Reference Assay for that analyte and all the remaining conditions of the space are analyzed with the surrogate Test Assay; (b) Intermediate Case Scenario (ICS): The Test Assay can only be used to obtain a rough description of the response surface of its assayed analyte, but promising areas of the surface will be assayed further by the Reference Assay to obtain more accurate measurements in an area of interest; and, (c) Worst Case Scenario (WCS): The Test Assay cannot be used. In this case the Assay Evaluation Procedure leads to an irretrievable loss of experimental resources without any benefit.

Equations 1 and 2 are used in conjunction with the BCS and WCS outcomes of the Assay Evaluation procedure to derive the estimated performance of the Test Assays in the respective criteria within the SAD context. The two equations are used directly to obtain the performance of the Reference Assays in the ExperimentationTime and SampleConsumption criteria, respectively. Equation 3 is used to describe the performance of both Test and Reference Assays in the Specificity criterion.

The Ranking Procedure ranks the available analytical methods for each analyte by formulating and solving a Multicriteria Decision Making (MCDM) problem using an Outranking Flow Method. Within this method

[1]

[2]

[3]

Samples × Replicates × DilutionsFExperimentationTime =

=

× PreparationTimeThroughput

Samples × Replicates × Dilutions× TransferSlope

Tips

+ Samples × Replicates × Dilutions × AnalysisTime

FSampleConsumption = Samples × Replicates × Dilutions × Volume

1,

0.5,

0, Specificity

Target Analyte Specific

F Total Analyte Specific

Impurity Specific

=


ExperimentationTime and SampleConsumption were minimized since fast assays are preferred over slow assays and assays consuming low sample volumes are preferred over assays consuming high sample volumes. Specificity can be minimized or maximized if screening for a single specific material or if the overall level of non-product species (i.e., impurities) is the target of the study. The three criteria are assigned weights and the Outranking Flow Method returns a Global Hierarchy of the analytical methods with a decreasing order of preference that includes both the Reference Assays and the suggested alternative Test Assays.

The two procedures outlined above are combined in an iterative framework (Fig 1). At each iteration, the Outranking Flow Method will generate a Global Hierarchy, suggesting the assay to be deployed, and the Assay Evaluation Procedure will then determine whether it will be adopted or excluded from further consideration. Upon occurrence of WCS for a Test Assay, the performance of the Reference Assay for that analyte in the criteria ExperimentationTime and SampleConsumption is updated by taking into account the analyte losses resulting from the use of the test method in the Assay Evaluation Procedure. This ensures that the action of deploying additional Test Assays is more preferable than simply opting for the Reference Assay itself. This procedure terminates once an analytical method has been identified for adoption to test each assayed analyte.

ResultsA single analytical method was selected as the Reference Assay for three analytes chosen in this study: SEC for GFP, Pierce® BCA for Total Protein, and ELISA for HCP determination. For the purposes of illustration the weights on the three criteria were set at 0.4, 0.4 and 0.2 for ExperimentationTime, SampleConsumption, and Specificity, respectively. This decision was made since the first two criteria are directly related to practical aspects of experimentation of equal importance and are deemed more important than Specificity. Fourteen experimental conditions were selected from the total experimental space by application of the Assay Evaluation Procedure to compare the Reference and Test Assays. The SAD experimentation procedure terminated after four iterations. The Global Hierarchies along with the deployed analytics and the results of the Assay Evaluation Procedure are summarized in Table 1.

At each iteration the Assay Evaluation Procedure determined the correct use of the selected analytic. For example, total protein measurements provided by the OD280 assay were found to be weakly related to the measurements of the Pierce BCA Reference

Assay (Figs 2A and 2B) and as such they were not used. In the case of the GFP product measurements the agreement between the Reference Assay (SEC) and the OD490 Test Assay was found to be strong (Figs 2C and 2D). HCP measurements based upon the use of the BioaffyTM 20HC Test Assay were shown to only account qualitatively for the relationships between process inputs and HCP concentration levels (Figs 2E and 2F). Consequently, an additional seven conditions were evaluated with the ELISA Reference Assay. These were selected once the analyses of the additional two analytes were complete in order to obtain accurate HCP measurements in the area of the experimental space with the richest information. This combinatorial way of measuring HCP content was still more preferable than the use of the ELISA Reference Assay and thus accepted.

START

FCriterion

CriterionRef. assay

CriterionTest assayRef. assayTest assay

WeightsWCriterion

Analyticalmethods

Assayedanalytes

Selectedassay

Outranking flow method

Assay evaluation procedure

ICS

Accepteduse?

Allanalytes

analysed?

STOP

No

No

Yes

Yes

WCS BCS

Update performance ofreference assay and

exclude selected assay

Deploy selected assayand exclude analyte

related assays

Fig 1. Flowchart of the iterative SAD framework. WCS: Worst Case Scenario, ICS: Intermediate Case Scenario, BCS: Best Case Scenario.


SEC CGFP (mg/mL)

[NaC

l] (m

M)

pH

54

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

36

18

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54

36

18

0

54

36

18

0

54

36

18

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54

36

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36

18

0

pH

pH pH

pH pH

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

OD490 CGFP (mg/mL)

ELISA CHCP (mg/mL) Bioaffy™ CHCP (mg/mL)

Pierce BCA CTotal protein (mg/mL) OD280 CTotal protein (mg/mL)

SEC CGFP (mg/mL)

[NaC

l] (m

M)

pH

54

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

36

18

0

54

36

18

0

54

36

18

0

54

36

18

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54

36

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54

36

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pH

pH pH

pH pH

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

OD490 CGFP (mg/mL)



SEC CGFP (mg/mL)

[NaC

l] (m

M)

pH

54

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0

36

18

0

54

36

18

0

54

36

18

0

54

36

18

0

54

36

18

0

54

36

18

0

pH

pH pH

pH pH

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

[NaC

l] (m

M)

OD490 CGFP (mg/mL)



Table 1. Generated Global Hierarchies per iteration and results of the Assay Evaluation Procedure for the deployed analytical method at each iteration

Series of Global Hierarchies Iteration 1 Iteration 2 Iteration 3 Iteration 4

Net

rank

ed o

rder

(d

ecre

asin

g or

der o

f pre

fere

nce)

←OD280 Pierce BCA OD490 Bioaffy 20HC

Pierce BCA Bradford HT Protein Express ELISA

Bradford OD490 SEC

OD490 HT Protein Express Bioaffy 20HC

HT Protein Express SEC RPC

SEC RPC ELISA

RPC Bioaffy 20HC

Bioaffy 20HC ELISA

ELISA

Deployed assay per iteration OD280 Pierce BCA OD490 Bioaffy 20HC

Assay evaluation procedure result per iteration

WCSrα = 0.53

BCS(Reference Assay)

BCSrα = 0.99

ICSrα = 0.93

α = correlation coefficient between the measurements of Reference and Test Assay pairs from the 14 conditions selected via the Kennard Stone Algorithm.

Once terminated the SAD method resulted in a total assay time of 35.56 h and total volume of analyte consumption of 6.52 mL. These compared favorably to the conventional approach of deploying only the standard Reference Assays that would have required a 45.27% longer total assay time and an increase of 16.79% in the total consumed analyte.

ConclusionsThe use of the SAD method generated comparable conclusions regarding the characterization of the experimental space while returning savings in total analysis time and total analyte consumption versus the conventional route of using only standard analytics (Reference Assays). The SAD method appears to be a promising tool for countering the analytical bottleneck, often present in HTPD, by making efficient use of available analytics.

Fig 2. (A) Response surface for Total Protein measurements obtained by the Pierce BCA Reference Assay; (B) Response surface of Total Protein measurements obtained by the OD280 Test Assay; (C) Response surface of GFP measurements obtained by the SEC Reference Assay; (D) Response surface of GFP measurements obtained by the OD490 Test Assay; (E) Response surface for HCP measurements by the ELISA Reference Assay; (F) Response surface for HCP measurements by the Bioaffy 20HC Test Assay. Red circles denote the 14 conditions used in the Assay Evaluation Procedure. Cyan circles denote the seven conditions revisited with the ELISA Reference Assay after the deployment of the Bioaffy 20HC Test Assay. The enclosed area in (F) represents the information rich area of the experimental space.

A B

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E

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Calibration of mathematical models of preparative chromatography: removal of antibody aggregatesNiklas Borg1, Line Naomi Lund2,3, Marcus Degerman2, Arne Staby1,2, and Bernt Nilsson1*1 Department of Chemical Engineering, Lund University, Box 124, 221 00 Lund, Sweden. 2 Novo Nordisk A/S, Hagedornsvej 1, DK-2880 Gentofte, Denmark.3 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense,

Denmark. * Corresponding author


The wells were seen as ideally mixed tanks with no flux either in or out of the system:

model to describe the effect of pH on the equilibrium constant of the adsorption:

The mass action law requires that the counter ions on the ligands that are bound to are displaced into the mobile phase:

IntroductionModelling is getting more common in the design of preparative chromatography in pharmaceutical production, but is still far from an everyday tool. The biggest obstacle to pass to start modelling is to quickly generate and calibrate the models. High throughput screening can be used to calibrate adsorption models. Batch uptake experiments on a HTS system will mimic the adsorption uptake of resin, but lacks the ability to predict the column effect on the separation.

A human isotype 1 immunoglobulin (hIgG1) aggregate removal on a strong cation exchange chromatographic material, Poros HS 50, is studied. This work will focus at how isotherms from batch uptake experiments can be used for calibration of a model describing a chromatographic system. The batch experiments will be used to determine the isotherm parameters of the system, and the HTS results are compared to experiments on a packed column.

TheoryModelThe chromatographic model describes how the mobile phase moves through the column and interacts with the stationary phase:

OptimizationThe models were used to numerically find an optimum operation point with respect to productivity. The decision variables for the optimization were the load volume, the start concentration of the gradient, the gradient slope and the pH in the range 4.5 to 5.5.

ExperimentsThe buffer in the IgG1 samples used in the batch uptake experiments was changed to 20 mM Sodium Citrate at pH 4.5, 5.0 or 5.5 and 20 mM Sodium Citrate and 350 mM Sodium Chloride at pH 4.5, 5.0 or 5.5 prior to the experiments. The Tecan was set up with 20 mM Sodium Citrate with or without 350 mM Sodium Chloride at pH 4.5, 5.0 or 5.5 as well as the appropriate IgG1 samples in 10 mL tubes. Using a MediaScout ResiQuot plug device, 7.7 µL Poros HS 50 resin plugs was transferred to a 96 well 1.2 mL Deep Well Plate (DWP). Six concentrations of IgG1 in duplicates at eight different sodium chloride concentrations were mixed in an empty 1.2 mL DWP, analyzed at 280 nm, and 500 µL transferred to the DWP containing Poros HS 50. The resin was incubated with IgG1 for 120 minutes at

The Steric Mass-Action model (SMA), was used as isotherm model for the competitive adsorption of components. A parameter, δ, was introduced to the


Experiment pH = 4.5Experiment pH = 5.0Experiment pH = 5.5Simulation pH = 4.5Simulation pH = 5.0Simulation pH = 5.5

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room temperature on a shaker at 900 rpm, followed by centrifugation at 4000 rpm for 5 min. 200 µL of the supernatant was removed and analyzed at 280 nm.

Poros HS 50 was slurry packed in HR 16/5 glass column from GE Healthcare to give a 2 mL column. The flow in all chromatographic experiments was set to 2 mL/min. Two types of chromatographic runs were done as summarized in table 1 with either the same loading but different elution gradients at pH 4.5, 5.0 or 5.5. In the elution gradient runs 2 mg IgG, 1 column volume (CV), was loaded onto the column and eluted with a gradient from 20 to 720 mM over 10, 18, 25, 40 and 85 CV.

Results and discussionTo get , qmax, ν and δ for monomer in the model we calibrated them to the experimental data from the HTS system, see Figure 1.

Fig 1. Experimental behavior of the total mixture and simulated behavior of the monomer in the HTS system.

Fig 2. Validation of model. (A) Model compared with experiment at pH 4.5. (B) Model compared with experiment at pH 5.5. (C) Model compared with experiment at high load, pH 5.0.

In Figure 2B the front of the aggregates and the tail of the monomers merge more than in Figure 2A. This is supposedly due to the equilibrium. With a lower pH the activation energy for the aggregation is lower, which would give this behavior.

When the chromatographic system was overloaded the behavior of the monomer was still predicted. The shape of the aggregates changes very little even with overloading, something that was not expected or predicted, see Figure 2C.

The optimum found with the model is shown in Figure 3. Load volume (32 mL), wash volume (2 mL), gradient slope (46 mM/mL) and gradient start (778 mM) at pH 4.5. When evaluated with the model a productivity of 73 kg/h/m3

CV (95% Yield) was predicted.

Fig 3. Optimimum for the model. 73 kg/h/m3CV production and 95%

Yield was predicted.From the HTS data all parameters except how the aggregate spread out could be found. The spreading of the aggregates was predicted for sharp gradients, see Figure 2A and 2B.

ConclusionsThe HTS system worked well to calibrate the model parameters and the parameters found with the HTS system could predict the behavior of the monomer well.

Fast analysis methods for the impurities are required to develop a model that is able to predict the impurities with accuracy. It is easy to calibrate a model for the main product with HTS if its concentration can be measured with UV absorption, but to calibrate the behavior of the impurities fast and easy analysis method needs to be developed and linked to the HTS system. Ordinary SEC analyses take too long and had in this case a too low resolution.


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)Mab AMab BMab A HCCF/Resin Contact TimeMab B HCCF/Resin Contact Time

Achieving comparability between batch binding and packed column methodsDebbie O’Connor, Tony Cano, Joseph Cheng, Brian Kelley, Mike Lee, Ivy Lin, Karthik Mani, Paul McDonald, Vikram Sisodiya, Frances Xia, and Charles WinterPurification Department, Genentech, Inc., South San Francisco, CA, USA


The benefit of high-throughput screening techniques, such as batch binding models of chromatographic purification steps, has been shown in context of early stages of process development. The high-throughput batch binding method has the efficiency to run and obtain data on more than ninety-six different conditions in one week, compared with the traditional packed column method, which takes two weeks to evaluate nine different conditions. The benefit of being able to screen wider ranges of more parameters, acquire more data in a shorter amount of time, and gain a better understanding of the operating range is no doubt a useful tool for late stage development to implement. A few potential applications include early identification of main effects and critical process parameters as well as to serve as the basis for risk assessments.

To become a useful tool for these proposed applications in the later stages of process development and early stages of process characterization, the differences between high-throughput batch binding and packed bed chromatography methods must be identified and understood. The load procedure is an example of a difference that exists between methods. Due to volume constraints in a 96-well plate, the batch binding method performs load in multiple phases with an incubation time for each phase until the targeted load density is achieved, while the packed column method uses constant flow to target a residence time. For most impurities and product quality attributes this difference is insignificant, but when using affinity resin the longer contact time of the harvested cell culture fluid (HCCF) with the resin can result in higher levels of leached protein A in the elution pool (Fig 1). When comparing two MAbs using the batch binding load and elution method the leached Protein A levels are more

than twice as high than when using the packed column method. Further evaluation of the packed column and offline batch binding load methods coupled with a batch binding elution revealed that when the HCCF to resin contact times are similar, comparable leached Protein A levels are achieved. Future batch binding studies conducted to evaluate leached Protein A levels will be performed by adding resin to the well plate that has undergone an offline equilibration and load to minimize the HCCF to resin contact time.

Studies performed on cation exchange and affinity chromatography resins demonstrated the ability of batch binding studies conducted in 96-well plates to qualitatively and quantitatively identify the main effects and interactions of process parameters on separation of product from aggregates, charged variants, host cell proteins, leached Pro A, and yield comparable to packed column studies. In a side by side comparison of an elution pH and conductivity screening on a cation exchange chromatography resin, both methods detected elution pH as having a stronger main effect on the level of basic variants compared to elution salt molarity (Fig 2). When increasing the

Fig 1. Comparison of leached Protein A levels of two MAbs run under varying load and elution conditions. The circle and “X” are contact time of HCCF with resin when load is performed using the different methods. Longer contact time results in higher leached protein A in elution pools.


Batch Binding Packed Column

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R2 = 0.712

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R2 = 0.089 0

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Linear Fit Method = "HTBB"Linear Fit Method = "PC"

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Batch Binding Packed Column

90

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25 30 35 40 45Load Density (g/L)

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Linear Fit Equil Buffer Linear Fit Equil Buffer

Bivariate Fit of Yield (%) By Load Density (g/L)

elution pH from 5.1 to 6.0, both methods resulted in a 9% increase in the level of basic variants. Although comparable increasing basic variant trends were achieved, due to the volume limitations of the batch binding elution to finely separate the backside of the peak, there is an offset in the absolute values with the batch binding levels being higher. Despite the offset, both methods concluded that elution pH, not elution molarity, is the critical parameter for controlling the level of basic variants.

Fig 4. Interaction plots showing impact of affinity resin equilibration buffer pH and load density on percentage yield. Line color is the same in both plots with the equilibration buffer pH at 7 (blue), 7.7 (black), and 8.7 (red). Results with both methods demonstrate the same trend.

Fig 2. Comparison of batch binding and packed column performance in a cation exchange chromatography elution pH and salt molarity screening study. The data show similar results, with increasing elution pH having the strongest main effect on the level of basic variants.

Although challenges have been identified with the small volume processing of the batch binding method, qualitative results to the packed column method have been achieved. Further method optimization will allow for improved quantitative results. Whether qualitative or quantitative, the large data sets generated by the batch binding method can be used in late stage development to rank key process parameters and detect main effects and interactions. This information will lead to a reduced number of pre-characterization packed column studies and the ability to focus earlier on key process parameters.

Fig 3. Main effect plot illustrating the level of aggregate obtained in cation exchange elution pools with increasing elution buffer pH. Red line is batch binding, blue line is packed column. Results demonstrate similar qualitative trends with increasing elution buffer pH causing higher aggregate levels.

After further optimizing the batch binding elution to include more phases with reduced volume to better fractionate and represent the packed column method, comparable aggregate levels were obtained (Fig 3). In looking at the impact of elution pH on percent aggregate in the pool, both the batch binding and packed column methods detected the main effect of increasing aggregate with increasing elution pH. When increasing the elution pH from 7.9 to 8.1, the highest level of aggregate is seen to increase by about 0.2% in both methods. Besides aggregate levels, this optimized elution will likely also decrease the offset seen in previous studies with charged variant levels. Although the offsets can be minimized, the error in small volume handling will always exist with the batch binding method and higher variability will be observed. Despite this variability, qualitative batch binding results have been able to identify comparable main effects and critical process parameters as the packed column method. This Indicates that the trend and not the absolute quantitative value is sufficient for our intended initial implementation.

Besides main effects, comparable interactions have also been detected by the batch binding and packed column methods (Fig 4). In an affinity DOE study, both methods picked up the same main effect of decreasing yield with increasing load density. Both methods also picked up the interaction between equilibration buffer pH, load density, and yield. Results showed that as the load density was increased from 25 to 45 g/L the higher equilibration buffer pH led to a steeper decrease in yield. Comparing the plots it is easy to see that the batch binding method has the benefit of providing more data in a few days than the packed column method can provide over several weeks.


Combination of two modeling approaches for a rapid and highly predictive optimization of an ion exchange chromatography step based on HTE dataAnna Osberghaus, Stefan Hepbildikler, Hans Rogl, Susanne Nath, Eric von Lieres, and Jürgen HubbuchKarlsruhe Institute of Technology, Institute of Process Engineering in Life Sciences, Biomolecular Separation Engineering, 76131 Karlsruhe, Germany


The optimization of ion exchange chromatography steps is one of the main challenges in biopharmaceutical downstream processing. The search for a favorable and robust operating point of a separation process represents a complex multiparameter optimization problem. This problem can be approached with intelligent High Throughput Experimentation (HTE) on robotic platforms. For example, the application of genetic algorithms supports the search for optimal process conditions. Moreover, DoE-based screenings deliver information close to the optimum, analyze robustness, and characterize the importance of process-influencing factors. Both methods, genetic algorithms and DoE screenings, have been widely applied in HTE.

However, HTE-based empirical modeling, like surface response modeling, can be error-prone with regard to several characteristics of the design space. Optima, situated at the border or in the corner of a design space, possibly corrupt the results from response surface modeling. The empirical method has particular problems when several optima exist, or when facing low robustness or high process complexity. Furthermore, taking the complete process development into account, DoE-based results give no information as to their transferability to different process scales. Another challenge is the considerable amount of data produced by HTE. Such data could be analyzed more intensely and used for a better understanding of the mechanistic processes influencing the results. These analyses would probably support and improve the process scaling.

The application of mechanistic modeling for the optimization of chromatographic steps is currently on the rise, due to time efficiency of algorithms and rising calculation power. The mechanistic method

has several advantages compared to empiric response surface modeling. As the equations in the model setup simulate the mechanistic processes leading to chromatographic results, they support a deeper process understanding and are independent of the design space. Furthermore, even complex elution behavior could be predicted by mechanistic modeling. Another advantage (in comparison to empirical models) is that not only retention times but peak shapes and complete chromatograms can be predicted. In other words, mechanistic modeling offers the opportunity to optimize complex processes - it allows a sensible use of the supplied data and could be employed as a supporting application in process scaling and control.

The efficiency of HTE combined with mechanistic modeling was demonstrated along the optimization process of a gradient separation of ribonuclease A, cytochrome C, and lysozyme on robotic scale and on 1 mL scale. A lumped rate model for chromatography was calibrated and validated with the experimental results and with preliminary information on parameter influences from the DoE-based analyses. Consequently, the mechanistic model was used to numerically optimize the profile of the elution gradient and to predict accurate separation results. These results were compared to predictions obtained by the response-surface modeling method. The response surface model’s predictions were inaccurate or in some cases failed for several reasons (mentioned previously in the abstract). The mechanistic model succeeded in predicting optimal elution gradients for separation, both for the RoboColumn and for the 1 mL scale. Thus, the mechanistic model was employed efficiently with HTE data for consolidation of mechanistic understanding and support of the scale-up.


Missing info on a key impurity? Test at constant Kp defining the minimum product yield

12

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1, 2, 3 should all have equal yieldsEfficient test of mobile phase impact on impurity clearance

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The next decade of HTPD: evolution or revolution?Brian Kelley and Phil LesterGenentech Inc., 1 DNA Way, South San Francisco, CA 94080 USA


IntroductionHigh-throughput process development (HTPD) is a relatively recent innovation. Yet despite this short history, it is already in regular use in many labs and is clearly influencing industrial process development and academic studies. Many different bioprocess applications have been evaluated, including most modes of chromatography, protein refolding, analytics, and formulation. Given this rapid establishment of the technology, what might the next decade of HTPD hold for the development of chromatography steps?

Often, chromatography steps for protein purification are developed and optimized based on iterative cycles of experimentation, with decisions based on qualitative information and heuristics. For instance, load, wash, and elution conditions are locked based on a modest series of experiments. HTPD enables broad parameter ranges to be evaluated rapidly for multiple parameters that influence product binding and selectivity. The quantitative information that HTPD generates allows chromatography steps to be run under more consistent conditions, which should enable improvements in step performance, including selectivity, product yield, and robustness.

Current state of the artMany different modes of chromatography have been evaluated with HTPD systems. Multi-dimensional designs that evaluate several mobile phase parameters and their effects on product binding and selectivity are commonplace. Complicated chromatographic modes like hydrophobic interaction, mixed mode, and hydroxyapatite, which are influenced by multiple solution parameters, are becoming more tractable through the use of HTPD tools. Correlations of column performance to the Henry’s law constant of the protein-resin interaction have proven to be a simple and practical means of establishing process operating windows.

Given the limitations in analytics (which may be eliminated in many instances by improved

throughput of analytical methods) and data interpretation for this relatively new technology, rules of thumb are often needed to translate HTPD results into practical guidelines for process development. If the level of a mobile phase parameter that takes the process to the limit of minimum product yield is known, then column runs can test the resulting impurity clearance at these limiting conditions quickly (Fig 1). One could clearly answer questions like, ‘Is the selectivity impacted by pH, or counterion concentration?’ with a few column runs.

Several companies have now established databases of MAb–resin interactions (Fig 2), complete with impurity removal profiles. Novel MAbs can then be compared to other MAbs in the database at a glance. Both graphical and quantitative comparisons are under development, emphasizing the importance of data visualization. Databases testing multiple resins of the same chromatographic mode are also being created, which will be useful for development of non-MAb purification processes.

Fig 1. Using HTPD to design efficient sets of chromatographic column tests.


MAb C

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[Acetate] (mM) [Acetate] (mM) [Acetate] (mM) [Acetate] (mM) [Acetate] (mM) [Acetate] (mM) [Acetate] (mM) [Acetate] (mM)



Future applicationsHTPD may have the unique ability to bring modeling into the realm of everyday use in industrial protein purification labs. The use of quantitative models (either empirical or mechanistic) could be accelerated by the virtue of HTPD offering rapid measurements of product and impurity interactions with resins in a relevant multidimensional operating space. This enables new questions to be asked: What is the minimum selectivity factor needed for an effective separation? What range of Henry’s law constants are allowed for an effective load, wash, or elution phase?

Could HTPD also become a key to understanding how protein sequence and structure govern interactions with resins? When libraries of proteins (including sequence variants) can rapidly (using quantitative models) be profiled for their interaction with chromatographic resins, the proteins’ molecular descriptors could then be regressed against the HTPD datasets (including pH effects, which are not often evaluated in these studies).

Design of experiments (DOE) and HTPD form a natural partnership. Programmed designs enable complicated studies that would be nearly impossible to execute by hand. Consider a set of three 32-run studies (each is a 5-factor central composite (half-fraction) with six centerpoints) on one plate (Fig 3). Each study could expand to different parameter ranges if stocks solutions are used. Some parameters could be tested at wider ranges, nested designs are

possible, and categorical studies testing resin lot/feedstock/excipients could all be run. Risky or unknown combinations can be tested, as not all wells have to produce acceptable results.

Resin manufacturers are beginning to use HTPD routinely during their development of new resins, especially for complicated mixed-mode products. Vendors could provide preconfigured plates and HTPD designs as part of their product launch. Using protein libraries and DOE, the effect of changes in ligand density and mobile phase modifiers can be approached systematically.

Naturally there are limitations to HTPD, and some applications may be out of reach. Can HTPD systems serve as qualified scale-down models of a chromatography step? This presents significant challenges, as there are many dynamic phenomena active in process columns that do not scale well from batch-binding, single-stage HTPD systems.

In the next decade of purification development, a full toolkit will include HTPD. HTPD connects with platform processing, protein conjugation (to toxins, or PEG), chromatographic modeling, facility fit, and dual sourcing. When new protein scaffolds enter a pipeline, HTPD will accelerate process development; given a rapid clinical production schedule and multiple products, establishing a prototype processing platform would prove very useful. HTPD combined with facility fit assessments and chromatographic modeling will make establishing a new platform easier and will require fewer resources.

Fig 2. Database of MAb-anion exchange resin interactions.


3 blocks of 32, each a central composite (5Vs at 5 levels)– This is a half-fraction plus axial points

– Same design accommodates 4 variables –full factorial

Each block is its own experiment– Same or additional parameters, different ranges

– Adjust stock solutions to vary parameter ranges

– Overlap to expand one variable

Analysis – Each block separately –Use RS techniques

– Merge blocks (requires common scaling levels)

– With 4V studies, analyze 1/2 fraction (save 24 wells)

Broader implicationsOpportunities for HTPD use exist throughout the product development lifecycle. Molecular assessment reviews fit to a process platform; significant issues may warrant shifting to a backup molecule, or adjusting staff resourcing in anticipation of development challenge. Process optimization could define parameter targets based on comparisons to an HTPD dataset for a related commercial product. In-process stability studies would provide information on deamidation rates, low pH aggregation, and turbidity generated by during pH transitions. Process characterization studies used to establish a Design Space may be designed from supportive HTPD studies. License applications could include data on alternate or back-up resins, based in part on HTPD studies. HTPD can be a useful teaching tool for academic labs, emphasizing concepts central to chromatographic separations, experimental design, data manipulation, statistics, and scale-up.

ConclusionsHTPD is now being extensively used for purification process development, and has been applied to many different chromatographic modes. Large datasets may be established rapidly for use in screening, optimization, and process characterization. Empirical and mechanistic models derived from HTPD experiments that describe protein-resin interactions will likely be used to guide development more in the future. The future of HTPD will continue to evolve, and for those who adopt this technology, HTPD has the potential to revolutionize purification process development.

Fig 3. HTPD may enable complicated design of experiments (DOE) studies.


Development of a capture chromatographic step for purification of r-pro-insulin expressed in E. coliE. Heldin1, J. Shanagar1, S. Grönlund1, E. Hallgren1, K. Eriksson1, L. Vilela2, H. Tunes2, M. Xavier2 1 GE Healthcare Biosciences AB, Björkgatan 30, 751 84 Uppsala, Sweden2 BIOMM S.A., Belo Horizonte, Brazil


We describe a case study of the development of the first purification step for a recombinant pro-insulin expressed in E. coli in which both the chromatographic resin and the operating conditions have been screened and optimized. The workflow for the development of a purification step starts with screening of both binding and elution conditions in the parallel 96-well format. When promising conditions have been indentified an optimization study is performed in column format to also include the dynamic aspect. Dynamic binding capacities are determined and are followed by a more thorough study of elution conditions with respect to purity and yield of the target protein. The robustness of the optimized conditions is investigated and when proven robust, the purification is scaled up to larger format, in this case a 400 mL AxiChrom™ column (50/300).

The pro-insulin sample used in this study was a protein suspension solubilized in 8 M urea. The clarified suspension contained approximately 18 mg/mL of total protein and 10 mg/mL pro-insulin at a conductivity of

12 mS/m. In order to minimize sample pretreatment before the first chromatographic step, it was of interest to monitor if only adjusting the pH would be enough to achieve binding and if that not, also assess the influence of ionic strength. All experiments had to be performed in the presence of 8 M urea. A parallel screening in the 96-well PreDictor™ filter plate format was chosen to enable both pH and salt concentration screening and Assist software was used to outline the experiments in the plates. As only a few 96-well plates were to be used, the experiments were performed manually.

The results of the screening, which was performed in overload mode with low resin volumes (2 or 6 µL) in the wells, was that by using Capto™ MMC only the pH of the clarified suspension had to be adjusted (pH 5.2). The other resins investigated were Capto S and SP Sepharose™ FF, both of which showed high binding, especially at lower salt conditions (Fig 1). As the first set of experiments performed in the PreDictor screening plates revealed an optimal binding condition at the

Fig 1. Response surfaces, from Assist software, for pro-insulin binding as a function of NaCl concentration (x-axis) and buffer pH (y-axis) for the three resins identified in the legend. The range of binding is shown to the right of each surface. The black crosses represent actual results and the surface is obtained from interpolation (not modeling) of results.

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3.35

1.55


border of the experimental layout, the pH range for Capto MMC was expanded in a second experiment and pH 5.2 was identified as the optimal pH for binding (Fig 2). A set of anion exchangers, including the mixed-mode resin Capto adhere, were also screened but none of them bound pro-insulin at the conditions studied.

Fig 2. Binding of pro-insulin on Capto MMC as a function of NaCl concentration (0 to 300 mM) and buffer pH (4 to 7.5).

The binding conditions found were used in a thorough elution study on the Capto MMC resin. In order to facilitate analysis of minor impurities it is recommended to use a resin volume of 50 µL in the elution study. The pro-insulin was loaded to about 80% of the maximum binding capacity and elution was found to be favored by adjusting both pH and salt concentrations. Consequently, these two factors were included in a Tricorn™ 5/50 column study performed using the Design of Experiment (DoE) functionality in ÄKTA™ avant 25. The study revealed that pH was the most important factor for elution and thus, only by increasing pH to 8.0, a yield of more than 80% of loaded pro-insulin could be achieved at 150 mM salt (i.e., no extra salt addition was needed in order to elute the pro-insulin; see Fig 3).

Fig 3. Full factorial elution study in ÄKTA avant 25 using Tricorn Capto MMC columns with yield as response and pH and NaCl concentration as factors. The factors were studied at three levels including center points. The coefficient plot shows that pH is the most important factor.

The robustness of the elution conditions was studied in Tricorn columns, varying pH from 7.8 to 8.2 and sample loading from 2.3 to 2.7 column volumes. As a Placket Burman DoE was used, two lots of Capto MMC and two pro-insulin batches could also be included in the study, giving in total 12 experimental points of which three were replicates. The system was found to be robust.

Finally, the purification scheme was scaled up from the 2 × 4.7 mL HiScreen™ columns (two 10 cm columns in series) to a 40 mL HiScale™ column (16/40), and then further to a 400 m AxiChrom™ column (50/300) all with a bed height of approximately 20 cm. All these column sizes gave a pro-insulin purity of about 84% and a yield of 85%.


0 50 100 150 200 250 300


4

4.5

5

5.5

6

6.5

7

7.5

pH -

Eq.

/Loa

ding

/Was

h

15.87

14.86

14.36

13.85

13.35

12.33

11.83

11.32

10.82

10.31

9.30

8.79

8.29

7.78

7.27

6.26

5.76

5.25

4.74

4.24

3.23

2.72

2.21

1.71

1.20

Result: r-Pro-Insulin yield as a function of pH and NaCl concentration

200 300 400 500 600 700mM NaCl

7.8

7.6

7.4

7.2

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6.8

6.6

6.4

6.2

pH

-50

0

50

100

Conclusion: pH is most important for elution of r-Pro-Insulin.Adjustment of pH is enough for elution.

Coefficient plot

pH salt pH × pH pH × salt

R2 = 0.989Q2 = 0,964


1101009080706050403020100

6000100% elution buffer

100% elution buffer

100% elution buffer

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0

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AU)

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)

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Pro-insulin chromatogram on Capto MMC, HiScreen 2 × (7/100), ÄKTA avant 25.

Pro-insulin chromatogram on Capto MMC, HiScale 16/200, ÄKTA avant 150.

Pro-insulin chromatogram on Capto MMC, AxiChrom 50/195, ÄKTA avant 150.

Fig 4. Chromatograms from the loading of pro-insulin crude sample on Capto MMC at pH 5.2 and 150 mM NaCl followed by step elution to pH 8 using the following columns: (A) 2 × 4.7 mL HiScreen; (B) 40 mL HiScale; (C) 400 mL AxiChrom.


Review and outlook on automated, small-scale parallelized biochromatographyJürgen FriedleAtoll GmbH, Ettishofer Straße 10, D-88250 Weingarten, Germany


The demand for pharmaceutical products, in particular MAbs, has grown rapidly in recent years. The necessity for pharmaceutical companies to be “first on the market”, combined with the need to cut development costs makes the application for HTS techniques and tools in downstream process development indispensable.

Fig 1. Liquid handling workstation Freedom EVO® modified for use with MediaScout RoboColumn.

Atoll’s 96 MediaScout® RoboColumn® array together with Tecan’s Freedom EVO® liquid handling workstation, enables fully-automated parallel column chromatography for the first time. The Robocolum design allows the user to select any chromatographic material that is packed with due consideration to individual material compression requirements. Bed containment between two filter frits ensures high efficiency and peak symmetry similar to that of preparative and process separation columns, and distinguishes the system from the current filter-based systems for simple on/off sample equilibration operation.

This presentation describes how automated, parallel chromatography can contribute to accelerate the development and optimization of resin screening, method development, process analytics, and protein drug screening.

IntroductionA miniaturized column system in standard microplate format, containing exchangeable arrays with up to 96 individual MiniColumns, was adapted for automated operation in a modified commercial liquid handling workstation.

For this purpose the eight channel liquid delivery system of the robotic workstation was reversibly connected to the columns, in order to allow uptake and loading of different volumes of samples and buffer solutions in the individual steps of the separation procedure.

Liquid flow in the columns (CV up to 0.6 mL) was driven by positive pressure liquid displacement, rather than by air pressure, thus mimicking the situation in columns individually connected to a one channel, stand-alone chromatography system. Fractions from step elution were collected into standard microplates, utilizing an automated microplate transport system from Tecan, called a Te-Chrom™ Shuttle, and subsequently submitted to analysis in a plate reader or transferred to additional methods like UV, MS, ELISA, HPLC, or SDS-PAGE.

The Tecan robotic system allowed automated, high-throughput, small-scale bio-chromatographic separations of protein samples by running up to eight individual columns simultaneously.

These applications were successfully implemented for parameter elucidation and optimization in process development of therapeutic protein production, in-process monitoring of fermentation broth for MAb-production, and sample preparation for mass spectrometry analysis in antibody screening (as demonstrated here).

The setup is suited to process a large number of samples, fully automated with a high reproducibility applied in depletion of abundant components from CSF and plasma.


Prot A

AIEC

CHT

Capto QQ Sepharose FFUNOSphere QFractogel TMAE Hicap (M)Fractogel TMAE (M)Toyopearl SuperQ-650MQ Ceramic HyperD FPOROS 50HQ

00 100

LOAD PLW STRIP

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2

C [m

g/m

L]C

[mg/

mL]

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10 mM PBS/10 mM NaCl/pH 7.510 mM PBS/40 mM NaCl/pH 7.520 mM PBS/80 mM NaCl/pH 7.530 mM PBS/120 mM NaCl/pH 7.510 mM PBS/10 mM NaCl/pH 6.510 mM PBS/40 mM NaCl/pH 6.520 mM PBS/80 mM NaCl/pH 6.530 mM PBS/120 mM NaCl/pH 6.5

Prot A

AIEC

CHT


00 100

LOAD PLW STRIP

200 300 400 500 600

2

C [m

g/m

L]C

[mg/

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V [µL]

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g/m

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[mg/

mL]

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LOAD PLW STRIP

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A

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C

Experimental setupA sealing inlet port for connection of small chromatography columns to fixed tips of a liquid delivery system was constructed, utilizing an appropriately sized Viton o-ring located at the top of the column. The inlet port of the RoboColumn column was checked for its ability to allow thousands of repeated insertions and removals of the fixed stainless steel tip, without becoming leaky.

Utilizing an injection molding technique, a commercial version made from polypropylene was developed with fixed bed height. For each experiment eight of these columns were packed individually with the desired chromatography material. The packed RoboColumns were mounted in eight rows on standard 96-well compatible base plates. The plates are accepted by a dedicated plate holder in a modified robotic workstation (Fig 1).

Chromatographic separations were carried out by applying an individually controlled flow of solutes (buffers, samples) to eight columns simultaneously in subsequent delivery steps by the eight channel liquid handler of an appropriately modified Tecan Freedom EVO workstation. Fractions were collected into microplates and automatically evaluated in a system-integrated microplate reader.

Downstream process developmentThe purification of MAbs is usually well optimized and based on a platform strategy with a protein A adsorbent as a capture step followed by an intermediate and a polishing step (Fig 2A). Using AIE chromatography as an intermediate purification step is common. Running it in a negative chromatography mode, binding the impurities, and letting the pure target antibody pass through the chromatography column has some major advantages. The benefits include smaller column volume, less consumption of process relevant solvents and resources, and almost no dilution of the highly concentrated, pure target antibody.

For this purpose, a screening experiment was performed by loading protein A capture eluate on an 8 row set-up of MediaScout® RoboRolumn® (5 mm ID × 2.5 mm height, CV = 50 µL) packed with 8 different AIEC chromatography media under non-binding conditions, followed by a post load wash (PLW) and a STRIP step (Fig 2B).

The AIEC medium that showed the lowest protein concentration in the STRIP fraction was taken as the most suitable candidate out of the screening experiment and optimized (regarding protein binding) by varying salt concentration and pH of LOAD and PLW buffer (Fig 2C).

Fig 2. (A) 3-step MAb purification platform; (B) Elution profile of protein A capture eluate on eight different AIEC resins. Toyopearl SuperQ-650 M shows the lowest protein binding using negative chromatography mode of operation. (C) Elution profile of protein A capture eluate on Toyopearl SuperQ-650 M. Protein binding was optimized by varying salt concentration and pH of LOAD and PLW buffer. Best results were achieved using the following buffer characteristics: 20 mM PBS, 80 mM NaCl, pH 6.5.

Finally, the intermediate AIEC step was scaled up by running a 10 mL MediaScout® MiniChrom column packed with Toyopearl® SuperQ-650 M under optimized conditions using an ÄKTA™ LC system. A protein recovery of 98% was achieved.


0

5 10 15 20 25 30 35 min

5 10 15 20 25 30 35 40 45 50 min

mAU

mAU

Fig 3. Process flow of 2-D, in-process bioreactor monitoring. Step 1: MAb isolation from clarified fermentation broth by Protein A capture resin; eluted at low pH into a microtiter plate, neutralized, then transferred to a HPLC autosampler for consecutive CIEX-HPLC analysis in Step 2.

A B

Process analyticsAll biologically produced active pharmaceutical ingredients need to be checked after each production run to ensure that the integrity of the product is identical to the product registered at FDA or EMEA. For example, in MAbs the pattern of the heavy chains, the light chains, and the glycosylation is checked against the registered standard. This is done by mass spectrometry directly from the filtrated culture supernatant after desalting. The rate-limiting step here is desalting, which could be shortened by a factor of ten when switching from gravitational flow to a liquid handling system using RoboColumns.

Another application field is finding the optimal harvesting point for a MAb fermentation. Samples are taken after incubation day 11. The MAbs are bound from filtrated supernatant to the same Protein A resin

as used in the main production. Host cell proteins are washed away and the MAbs are eluted and analyzed by CIEX-HPLC. The resulting pattern governs the decision as to when the fermenter should be harvested in order to give the best yield of target MAb (Fig 4).

SummaryThe combination of Tecan’s Freedom EVO robotic workstation and Atoll’s 96 MediaScout RoboColumn array has shortened development time for biopharmaceutical production significantly and will contribute to overcoming future bottlenecks. Some automated screening procedures showed 80% savings in project duration and up to 35% reduction in manpower.

The transfer from a common, one channel, stand-alone chromatography system into a fully automated, parallel chromatography system was sucessfully completed as well.

Small-scale automated HTS separations by bio-chromatography were successfully applied in screening of cell culture supernatants for recombinant MAbs, both in research and development, as well as in full-scale production.

Time-consuming manual desalting of protein samples (MAb), using common gravity induced columns, was effectively replaced by automated HTS-chromatography using robotically operated MediaScout RoboColumns. Process time per sample was reduced ten-fold.

Costs per experiment can be significantly reduced due to saving process time, API, process-relevant products, and solvents.

Fig 4. (A) Single chromatogram after CIEX-HPLC showing the ratio of MAb-monomers and MAb-aggregates and modifcations. When modifications increase, yield goes down. (B) Magnification of Figure 4A, with arrow indicating critical peak of modifications.


Vivawell Vac8-strip plate

Silicone gasket

Manifold top

Collection plate

Adaptor (Optional)

Manifold base

Quickreleasefitting

Control valve

High-throughput, downstream screening system for protein purification using membrane adsorbersMartin Leuthold and Rene FaberSartorius Stedim Biotech GmbH, Göttingen, Germany


Membrane adsorbers are a powerful alternative to conventional chromatography resins for downstream processing in the biopharmaceutical industry. Open pores and accessible ligands on cross-linked cellulose enable short cycle times by providing high flow rates with convective flow. Membrane adsorbers also fit into the industry standard of having ready-to-use and disposable applications.

The high load capacities (e.g., 10 kg monoclonal antibody per liter membrane) typically achieved with anion exchange membrane adsorbers in flowthrough mode implies that a significant amount of material is required for development of such a process step. Smaller devices can be helpful to decrease material consumption, and improve speed and save costs, especially for high-value experiments such as virus spiking studies.

High-throughput techniques have been successfully used with chromatography resins for process development (1, 2). In the current study, 8-strip plates with 96 wells, each having three layers and 0.017 mL membrane volume (MV), were used to evaluate the high-throughput process development approach for different polishing or capture applications of anion exchange membranes.

Experimental set-upThe membrane adsorber plates can be operated with vacuum or centrifuge, manually, or by automated robotic systems. The plates are built up from 12 individual 8-well units, “strips” assembled into a 96-well frame. Maximum operating volumes for the membrane wells are 0.5 mL per load step.

Fig 1. Adaptation to a robotic platform of Sartobind® 8-Strip IEX plate and the Vivawell Vac96 vacuum manifold.


Binding of BSA on Sartobind STIC [%]20

18

16

14

12

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2

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Sodium chloride [mM]

Sodi

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itrat

e [m

M]

Binding of BSA on Sartobind Q [%]20

18

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20

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60

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8

6

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00 50 100 150 200 250 300


Sodi

um c

itrat

e [m

M]

Collection wells can accommodate up to 2 mL of liquid. A silicone gasket seals the plate set-up of 12 8-strip units for vacuum processing. A vacuum device (Vivawell Vac96, Sartorius Stedim Biotech GmbH, Goettingen, Germany) has been designed specifically for use with Vivawell Vac 8-Strip IEX plates. Drip nozzles on the 8-strip outlet eliminate gaps between the sample flow and collection wells. Direct stacking prevents cross talk between individual wells. All pipetting and chromatography steps were processed by a robotic liquid handling system (Lissy 2002, Zinsser Analytic GmbH, Frankfurt a. M., Germany) with 8 dispensing tips. After each filling step the pressure is reduced to 350 mbar and the permeates are collected.

The robot can replace collection plates automatically using a gripper arm to collect different fractions. This enables the representation of all typical chromatographic steps.

Protein concentration of the initial solutions and each fraction collected were measured by a plate reader (Tecan Safire, Tecan Group AG, Switzerland) at 280 nm for bovine serum albumin (BSA). Salmon Sperm DNA (300–700 bp) was determined with the PicoGreen standard assay protocol according to the manufacturer (PicoGreen dsDNA Quant-iT P7581 Reagent, Life Technologies, Carlsbad, USA). The sampling was performed by the liquid handling system, sample volume was 300 µL, and 96-well micro plates (Greiner Bio-One International AG, Austria) were used.

Influencing factorsStudies explore factors like flow rate, determined by vacuum and error analysis. Data show that variation in the results is mainly caused by the liquid handling system and the precision of detection. Generally, the performance of membrane chromatography is

independent of flow rate. Using small-scale 96-well plates, much higher linear flow rates were reached than under standard applications. Often flow rates in the plates are much higher than 50 MV/min, which surpasses the pressure limit in large-scale devices and can result in higher breakthrough.

Example: Effect of salt on protein bindingDifferent substances can affect the protein binding. In ion exchange chromatography, buffer salt reduces the adsorbed amount of protein via competition. As a result, binding buffers with different salt concentrations (ionic strength) were used to show the effect of salt on the binding of BSA on different membranes. The buffer used was 20 mM Tris/HCl, pH 7.5. Two anion exchanger membranes were compared. The breakthrough of BSA for different concentrations and types of salt was determined. Figure 2 shows the results depending on the two parameters sodium chloride and sodium citrate.

The binding capacity of BSA, calculated by the concentration of the initial solution and flowthrough fractions, was monitored and the green areas indicate regions of low binding conditions.

Example: Performance of membrane adsorbers on DNA contaminant removalOne of the common applications of membrane chromatography is contaminant removal in flowthrough mode. Trace levels of contaminants are bound and the molecules of interest flow through the membrane. Screening with 8-well strips helps to identify optimal conditions for such a polishing unit operation. The aim here was to analyze DNA breakthrough with two anion exchange membranes, Sartobind Q and Sartobind STIC® PA.

Fig 2. Binding of BSA relative to the maximum value. After conditioning with 0.5 mL 1 M sodium chloride in binding buffer and equilibration with 1 mL buffer, 2.7 mg/cm² of BSA were added in 2 steps of 0.5 mL each. Black dots represent data points measured followed by interpolation.


Breakthrough of DNA [c/c0] on Sartobind Q

10

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40

50

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0

20

2000

1500

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500500

1000

0

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Load [µL/cm2]

c/c0

[%]

Breakthrough of DNA [c/c0] on Sartobind STIC

1.5

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20

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500500

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Load [µL/cm2]

c/c0

[%]

Fig 3. Breakthrough of DNA. After equilibration with 1 mL buffer, 50 up to 1800 µg/cm² DNA (in binding buffer) were added in four steps of different load volumes and concentration of DNA in flowthrough fractions were measured. The grid in both figures marks the limit of 10% breakthrough.

Figure 3 shows the influence of sodium chloride concentration and amount of DNA on binding to anion exchanger membranes. On one 96-well plate eight different buffer conditions were tested with increasing load volume up to 2 mL in 12 individual wells. The load concentration of DNA remained constant.

In this example, the results are used to determine optimal conditions for a polishing application. Sartobind STIC PA shows DNA binding at higher concentrations of salt , compared to Sartobind Q membrane. Because of high flow rates it was possible to test up to 96 conditions per plate in only 2 h.

ConclusionA high-throughput downstream screening system for membrane chromatography was presented, allowing for manual or automatic operation as a basic step in process development. Further studies for screening

of contaminant removal (e.g., host cell proteins), DNA, viruses, or endotoxins showed the high potential of this technique to support the development of membrane adsorber-based process steps. Tests showed the scalability of the relative binding properties using different buffer conditions compared to the larger membrane adsorber devices. Results help to identify conditions of high separation for further upscale studies. The large number of data values help to identify a design space for future applications.

References1. Nilsson-Valimaa, K., et al. High-Throughput Process

Development: Determination of Dynamic Binding Capacity Using Microtiter Filter Plates Filled with Chromatography Resin. Biotechnol. Prog. 24, 632-639 (2008).

2. Kelley, B.D., et al. High-Throughput Screening of Chromatographic Separations: IV Ion-Exchange. Biotechnol. Bioeng. 100, 950-963 (2008).


1 Resources from the US National Cancer Institute to develop innovative technologiesMark LimProgram for Innovative Molecular Analysis Technologies, National Cancer Institute, NIH, Bethesda, MD, U.S.A.

2 A new strategy for the capture step of a recombinant allergen named rBetv1 expressed in Escherichia coliVirginie BrochierPALL BioSepra, CERGY St Christophe, France

3 High-throughput screening for the development of protein purification processesXiaonan Li*, Kim Burgers, and Michel EppinkSynthon BV, Nijmegen, The Netherlands

4 Fast analytical techniques to complement high-throughput downstream drocess developmentPatrick Diederich* and Jürgen HubbuchInstitute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering; Karlsruhe Institute of Technology, Germany

5 Advances in chromatographic high-throughput screening techniquesJörg Kittelmann*, Katrin Treier, and Jürgen Hubbuch Institute of Engineering in Life Sciences, University of Karlsruhe, Germany

6 High-throughput screening and optimization of intermediate wash conditions for a Protein A chromatography step using a Design of Experiment (DoE) approachKristina Nilsson-Välimaa*, Gustav Rodrigo, Tuomo Frigård, and Hans J JohanssonGE Healthcare Bio-Sciences AB, Uppsala, Sweden

7 Automated high-throughput process development technology for design of Cleaning-In-Place protocols for chromatography resinsAnna Grönberg*, Hans J. Johansson, Kjell Eriksson, and Enrique CarredanoGE Healthcare Bio-Sciences AB, Uppsala, Sweden

8 The use of high-throughput techniques for investigating adsorbent manufacturing consistencyChloe Booth, Bastiaan Lobbezoo, and Sharon Williams*ProMetic BioSciences Ltd, Cambridge, U.K.

9 Statistical profiling of an automated screening method in the case of an ATPS screening toolStefan Oelmeier* and Jürgen HubbuchInstitute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Techonolgy - University of Karlsruhe, Karlsruhe, Germany

10 Development of a HTPD mixing system for ultra scale-down characterisation of protein precipitationJ.P. Aucamp*, I. Papantoniou, and M. Hoare Department of Biochemical Engineering, University College London, London, U.K.

11 The use of an automated multiwell filtration technique for cell removal and clarification studies of mammalian cell culture brothsSimyee Kong*, Andrew Tait, Jean Aucamp, Nigel Titchener-Hooker, and Mike HoareUniversity College London, The Advanced Centre for Biochemical Engineering, London, U.K.

12 Using high-throughput formulation screening and DoE to understand and prevent molecule self associationBrian Connolly* and Jamie MooreGenentech, Inc., South San Francisco, CA, U.S.A.

List of posters presented at HTPD 2010* denotes author for correspondence


13 High-throughput screening systems enable Quality-By-Design approachSusanne Nath, Stefan Hepbildikler*, and Wolfgang KuhnePharma Biotech Production & Development, Roche Diagnostics GmbH, Penzberg, Germany

14 Comparison of automated, micro methods for product characterization assaysMartin Vanderlaan*, Yun Tang, Mansour Jazayri, and Kevin Lin Analytical Operations, Pharma Technical Development, Genentech, Inc., South San Francisco, CA, U.S.A.

15 A case study evaluation of protein refolding using high-throughput process development techniquesKimberly A. Kaleas*, Maricel Rodriguez, Shelly A. Pizarro, and Paul J. McDonaldPharma Technical Development-US, Genentech Inc., South San Francisco, CA, U.S.A.

16 A high-throughput approach to process development capture of green fluorescent proteinCharlotte Brink*, Carina Engstrand, Anders Ljunglöf, and Bengt WesterlundGE Healthcare Bio-Sciences AB, Uppsala, Sweden

17 Automated parallel chromatographic separations in downstream process developmentTim Schroeder* and Jürgen FriedleAtoll GmbH, Weingarten, Germany

18 Affinity chromatography optimization by DoE in 96 well platesD. Bataille*, S. De Marco, A. Lejars, A. Poncin, and M. Ollivier LFB Biotechnologies, Les Ulis, France

19 High-throughput process development of cation exchange chromatography for monoclonal antibodiesHai Hoang, Junfen Ma, James Cheung, and Judy H Chou*Genentech, Inc., South San Francisco, CA, U.S.A.

20 Setup of a fast flocculation screenings toolP. van Hee*, A.M.C. Janse, J. Vente, H. Robers, and T. VerkaikDSM Biotechnology Center, Dep. Downstream Processing, Delft, The Netherlands

21 Automated monoclonal antibody screeningWim Decrop* and Remco SwartDionex Corporation, Amsterdam, The Netherlands

22 A highly sensitive quantitative assay for CHO host cell DNA with automated test sample preparationMichael T. Brewer*, Jenkuei Liu, Sueh-Ning Liew, Nan Liu, Craig Cummings, Olga Petrauskene, and Manohar FurtadoLife Technologies Inc., Foster City, CA, U.S.A.

23 Evaluation of high-throughput methods for the purification process development of vaccine antigensYan-Ping Yang* and Ernst BraendliBioprocess Research & Development, Sanofi Pasteur, Toronto, Canada

24 Multivariate evaluation of low resolution chromatograms for high-throughput protein quantificationSigrid Hansen* and Jürgen HubbuchInstitute of Process Engineering in Life Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany

25 Characterization of host cell protein patterns to increase process understandingGunnar Malmquist*, Susanne Grimsby, Anneli Jorsback, Åsa Hagner-McWhirter,Tomas Björkman, Lena Kask, Maria Winkvist, and Lennart BjörkestenGE Healthcare Bio-Sciences AB, Uppsala, Sweden


Author index

Ahnfelt, M. 10

Bergander, T. 10, 14 Borg, N. 24 Brekkan, E. 10

Cai, N. 12 Cano, T. 12, 26 Cheng, J. 12, 26 Chhatre, S. 21

Dasnoy, S. 8 Degerman, M. 24 Dezutter, N. 8

Eriksson, K. 32

Faber, R. 38 Fahrner, R. 7 Farnan, D. 19 Fattinger, C. 6 Florén, A. 14 Friedle, J. 35

Grönlund, S. 32

Hallgren, E. 32 Hansen, S. 16 Heldin, E. 21, 32 Hellman, A.-K. 14 Hemström Nilsson, C. 10 Hepbildikler, S. 28 Hopp, J. 12 Hubbuch, J. 16, 28

Kelley, B. 12, 26, 29 Konstantinidis, S. 21

Łącki, K. 10 Lazzareschi, K. 7 Le Bras, V. 8 Lee, M. 26

Lemoine, D. 8 Lester, P. 12, 29 Leuthold, M. 38 Lin, I. 12, 26 Lund, L. N. 24

Malmquist, G. 10 Mani, K. 26 McDonald, P. J. 7, 12, 26 Moreno, T. 19

Nath, S. 28 Nilsson, B. 24

O’Connor, D. 26 Osberghaus, A. 28

Préat, V. 8

Rea, R. 19 Rodriguez, M. 7 Rogl, H. 28

Sandblad, P. 14 Shanagar, J. 32 Sisodiya, V. N. 7, 26 Skibsted, E. 16 Staby, A. 16, 24 Stults, J. T. 19

Titchener-Hooker, N. 21 Tunes, H. 32

Vilela, L. 32 von Lieres, E. 28

Winter, C. 26 Wong, M. 12

Xavier, M. 32 Xia, F. 26

imagination at work

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© 2011 General Electric Company – All rights reserved. First published Apr. 2011

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