Exploring the effects of resin particle sizes on enhancing antibody binding capacity of a hybrid biomimetic ligand.

Particle size is a critical parameter of chromatographic resins that significantly affects protein separation. In this study, effects of resin particle sizes (31.26 µm, 59.85 µm and 85.22 µm named Aga-31, Aga-60 and Aga-85, respectively) on antibody adsorption capacity and separation performance of a hybrid biomimetic ligand were evaluated. Their performance was investigated through static adsorption and breakthrough assays to quantify static and dynamic binding capacity (Qmax and DBC). The static adsorption results revealed that the Qmax for hIgG was 152 mg/g resin with Aga-31, 151 mg/g resin with Aga-60, and 125 mg/g resin with Aga-85. Moreover, the DBC at 10% breakthrough for hIgG with a residence time of 2 min was determined to be 49.4 mg/mL for Aga-31, 45.9 mg/mL for Aga-60, and 38.9 mg/mL for Aga-85. The resins with smaller particle sizes exhibited significantly higher capacity compared to typical commercial agarose resins and a Protein A resin (MabSelect SuRe). Furthermore, the Aga-31 resin with the hybrid biomimetic ligand demonstrated exceptional performance in terms of IgG purity (>98%) and recovery (>96%) after undergoing 20 separation cycles from CHO cell supernatant. These findings are helpful in further chromatographic resin design for the industrial application of antibody separation and purification.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Residence time distribution in continuous virus filtration.

Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( R 2 > ${R}^{2}\gt $ 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved R 2 > ${R}^{2}\gt $ 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation.

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography.

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

Evaluation of dynamic control of continuous capture with periodic counter-current chromatography under feedstock variations.

Multi-column periodic counter-current chromatography is a promising technology for continuous antibody capture. However, dynamic changes due to disturbances and drifts pose some potential risks for continuous processes during long-term operation. In this study, a model-based approach was used to describe the changes in breakthrough curves with feedstock variations in target proteins and impurities. The performances of continuous capture of three-column periodic counter-current chromatography under ΔUV dynamic control were systematically evaluated with modeling to assess the risks under different feedstock variations. As the concentration of target protein decreased rapidly, the protein might not breakthrough from the first column, resulting in the failure of ΔUV control. Small reductions in the concentrations of target proteins or impurities would cause protein losses, which could be predicted by the modeling. The combination of target protein and impurity variations showed complicated effects on the process performance of continuous capture. A contour map was proposed to describe the comprehensive impacts under different situations, and nonoperation areas could be identified due to control failure or protein loss. With the model-based approach, after the model parameters are estimated from the breakthrough curves, it can rapidly predict the process stability under dynamic control and assess the risks under feedstock variations or UV signal drifts. In conclusion, the model-based approach is a powerful tool for continuous process evaluation under dynamic changes and would be useful for establishing a new real-time dynamic control strategy.

Physics-informed neural networks to solve lumped kinetic model for chromatography process.

Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.

Model-assisted process development, characterization and design of continuous chromatography for antibody separation.

Continuous manufacturing in monoclonal antibody production has generated increased interest due to its consistent quality, high productivity, high equipment utilization, and low cost. One of the major challenges in realizing continuous biological manufacturing lies in implementing continuous chromatography. Given the complex operation mode and various operation parameters, it is challenging to develop a continuous process. Due to the process parameters being mainly determined by the breakthrough curves and elution behaviors, chromatographic modeling has gradually been used to assist in process development and characterization. Model-assisted approaches could realize multi-parameter interaction investigation and multi-objective optimization by integrating continuous process models. These approaches could reduce time and resource consumption while achieving a comprehensive and systematic understanding of the process. This paper reviews the application of modeling tools in continuous chromatography process development, characterization and design. Model-assisted process development approaches for continuous capture and polishing steps are introduced and summarized. The challenges and potential of model-assisted process characterization are discussed, emphasizing the need for further research on the design space determination strategy and parameter robustness analysis method. Additionally, some model applications for process design were highlighted to promote the establishment of the process optimization and process simulation platform.

Improved process design for monoclonal antibody charge variants separation with multicolumn counter-current solvent gradient purification.

The multicolumn counter-current solvent gradient purification (MCSGP) method has proven effective in addressing the issue of elution profile overlap for difficult-to-separate proteins, leading to improved purity and recovery. However, during the MCSGP process, the flow rate and proportion of loaded proteins undergo changes, causing a significant discrepancy between the elution profiles of batch process design and the actual MCSGP process. This mismatch negatively impacts the purity and recovery of the target protein. To address this challenge, an improved process design (reDesign) was proposed with the first-run MCSGP to mimic the actual continuous process and obtain elution profiles that closely resemble the real ones. The reDesign was demonstrated with both a model protein mixture and a sample of monoclonal antibody (mAb) with charge variants. For model protein mixture, the reDesign-based MCSGP process (reMCSGP) showed a remarkable improvement in recovery, increasing from 83.6% to 97.8% while maintaining a purity of more than 95%. For mAb sample, the recovery of reMCSGP improved significantly to 93.9%, surpassing the performance of normal MCSGP processes at a given purity level of more than 84%. In general, the new process design strategy developed in this work could generate a more representative elution profile that closely mirrors actual conditions in continuous processes, which enhances the separation performance of MCSGP.

Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Simplified estimation for steric shielding factor.

Mechanistic models play a crucial role in the process development and optimization of ion-exchange chromatography (IEC). Recent researches in steric mass action (SMA) model have heightened the need for better estimation of nonlinear parameter, steric shielding factor σ. In this work, a straightforward approach combination of simplified linear approximation (SLA) and inverse method (IM) was proposed to initialize and further determine σ, respectively. An existed, unique, and positive σ can be derived from SLA. Compared with linear approximation (LA) developed in our previous study, σ of the multi-component system can be calculated easily without solving the complex system of linear equations, leading to a time complexity reduction from O(n3) to O(n). The proposed method was verified first in numerical experiments about the separation of three charge variants. The calculated σ was more reasonable than that of LA, and the error of elution profiles with the parameters estimated by SLA+IM was only one-sixth of that by LA in numerical experiments. Moreover, the error accumulation effect could also be reduced. The proposed method was further confirmed in real-world experiments about the separation of monomer-dimer mixtures of monoclonal antibody. The results gave a lower error and better physical understanding compared to LA. In conclusion, SLA+IM developed in the present work provides a novel and straightforward way to determine σ. This simplification would help to save the effort of calibration experiments and accelerate the process development for the multi-component IEC separation.

Model-assisted process design for better evaluation and scaling up of continuous downstream bioprocessing.

Continuous process is a promising alternative for tradition batch process in biomanufacturing, which has higher productivity and lower material consumption. However, despite the maturation of necessary technologies for continuous process, there are few discussion about optimization of full continuous process. One possible reason is that the continuous process is such a complex and interacted process that the traditional experiment-based optimization approach is not sufficient anymore. To address that problem, the process simulation tool SuperPro Designer and continuous capture chromatography model were integrated into a model-assisted design platform for better development of continuous process. The influences of different continuous capture operation modes and sub-lot number under various upstream conditions were analyzed for pilot-scale production. The best combination of operation mode and sub-lot number were determined for the highest equipment utilization without any conflict. After the process optimization, the equipment utilization of continuous process was significantly improved to 27.3%. Then, a pilot-scale case study was carried out to validate the proposed approach. The results showed that the scaling up and process design of continuous process were successful. No time conflict and process failure happened and the final product met the release specification. Finally, the cost of goods was evaluated with SuperPro Designer, and the results showed a 17.4% cost reduction for optimized continuous downstream process compared to batch process, which is promoting for the future industrial applications.

One kind of challenging tetrapeptide biomimetic chromatographic resin for antibody separation.

Short peptide biomimetic chromatography technology as a developing protein separation technology has huge potential for antibody purification. In this study, four tetrapeptide ligands (Ac-FYKH, Ac-YEHF, Ac-YFLH and Ac-FYHI) with high potential binding ability to antibody were selected for the optimal ligand to antibody purification. The results showed that Ac-YEHF-4FF resin had higher binding capacity and selectivity for hIgG among the four resins. And at pH 7.0 and 0.3 ml/min, the highest Q10%-hIgG of Ac-YEHF-4FF resin was 26.2 mg/ml resin while its Q10%-BSA was just 2.2 mg/ml resin. Further, Ac-YEHF-4FF resin was used to purify protein mixtures. By binding at pH 7.0 and being eluted at pH 5.0 and pH 4.0, Ac-YEHF-4FF resin was well used to separate hIgG from BSA containing feedstock, HSA containing feedstock and human serum with the purity and yield both more than 95 %. And the screened resin could also separate mAb from CHO cell culture supernatant with purity 94.3 % and yield 97.5 %. The adsorption and separation results of Ac-YEHF-4FF resin indicated that the goal of getting the efficacy of critical residues from protein A to biomimetic its structure and function could be achieved, which had great significance to the establishment and improvement of tetrapeptide biomimetic chromatography, and also provided a new method for the field of antibody separation and purification.

Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Theoretical considerations and experimental verification.

Ion exchange chromatography (IEC) is one of the most widely-used techniques for protein separation and has been characterized by mechanistic models. However, the time-consuming and cumbersome model calibration hinders the application of mechanistic models for process development. A new methodology called "parameter-by-parameter method (PbP)" was proposed with mechanistic derivations of the steric mass action (SMA) model of IEC. The protocol includes four steps: (1) first linear regression (LR1) for characteristic charge; (2) second linear regression (LR2) for equilibrium coefficient; (3) linear approximation (LA) for shielding factor; (4) inverse method (IM) for kinetic coefficient. Four SMA parameters could be one-by-one determined in sequence, reducing the number of unknown parameters per species from four to one, and predicting almost consistent retention. Numerical single-component experiments were investigated firstly, and the PbP method showed excellent agreement between experiments and simulations. The effects of loadings on the PbP and Yamamoto methods were compared. It was found that the PbP method had higher accuracy and robustness than the Yamamoto method. Moreover, a five-experiment strategy was suggested to implement the PbP method, which is straightforward to reduce the cost of calibration experiments. Finally, a real-world multi-component separation was challenged and further confirmed the feasibility of the PbP method. In general, the proposed method can not only reliably estimate the SMA parameters with comprehensive physical understanding but also accurately predict retention over a wide range of loading conditions.

Model-based evaluation and model-free strategy for process development of three-column periodic counter-current chromatography.

Multi-column counter-current chromatography is an advanced technology used for continuous capture processes to improve process productivity, resin capacity utilization and product consistency. However, process development is difficult due to process complexity. In this work, some general and convenient guidances for three-column periodic counter-current chromatography (3C-PCC) were developed. Boundaries and distributions of operating windows of 3C-PCC processes were clarified by model-based predictions. Interactive effects of feed concentration (c0), resin properties (qmax and De), recovery and regeneration times (tRR) were evaluated over a wide range for maximum productivity (Pmax). Furthermore, variation of Pmax was analyzed considering the constraint factors (capacity utilization target and flow rate limitation). The plateau value of Pmax was determined by qmax and tRR. The operating conditions for Pmax were controlled by qmax, tRR and c0 interactively, and a critical concentration existed to judge whether the operating conditions of Pmax under constraints. Based on the comprehensive understanding on 3C-PCC processes, a model-free strategy was proposed for process development. The optimal operating conditions could be determined based on a set of breakthrough curves, which was used to optimize process performance and screen resins. The approach proposed was validated using monoclonal antibody (mAb) capture with a 3C-PCC system under various mAb and feed concentrations. The results demonstrated that a thorough model-based process understanding on multi-column counter-current chromatography is important and could improve process development and establish a model-free strategy for more convenient applications.

Converting a mAb downstream process from batch to continuous using process modeling and process analytical technology.

The biopharmaceutical market is driving the revolution from traditional batch processes to continuous manufacturing for higher productivity and lower costs. In this work, a batch mAb downstream process has been converted into an integrated continuous process with the combination of multiple techniques. For process intensification, two batch mode unit operations (protein A capture chromatography, ultrafiltration/diafiltration) were converted into continuous ones; for continuity, surge tanks were used between adjacent steps, and level signals were used to trigger process start or stop, forming a holistic continuous process. For process automation, manual operations (e.g., pH and conductivity adjustment) were changed into automatic operation and load mass was controlled with process analytical technology (PAT). A model-based simulation was applied to estimate the loading conditions for the continuous capture process, resulting in 21% resin capacity utilization and 28% productivity improvement as compared to the batch process. Automatic load mass control of cation exchange chromatography (CEX) was achieved through a customized in-line protein quantity monitoring system, with a difference of less than 1.3% as compared to off-line analysis. Total process time was shortened from 4 days (batch process) to less than 24 hours using the continuous downstream process with the overall productivity of 23.8 g mAb per day for the bench-scale system. Comparable yield and quality data were obtained in three test runs, indicating a successful conversion from a batch process to a continuous process. The insight of this work could be a reference to other similar situations.

Study on antibody adsorption and elution performance of carboxyl and hydrophobic groups on mixed-mode ligands.

Molecular interactions between ligands and target biomolecules are crucial in the development of chromatographic techniques for the separation and purification of biotherapeutics. In this study, the role of functional moieties on a mixed-mode ligand (phenylalanine-tyrosine-glutamate-5-aminobenzimidazole) for human immunoglobulin G purification was investigated and a detailed mechanism was discussed. A similar ligand with glutamic acid substituted by glutamine (phenylalanine-tyrosine-glutamine-5-aminobenzimidazole) together with other resins including a commercial resin (CM Bestarose Fast Flow), phenylalanine-tyrosine-glutamate, glutamate-5-aminobenzimidazole, and 5-aminobenzimidazole resins were prepared for comparison. Molecular dynamics simulation and experimental studies were used to analyze the difference between these ligands. The results showed that the carboxyl group of phenylalanine-tyrosine-glutamate-5-aminobenzimidazole contributed 70% of the electrostatic interaction during human immunoglobulin G binding, and 5-aminobenzimidazole provided electrostatic repulsion for desorption, which showed low selectivity and binding capacities at pH 4.0 (dynamic binding capacities at 10% breakthrough of human immunoglobulin G = 1.0 mg/ml resin, dynamic binding capacities at 10% breakthrough of human serum albumin = 1.2 mg/ml resin) when used as an individual resin ligand. The results showed in this study demonstrated that it is possible to achieve optimal antibody separation and purification through reasonable ligand design by understanding the performance of key functional moieties in binding and elution processes.

Downstream processing of virus-like particles with aqueous two-phase systems: Applications and challenges.

The advancement of recombinant virus-like particle-based vaccines has attracted global attention owing to substantially safety and high efficacy in provoking a protective immunity against various chronic and infectious diseases in humans and animals. A robust, low-cost, and scalability separation and purification technology is of utmost importance in the downstream processing of recombinant virus-like particles to produce affordable and safe vaccines. Being a relatively simple, environmentally friendly, and efficient biomolecules recovery approach, aqueous two-phase systems have received great attention from researchers worldwide. This review aims to highlight the challenges and outlook in addition to the current applications of aqueous two-phase systems in downstream processing of virus-like particles. The efforts will confidently reinforce scholars' knowledge and fill in the valuable research gap in the aspect of concerning recombinant virus-like particle-based vaccines development, particularly related to the virus-like particles downstream production processes.

Salt-tolerant and thermostable mechanisms of an endoglucanase from marine Aspergillus niger.

The cellulase cocktail of marine Aspergillus niger exhibited salt-tolerant and thermostable properties, which is of great potential in industrial application. In order to excavate the single tolerant cellulase components from complex cellulase cocktail, constitutive homologous expression was employed for direct obtainment of the endoglucanase (AnEGL). Enzymatic property study revealed that AnEGL exhibited a property of salt tolerance and a strong thermostability in high salinity environment. Significantly, its activity increased to 129% and the half-life at 65 °C increased to 27.7-fold with the presence of 4.5 M NaCl. Molecular dynamics simulation revealed that Na+ and Cl- could form salt bridges with charged residues, and then influenced the activity of loops and the stability of substrate binding pocket, which accounted for the salt tolerance and thermostability. Further, site-specific mutagenesis study proved that the residues Asp95 and Asp99 in the pocket were of great concern for the tolerant properties. The salt-tolerant and thermostable AnEGL was of great value in lignocellulosic utilization and the conjectural mechanisms were of referential significance for other tolerant enzymes.

Comparison of Protein A affinity resins for twin-column continuous capture processes: Process performance and resin characteristics.

Continuous chromatography is a promising technology for downstream processing of biopharmaceuticals. The operation of continuous processes is significantly different to batch-mode chromatography and needs comprehensive evaluation. In this work, the performances of four Protein A affinity resins were studied systematically for twin-column continuous capture processes. A model-based approach was used to evaluate the process performance (productivity and capacity utilization) under varying operation conditions, and the objective was to reveal the crucial resin properties for continuous capture. The trade-off between productivity and capacity utilization was found, and it is necessary to select appropriate resins for different feedstock and operation conditions. The capacity utilization heavily depends on mass transfer, and steep breakthrough curves are favorable for high capacity utilization. The productivity is determined by both equilibrium binding capacity and mass transfer, and the balance of feed amount and feed time is critical. Moreover, the influence of binding capacity and mass transfer on process productivity and parameter sensitivity with two important resin properties (equilibrium binding capacity qmax and effective pore diffusion coefficient De) were assessed by the model, and suitable resin parameter ranges for twin-column continuous capture were determined. The model-based approach is an effective and useful tool to evaluate the complex performance of different resins and guide the design of next-generation resins for continuous processes.

Optimization and comparison of the production of galactooligosaccharides using free or immobilized Aspergillus oryzae ß-galactosidase, followed by purification using silica gel.

The aim of this study was to optimize and compare the production of galactooligosaccharides (GOSs) by free and cotton cloth-immobilized Aspergillus oryzae ß-galactosidase, and perform economical evaluation of production of GOSs (100%) between them. Using the response surface method, the optimal reaction time (3.9 h), initial lactose concentration (57.13%), and enzyme to lactose ratio (44.81 U/g) were obtained for the free enzyme, which provided a GOSs yield of 32.62%. For the immobilized enzyme, the optimal yield of GOSs (32.48%) was obtained under reaction time (3.09 h), initial lactose concentration (52.74%), and temperature (50.0 ℃). And it showed desirable reusability during five successive enzymatic reactions. The recovery rate of GOSs (100%) is 65% using silica gel filtration chromatography. The economical evaluation showed almost no difference in the manufacturing cost for the GOSs (100%) between these two systems, and that the recovery rate had a great impact on the cost.