An optimization study of an integrated periodic counter-current chromatography (PCC) process in a monoclonal antibody (mAb) downstream process at lab scale, is presented in this paper. The optimization was based on a mechanistic model of the breakthrough curve in the protein-A capture step.
Journal of Chromatography A 1621 (2020) 461055 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma Optimization study on periodic counter-current chromatography integrated in a monoclonal antibody downstream process Joaquín Gomis-Fons a,b,∗, Niklas Andersson a, Bernt Nilsson a,b a b Dept of Chemical Engineering, Lund University, Lund, Sweden Competence Centre for Advanced BioProduction by Continuous Processing, Royal Institute of Technology, Stockholm, Sweden a r t i c l e i n f o Article history: Received 13 January 2020 Revised March 2020 Accepted 17 March 2020 Available online 19 March 2020 Keywords: Periodic counter-current chromatography Process optimization Process integration Downstream processing Monoclonal antibody purification a b s t r a c t An optimization study of an integrated periodic counter-current chromatography (PCC) process in a monoclonal antibody (mAb) downstream process at lab scale, is presented in this paper The optimization was based on a mechanistic model of the breakthrough curve in the protein-A capture step Productivity and resin utilization were the objective functions, while yield during the loading of the capture column was set as a constraint Different integration approaches were considered, and the effect of the feed concentration, yield and the protein-A resin was studied The breakthrough curve and the length of the product recovery, which depended on the integration approach, determined the process scheduling Several optimal Pareto solutions were obtained At 0.5 mg mL−1 and 99% yield, a maximum productivity of 0.38 mg mL−1 min−1 with a resin utilization of 60% was obtained On the other hand, the maximum resin utilization was 95% with a productivity of 0.10 mg mL−1 min−1 Due to the constraint of the process scheduling, a lower productivity could be achieved in the integration approaches with higher recovery time, which was more remarkable at higher concentrations Therefore, it was shown that a holistic approach, where all the purification steps are considered in the process optimization, is needed to design a PCC in a downstream process © 2020 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 The biopharmaceutical market demand is constantly changing and there is an increasing pressure on a price reduction for a global access to biological drugs [1,2] Continuous bioprocessing is a way to reduce biologics price by increasing the productivity and diminishing the manufacturing costs [2,3] A significant improvement has been carried out in upstream by progressively shifting from fed-batch to perfusion bioreactors However, the productivity in downstream has not increased accordingly and nowadays a big proportion of the manufacturing cost are due to the product purification [4,5] Continuous downstream processes, like periodic counter-current chromatography (PCC) [6,7], Capture SMB [7,8] or multi-column counter-current solvent gradient purification (MCSGP) [9,10], have gained interest in the last years These processes offer a higher productivity and resin utilization, while keep- ∗ Corresponding author: Dept of Chemical Engineering, Lund University, P.O Box 124, SE-21100 Lund, Sweden E-mail addresses: joaquin.gomis_fons@chemeng.lth.se (J Gomis-Fons), niklas.andersson@chemeng.lth.se (N Andersson), bernt.nilsson@chemeng.lth.se (B Nilsson) ing a similar yield than the one obtained in a batch process [7,10] They all are based on multiple columns, in a way that a column is loaded with the outlet of another column In MCSGP, the eluted impurities containing product is loaded onto another column, and it is usually applied for polishing steps [10] For the capture step, the Capture SMB (2-column PCC) and the 3-column or 4-column PCC are common alternatives [7] In a PCC operation, two columns are interconnected and loaded while the product is recovered in one or two more columns (depending if it is a 3-column or 4-column PCC) [6] To be able to run a PCC process, a feed continuity constraint must be fulfilled so that the harvest can be continuously loaded onto the capture columns [6] Additional scheduling constraints can also be applied to avoid product loss during the loading Furthermore, to make the most of the potential of a PCC process, resin utilization and productivity should be maximized For those reasons, the PCC is a process that must be carefully designed Several authors have used empirical models of the breakthrough curve to design a PCC [6,11] While these models are useful to get the process conditions that make the PCC operate, they fail in obtaining an optimal process, since empirical models are only valid for the residence time and feed concentration at which the experiments are run On the contrary, https://doi.org/10.1016/j.chroma.2020.461055 0021-9673/© 2020 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/) J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 mechanistic models take into account the adsorption equilibrium and kinetics, and the mass transfer limitations [12] Therefore, once the model is calibrated, an optimization over the whole range of residence times and concentrations, and other process parameters like loading time, can be performed to obtain one or several optimal solutions With the help of the model, investigations around the PCC process, like the effect of the feed concentration or the particle diameter can be carried out [7,13] Model-based optimization of a PCC process has been done previously [7,13] However, in these cases only the capture step is considered when optimizing the PCC process But in a purification train, the capture step is usually followed by a virus inactivation process and several polishing steps The integration of the capture step with the rest of the purification steps affects the PCC optimization strongly, and depending on the integration approach, the effect is more or less significant This is because the time that takes to recover the product (recovery time), which is affected by the integration approach, has to be lower than the cycle time to meet the feed continuity constraint in PCC [6] Therefore, if the whole purification process is considered instead of only the capture step, the recovery time is longer, and the process scheduling is affected In this work, we performed an optimization study of a PCC integrated in a monoclonal antibody (mAb) downstream process, for which a mechanistic model of the breakthrough curve in the protein-A capture step was used Three integration approaches were evaluated and their influence on the Pareto front of optimal solutions is presented The effect of the chromatography resin, the feed concentration and the yield were also studied, and the process was compared to a 1-column batch capture step The integrated PCC process was verified experimentally at lab scale with two different Protein-A resins Process integration alternatives The way the PCC process in the capture step is integrated with the rest of the purification train is not an obvious choice In this work, several alternatives are considered depending on the number of systems or pumps that are used, and whether a surge vessel after the PCC is used or the eluate is directly sent to the next step (see Fig 1) In this case, a 3-column PCC is chosen because it provides a higher productivity and resin utilization, compared to Capture SMB or a 4-column PCC [7] However, the same reasoning around the PCC and conclusions extracted from this work could be applied for a four-column PCC or Capture SMB In all cases, the process consists of the same process steps corresponding to a typical mAb purification process: 1) a capture step with a protein A resin, 2) a virus inactivation (VI) at pH 3.5 for 60 min, 3) a cation exchange chromatography (CEX) step in bind-and-elute mode with a gradient elution, 4) an anion exchange chromatography (AEX) step in flow-through mode A final ultrafiltration-diafiltration step for product formulation is not included in this process because the volume of the final product from the chromatography steps at lab scale is much lower than the minimum starting volume in the ultrafiltration step, as shown in [14], which would require to use much higher column volumes in the process validation Anyway, at larger scales, the integration of the ultrafiltration step with the rest of the downstream process including PCC, could easily be carried out the same way it is presented in [14] This could affect the recovery times, but the deductions and conclusions from this work would still apply 2.1 Integrated PCC process in one system The first alternative to run the PCC process with the virus inactivation and the polishing steps (CEX and AEX steps) is to integrate all steps in one chromatography system without any hold-up volume between the steps (Fig 1A) In order to avoid hold-up volumes, the methodology followed in [14–16], called Integrated Column Sequence (ICS), was used The basic principle is that the eluate of one column is loaded directly onto the next column That requires synchronization between the elution of a column and the loading of the next one In addition, since all steps are performed in a single chromatography system, they have to be carried out in series Consequently, in this case the recovery time corresponds to the time it takes for the capture step and the polishing steps to recover the product The incubation time of the virus inactivation is not included because during this time, regeneration and equilibration steps of the other columns are carried out This is a constraint in the minimum cycle time that limits the productivity On the other hand, an integrated and continuous mAb purification process in only a chromatography system is a compact and cheap alternative that can be specially interesting for lab-scale PCC runs 2.2 Integrated PCC process in two systems In this alternative, the process is integrated the same way as in the previous one, but two chromatography systems are used instead (Fig 1B) A system is used for the PCC process and the virus inactivation, and another system is used for the polishing steps The main difference compared to the one-system alternative is that the capture step and the polishing steps are run in parallel That means that the minimum cycle time is not the sum of the process times of the capture and polishing steps, but the highest of them instead, that is, when one system is done, it waits for the slower one That leads to an increase of productivity due to a reduction of the process time However, the investment cost and the space occupied in the lab is twice as high as in the first process alternative 2.3 PCC process with a surge vessel in two systems The last process choice considered in this work is the use of a surge vessel between the capture step and the virus inactivation step (Fig 1C) The vessel allows for a desynchronization of the capture step respect to the polishing steps, that is, the minimum cycle time would be only the recovery time in the capture step Therefore, in terms of the PCC design, this alternative is equivalent to the PCC with the capture alone, without the rest of steps That is an improvement respect to the previous alternative, since the capture step is usually shorter than the polishing steps, based on a typical mAb purification process [17], thus leading to a lower minimum PCC cycle time, which means that higher productivity can be achieved However, this option has some drawbacks Firstly, the complexity of the process is significantly increased Since the filling and the emptying of the surge vessel, both discrete operations, are not synchronized (as it can be seen in Fig 4), the liquid level before emptying the vessel is different from cycle to cycle This means that a level sensor and a controller are needed to keep the liquid level in the vessel the same after each cycle In addition, this desynchronization involves that, for certain PCC cycles the volume in the vessel is higher than for other cycles Therefore, the column volumes of the polishing steps are over-designed for those cycles with lower volume in the surge vessel Furthermore, the presence of a surge vessel increases the risk of product degradation and the residence time of the product, increases the capital cost, and contributes to slowing down the scaling up time [2] Another pitfall of this process is the slower start-up due to the need of filling the vessel up to a minimum level before starting the polishing steps Longer start-ups lead to product loss and higher cost [2] J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 Fig Process alternatives for the integration of PCC with virus inactivation and polishing steps: (A) Process alternative 1: All steps in one chromatography system, (B) Process alternative 2: the capture and the virus inactivation in a system (on the left), and the polishing steps in another one (on the right), (C) Process alternative 3: a surge vessel is used between the capture and the virus inactivation, and two systems are used Materials and methods 3.1 Materials Two ÄKTATM pure 150 units were used to perform all the calibration and validation experiments Each of the chromatography systems is equipped with the following devices: three pumps (pumps A and B, and sample pump) with inlet valves for each of them to be able to select different buffers, a column valve with inbuilt pressure sensors, a loop valve, an outlet valve, several versatile valves, with which different flow paths can be applied, a conductivity sensor, a pH sensor, and two UV monitors For the capture step, two protein A resins with different particle size were evaluated One is mAb Select SuReTM , with 85 μm in particle diameter, and the other one is mAb Select PrismATM , with a particle diameter of 60 μm The buffers and flow rates for the capture step, except for the loading flow rate, were based on [18] The VI was done in a 50 mL SuperloopTM provided by GE Healthcare Life Sciences (Uppsala, Sweden) The CEX resin was a CaptoTM S Impact, and the AEX resin was a CaptoTM Adhere The process information regarding buffers and flow rates for these two steps, was taken from [17] HiTrapTM prepacked columns with a volume of mL were used for all chromatography steps All columns and resins, along with the chromatography systems, were provided by GE Healthcare Life Sciences (Uppsala, Sweden) An in-line conditioning between the steps was performed by dilution Regarding the conditioning buffers, 100 mM acetic acid with a dilution factor of 0.5 respect to the eluted volume, was used to set the pH at 3.5 J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 in the VI step, whereas a buffer with 50 mM sodium acetate and 100 mM sodium hydroxide with a dilution factor of 0.3 was used to increase the pH after the VI for the loading of the CEX column The eluate of the CEX column was diluted with a factor of 1, with a 50 mM sodium phosphate solution at a pH of 6.8 A Cleaning-InPlace (CIP) was performed after the elution of the columns, with 0.1 M NaOH for the protein-A resins and M NaOH for the rest of steps 3.2 Methods 3.2.1 Process modelling The breakthrough curve of the capture step was modelled in order to simulate and optimize the PCC process, based on previous implementations of the general rate model in the research group [19–21] For this particular application, a modification was introduced based on the model from Perez-Almodovar and Carta, 2009 [12] This model assumes heterogeneous binding mechanism with fast and slow binding sites The concentration in the mobile phase and in the particle are described by Eqs (1) and (2), with the boundary conditions in Eqs (1a), (1b), (2a) and (2b), respectively Eq (3) is the description of the kinetics: ∂c ∂ c v ∂ c − εc = Dax − − k c − c p |r=r p ∂t εc ∂ z εc r p f ∂z (1) ∂c v = (c − cF ) at z = ∂ z εc Dax (1a) ∂c =0 ∂z (1b) ∂ cp ∂ cp ∂ = De f f r2 ∂t ∂r r ∂r ∂ cp =0 ∂r at z = L − εp ∂ ( q1 + q2 ) ∂t at r = kf ∂ cp = (c − c p ) at r = r p ∂r De f f ∂ qi = ki [(qmax,i − qi )c p − qi /K ] ∂t (2) (2a) (2b) (3) Where c is the mobile phase mAb concentration, cF is the inlet mAb concentration, cp is the mAb concentration inside the particle, q is the adsorbed mAb concentration Dax is the axial dispersion coefficient, v is the superficial fluid velocity, kf is the mass transfer coefficient in the particle layer, Deff is the effective pore diffusivity, ɛc is the column void, ɛp is the particle porosity, rp is the particle radius, L is the column length, qmax is the maximum column capacity, K is the Langmuir equilibrium constant, and k is the adsorption rate constant, where i can be (fast kinetics) or (slow kinetics) 3.2.2 Multi-response experiments The heterogeneous model contains several parameters that were obtained in different ways The column void (ε c ) and particle porosity (ε p ) were obtained from [22] and were based on isocratic elution experiments with dextran with molecular weights from 10 to 670 kDa The axial dispersion coefficient Dax using the Peclet number (Pe) correlation [23] The mass transfer coefficient is also obtained through a correlation based on the Sherwood, Reynolds and Schmidt numbers [24], where the density and the viscosity are assumed the same as for water at 20 °C The rest of the parameters were obtained from frontal analysis of the breakthrough experiments at different mAb concentrations and flow rates The column volume (Vc ) was mL both for mAb Select SuRe and PrismA Flow rates (FF ) of 0.2, 0.5, and 1.5 mL min−1 (30, 75, 150 and 225 cm h−1 ) were applied at a constant mAb concentration of 0.5 mg mL−1 Several feed concentration values were also tested (0.25, 0.5, 1.7 and mg mL−1 ) at a constant flow rate of 0.5 mL min−1 (75 cm h−1 ) The columns were loaded until the outlet concentration was almost as high as the feed concentration (at t = tf ) The last part of the breakthrough curve until reaching the feed concentration was extrapolated to calculate the total amount of adsorbed protein per resin volume (κ ) as follows: κ= tf FF cF t f − ∫ c|z=L dt Vc (1 − εc ) (4) With the adsorbed concentration for every corresponding mobile phase concentration, the isotherm parameters (the equilibrium constant K and the total column capacity qmax in Eq (5) were obtained by fitting the data to a Langmuir adsorption isotherm with the least-square method (Fig S1, in Supplementary Material), where qmax is expressed as adsorbed product per volume of resin κ = qmax K cF + K cF (5) To obtain the maximum capacity for the fast and the slow kinetics (qmax,1 and qmax,2 ), a parameter between and was introduced (w), where meant that all sites were adsorbed with slow kinetics, and meant the opposite: qmax,1 = wmax, qmax, = (1-w) The effective diffusivity (Deff ), the kinetics constants (k1 and k2 ), and the parameter w were obtained by running a calibration using the MATLAB nonlinear least square curve-fitting solver lsqcurvefit Since the sampling frequency of the UV sensor was constant, the amount of experimental points was larger in the longer experiments In order to have a balanced calibration with equal importance for all the experiments, the number of points was adjusted to 200 for all curves by interpolating the raw data to obtain a new point for each time, which resulted in identical curves as the raw ones but with the same number of points Before this reduction of points, the curves were also smoothed to avoid the noise The spatial derivatives were discretized using the Finite Volume Method, as shown in [25], with 10 particle grid point and 30 axial grid points This is a considerably low number, which leads to an increased numerical dispersion, but it was shown to be enough for a good fitting of the experimental data, and increasing this number would have involved much longer calculation times The resulting ordinary differential equations were solved with MATLAB’s ode15s The value of all the parameters and properties used in the model are presented in Table S1, in Supplementary Material 3.2.3 PCC optimization The Periodic Counter-current Chromatography (PPC) operation requires synchronization between the columns In a three-column PCC in particular, two columns are loaded while the product is being recovered in the third one [6] That means that the product recovery is at least so long as the loading of the columns In other words, the cycle time (tcycle ), which is the time that takes to completely load a capture column, must be equal or higher than the recovery time, defined as the time to recover the product in the capture step plus any necessary waiting time, which depends on the process alternative Fulfilling this constraint would be enough to make the process work [6] However, to have an optimal process, it is desired to: maximize productivity (P), defined as adsorbed product per column volume and time (Eq (6)), where τ is the residence time during the loading; and maximize resin utilization (U), defined as adsorbed product divided by the maximum adsorbed amount of product at that feed concentration, which de- J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 pends on the Langmuir adsorption isotherm (Eq (7)) t P= tcycle cF − ∫0cycle c|z=L dt τ (1 − εc )tcycle (6) t U= tcycle cF − ∫0cycle c|z=L dt τ (1 − εc )qmax 1+K KcFcF (7) Apart from the time constraint, the yield (Y), defined as the amount of the adsorbed product divided by the loaded product (Eq (8)), is also set as a constraint, in order to avoid loss of product breaking through the columns The yield constraint was set to 99% In a second step of the process, two columns are interconnected during the wash so that the non-adsorbed product of the first column gets captured in the second column, and meanwhile the third column gets loaded Setting a yield constraint also avoids product loss in this step Both the objective functions and the yield constraint were calculated at steady state, when the breakthrough curves from the three columns were constant and equal to each other t Y =1− ∫0cycle c|z=L dt cF tcycle (8) The decision variables were the residence time during the loading (τ ), which relates to the loading flow rate, and the fraction of the breakthrough curve height respect to the maximum level during the interconnected step (xf ), i.e the step where the two columns being loaded are interconnected The lower and upper bounds for these two variables were 0.25 and for the residence time, and 0.20 and 0.95 for the xf The pressure drop was also considered, but for the lower limit of the residence time (corresponding to the highest flow rate), the pressure drop was less than the maximum, so no explicit pressure constraint was then necessary in the optimization To sum up, the optimization problem consists of the following elements: minimize w.r.t f (x ) = −[P, U ] ∈ R2 x= τ , x f ∈ R2 s.t 0.25 < τ < 0.20 < x f < 0.95 Y > 0.99 tcycle > trec An optimization was run for each process alternative, where the only difference was the last constraint (tcycle > trec ), with trec being the recovery time The optimization solver was gamultiobj, a function of the Global Optimization toolbox in MATLAB This method is based on genetics algorithms, and it allows to run constrained multi-objective optimization problems to obtain a set of optimal solutions, the Pareto front The population size was 150, with a stop criterion based on a normalized function tolerance of 10−6 , a Pareto fraction of 0.35, and a migration faction of 0.2, which takes place every 20th generation The fitness and the constraint functions were computed in parallel, thus allowing to reduce the calculation time, which was around 42 h per optimization 3.2.4 Experimental set-up For the process validation, a chromatography system is used for the capture step, with the three-column PCC, and the virus inactivation, and another system is used for the polishing steps, i.e the CEX and the AEX steps The implementation of a PCC process in an ÄKTA pure system requires the use of versatile valves, which enable the different flow paths present in this process In Fig 2, the loop valve (LV) determines which column is loaded first (red solid line), and which column goes through the recovery step (blue dotted line) Three versatile valves are used to lead the flow to the next column, to waste or to the virus inactivation loop, which is placed in another versatile valve Pump A is used for wash, equilibration, elution and regeneration buffers, Sample pump is used for the feed, and Pump B is used to dilute the eluate, being the dilution point just before the VI loop The polishing steps are implemented in another ÄKTA pure system following the same flow path and process concept as in [14– 16] The only difference in the set-up is the use of the column valve In this case, this valve is right after the VI loop (see Fig 2) By using three different positions, this valve enables the simultaneous regeneration and equilibration of the CEX and AEX column by using the pumps A and B for the CEX column, and the Sample pump for the AEX column This is a way of decreasing the process time and eventually increase the productivity In addition, the column valve allows to empty the VI loop onto the CEX column by interconnecting the two chromatography systems The Sample Pump is used to increase the pH after virus inactivation and to condition the eluate from the CEX column before being loaded onto the AEX column In Figure S2, in Supplementary Material, several possible flow paths are shown for a better understanding of the process set-up 3.2.5 Process control Both chromatography systems are controlled by the research software Orbit [26] Details about how Orbit is applied to an industrial purification case can be found in [14,15] or [16] In this work, the operation of Orbit is similar, but an additional feature is included so that the two systems can communicate to each other and synchronize Two Orbit programs are created, one for each system, and the synchronization between them is based on flags in form of binary communication When one of the systems is finished with a process, the corresponding Orbit program sends a flag to the other Orbit controlling the other system, and it remains waiting for another flag from the other Orbit When the other system is also finished with its task, its corresponding Orbit sends a flag too Once both Orbit programs have sent a flag to each other and interpreted the other system’s flag, they are synchronized, and the overall process can continue This process is repeated every time there are parallel processes, and both systems must synchronize with each other to continue to the next step 3.2.6 Analytics Each ÄKTA system includes one conductivity, one pH and two UV sensors at 280 nm that measure continuously inline In the PCC process, a UV sensor is used after the first column that is loaded, and the other UV sensor is used for the elution In the polishing steps, a UV sensor is used after each column, and the outlet of the VI loop is also detected by one of the UV sensors in the polishing ÄKTA system Additionally, the pool from the AEX column is collected every cycle, and for the last cycle the pool from the capture step is also collected to check the concentration, and then be able to calculate the experimental resin utilization, yield and productivity of the capture step, and compare it with the model to validate the obtained optimal solutions All samples taken were measured on an ÄKTA pure 150 system by injecting a known volume onto a J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 Fig Process diagram of the PCC process integrated in a downstream process with two systems: one for PCC and virus inactivation (on the right area), and another one for the polishing steps (on the left area) The red solid line represents the raw material being loaded onto capture columns and (C1 and C2) Capture column (C3) is washed, eluted and regenerated (blue dotted line) On the left, the CEX column is eluted and the product is directly loaded onto the AEX column (blue dashed line) and collected, and the sample pump is used to dilute the stream between the two columns (green dashed line) Versatile valves (VV), a loop valve (LV), a column valve (CV) and an outlet valve (OutV) are used to define the flow paths Grey lines represent inactive flow paths On the right, a simplified block diagram is shown for an easier understanding of the flow paths (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) mL mAb Select PrismA column and measuring the elution chromatogram with a UV sensor at a wavelength of 280 nm The sample concentration was calculated as the area under the eluted peak divided by the injected volume The extinction coefficient used was 1.4 (mg/mL)−1 cm−1 and it was taken from Maity et al., 2015 [27] Results and discussion 4.1 Model calibration The aim of using a mechanistic model was to get a good estimation of the breakthrough curves under a variety of conditions with different mAb concentration and residence times In Fig 3, it can be seen that the breakthrough curves are well fitted for both resins except for the lowest flow rate At higher residence times (low flow rates), the General Rate model deviates more from the experimental breakthrough curve, something that was already shown by Hahn et al., 2005 [28] and Perez-Almodovar and Carta, 2009 [12], which the model used in this work is based on Fig also shows a good breakthrough curve fitting for all concentrations, except for the lowest concentration Similarly to the deviation shown with high residence times, this discrepancy between the model and the experiment corresponding to the lowest mAb concentration has also been observed in the model calibration performed by Perez-Almodovar and Carta, 2009 [12], where they obtained a good fitting for the early rising part of the curve, but a big deviation for the rest of the curve Other authors [28,29] have obtained similar results with either higher effective diffusivity or lower binding capacities than expected for the low protein concentration case Despite the deviations in the model for the lowest flow rate and mAb concentration, it was not expected to affect the optimization of the PCC process On the one hand, although there are deviations at the lowest feed concentration (0.25 mg mL−1 ), the area under the curve, which is what is used to calculate the objective functions and the yield constraint in the optimization, is approximately the same for the simulated and the experimental data On the other hand, although for the lowest flow rate (0.25 mL min−1 ) there is a larger deviation, the simulation provides a more conservative solution than the reality, since the breakthrough curve appears earlier than in the experimental results Regarding the suitability of the model for the simulation of a multi-column process like PCC, it has been shown that model calibration from batch experiments can be used to predict the performance in a continuous multi-column process [7,8] 4.2 PCC scheduling The choice of the integration approach affects the product recovery time, as seen in Fig 4, where Gantt diagrams are shown for the three process alternatives Process alternative has the longest recovery time because the whole downstream process is run in one system Therefore, due to the lack of enough pumps, all steps must be run in series, thus making the total recovery time longer In process alternative 2, where two systems are used, the capture step and the rest of the steps are run in parallel, thus reducing the recovery time Finally, process alternative has the lowest recovery time, since the PCC and the rest of steps are de-coupled or de-synchronized due to the presence of the surge vessel, i.e., when the product recovery is done in the capture step, the eluate from the protein-A resin will be hold in the surge vessel (after a pH adjustment to avoid mAb aggregation during the hold-up time), and the next PCC cycle can then be run directly, without the need to wait for the polishing steps to finish When the virus inactivation J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 Fig Calibration of the general rate model: (A) Breakthrough curve fitting at different flow rates and at a constant concentration of 0.5 mg mL−1 for mAb Select PrismA (black curves), and (B) mAb Select SuRe (grey curves) (C) Breakthrough curve fitting for different mAb concentrations and at a constant flow rate of 0.5 mL min−1 for mAb Select PrismA, and (D) mAb Select SuRe The dots are experimental points and the solid lines are fitted curves corresponding to the general rate model loop is ready to be filled, which is determined by the scheduling of the polishing steps, as shown in Fig 4C, the surge vessel will be emptied Therefore, the filling frequency of the surge vessel is determined by the PCC cycle time, and its emptying depends on the scheduling of the virus inactivation and the polishing steps As mentioned before, the cycle time must be higher than the recovery time In other words, process solutions with a PCC cycle time lower than the recovery times stated in Fig for each process alternative, are unfeasible That means that the PCC cycle time must be chosen so that it is at least as high as the recovery time, and the loading flow rate must then be adapted so that the yield is kept within the constraint for that particular cycle time A possibility to reduce the recovery time could be to duplicate the virus inactivation loop and run a semi-continuous virus inactivation where a loop is filled while the other one is emptied, like in Pall’s CadenceTM Virus Inactivation System [30] However, in Fig 4, it can be seen that the time for the polishing steps is higher than the one for the virus inactivation Therefore, the duplication of the VI loop would not involve any improvement in the recovery time in the long run, unless the polishing columns were also duplicated Other options include the implementation of a fully continuous virus inactivation process with, for example a packed bed reactor [31] or a Jig in a Box (JIB) approach [32] The impact of this implementation on the recovery time would depend on the way of integrating the PCC with the continuous virus inactivation, but the length of the polishing steps would still limit the minimum overall recovery time 4.3 PCC optimization The result of the two-objective optimization is a set of optimal solutions with different values of productivity and resin utilization, the so-called Pareto front Solutions with higher productivity have higher loading flow rate in order to treat more material in less time, but this makes the breakthrough curve flatter [12,13,22] and it forces the cycle to be shorter and the resin utilization to be lower to keep a high yield On the contrary, solutions with high resin utilization have higher cycle times, and lower loading flow rate and productivity (Fig 5) The optimization was executed for different process conditions to evaluate the effect of several parameters of the process 4.3.1 Effect of the integration approach As explained before, the integration approach influences the minimum cycle time This means that the solutions with a lower cycle time than the recovery time for a particular integration alternative, are unfeasible That is what is shown in Fig 5A Process alternative has the highest recovery time (184.1 min) This implies that the points with higher productivity (with lower cycle time) are unfeasible for this process alternative, and only the solutions with high resin utilization are viable in this case Process alternatives and have lower recovery time (118.4 and 60.1 min, respectively), thus the range of viable solutions is broader It is therefore shown that the higher the recovery time, the lower the process flexibility and freedom to choose between high resin utilization J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 Fig Gantt diagrams for the three integration alternatives: (A) Alternative 1, integrated PCC process in one system, (B) Alternative 2, integrated PCC process in two systems, and (C) Alternative 3, PCC process with a surge vessel in two systems (For interpretation of the references to color in the figure legend, the reader is referred to the web version of this article.) and high productivity solutions The optimal Pareto solutions for process alternative are equivalent to the ones that would be obtained for an optimized capture PCC alone without the rest of the downstream process Therefore, as evidenced in Fig 5A, the optimal design of a PCC process must be done in a holistic approach, i.e., taking into account the process integration with the polishing steps already in the optimization problem Not doing so may lead to optimal solutions that are feasible when running only the capture step but are unfeasible when the whole downstream process is implemented, if no surge vessel is used 4.3.2 Effect of the feed concentration The inlet loading concentration affects in different ways Firstly, the equilibrium adsorbed product concentration (κ ) is affected by the mobile phase concentration according to the Langmuir isotherm (Eq (5)) This means that at a higher concentration, the adsorbed concentration at equilibrium is higher and therefore the amount of product that can potentially be loaded in each cycle is generally higher Secondly, a higher inlet concentration increases the productivity, because a higher amount of product is being loaded by amount of time and volume In addition, it can also improve resin utilization, because a higher concentration means that a lower flow rate can be applied to load the same amount of product as in a low concentration process, thus making the breakthrough curve sharper, which leads to a higher resin utilization In Figure 5, several Pareto fronts for concentrations ranging from 0.25 to mg mL−1 are shown It is interesting to notice the decrease in the slope of the fronts for an increasing concentration For the high concentration cases, a small increase in loading flow rate implies a bigger increase in productivity due to a higher amount of product per volume being loaded, but the reduction of the resin utilization due to this flow rate increase is small On the contrary, for the low concentration solutions, in order to achieve a significant rise of the productivity, a higher increase in flow rate must be applied, with the consequent large sacrifice in the resin utilization This behavior justifies the decrease of the slope at higher inlet concentrations Another interesting fact is that the Pareto curves get increasingly flatter when approaching a very high resin utilization In these operating points, the flow rate and the percentage of the unutilized resin are very low That implies that a greater decrease of flow rate (with the corresponding drop of productivity) is needed to get a slightly higher resin utilization This is more pronounced at higher concentrations because this flow rate decrease affects more the productivity than if the concentration was lower The lower flow rate that it is needed to apply in the process as a result of a higher concentration, affects the choice of integration approach As shown in Fig 5A, at mg mL−1 , only a few operating points are feasible for process alternative 1, and at mg mL−1 only process alternative is feasible for the solutions of the Pareto front The reason the optimization method cannot find viable points for alternatives and at mg mL−1 is because it would require a very low loading flow rate to avoid product breakthrough with a cycle time higher than the recovery time for these two process alternatives But there is a low limit in the loading flow rate in the simulation of the process set in 30 cm h−1 , because the model was not calibrated for lower flow rates Simulations of the process at lower flow rates than the model was calibrated for, would have provided unreliable solutions It should be noticed that, despite this fact, it is possible to run process alternatives and at high concentrations and very low flow rates experimentally, but the simulation and prediction of these processes would be unreliable and further model calibration for lower flow rates would be needed J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 4.3.3 Effect of the chromatography resin The optimization was run for two different resins: mAb Select PrismA and mAb Select SuRe, with particle diameters of 60 μm and 85 μm, respectively A lower particle diameter allows for a better mass transfer due to a shorter way from the particle surface to the adsorption sites [28] This is translated in a sharper breakthrough, which in turn leads to a higher resin utilization for equal flow rate or the possibility to run at higher flow rates, thus increasing the productivity, without sacrificing the resin utilization too much In addition, mAb Select PrismA has a higher capacity, as shown in Table S1 Therefore, it is expected that this resin performs better than mAb Select SuRe In Fig 5A, it is confirmed that mAb Select PrismA has a better compromise of productivity-resin utilization for all the concentrations It is remarkable that the difference between the two resins is bigger at higher productivities This is due to a lower slope of the curve corresponding to mAb Select PrismA, which is explained by the faster mass transfer in this resin For a certain desired increase of productivity, which is carried out by a corresponding flow rate increase, the sacrifice in resin utilization for mAb Select PrismA is lower than for mAb Select SuRe That is the reason of the different slopes of the two curves, and, in turn, of the larger difference between the two resins at operating points with higher productivity Considering two operating points with the same resin utilization for both resins, the cycle time is higher for mAb Select SuRe than for mAb Select PrismA That means that the number of feasible solutions for the process alternatives and is slightly higher with mAb Select SuRe, because these alternatives, which have higher recovery time, benefit from an increase of the cycle time For example, as it can be seen in Fig 5A, at 0.25 mg mL−1 and at a resin utilization of around 63%, an optimal solution can be operated with process alternative in the case of mAb Select SuRe, but no solution would be feasible with the process alternative at that resin utilization with mAb Select PrismA Fig Pareto fronts with optimal solutions (A) Three different integration alternatives (filled, shaded and crossed points) at four load concentrations (0.25, 0.5, and mg mL−1 ) for mAb Select PrismA (circles) and mAb Select SuRe (squares) (B) Three yields: 95%, 98% and 99%, for mAb Select PrismA at a load concentration of 0.5 mg mL−1 (C) Four operation modes: 3-column PCC, 1-column batch, 2-column sequential batch and 3-column sequential batch, for mAb Select PrismA at a load concentration of 0.5 mg mL−1 The legend of the process alternatives is the same for all the panels 4.3.4 Effect of the yield The optimization was run for three yields: 95%, 98% and 99%, and the three Pareto fronts corresponding to the resin mAb Select PrismA are shown in Fig 5B As expected, if the yield is lower, the productivity and the resin utilization are higher A process with very high yield implies that the losses due to product breakthrough must be very low, which means that the process must be run at a lower velocity to get a sharper breakthrough curve, thus reducing the productivity, or finish the cycle earlier, with a corresponding drop of the resin utilization Besides, it is shown that the yield does not significantly affect the feasible range of optimal points for each integration alternative, since for the three yields there are optimal solutions that are feasible for the three process alternatives Remarkably, the difference between the curves is smaller in the solutions with higher resin utilization, whereas this difference is more pronounced in the solutions with higher productivity This is due to the different slopes of the breakthrough curve in both cases At lower yields, a longer loading can be applied because the allowed amount of product loss is higher, and therefore higher resin utilization can be achieved But if the breakthrough curve is very sharp, the loss of product will be too high at a slight increase of the loading time Therefore, the benefit of reducing the yield constraint does not lead to a significantly higher resin utilization in that case For that reason, the solutions with higher resin utilization, which have a sharper breakthrough curve, not differ much at different yields, while in the high productivity solutions, the difference in yield is more significant 10 J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 Fig Chromatogram of the capture step (A) and the polishing steps (B) during a PCC run with mAb Select PrismA The shaded areas represent the PCC cycles 4.3.5 Comparison of PCC and batch chromatography Periodic counter-current chromatography enables to treat a continuous stream, but it also provides higher productivity and resin utilization for the same yield, in comparison to batch chromatography [7,8] For this reason, it is interesting to see the differences between the studied 3-column PCC and a traditional batch chromatography process (Fig 5C) In addition, two sequential batch processes with and columns, respectively, are also considered, to compare PCC with other simpler periodic processes In a sequential batch process, one column is always being loaded, and the product recovery is carried out in the other columns, just as in PCC, with the difference that there is no interconnection between the columns A multi-objective optimization was solved for the four cases, considering there is no limitation due to the integration with the rest of the downstream processing steps, since the effect of this limitation is already shown in Fig 5A and discussed in Section 4.3.1 The volume for each column was assumed to be the same, therefore the total resin volume depended on the number of columns As expected, PCC provides the highest productivity and resin utilization, since the PCC process can be run at higher flow rates, compared to the batch processes, without compromising the yield or the resin utilization This is due to the interconnection of the two columns, which avoids losing the product that is not adsorbed in the first column On the other hand, the 1-column batch process performs better than the sequential batch processes, as shown in Fig 5C This is because the same amount of product can be loaded in the 1-column batch process and in the sequential processes, but the number of columns is different Since the productivity is defined by the total resin volume, and the sequential processes have more columns, the productivity is, in general, higher for the 1-column batch process However, it is noteworthy that the sequential batch processes can achieve a higher productivity than the 1-column batch process, although at a lower resin utilization, as seen in Fig 5C The reason is that in the sequential processes, the wash, elution and CIP steps are performed at the same time as the loading, whereas in the 1-column batch process, these steps are run after the loading Therefore, in some of the solutions, the processing time in the sequential processes is significantly shorter than in the 1-column batch process, thus compensating the fact that more columns are used Fig 5C clearly shows that, in the conversion from batch to continuous capture, the choice of the process is very important While a PCC process may be a more complex alternative to implement than a sequential batch process, it provides almost times more productivity at a constant resin utilization (in Fig 5C, at a resin utilization of approximately 60%, the productivities are circa 0.38 and 0.08 mg mL-1 min-1 , respectively for the 3-column PCC and the 3-column sequential batch process) 4.4 Experimental validation The process was implemented with the process set-up shown in Fig The process was run for the two resins, and a solution with similar resin utilization was chosen from the Pareto front of each resin Both processes were run at 0.5 mg mL−1 The column volume of the protein-A resin was mL, and the rest of columns were sized according to the same procedures used in [14] Regarding the process integration, alternative was chosen Compared to alternative 1, where the whole downstream process is run in one system, it was simpler and more flexible to carry out all the steps in two systems than trying to fit every step in one system, since the num- J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 11 Table Summary of the experimental validation of a 3-column PCC integrated in a downstream process Parameter Cycle time Loading flow rate Load per cycle Specific buffer consumption Capture eluate concentration Capture yielda) Overall yieldb) Resin utilization Capture productivitya) Overall productivityb) Units MAb Select PrismA MAb Select SuRe mL min−1 mg cycle−1 mL mg−1 mg mL−1 % % % mg mL−1 min−1 mg mL−1 min−1 Simulation 140.40 0.98 68.80 0.47 5.55 99.00 77.80 0.25 - Simulation 165.60 0.66 54.60 0.59 4.44 99.00 77.60 0.16 - Experimental 140.40 0.98 68.80 0.49 5.28 94.60 78.20 74.00 0.23 0.20 Experimental 165.60 0.66 54.60 0.63 4.16 93.60 75.20 72.80 0.15 0.11 Notes: a) Yield/productivity for the capture step The productivity is based on the protein-A resin volume b) Yield/productivity for the whole downstream process The productivity is based on the protein-A resin volume ber of valves and pumps available in two systems are higher than those in one system In addition, as shown in Fig 5A, it offered a broader range of possible solutions with higher productivity Alternative was even better in this aspect, but the introduction of a surge vessel leads to a hold-up volume in the system that implies that process is not completely integrated anymore [2], apart from a much more complex implementation and other disadvantages already exposed in Section In Fig S3, in Supplementary Material, a picture of the two systems connected to each other for the implementation of the integrated PCC process, is shown Fig reveals that the process gets to steady state very quickly, already in the second cycle This is because in the start-up of the process, a longer loading is applied, so that approximately the same amount of product is loaded in the start-up and the following cycles Both the initial start-up time and the steady-state cycle time were determined by the simulation of the process In the panel A of Fig 6, the absorbance of the outlet of the capture column is displayed There are three peaks: the first one corresponds to the washed impurities, the second one is the eluted product that is directly loaded onto a virus inactivation loop, and a third peak that corresponds to the strongly adsorbed impurities that get out of the column during the cleaning-in-place (CIP) The panel B of Fig shows the absorbance of the outlets from the CEX and the AEX columns The first CEX peak is linked to the loading of the CEX column, or the emptying of the VI loop The second CEX peak is the eluted product that gets directly loaded onto the AEX column Since the AEX step is in flow-through mode, the product gets out of this column at the same time it is being loaded (AEX peak), with some delay corresponding to the column volume, and at a lower concentration as a result of the dilution that is carried out before the loading, in order to condition the product to the right pH and salt concentration The cycles in the polishing steps are delayed respect to the ones in the capture step due to the process scheduling, as revealed in Fig The results shown in Fig corresponds to the resin mAb Select PrismA Similar results are presented in Figure S4 for mAb Select SuRe In Table 1, a summary of the experimental results is presented The loading flow rate is significantly higher for mAb Select PrismA, and therefore the productivity is also higher As a result of the higher flow rate, the cycle time is lower for this resin, while the resin utilization is similar for both processes, around 73–74% The amount of product treated per cycle is higher for mAb Select PrismA, so the specific buffer consumption is lower for this resin, since all flow rates (except for the loading flow rate of the capture step) and volumes are the same for both resins The constraint on the capture yield was set to 99%, and the experimental one was around 94% for both processes This difference is due to the fact that the yield used in the optimization is a theoretical one that only considers the product lost in the breakthrough dur- ing the loading, and it does not take into account the losses during the elution, wash or regeneration of the capture column The lower overall yield relates to the product loss in the subsequent steps (virus inactivation, CEX and AEX) The polishing steps were not modelled, and that is why the simulated overall yield and productivity were not provided in Table Overall, the process was successfully validated since productivity and resin utilization were similar as the simulated results, while keeping a very high yield Conclusions The implementation of a periodic counter-current chromatography integrated in a downstream process requires an optimal design in order to operate at maximum productivity, resin utilization and yield In this work, it was shown that the optimal results obtained from an optimization of a PCC process for the capture of antibodies without considering the integration with the following steps cannot always be used in an integrated process due to scheduling mismatches, and a holistic approach is needed to design the PCC in a downstream process Three different integration alternatives were presented: 1) an integrated PCC process in one system, 2) an integrated PCC process in two systems, and 3) a PCC process with a surge vessel in two systems The main difference lies in the total time that takes to purify the product, the so-called recovery time The first two alternatives have a higher recovery time than the third one because a synchronization between the capture step and the following steps is required, whereas with a surge vessel once the product is purified in the capture step, there is no need to wait for the polishing steps to finish Since a requirement for feed continuity in the PCC process is that the cycle time is equal or higher than the recovery time, the choice of integration alternative affects the optimal design of the PCC A multi-objective optimization based on a mechanistic model of the protein-A capture step was carried out Several Pareto fronts with optimal solutions were obtained for different conditions of feed concentration, yield and protein-A resin, and the feasibility of the solutions depending on the integration approach was studied The highest productivity could be achieved with the third process alternative (for example at 0.5 mg mL−1 of feed concentration, up to 0.38 mg mL−1 min−1 and 99% yield), since the minimum cycle time in this process is lower than in the other two alternatives In the second process alternative, with two systems and no surge vessel, a reasonable high productivity and resin utilization was obtained, and in the integration approach with only one system, the recovery time is high, and the process cannot operate at high productivities, but the resin utilization is very high (up to 95%) At a higher feed concentration, both the productivity and the resin utilization are heavily increased, and the cycle time is in general lower, which affects the feasibility of the solutions in 12 J Gomis-Fons, N Andersson and B Nilsson / Journal of Chromatography A 1621 (2020) 461055 relation to the integration approach At the highest feed concentration (2 mg mL−1 ), only the process with surge vessel can be run at optimal conditions, whereas the other two alternatives would have to be operated at sub-optimal conditions The choice of the protein-A resin also affects the performance of the process A better mass transfer in mAb Select PrismA due to a lower particle diameter, together with a higher resin capacity, compared to mAb Select SuRe, allows for a much better compromise between productivity and resin utilization In addition, the effect of the yield was also studied At a lower yield, the allowed amount of product loss is higher, and therefore a higher flow rate or a longer cycle can be applied, thus increasing the productivity and/or the resin utilization Finally, the 3-column PCC was compared to a 1-column batch process and two sequential batch processes with and columns, respectively As expected, the PCC optimization provided much better operation points in terms of productivity and resin utilization The 1-column batch process outperformed the sequential batch processes due to the use of less columns Therefore, in this work, it is shown that, in order to convert a batch process into a continuous process, the simpler alternative of running a multi-column process sequentially with no column interconnection leads to a more inefficient process However, the conversion to a PCC process, not only allows a continuous load of the column, but also a significant increase of the productivity and the resin utilization The optimized design was validated experimentally using the integration approach with no surge vessel and two ÄKTA pure systems centrally controlled by the research software Orbit The process was run at a feed concentration of 0.5 mg mL−1 and a theoretical yield of 99% A solution with similar resin utilization was selected from the Pareto front of both resins A steady-state operation was achieved already in the second cycle A productivity in the capture step of 0.23 and 0.15 mg mL−1 min−1 was obtained for mAb Select PrismA and mAb Select SuRe, respectively The resin utilization was around 73-74% in both cases, and the total recovery yield, including the polishing steps, was in the range of 75–78%, whereas the yield in the capture step was around 94% for both cases Therefore, it was shown that the optimization study can be used to obtain an optimal design of the PCC integrated in a downstream process, which was successfully validated experimentally Declaration Competing of Interest The authors declare no commercial or financial conflict of interest Acknowledgements The authors acknowledge that this research is part of the Competence Centre for Advanced BioProduction by Continuous Processing (AdBIOPRO), which is co-funded by VINNOVA, the Swedish Agency for Innovation (grant ID: 2016-05181) and the industrials partners: Swedish Orphan Biovitrum, Cobra Biologics, BioInvent, GE Healthcare Life Sciences, Valneva, Lab-on-a-Bead, and CellProtect Nordic Pharmaceutical The authors would also like to thank Andreas Castan (GE Healthcare Life Sciences) for providing the raw material, and Dennis Bogren (Department of Chemical Engineering, Lund University) for his assistance with the calibration experiments Supplementary materials Supplementary material associated with this article can be found, in the online version, at 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Healthcare Bio-Sciences AB, Continuous chromatography, Downstream Processing of a Monoclonal Antibody, 2015 Report 29170800 AA [18] GE Healthcare Bio-Sciences AB, Evaluation of Protein A resin... is longer, and the process scheduling is affected In this work, we performed an optimization study of a PCC integrated in a monoclonal antibody (mAb) downstream process, for which a mechanistic