A regressive approach to the design of continuous capture process with multi-column chromatography for monoclonal antibodies

Although empirical methods have been introduced in the process development of continuous chromatography, the common approach to optimize a multi-column continuous capture chromatography (periodic counter-current chromatography, PCCC) process heavily relies on numerical model simulations and the number of experiments.

Journal of Chromatography A 1658 (2021) 462604 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma A regressive approach to the design of continuous capture process with multi-column chromatography for monoclonal antibodies Chyi-Shin Chen a,b, Fuminori Konoike a,b, Noriko Yoshimoto a,b,c, Shuichi Yamamoto a,b,c,∗ a Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube,755-8611 Japan Manufacturing Technology Association of Biologics, Shin-kawa, Chuo-ku, Tokyo, 104-0033, Japan c Biomedical Engineering Center (YUBEC), Yamaguchi University, Ube, 755-8611, Japan b a r t i c l e i n f o Article history: Received 19 June 2021 Revised 20 September 2021 Accepted October 2021 Available online October 2021 Keywords: Continuous chromatography Monoclonal antibody Periodic counter-current chromatography Process development Protein A a b s t r a c t Although empirical methods have been introduced in the process development of continuous chromatography, the common approach to optimize a multi-column continuous capture chromatography (periodic counter-current chromatography, PCCC) process heavily relies on numerical model simulations and the number of experiments In addition, different multi-column settings in PCCC add more design variables in process development In this study, we have developed a rational method for designing PCCC processes based on iterative calculations by mechanistic model-based simulations Breakthrough curves of a monoclonal antibody were measured at different residence times for three protein A resins of different particle sizes and capacities to obtain the parameters needed for the simulation Numerical calculations were performed for the protein sample concentration in the range of 1.5 to g/L Regression curves were developed to describe the relative process performances compared with batch operation, including the resin capacity utilization and the buffer consumption Another linear correlation was established between breakthrough cut-off (BT%) and a modified group composed of residence time, mass transfer coefficient, and particle size By normalizing BT% with binding capacity and switching time, the linear regression curves were established for the three protein A resins, which are useful for the design and optimization of PCCC to reduce the process development time © 2022 The Authors Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Introduction Compared to the traditional batch chromatography in the downstream process of manufacturing monoclonal antibodies (mAbs), continuous chromatography for the capture process aims to reduce the production cost by decreasing the amount of column medium and buffer required, and shortening process time In a periodic counter-current chromatography (PCCC), multiple columns are switched in between loading step, post-load wash (PLW) step, and turnaround steps including wash, elution, clean-in-place (CIP), and equilibration Two columns are connected in series for loading while the other column(s) undergoes turnaround cycles The connected tandem columns can thus capture additional product breakthrough compared to a single column In order to accomplish the continuous sample feed, the time for non-loading operation needs to be equal or shorter than the loading time To prevent product loss from the outlet column, the loading needs to be controlled in a threshold range, which is generally defined as – % break- ∗ Corresponding author E-mail address: shuichi@yamaguchi-u.ac.jp (S Yamamoto) through (BT%) from the outlet column or the corresponded % in the dynamic binding capacity (DBC) [1,2] Theoretically, the utilization of column capacity can be increased by PCCC operation compared to a batch process at the same productivity, and the material cost can be cut down because of the buffer consumption reduction [3– 5] Commercial apparatus for PCCC with to 16 columns are already available The control system for column switching in PCCC can be categorized as static control and dynamic control Static control switches columns at a fixed time duration, while dynamic control switches columns when a trigger signal is received from an in-line UV detector As the column performance degradation or the variation of the sample feed concentration with time is not considered in static control, dynamic control can better maintain a stable performance if the monitoring is precise and reliable Although the hardware is well constructed and ready for continuous operation, the process parameters including column switching time and the flow rate corresponding to the feed concentration and desired performances are difficult to obtain without pre-knowledge of the columns, feed materials, adsorption isotherms and mass transfer mechanism https://doi.org/10.1016/j.chroma.2021.462604 0021-9673/© 2022 The Authors Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) C.-S Chen, F Konoike, N Yoshimoto et al Journal of Chromatography A 1658 (2021) 462604 Many studies have been published for the methodology in modeling and simulation of PCCC in different column settings, including the schematic design and loading strategy [2,6–8] Generally, the design of the model-based PCCC approaches includes the adsorption isotherm of mAb for protein A chromatography columns related to the binding capacity, and the mass transfer kinetics dominated by the (intraparticle) pore diffusion Although analytical solutions such as constant pattern approximation (CPA) can be used in batch loading [9,10], numerical calculation is more widely adopted as the limitation of continuous flow in PCCC requires more process parameters to be synchronized The modeling of PCCC requires the knowledge of the adsorption, mass transfer, and the optimal loading threshold, which can be determined by iterations of the numerical calculation However, the numerical solution is necessary every time when there are changes in the column or the feed The number of columns in PCCC will also affect the switching condition The cost in development in a continuous multi-column chromatography process is thus higher than a batch operation [11] In this study, we aim to develop a rational method to assess the column switching threshold in PCCC Three commercially available protein A media were used By simulating the loading profile in PCCC using mechanistic models, the operation conditions with the maximum productivity can be obtained Series of experiments in column (2C) - PCCC and column (4C) - PCCC under different columns and concentrations were conducted for verification The process performances including productivity (P), resin utilization, and buffer consumption were also examined for each PCCC process Based on the simulations, the effects of feed concentration and resin particle size to the loading cut-off BT% were investigated The results were used to develop a linear relationship between BT% and new groups including particle size and mass transfer coefficients in PCCC processes, which can be used to determine the BT% threshold IV Loading step: The sample feed is loaded to the (re-connected) tandem column at Fv1 for the duration t2C The steady state PCCC cycle is composed of step (Ⅱ) to step (Ⅳ) The general assumptions for each PCCC calculation in this study include (a) constant amount of loading for every cycle, (b) PLW, (c) identical columns in multi-column settings, (c) 100% recovery during elution, and (e) no degradation or fouling in the column and the sample The schematic diagram of a 2C-PCCC process can be shown as Fig 1(A) The design variables in a 2C-PCCC process in this study include the time duration at each step (tnonload , tPLW , and t2Cload ), BT% at the switching point, and flow rates Fv1 and Fv2 The non-loading protocol and initial loading flow rate are pre-defined, and BT% at switching point and Fv2 are calculated by satisfying the constraints in the PCCC setup The calculation results for BT% and Fv2 are aimed to achieve the highest productivity while maintaining less than BT1% (C/C0 1, it is more beneficial to use PCCC Instead of conducting numerous experiments or simulations for several C0 values every time, the RU∗ and BF∗ vs P may be developed with just the extremum of C0 as high linearity exists (R2 > 0.9) based on the results in Fig The trends shown in Fig indicate that at lower C0 , PCCC has more advantages in buffer consumption and resin utilization compared to the batch operation It is also shown that PCCC is more beneficial when mass transfer rates are low such as large particles In the ideal case where the diffusion mass transfer is very fast, the performance difference between PCCC and batch operation will disappear 4.3 Process efficiency Since the feed concentration C0 influences the shape of BTC heavily, it also affects the process parameters in PCCC The product titer from upstream is increasing with the improvement in fermentation technology for mAbs [23–25], which makes the capability to process high C0 essential in the downstream process The maximum P for 2C-PCCC with MSS, AA3 and KC3 were obtained by the simulation The C0 from 1.5 to g/L were examined as the average titer for mAb products is expected to be above g/L with recent industrial standard [25] Corresponded process performances were calculated based on the loading results derived from R, and the process parameters were listed in Supplementary Material Table S3 To further evaluate the benefit of PCCC compared with the batch chromatography, the two process evaluation parameters were introduced; relative buffer consumption (BF∗ ) and relative resin utilization (RU∗ ) They were defined as the ratio of the performance in PCCC over batch at the same productivity by Eq (22) Fig shows BF and RU as a function of P for 2C-PCCC and a batch repeated cyclic operation (RCO) By comparing the results between PCCC and RCO, the relative process performances can be calculated Lower BF∗ (< 1.0) and higher RU∗ (> 1.0) imply the superiority of PCCC 4.4 Determination of breakthrough threshold in PCCC We have already shown that the DBC normalized by the SBC, E∗ = DBC/SBC is a linear function of the dimensionless group F∗ = dp /[Ds (Z/u)] [16,19,26] E∗ = DBC = f (F ∗ ) = f SBC dp Ds (Z/u ) (23) where Ds is the stationary phase (pore) diffusivity The E∗ - F∗ linear relationship was used in batch operation with DBC10% or DBC1% [26] However, as the switching BT% in PCCC is influenced by other factors such as the number of columns and the non-loading protocols, the application of Eq (23) to PCCC is not straightforward The important parameter for PCCC, BT%, changes with the loading flow rate Although automatic iterations can be used to find the optimal BT%, calculation time heavily depends on the computation power and algorithms applied To improve the process development speed and the accessibility for general users of PCCC, we examined the simulation data for different protein A media including MSS, AA3, and KC3 with the same non-loading protocol (Supplementary Material Table S2) at different C0 to find a good correlation for BT% As the switching BT% in PCCC over different RTs is not a constant DBCx% , BT% was normalized as BT%norm with SBC and tswitch After replacing Ds /dp with dp Ks /60, the following relationship BF ∗ = BFPCCC /BFbatch RU∗ Productivity (g/L/hr) Sim 10 cycles performed cycles performed Averaged values from cycles Overall productivity (start-up time and end time included) Calculated values from Contichrom software sidered in the simulation Since extra holding time will be needed from the start to the end with additional line-wash step employed in the PCCC apparatus, slight deviation exists between the overall productivity and the estimated productivity from Eq (14) Nevertheless, the results show that our model can simulate the concentration profiles in the columns in PCCC, and can be used to derive the critical operation parameter, tswitch accurately with errors below 6% The model can be applied to different process conditions such as Ncol and non-loading protocol RU ∗ = RUPCCC /RUbatch tswitch (min) Expc (22) BF∗ Fig shows and vs P curves as a function of C0 for 2C-PCCC Each curve has a parabolic to polynomial profile with an optimal point, the lowest BF∗ or the highest RU∗ The minimum/maximum values were not clearly shown at high C0 for MSS and KC3 as the loading amount due to low DBC is so small that the short loading time cannot improve the productivity anymore By using the same non-loading protocols for all three resins, the highest RU∗ or the lowest BF∗ values can be linearly correlated with P as shown in Fig Note that (BF∗ × RU∗ ) calculated by the correlation equations are constant, which can be easily understood by Eqs (8) and (9) From these correlations, the effect of C0 can be understood For instance, the resin utilization can be improved up to C.-S Chen, F Konoike, N Yoshimoto et al Journal of Chromatography A 1658 (2021) 462604 Fig The process performances including RU (open symbols) and BF (filled symbols) over P from AA3 at C0 = g/L of 2C-PCCC (circle) and batch RCO (square) The following non-loading protocol was used 1) equilibration 3CV, RT= 2) PLW 2CV, RT= 0.5 min, 3) Wash 2CV, RT= 0.5 min, 4) Elution 4CV, RT= 0.5 min, 5) CIP 3CV, RT= 1min Fig RU∗ and BF∗ vs P (A) – (B) MSS, (C) – (D) AA3 and (E) – (F) KC3 Comparison was made between values from 2C-PCCC and batch processes Regression curves were developed among the optimal points (filled or bold) with the highest RU∗ or lowest BF∗ from each concentration The mass transfer coefficients used are shown in Table S3 C.-S Chen, F Konoike, N Yoshimoto et al Journal of Chromatography A 1658 (2021) 462604 Fig Plots of BT%norm (y-axis) over the new functional group F’ (x-axis) from three protein A media (A), (C), and (E) are for different column PCCC settings and feed concentration as marked The scatter plots of switching time over RT from the same dataset are illustrated in (B), (D), and (F) with the same order The linear correlation of BT%norm and F’ are labeled with regression coefficient R2 The mass transfer coefficients used for the simulation are labeled for each resin RT are presented in Table The differences of BT% between the linear regression and iterative results from numerical solutions are within 10% As the calculation time of iterations usually takes up from minutes to hours (depending on the scanning area), the linear regression can reduce significant calculation resources and provide the comparable results The schematic illustration for the determination method of BT% is presented in Fig Although the example was for 3C-PCCC settings, 4C-PCCC will have the same trend since tswitch and BT% will not change with the number of columns, only productivity and performance have dependency on column numbers when we compare 3C-PCCC with 4C-PCCC The linear relationship still holds for C0 = g/L with the same resins and the non-loading protocol as shown in Fig (C) with R2 = 0.98 and similar performance between BT%reg and BT%sim The slope of BT%norm and F’ becomes steeper for higher feed concentration, which matched to the BTCs (Fig 2) Higher C0 leads to steeper BTCs and shorter VB,1% and makes the switch time shorter, which is reflected in Fig (D) can be derived between the normalized BT% and a new modified group (F’) BT%norm = BT% (SBC )tswitch = f( 60 ε (RT )Ks dp ) = f (F ) (24) In an example using 3.5 g/L of IgG as feed for the three different resins in a 3C-PCCC setting, results from Eq (24) is shown in Fig (A) – (B) A linear relation of BT%norm and F’ was observed for the three different media with R2 = 0.97 The points that diverged out from the linear regression are the extreme values at the lowest RT in 3C-PCCC Since higher RT is usually adopted in actual processes, the outliers in Fig (A) could be possibly excluded from the linear trend The switch time can also be expressed as a linear function of RT as shown in Fig (B) As a result, the BT% at different conditions can be estimated by the relationship in Eq (24) once the regression has been obtained The comparison of BT% from the regression (BT%reg ) and BT% from numerical simulations (BT%sim ) at C.-S Chen, F Konoike, N Yoshimoto et al Journal of Chromatography A 1658 (2021) 462604 Table Comparison of BT% from linear regression and numerical simulation in 3C-PCC at C0 = 3.5 g/L under RT = Resin SBC (g/L)a tswitch (min) F’ (μm−2 ) BT%norm (%Lg−1 min−1 ) BT%reg (%) BT%sim (%) Error (%)b MSS AA3 KC3 91.00 72.10 109.9 31.82 40.86 38.04 0.14 0.27 0.15 0.021 0.035 0.021 60.28 103.3 87.78 67.28 99.43 83.70 -10.4 3.93 4.87 a b Calculated from fitted Q, Vt , and ɛ (BT%reg − BT%sim )/BT%sim Fig Steps for determining BT% for PCC switching from regressions developed in Fig In 2C-PCCC, the starting RT cannot represent the whole loading since the flow rates may be different during the single column loading (Fv2 ) and the tandem column loading (Fv1 ) The tswitch in Eq (24) was replaced by t2C , which is the loading time for the tandem column The modified BT%norm is re-written to Eq (25) BT%norm = BT%/SBC/t2C = f ( 60 ε (RT )Ks dp ) = f (F ) matography [1–8] However, compared with the standard batch capture chromatography, choosing the proper operating conditions and/or column properties is difficult By replacing experimental runs with in silico simulations based on mechanistic models, process performances and operation parameters can be determined in a systematic way Design strategies for PCCC processes that match to the current available PCCC instruments were presented in the study The methods of modeling and process simulation for repeated cyclic batch operation (RCO) and PCCC were developed Among the process performances, productivity P describes the amount of product being processed per volume of resin per unit of time, which can connect to the time needed to process a certain amount of the material As buffer consumption BF is defined as per liter of buffer required to process per gram of mAb, it can be related to buffer cost easily Resin utilization RU, on the other hand, expresses the amount of product can be loaded per volume of resin per cycle, which correlates to the cleaning cycles on the resin affecting its lifetime As easily shown by Eq (9), BF is inversely proportional to RU While protein A resin is expensive, higher resin utilization can lead to lower frequency of the replacement of resin hence reducing overall production cost [28–32] Although PCCC cannot eliminate the tradeoff between P and other performances, it is possible to achieve better balance in RU or BF while maintaining the same or higher P compared to batch operation Previous studies [5–7] have also shown that P of PCCC is not higher than that of batch operation whereas BF of PCCC can be reduced at the same P In this study effect of feed concentration C0 , and mass transfer coefficient (particle diameter, dp ) on the relative productivity and buffer consumption values were shown Higher C0 and smaller dp resulted in higher P and lower BF, which are simi- (25) With the same non-loading protocol, results for BT%norm in 2C-PCCC are shown in Fig (E) By considering the two flow rates with t2C , a linear relationship between BT%norm and F’ with R2 = 0.96 was obtained The slope was steeper than the one in 3C-PCCC with the same C0 By reducing Fv2 for loading, DBC increases and elevates the possible loading amount in the column Hence, the BT% for column switching can be increased in 2C-PCCC although t2C in Fig (F) is similar to tswitch in the 3C-PCCC By expressing a normalized BT% as a function of F’, a generalized linear relationship can be acquired across resins in different particle diameters without dependency on column geometry with the same non-loading protocol applied As shown in our previous study [19], by using such resin properties as particle size, SBC, and mass transfer coefficient (pore diffusion coefficient), PCCC operating conditions can be easily estimated The linear relationship can be constructed under different feed concentrations or the number of columns in PCCC settings including 2C-, 3C-, and 4C-PCCC Discussion In manufacturing of bio-based drugs, continuous integrated process has become a production strategy enabling agile production of high quality drugs with less regulatory oversight [27, ] PCCC is a well-established method for continuous capture chro9 C.-S Chen, F Konoike, N Yoshimoto et al Journal of Chromatography A 1658 (2021) 462604 lar to the repeated batch cyclic operation (RCO) [19] However, the benefit of PCCC over RCO decreases with decreasing dp In addition, the column back pressure p increases with 1/dp [19] The calculations in this study were carried out with the column height Z = –5 cm For such short columns, p was below 0.2 MPa at residence time RT = even for dp = 50 μm Process-scale chromatography column height is longer than 10–15 cm As p is over MPa at RT =1 for Z = 15 cm with dp = 50 μm, longer RT (lower flow velocity) may be needed to lower p as the process pressure It is also important to keep in mind that higher P with small dp results in shorter tC , which in turn requires a larger number of cycles Therefore, understanding the fouling mechanism to develop a proper cleaning protocol or monitoring the fouling is essential for a stable PCCC operation [33–35] Although column fouling is not considered in the present study, it is possible to introduce a safety factor (e.g 0.9) in the normalized BT% or lower the BT% from 1% to 0.5 – 0.8% depending on the expected resin life cycle, and develop the corresponded linear correlation next In our 2C-PCCC experiments (five runs), the column performance did not change after 72 hour operation according to the yield (>80%) measured by the UV absorbance at 280 nm and the purity (>95%) determined by size exclusion chromatography with a TSK G30 0SWXL HPLC column (data not shown) Another important point to notice is that the fitting of BTC at shorter RTs was not good compared with the BTC at longer RTs as shown in Fig Therefore, simulated results for high P values in Fig are not very precise However, the trends shown in Fig not change even when somewhat different fitted values from different chromatography models are used It was not our purpose to show how to find the optimum conditions As is clear from Figs and 4, BF increases sharply near the maximum P, and hence it is not beneficial to choose the maximum P conditions Acknowledgment This research was partially supported by AMED under Grant Number JP20ae0101056, JP20ae0101058, JP21ae0121015, and JP21ae0121016 Supplementary materials Supplementary material associated with this article can be found, in the online version, at 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D.G Shirazi, A Felinger, A. M Katti, Fundamentals of Preparative and Nonlinear Chromatography, Academic Press, 2006 [18] N Yoshimoto, Y Sugiyama, S Yamamoto, A simple method for calculating the productivity... such as the number of columns and the non-loading protocols, the application of Eq (23) to PCCC is not straightforward The important parameter for PCCC, BT%, changes with the loading flow rate Although