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Quality by Design approaches to assessing the robustness of tangential flow filtration for MAb
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Biologicals journal homepage: www.elsevier.com/locate/biologicals
Quality by Design approaches to assessing the robustness of tangential flow filtration for MAb Hyun Weea,∗, KeunHoe Koob, EunJeong Baea, TaekYeol Leea,c a
Biologics Process 1, Biologics Research Laboratories, Dong-A Socio Holdings R&D Center, Yongin, 17073, South Korea Merck Ltd. Korea, 24 Fl., Bldg M, Songdo Technopark IT Center, Incheon, 21984, South Korea c Department of Life Science, Sogang University, Seoul, South Korea b
ARTICLE INFO
ABSTRACT
Keywords: Quality by design (QbD) Tangential flow filtration (TFF) Multivariate analysis Process robustness Process characterization
Quality by Design (QbD) is a modern approach for quality assurance in pharmaceutical production. This article illustrates a case study of TFF robustness performed for a process characterization of a monoclonal antibody under QbD principles by exploring functional relationships that link the process parameters to quality/process attributes with prior process knowledge, risk assessment, and multivariate experiments. In every case of quality or process attributes, all measured values were in alignment with the allowable specification range, and the developed models were non-significant and had no lack of fit, thus confirming the robustness of the TFF process within the tested ranges of process parameters.
1. Introduction Quality by Design (QbD) is defined as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management” [1]. The publication of PAT – A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance by the US Food and Drug Administration (FDA) triggered the implementation of QbD concepts in pharmaceutical manufacturing [2]. The principles, concepts, relevant tools, and applications are described in the International Conference on Harmonization (ICH) guidelines Q8, Q9, Q10 and Q11 [1,3–5]. The relevant strategies of how to apply the QbD concepts for a monoclonal antibody had been suggested and explored in the A-Mab case study [6]. EMA-FDA pilot program for QbD applications has led to having an in-depth understanding of critical requirements and concerns of the regulatory agencies, and further refined the QbD tools and concepts for the implementation of QbD for biopharmaceutical products. In recent practice, Roche/Genentech presented how the QbD concept and critical elements of the QbD approaches have been applied throughout the whole development stages and during the commercial phases of a product. The application of QbD principles significantly improved overall process robustness and product quality during development and lifecycle [7].
Tangential Flow Filtration (TFF) is widely used in various areas of the biopharmaceutical industry such as an antibody, recombinant protein, vaccines, and plasma-derived products. The general applications for TFF are volume reduction, buffer exchange to prepare the product ready for purification through chromatography, and mostly, in a final formulation where the protein product is concentrated to the required final concentration, buffer exchanged into the desired formulation excipients while removing residual excipients from previous purification steps [8]. QbD approaches have been applied by the pharmaceutical industry to characterize each unit operation of the manufacturing process; however, examples of TFF robustness studies exploring the effect of process parameters on quality or process attributes are rarely found in the literature. Even though the application of QbD principles to TFF has been explored in some publications, these cases mainly relied on theoretical understanding and the mechanistic model of TFF operation without a DoE study, which is one of the essential elements of QbD [8,9]. This mechanistic approach is distinguished from a typical DoE approach and may not fully assess the robustness of TFF. Here, we illustrate a QbD case study of TFF robustness performed for a process characterization of a monoclonal antibody by exploring functional relationships that link the process parameters to quality or process attributes with prior process knowledge, risk assessment, and
Abbreviations: ANOVA, Analysis of Variance; CPP, Critical Process Parameter; CQA, Critical Quality Attribute; DF, Diafiltration; DoE, Design of Experiment; KPA, Key Process Attributes; MAb, Monoclonal antibody; NWP, Normalized Water Permeability; OFAT, One Factor at a Time; pCPP, Potential Critical Process Parameter; Q2, coefficient of prediction; QbD, Quality by Design; R2, coefficient of determination; TFF, Tangential Flow Filtration; UF, Ultrafiltration ∗ Corresponding author. E-mail address: [email protected] (H. Wee). https://doi.org/10.1016/j.biologicals.2019.12.001 Received 5 June 2019; Received in revised form 6 December 2019; Accepted 7 December 2019 1045-1056/ © 2019 International Alliance for Biological Standardization. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Hyun Wee, et al., Biologicals, https://doi.org/10.1016/j.biologicals.2019.12.001
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Table 1 How to evaluate the impact of process parameter on quality or process attributes. Impact
The impact of each process parameter on each quality or process attribute
High Middle Low
9: Definite impact known 3: Moderate or indirect impact expected 1: Negligible impact expected
Table 3 Criticality classification.
2.1. Materials An IgG1 monoclonal antibody (Dong-A Socio Holdings) was used for these studies. 2.2. Risk assessment The criticality score was calculated using Lean QbD™ (QbDWorks) software as below. The criticality score of each process parameter = [Sum of the impact of parameter] x Occurrence x Detectability. The impact, occurrence, and detectability of each process parameter on each CQA were assigned based on available literature or experimental data as shown in Table 1 and Table 2. Based on the criticality score, the process parameters were classified, and the experimental strategy was established accordingly (see Table 3). 2.3. DoE robustness study design and data analysis The fractional multivariate study was performed with 5 factors at 2 levels, namely diavolume (DV), TMP, feed protein concentration (Conc.), load mass (Load) and feed flow (Flow). These 5 factors were investigated in a fractional factorial design (25−1, Resolution V), resulting in 16 experiments and 3 center points. Resolution V design was chosen because it allows precise estimation of all main factors and twofactor interactions upon neglecting three and higher-order interactions [10]. The experimental design is provided in Table 7 with the test results. The results of the robustness study were analyzed using MODDE 11 software (Umetirics) to estimate functional relationships between the process parameters and quality/process attributes. The relative strength of each process parameter affecting quality or process attributes was estimated with raw data assessment and regression coefficient plots. A regression coefficient was considered as statistically significant if the corresponding 95% confidence interval was smaller than the coefficient. The developed model was evaluated employing a set of diagnostic tools such as the coefficient of determination (R2) and the coefficient plot of prediction (Q2) and analysis of variance (ANOVA) with a significance level of 0.05. R2 indicates how well the regression model can be made to fit the raw data and Q2 is a measure of estimating the predictive power of the model [11].
Size exclusion chromatography was performed on a Thermo HPLC system equipped with a UV detector (Thermo scientific) and a TSKGelGWXL column (Tosoh Bioscience) with an oven temperature set to 25 °C. The mobile phase consisted of a 20 mM sodium phosphate monobasic/sodium phosphate dibasic buffer and 150 mM sodium chloride (pH 7.3) at a flow rate of 0.5 mL/min for 35 min. Samples were diluted to 5 mg/mL protein concentration, and 20 μL was injected, corresponding to a loading amount of 1 μg. Detection was performed at a wavelength of 280 nm. Data analysis was performed using the software Chromeleon (Thermo scientific). The results were reported as area % monomer, which is the area of the main peak.
How likely the process parameter deviation goes undetected Detectability
Multivariate (DoE) Multivariate (DoE) or Univariate (OFAT) Univariate (OFAT) if required
2.5. SEC HPLC
Table 2 Scoring of occurrence and detectability.
3: High 2: Middle 1: Low
≥50 20–49 ≤19
All tests were performed at room temperature using Merck Ultracell 30-kD C-Screen membranes in 88 cm2 Pellicon 3 devices (Merck Millipore) installed in the Cogent® μScale TFF system (Merck Millipore) which includes a 1 L polypropylene tank with a magnetic impeller. Also included with the system is a filter holder for the Pellicon® 3 88 cm2 cassettes, and a complete high-pressure tubing assembly (Silicone and GORESTA-PURE®) capable of running up to 80 psi [12]. The test materials were filtered using a 0.22 μm filter (Corning) before processing. Ultrafiltration (UF) and diafiltration (DF) were executed at a desired transmembrane pressure (TMP) and feed flow. The initial feed was composed of sodium acetate and acetic acid. Its concentration ranged from 0.5 to 4.5 g/L and concentrated to 10 g/L followed by diafiltration with 7–13 diavolumes. The buffer for diafiltration is composed of one of the components of the final formulation. The retentate was continuously stirred and recirculated through the system using a peristaltic pump [13]. After diafiltration, samples were further concentrated to more than 40 g/L. A final concentrated sample was taken from the feed tank for UV spectrophotometry, pH, and SEC analyses. Table 4 provides a summary of the ranges of the process parameters tested. During processing, membrane cassettes were initially washed with 0.1 NaOH at room temperature for 30–45 min and cleaned using the same procedure immediately after each experiment with 0.1 N NaOH at room temperature. Following each cleaning cycle, deionized water flush, water pH levels were tracked in the permeate and retentate lines, and the Normalized Water Permeability (NWP) was measured before and after the process at standard conditions. Also, the membrane cassettes evaluated the integrity testing by automatic diffusion testing [14]. Membrane recovery was defined as the percent ratio of the water NWP (normalized water permeability) after the standard cleaning (recirculation of 0.1 N sodium hydroxide for 30–45 min) to the initial NWP measured before the membrane came into contact with the process fluid. The acceptance criteria of membrane recovery for this study was ≥70%.
2. Materials and methods
Occurrence
Experimental Strategy
2.4. TFF
the DoE approach.
How likely the process parameter goes out of specification
Criticality Score
2.6. Protein concentration
3: High 2: Middle 1: Low
Protein concentration in solution was determined by photometric analysis of the diluted sample at 280 nm using a spectrophotometer (Shimadzu). The extinction coefficient (E1%) for determining protein 2
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process parameter is summarized in Table 6.
Table 4 Summary of the ranges of the process parameters tested. Process parameter
The tested range
Load mass (g/㎡) Feed protein concentration (g/L) Feed flow (L/㎡/min) Transmembrane pressure (psi) Diavolume (DV) Diafiltration buffer concentration (Times)
40–200 0.5–4.5 4–6 8–22 7–13 0.9–1.1
3.2. DoE study Generally, where predefined acceptance limits are available, the following four robustness study outcomes could be categorized as four cases [11,15]. The case type determination was based upon whether the CQA/KPA values were lying in the acceptance range specified for the process and the statistical significance of the model. (A) A non-significant statistical model and all outputs are inside the specification; (B) a significant statistical model and all outputs are inside the specification; (C) a significant statistical model and not all outputs are inside the specification; (D) a non-significant statistical model and not all outputs are inside the specification.
concentration was 15.8. 3. Results 3.1. Risk assessment 3.1.1. Criticality assessment of process parameters on quality or process attributes In accordance with ICH8(R2) guidelines, the criticality of process parameters was assessed to prioritize the list of potential critical process parameters (pCPPs). The assessed impact of each process parameter on each quality/process attribute is summarized in Table 5. The criticality score of each process parameter was calculated as stated in Materials and Methods (2.2), and the overall results are summarized in Table 6.
3.2.1. Monomer (case A) The initial raw data assessment of the monomer shows that this response is robust because all measured values of the monomer were in alignment with the allowable specification range, which is above 99% (Table 7). The obtained model was insignificant with an R2 of 0.525 and a Q2 of 0.002. The coefficient plot indicates that none of the tested main factors had a statistical effect on the monomer, although a significant interaction term [Feed protein concentration] x [Feed flow] was observed (Fig. 1a). The analysis of variance revealed that a non-significant model was built for the monomer (p > 0.05), and the model has no lack of fit (p > 0.05, Fig. 1b). Taken together, the TFF process can be thus considered to be robust regarding the monomer within the tested ranges of process parameters.
3.1.2. Experimental strategy for process characterization The criticality score was used as the basis for assessing the required level of experimental complexity and strategy for the TFF process characterization study. The experimental strategy established for each
Table 5 Risk assessment of the process parameters: Evaluating the impact of each process parameter on each quality or process attributea.
1) This process parameter is redundant because the retentate volume was determined by the feed protein concentration. a How the impact of each process parameter on each quality or process attribute was assigned is described in Table 1.
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Table 6 Criticality assessment and experimental strategy for the process parameters.
3.2.2. pH (case A) As for the pH, the initial raw data assessment indicates that this response is robust because all the measured values of the pH were within the specification limit, which is between 5.7 and 6.3 (Table 7). The built model was insignificant with an R2 of 0.194 and a Q2 of 0.027. The coefficient plot indicates that none of the tested main factors had a statistical effect on pH, although a p-value of feed flow (p = 0.059) was close to the significance level (0.05) (Fig. 2a). When the statistical significance of the regression model was evaluated with the analysis of variance, a non-significant model was built for the pH (p > 0.05), and
the model has no lack of fit (p > 0.05) (Fig. 2b). It suggests that the TFF process parameters within the tested ranges do not significantly affect the distribution of pH, confirming the non-significant correlation between the tested process parameters and the pH. Thus, the TFF process can be considered to be robust with regard to pH within the tested ranges of process parameters. 3.2.3. Retentate concentration (case A) In the case of retentate concentration, the initial raw data assessment showed that all measured values were within the acceptance
Table 7 TFF DoE study result. Exp No
Run Order
Diavolume (DV)
TMP (psi)
Feed protein Concentration (mg/ml)
Load mass (g/m2)
Feed flow (LMM)
Monomer (%)
pH
Yield (%)
Concentration (mg/ml)
Integrity test (Before/ After)
Membrane recoverya
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
11 13 10 12 5 9 2 8 18 16 19 15 6 4 14 1 17 3 7
7 13 7 13 7 13 7 13 7 13 7 13 7 13 7 13 10 10 10
8 8 22 22 8 8 22 22 8 8 22 22 8 8 22 22 15 15 15
0.5 0.5 0.5 0.5 4.5 4.5 4.5 4.5 0.5 0.5 0.5 0.5 4.5 4.5 4.5 4.5 2.5 2.5 2.5
40 40 40 40 40 40 40 40 200 200 200 200 200 200 200 200 120 120 120
6 4 4 6 4 6 6 4 4 6 6 4 6 4 4 6 5 5 5
99.85 99.89 99.86 99.87 99.87 99.88 99.87 99.83 99.87 99.88 99.86 99.87 99.88 99.86 99.86 99.88 99.88 99.87 99.85
5.98 5.94 5.99 5.94 5.97 5.94 5.93 5.95 5.98 5.93 5.95 5.97 5.93 5.96 5.95 5.94 5.98 5.96 6.01
91.74 89.32 94.78 95.55 87.07 91.90 98.07 89.05 91.85 93.53 86.92 94.99 87.38 91.65 92.14 90.83 91.62 87.95 102.36
40.82 47.09 49.62 48.10 44.79 49.90 47.45 42.01 48.49 49.16 47.50 47.89 42.33 49.18 46.52 43.21 49.89 45.15 49.32
Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass
Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
a The membrane cassettes achieved NWP recovery criteria after the standard cleaning procedure, indicating that membranes were effectively cleaned back to their original state. The cleaned cassettes were used to perform a repeat run for data reproducibility analysis. There was no apparent evidence of the membrane fouling or loss of performance across the process runs.
4
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Fig. 1. Monomer. (a) Coefficient plot for the monomer (b) The ANOVA of the monomer showing a non-significant statistical model without lack of fit.
range allowed, which is above 40 mg/mL (Table 7), indicating that this response is robust. The obtained model was not significant with an R2 of 0.38 and a Q2 of 0.004. The coefficient plot demonstrates that none of the tested main factors had a statistical effect on retentate concentration, though a significant interaction term [Diavolume] x [TMP] exists (Fig. 3a). The analysis of variance demonstrated that the non-significant model was built for the retentate concentration (p = 0.06), and the model has no lack of fit (p > 0.05, Fig. 3b). Overall, it suggests that the TFF process can be thus considered to be robust regarding the retentate concentration within the tested ranges of the process parameters.
3.3. Univariate study The diafiltration buffer concentration within the tested ranges gave the monomer, pH, retentate concentration, and yield in the predefined acceptance range, indicating that it does not have a significant impact on quality or process attributes (see Table 8). 4. Discussion and conclusions Implementing QbD principles have been recommended by the regulatory agencies to better characterize the process, and QbD approaches have been applied to the unit operations of the downstream process; however, examples of QbD study exploring the robustness of TFF are rarely available. How QbD concepts have been applied to the process development of biopharmaceuticals was reviewed by Elliott et al., in 2013 [16]. Typical examples of the application of QbD to the unit operations of downstream processes follow the typical sequence of the following steps: Determining acceptable ranges for quality attributes, risk assessment, parameter screening, and modeling DoE. In the cases where a mechanistic model built from the extensive theoretical knowledge of the process is available, such as ion-exchange chromatography and TFF, a more mechanistic approach has been applied. When the theoretical model was compared to the DoE approach for parameter screening of
3.2.4. Yield (case A) The initial raw data assessment of yield demonstrates that this response is robust, because all the measured values of the yield were inside the specified limit, which is above 85% (Table 7). No significant main factor nor interaction term was found during the building of the regression model. When the statistical significance of the initial regression model was assessed with the analysis of variance, the p-value (p > 0.05) revealed that a non-significant model was built for yield, and the model has no lack of fit (p > 0.05, Fig. 4). Taken together, it suggests that the TFF process parameters within the tested ranges do not significantly affect the distribution of yield, thus confirming the robustness of the TFF process. 5
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Fig. 2. pH. (a) Coefficient plot for the pH (b) The ANOVA of the pH showing a non-significant statistical model without lack of fit.
ion-exchange chromatography, both approaches drew a similar conclusion, assuring that the mechanistic approach based on chromatographic theory was valid [17]. In 2009, an article by Watler et al. proposed a mechanistic QbD approach for TFF relying on a theoretical model built with an in-depth understanding of the TFF system in terms of its fluid dynamics, physics, and chemistry [9]. It was noted that the TFF operation is particularly well fitted to a mechanistic model approach because of its well-known mass transfer fundamentals and proven engineering principles and design equations. Also, the FDA's PAT Team and Manufacturing Science Working Group stated that a mechanistic understanding of how process factors affect product performance is a basis for product and process specifications [2]. The authors concluded that the theoretical proven design model for TFF could be used to identify optimal operating conditions. However, no further assessment was given on the robustness of the TFF process in the cases where the value of process parameters varies. This study aimed at assessing the robustness of the tangential flow filtration (TFF) process with QbD approaches. Initially, the criticality of the TFF process parameters was assessed by evaluating their impact on quality/process attributes, occurrence, and detectability based on prior/platform knowledge and available experimental data. Further, the experimental strategy using the design of multivariate and univariate studies was established based on the result of the criticality assessment to evaluate the impact of potential critical process parameters on quality/process attributes and the adequacy of the operation
range for claiming process robustness. This goal of the DoE study was achieved using a resolution V fractional factorial design (25−1) and the test results were analyzed using MODDE 11 software. In all cases of quality or process attributes, initial raw data assessment showed that all the measured values were clearly inside the predefined limits and the models developed were statistically non-significant, which is categorized as case A. Case A provides little information about how process parameters affect product quality or process attributes. However, this is the ideal outcome of the robustness test, because it indicates that the quality or process attributes distributes within the predefined specification ranges and the variation of process parameters within the tested ranges has no significant effect on them [11,15]. Therefore, the whole tested range of process parameters could be claimed as the acceptable operating range, ensuring a robust process and consistent product quality. Monoclonal antibodies (Mabs) often exhibit different molecular behaviors in terms of their response to shear stresses, tendency for aggregation, pH sensitivity, and denaturation. In this regard, DoE results regarding the TFF robustness might be different depending on the molecular characters of Mab. As stated in Result (3.2), the cases for the robustness study could be categorized as 4 cases based on whether the test results are lying in the specified limits and the statistical significance of the model. In the cases of A and B, we can deduce that the TFF process is robust within tested ranges of the process parameters. Case C offers information on how the process parameters affect product quality and process performance, but indicates that the process is not 6
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Fig. 3. Retentate concentration. (a) Coefficient plot for the retentate concentration (b) The ANOVA of the retentate concentration showing a non-significant statistical model without lack of fit.
Fig. 4. The ANOVA of yield showing a non-significant statistical model without lack of fit.
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48.95 47.73
Pass/Pass Pass/Pass
Pass Pass
robust. However, the statically significant model can be used to predict an acceptable process parameter range where all the values of the tested quality/process attributes will be inside the predefined limits. Case D is the least favored scenario because a statistically non-significant model offers poor information and predictability regarding the functional correlation between process parameters and the quality/ process attributes, and the process robustness cannot be claimed. There might be several underlying reasons for this limiting case, and mostly this could be caused by inappropriate parameter selection for the DoE test or by experimental deviation [11,15,18]. As described above, the mechanistic models of TFF derived from relevant theoretical principles are applicable to the TFF process development and optimization of operating conditions, so an extensive DoE study might not be necessarily required for a rapid and efficient TFF process development and optimization. More targeted experiments guided by the mechanistic model are expected to generate an optimized process with reduced experimental and analytical requirements. However, this approach might not fully assess the potential interactions across the process parameters and the adequacy of operation ranges for claiming process robustness. Therefore, verifying the robustness of the TFF process with a DoE study after development and optimization using the mechanistic model would give an assurance to claim the acceptable operating ranges of process parameters. In this regard, it is proposed that the initial development and optimization of the TFF process with the proven mechanistic models and further verification of the process using DoE approaches will enable and accelerate robust and optimized process development, ensuring product quality.
5.96 5.97 99.87 99.85
89.44 91.44
pH Monomer (%)
Yield (%)
Concentration (mg/ ml)
Integrity test (Before/After)
Membrane recovery
H. Wee, et al.
5 5
This work was funded and supported by Dong-A Socio Holdings and Merck Ltd. Korea. Hyun Wee, EunJeong Bae and Taekyeol Lee are employees of Dong-A Socio Holdings and KeunHoe Koo is an employee of Merck Ltd. Korea.
120 120
References
15 15
2.5 2.5
[1] International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use,. ICH harmonized tripartite guideline, pharmaceutical development; August 2009. Q8 R2. [2] US Food and Drug Administration. Guidance for industry PAT - Framework for innovative pharmaceutical development, manufacturing and quality assurance. 2004. [3] International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use. vol. 4. ICH harmonized tripartite guideline, quality risk management; 9 November 2005. Q9 Step. [4] International International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use. ICH harmonized tripartite guideline, pharmaceutical quality; 4 June 2008. Q10 Step. 4. [5] Q11 Step 4 International Conference on Harmonization of technical requirements for registration of pharmaceuticals for human use, ICH Harmonized tripartite guideline, development and manufacture of drug substances (chemical entities and biotechnological/biological entities). May 2012. [6] CMC Biotech Working Group A-MAb. A case study in bioprocess development. Emeryville, CA: CASSS; 2009. [7] Finkler C, Krummen L. Introduction to the application of QbD principles for the development of monoclonal antibodies. Biologicals 2016;44(5):282–90. [8] Shultz J, Lutz H, Rao S. Quality by design as applied to drug substance formulation using ultrafiltration and diafiltration. Quality by design for biopharmaceutical Drug product development. AAPS advances in the pharmaceutical sciences series, vol. 18. New York: Springer; 2015. p. 191–210. [9] Watler P, Rozembersky J. Application of QbD principles to tangential flow filtration operations. Quality by design for biopharmaceuticals: principles and case studies. NJ, USA: John Wiley & Sons, Inc.; 2009. p. 111–25. [10] Nageeb El-Helaly S, Habib BA, Abd El-Rahman MK. Resolution V fractional factorial design for screening of factors affecting weakly basic drugs liposomal systems. Eur J Pharm Sci 2018;119(1):249–58. [11] Eriksson L, Johansson E, Kettaneh-Wold N, Wikstrom C, Wold S. Design of experiments: principles and applications. MKS Umetrics AB; 2008. [12] Merck Millipore. “Cogent® μScale TFF system” datasheet, lit. No. DS0003EN00 ver. 4.0. 2018. 04193, 10/2018. [13] Merck Millipore. “Pellicon® ultrafiltration (UF)/Diafiltration (DF) operations” protocol guide, lit No. RF1159EN00 ver. 3.0 4. 2016. [14] Merck Millipore. “Pellicon® 3 cassettes installation and user guide”, lit No.
0.9 1.1 1 2
10 10
Diafiltration buffer concentration (Times) Exp No
Table 8 TFF univariate study result.
Diavolume (DV)
TMP (psi)
Feed protein Concentration (mg/ml)
Load mass (m2/L)
Feed flow (LMM)
Declaration of competing interest
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H. Wee, et al. AN1065EN00 rev E. 02/2014. [15] Wurth C, Demeule B, Mahler HC, Adler M. Quality by design approaches to formulation robustness-an antibody case study. J Pharm Sci 2016;105(5):1667–75. [16] Elliott P, Billingham S, Bi J, Zhang H. Quality by design for biopharmaceuticals: a historical review and guide for implementation. Pharmaceut. Bioprocess. 2013;1(1):105–22.
[17] Kaltenbrunner O, Giaverini O, Woehle D, Asenjo JA. Application of chromatographic theory for process characterization towards validation of an ion-exchange operation. Biotechnol Bioeng 2007;98(1):201–10. [18] Bhatia H, Read E, Agarabi C, Brorson K, Lute S, Yoon S. A design space exploration for control of Critical Quality Attributes of mAb. Int J Pharm 2016;512(1):242–52.
9
Contents lists available at ScienceDirect
Biologicals journal homepage: www.elsevier.com/locate/biologicals
Quality by Design approaches to assessing the robustness of tangential flow filtration for MAb Hyun Weea,∗, KeunHoe Koob, EunJeong Baea, TaekYeol Leea,c a
Biologics Process 1, Biologics Research Laboratories, Dong-A Socio Holdings R&D Center, Yongin, 17073, South Korea Merck Ltd. Korea, 24 Fl., Bldg M, Songdo Technopark IT Center, Incheon, 21984, South Korea c Department of Life Science, Sogang University, Seoul, South Korea b
ARTICLE INFO
ABSTRACT
Keywords: Quality by design (QbD) Tangential flow filtration (TFF) Multivariate analysis Process robustness Process characterization
Quality by Design (QbD) is a modern approach for quality assurance in pharmaceutical production. This article illustrates a case study of TFF robustness performed for a process characterization of a monoclonal antibody under QbD principles by exploring functional relationships that link the process parameters to quality/process attributes with prior process knowledge, risk assessment, and multivariate experiments. In every case of quality or process attributes, all measured values were in alignment with the allowable specification range, and the developed models were non-significant and had no lack of fit, thus confirming the robustness of the TFF process within the tested ranges of process parameters.
1. Introduction Quality by Design (QbD) is defined as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management” [1]. The publication of PAT – A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance by the US Food and Drug Administration (FDA) triggered the implementation of QbD concepts in pharmaceutical manufacturing [2]. The principles, concepts, relevant tools, and applications are described in the International Conference on Harmonization (ICH) guidelines Q8, Q9, Q10 and Q11 [1,3–5]. The relevant strategies of how to apply the QbD concepts for a monoclonal antibody had been suggested and explored in the A-Mab case study [6]. EMA-FDA pilot program for QbD applications has led to having an in-depth understanding of critical requirements and concerns of the regulatory agencies, and further refined the QbD tools and concepts for the implementation of QbD for biopharmaceutical products. In recent practice, Roche/Genentech presented how the QbD concept and critical elements of the QbD approaches have been applied throughout the whole development stages and during the commercial phases of a product. The application of QbD principles significantly improved overall process robustness and product quality during development and lifecycle [7].
Tangential Flow Filtration (TFF) is widely used in various areas of the biopharmaceutical industry such as an antibody, recombinant protein, vaccines, and plasma-derived products. The general applications for TFF are volume reduction, buffer exchange to prepare the product ready for purification through chromatography, and mostly, in a final formulation where the protein product is concentrated to the required final concentration, buffer exchanged into the desired formulation excipients while removing residual excipients from previous purification steps [8]. QbD approaches have been applied by the pharmaceutical industry to characterize each unit operation of the manufacturing process; however, examples of TFF robustness studies exploring the effect of process parameters on quality or process attributes are rarely found in the literature. Even though the application of QbD principles to TFF has been explored in some publications, these cases mainly relied on theoretical understanding and the mechanistic model of TFF operation without a DoE study, which is one of the essential elements of QbD [8,9]. This mechanistic approach is distinguished from a typical DoE approach and may not fully assess the robustness of TFF. Here, we illustrate a QbD case study of TFF robustness performed for a process characterization of a monoclonal antibody by exploring functional relationships that link the process parameters to quality or process attributes with prior process knowledge, risk assessment, and
Abbreviations: ANOVA, Analysis of Variance; CPP, Critical Process Parameter; CQA, Critical Quality Attribute; DF, Diafiltration; DoE, Design of Experiment; KPA, Key Process Attributes; MAb, Monoclonal antibody; NWP, Normalized Water Permeability; OFAT, One Factor at a Time; pCPP, Potential Critical Process Parameter; Q2, coefficient of prediction; QbD, Quality by Design; R2, coefficient of determination; TFF, Tangential Flow Filtration; UF, Ultrafiltration ∗ Corresponding author. E-mail address: [email protected] (H. Wee). https://doi.org/10.1016/j.biologicals.2019.12.001 Received 5 June 2019; Received in revised form 6 December 2019; Accepted 7 December 2019 1045-1056/ © 2019 International Alliance for Biological Standardization. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Hyun Wee, et al., Biologicals, https://doi.org/10.1016/j.biologicals.2019.12.001
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Table 1 How to evaluate the impact of process parameter on quality or process attributes. Impact
The impact of each process parameter on each quality or process attribute
High Middle Low
9: Definite impact known 3: Moderate or indirect impact expected 1: Negligible impact expected
Table 3 Criticality classification.
2.1. Materials An IgG1 monoclonal antibody (Dong-A Socio Holdings) was used for these studies. 2.2. Risk assessment The criticality score was calculated using Lean QbD™ (QbDWorks) software as below. The criticality score of each process parameter = [Sum of the impact of parameter] x Occurrence x Detectability. The impact, occurrence, and detectability of each process parameter on each CQA were assigned based on available literature or experimental data as shown in Table 1 and Table 2. Based on the criticality score, the process parameters were classified, and the experimental strategy was established accordingly (see Table 3). 2.3. DoE robustness study design and data analysis The fractional multivariate study was performed with 5 factors at 2 levels, namely diavolume (DV), TMP, feed protein concentration (Conc.), load mass (Load) and feed flow (Flow). These 5 factors were investigated in a fractional factorial design (25−1, Resolution V), resulting in 16 experiments and 3 center points. Resolution V design was chosen because it allows precise estimation of all main factors and twofactor interactions upon neglecting three and higher-order interactions [10]. The experimental design is provided in Table 7 with the test results. The results of the robustness study were analyzed using MODDE 11 software (Umetirics) to estimate functional relationships between the process parameters and quality/process attributes. The relative strength of each process parameter affecting quality or process attributes was estimated with raw data assessment and regression coefficient plots. A regression coefficient was considered as statistically significant if the corresponding 95% confidence interval was smaller than the coefficient. The developed model was evaluated employing a set of diagnostic tools such as the coefficient of determination (R2) and the coefficient plot of prediction (Q2) and analysis of variance (ANOVA) with a significance level of 0.05. R2 indicates how well the regression model can be made to fit the raw data and Q2 is a measure of estimating the predictive power of the model [11].
Size exclusion chromatography was performed on a Thermo HPLC system equipped with a UV detector (Thermo scientific) and a TSKGelGWXL column (Tosoh Bioscience) with an oven temperature set to 25 °C. The mobile phase consisted of a 20 mM sodium phosphate monobasic/sodium phosphate dibasic buffer and 150 mM sodium chloride (pH 7.3) at a flow rate of 0.5 mL/min for 35 min. Samples were diluted to 5 mg/mL protein concentration, and 20 μL was injected, corresponding to a loading amount of 1 μg. Detection was performed at a wavelength of 280 nm. Data analysis was performed using the software Chromeleon (Thermo scientific). The results were reported as area % monomer, which is the area of the main peak.
How likely the process parameter deviation goes undetected Detectability
Multivariate (DoE) Multivariate (DoE) or Univariate (OFAT) Univariate (OFAT) if required
2.5. SEC HPLC
Table 2 Scoring of occurrence and detectability.
3: High 2: Middle 1: Low
≥50 20–49 ≤19
All tests were performed at room temperature using Merck Ultracell 30-kD C-Screen membranes in 88 cm2 Pellicon 3 devices (Merck Millipore) installed in the Cogent® μScale TFF system (Merck Millipore) which includes a 1 L polypropylene tank with a magnetic impeller. Also included with the system is a filter holder for the Pellicon® 3 88 cm2 cassettes, and a complete high-pressure tubing assembly (Silicone and GORESTA-PURE®) capable of running up to 80 psi [12]. The test materials were filtered using a 0.22 μm filter (Corning) before processing. Ultrafiltration (UF) and diafiltration (DF) were executed at a desired transmembrane pressure (TMP) and feed flow. The initial feed was composed of sodium acetate and acetic acid. Its concentration ranged from 0.5 to 4.5 g/L and concentrated to 10 g/L followed by diafiltration with 7–13 diavolumes. The buffer for diafiltration is composed of one of the components of the final formulation. The retentate was continuously stirred and recirculated through the system using a peristaltic pump [13]. After diafiltration, samples were further concentrated to more than 40 g/L. A final concentrated sample was taken from the feed tank for UV spectrophotometry, pH, and SEC analyses. Table 4 provides a summary of the ranges of the process parameters tested. During processing, membrane cassettes were initially washed with 0.1 NaOH at room temperature for 30–45 min and cleaned using the same procedure immediately after each experiment with 0.1 N NaOH at room temperature. Following each cleaning cycle, deionized water flush, water pH levels were tracked in the permeate and retentate lines, and the Normalized Water Permeability (NWP) was measured before and after the process at standard conditions. Also, the membrane cassettes evaluated the integrity testing by automatic diffusion testing [14]. Membrane recovery was defined as the percent ratio of the water NWP (normalized water permeability) after the standard cleaning (recirculation of 0.1 N sodium hydroxide for 30–45 min) to the initial NWP measured before the membrane came into contact with the process fluid. The acceptance criteria of membrane recovery for this study was ≥70%.
2. Materials and methods
Occurrence
Experimental Strategy
2.4. TFF
the DoE approach.
How likely the process parameter goes out of specification
Criticality Score
2.6. Protein concentration
3: High 2: Middle 1: Low
Protein concentration in solution was determined by photometric analysis of the diluted sample at 280 nm using a spectrophotometer (Shimadzu). The extinction coefficient (E1%) for determining protein 2
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process parameter is summarized in Table 6.
Table 4 Summary of the ranges of the process parameters tested. Process parameter
The tested range
Load mass (g/㎡) Feed protein concentration (g/L) Feed flow (L/㎡/min) Transmembrane pressure (psi) Diavolume (DV) Diafiltration buffer concentration (Times)
40–200 0.5–4.5 4–6 8–22 7–13 0.9–1.1
3.2. DoE study Generally, where predefined acceptance limits are available, the following four robustness study outcomes could be categorized as four cases [11,15]. The case type determination was based upon whether the CQA/KPA values were lying in the acceptance range specified for the process and the statistical significance of the model. (A) A non-significant statistical model and all outputs are inside the specification; (B) a significant statistical model and all outputs are inside the specification; (C) a significant statistical model and not all outputs are inside the specification; (D) a non-significant statistical model and not all outputs are inside the specification.
concentration was 15.8. 3. Results 3.1. Risk assessment 3.1.1. Criticality assessment of process parameters on quality or process attributes In accordance with ICH8(R2) guidelines, the criticality of process parameters was assessed to prioritize the list of potential critical process parameters (pCPPs). The assessed impact of each process parameter on each quality/process attribute is summarized in Table 5. The criticality score of each process parameter was calculated as stated in Materials and Methods (2.2), and the overall results are summarized in Table 6.
3.2.1. Monomer (case A) The initial raw data assessment of the monomer shows that this response is robust because all measured values of the monomer were in alignment with the allowable specification range, which is above 99% (Table 7). The obtained model was insignificant with an R2 of 0.525 and a Q2 of 0.002. The coefficient plot indicates that none of the tested main factors had a statistical effect on the monomer, although a significant interaction term [Feed protein concentration] x [Feed flow] was observed (Fig. 1a). The analysis of variance revealed that a non-significant model was built for the monomer (p > 0.05), and the model has no lack of fit (p > 0.05, Fig. 1b). Taken together, the TFF process can be thus considered to be robust regarding the monomer within the tested ranges of process parameters.
3.1.2. Experimental strategy for process characterization The criticality score was used as the basis for assessing the required level of experimental complexity and strategy for the TFF process characterization study. The experimental strategy established for each
Table 5 Risk assessment of the process parameters: Evaluating the impact of each process parameter on each quality or process attributea.
1) This process parameter is redundant because the retentate volume was determined by the feed protein concentration. a How the impact of each process parameter on each quality or process attribute was assigned is described in Table 1.
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Table 6 Criticality assessment and experimental strategy for the process parameters.
3.2.2. pH (case A) As for the pH, the initial raw data assessment indicates that this response is robust because all the measured values of the pH were within the specification limit, which is between 5.7 and 6.3 (Table 7). The built model was insignificant with an R2 of 0.194 and a Q2 of 0.027. The coefficient plot indicates that none of the tested main factors had a statistical effect on pH, although a p-value of feed flow (p = 0.059) was close to the significance level (0.05) (Fig. 2a). When the statistical significance of the regression model was evaluated with the analysis of variance, a non-significant model was built for the pH (p > 0.05), and
the model has no lack of fit (p > 0.05) (Fig. 2b). It suggests that the TFF process parameters within the tested ranges do not significantly affect the distribution of pH, confirming the non-significant correlation between the tested process parameters and the pH. Thus, the TFF process can be considered to be robust with regard to pH within the tested ranges of process parameters. 3.2.3. Retentate concentration (case A) In the case of retentate concentration, the initial raw data assessment showed that all measured values were within the acceptance
Table 7 TFF DoE study result. Exp No
Run Order
Diavolume (DV)
TMP (psi)
Feed protein Concentration (mg/ml)
Load mass (g/m2)
Feed flow (LMM)
Monomer (%)
pH
Yield (%)
Concentration (mg/ml)
Integrity test (Before/ After)
Membrane recoverya
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
11 13 10 12 5 9 2 8 18 16 19 15 6 4 14 1 17 3 7
7 13 7 13 7 13 7 13 7 13 7 13 7 13 7 13 10 10 10
8 8 22 22 8 8 22 22 8 8 22 22 8 8 22 22 15 15 15
0.5 0.5 0.5 0.5 4.5 4.5 4.5 4.5 0.5 0.5 0.5 0.5 4.5 4.5 4.5 4.5 2.5 2.5 2.5
40 40 40 40 40 40 40 40 200 200 200 200 200 200 200 200 120 120 120
6 4 4 6 4 6 6 4 4 6 6 4 6 4 4 6 5 5 5
99.85 99.89 99.86 99.87 99.87 99.88 99.87 99.83 99.87 99.88 99.86 99.87 99.88 99.86 99.86 99.88 99.88 99.87 99.85
5.98 5.94 5.99 5.94 5.97 5.94 5.93 5.95 5.98 5.93 5.95 5.97 5.93 5.96 5.95 5.94 5.98 5.96 6.01
91.74 89.32 94.78 95.55 87.07 91.90 98.07 89.05 91.85 93.53 86.92 94.99 87.38 91.65 92.14 90.83 91.62 87.95 102.36
40.82 47.09 49.62 48.10 44.79 49.90 47.45 42.01 48.49 49.16 47.50 47.89 42.33 49.18 46.52 43.21 49.89 45.15 49.32
Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass Pass/Pass
Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
a The membrane cassettes achieved NWP recovery criteria after the standard cleaning procedure, indicating that membranes were effectively cleaned back to their original state. The cleaned cassettes were used to perform a repeat run for data reproducibility analysis. There was no apparent evidence of the membrane fouling or loss of performance across the process runs.
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Fig. 1. Monomer. (a) Coefficient plot for the monomer (b) The ANOVA of the monomer showing a non-significant statistical model without lack of fit.
range allowed, which is above 40 mg/mL (Table 7), indicating that this response is robust. The obtained model was not significant with an R2 of 0.38 and a Q2 of 0.004. The coefficient plot demonstrates that none of the tested main factors had a statistical effect on retentate concentration, though a significant interaction term [Diavolume] x [TMP] exists (Fig. 3a). The analysis of variance demonstrated that the non-significant model was built for the retentate concentration (p = 0.06), and the model has no lack of fit (p > 0.05, Fig. 3b). Overall, it suggests that the TFF process can be thus considered to be robust regarding the retentate concentration within the tested ranges of the process parameters.
3.3. Univariate study The diafiltration buffer concentration within the tested ranges gave the monomer, pH, retentate concentration, and yield in the predefined acceptance range, indicating that it does not have a significant impact on quality or process attributes (see Table 8). 4. Discussion and conclusions Implementing QbD principles have been recommended by the regulatory agencies to better characterize the process, and QbD approaches have been applied to the unit operations of the downstream process; however, examples of QbD study exploring the robustness of TFF are rarely available. How QbD concepts have been applied to the process development of biopharmaceuticals was reviewed by Elliott et al., in 2013 [16]. Typical examples of the application of QbD to the unit operations of downstream processes follow the typical sequence of the following steps: Determining acceptable ranges for quality attributes, risk assessment, parameter screening, and modeling DoE. In the cases where a mechanistic model built from the extensive theoretical knowledge of the process is available, such as ion-exchange chromatography and TFF, a more mechanistic approach has been applied. When the theoretical model was compared to the DoE approach for parameter screening of
3.2.4. Yield (case A) The initial raw data assessment of yield demonstrates that this response is robust, because all the measured values of the yield were inside the specified limit, which is above 85% (Table 7). No significant main factor nor interaction term was found during the building of the regression model. When the statistical significance of the initial regression model was assessed with the analysis of variance, the p-value (p > 0.05) revealed that a non-significant model was built for yield, and the model has no lack of fit (p > 0.05, Fig. 4). Taken together, it suggests that the TFF process parameters within the tested ranges do not significantly affect the distribution of yield, thus confirming the robustness of the TFF process. 5
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Fig. 2. pH. (a) Coefficient plot for the pH (b) The ANOVA of the pH showing a non-significant statistical model without lack of fit.
ion-exchange chromatography, both approaches drew a similar conclusion, assuring that the mechanistic approach based on chromatographic theory was valid [17]. In 2009, an article by Watler et al. proposed a mechanistic QbD approach for TFF relying on a theoretical model built with an in-depth understanding of the TFF system in terms of its fluid dynamics, physics, and chemistry [9]. It was noted that the TFF operation is particularly well fitted to a mechanistic model approach because of its well-known mass transfer fundamentals and proven engineering principles and design equations. Also, the FDA's PAT Team and Manufacturing Science Working Group stated that a mechanistic understanding of how process factors affect product performance is a basis for product and process specifications [2]. The authors concluded that the theoretical proven design model for TFF could be used to identify optimal operating conditions. However, no further assessment was given on the robustness of the TFF process in the cases where the value of process parameters varies. This study aimed at assessing the robustness of the tangential flow filtration (TFF) process with QbD approaches. Initially, the criticality of the TFF process parameters was assessed by evaluating their impact on quality/process attributes, occurrence, and detectability based on prior/platform knowledge and available experimental data. Further, the experimental strategy using the design of multivariate and univariate studies was established based on the result of the criticality assessment to evaluate the impact of potential critical process parameters on quality/process attributes and the adequacy of the operation
range for claiming process robustness. This goal of the DoE study was achieved using a resolution V fractional factorial design (25−1) and the test results were analyzed using MODDE 11 software. In all cases of quality or process attributes, initial raw data assessment showed that all the measured values were clearly inside the predefined limits and the models developed were statistically non-significant, which is categorized as case A. Case A provides little information about how process parameters affect product quality or process attributes. However, this is the ideal outcome of the robustness test, because it indicates that the quality or process attributes distributes within the predefined specification ranges and the variation of process parameters within the tested ranges has no significant effect on them [11,15]. Therefore, the whole tested range of process parameters could be claimed as the acceptable operating range, ensuring a robust process and consistent product quality. Monoclonal antibodies (Mabs) often exhibit different molecular behaviors in terms of their response to shear stresses, tendency for aggregation, pH sensitivity, and denaturation. In this regard, DoE results regarding the TFF robustness might be different depending on the molecular characters of Mab. As stated in Result (3.2), the cases for the robustness study could be categorized as 4 cases based on whether the test results are lying in the specified limits and the statistical significance of the model. In the cases of A and B, we can deduce that the TFF process is robust within tested ranges of the process parameters. Case C offers information on how the process parameters affect product quality and process performance, but indicates that the process is not 6
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Fig. 3. Retentate concentration. (a) Coefficient plot for the retentate concentration (b) The ANOVA of the retentate concentration showing a non-significant statistical model without lack of fit.
Fig. 4. The ANOVA of yield showing a non-significant statistical model without lack of fit.
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48.95 47.73
Pass/Pass Pass/Pass
Pass Pass
robust. However, the statically significant model can be used to predict an acceptable process parameter range where all the values of the tested quality/process attributes will be inside the predefined limits. Case D is the least favored scenario because a statistically non-significant model offers poor information and predictability regarding the functional correlation between process parameters and the quality/ process attributes, and the process robustness cannot be claimed. There might be several underlying reasons for this limiting case, and mostly this could be caused by inappropriate parameter selection for the DoE test or by experimental deviation [11,15,18]. As described above, the mechanistic models of TFF derived from relevant theoretical principles are applicable to the TFF process development and optimization of operating conditions, so an extensive DoE study might not be necessarily required for a rapid and efficient TFF process development and optimization. More targeted experiments guided by the mechanistic model are expected to generate an optimized process with reduced experimental and analytical requirements. However, this approach might not fully assess the potential interactions across the process parameters and the adequacy of operation ranges for claiming process robustness. Therefore, verifying the robustness of the TFF process with a DoE study after development and optimization using the mechanistic model would give an assurance to claim the acceptable operating ranges of process parameters. In this regard, it is proposed that the initial development and optimization of the TFF process with the proven mechanistic models and further verification of the process using DoE approaches will enable and accelerate robust and optimized process development, ensuring product quality.
5.96 5.97 99.87 99.85
89.44 91.44
pH Monomer (%)
Yield (%)
Concentration (mg/ ml)
Integrity test (Before/After)
Membrane recovery
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5 5
This work was funded and supported by Dong-A Socio Holdings and Merck Ltd. Korea. Hyun Wee, EunJeong Bae and Taekyeol Lee are employees of Dong-A Socio Holdings and KeunHoe Koo is an employee of Merck Ltd. Korea.
120 120
References
15 15
2.5 2.5
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0.9 1.1 1 2
10 10
Diafiltration buffer concentration (Times) Exp No
Table 8 TFF univariate study result.
Diavolume (DV)
TMP (psi)
Feed protein Concentration (mg/ml)
Load mass (m2/L)
Feed flow (LMM)
Declaration of competing interest
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