Process Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr® 250 High-throughput Parallel Bioreactors

Process Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr® 250 High-throughput Parallel Bioreactors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Process Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr ® 250 High-throughput Parallel Bioreactors Achinta Bordoloi, Farid Talebnia Rowshan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6831589/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Bioprocess and Biosystems Engineering → Version 1 posted 8 You are reading this latest preprint version Abstract The demand to accelerate monoclonal antibody (mAbs) process development timelines using Chinese hamster ovary (CHO) host cells to bring therapies to patients sooner is gaining momentum. The applicability of single use high throughput (HTP) bioreactor system such as ambr250 facilitating precise and automated control is very promising. This entails optimizing process parameters through design of experiments (DoE) using less resources and time, compared to traditionally employed large scale bench top reactors (2-5L). It is imperative to improve mAb productivity through robust process development to mitigate current manufacturing challenges. In this study, a systematic mapping approach was employed to identify critical process parameters (CPP) and improve process efficacy. A central composite design (CCD) was used in ambr250 bioreactors to investigate the impact of initial seeding density (SD) and feeding rate (FR) on mAb production. Variance in the SD and FR impacted the cell performance and mAb titer profile based on which parameter optimization was done using response surface methodology. Significant impact of FR and SD was identified leading to improved mAb titer of up to 5 g/L. Bioreactors operated at SD > 1 x 10 6 cells/mL and FR of > 2 % were more productive, and respective optimal FR and SD were estimated at 2.68 % and 1.1 x10 6 cells/mL. The cell viability and productivity were well-maintained at optimal conditions allowing extended cultivation time for higher mAb titer. These findings optimizing operating range of CPPs to improve productivity by using HTP ambr250 scaled-down platform would provide a framework for quicker early phase process development, allowing reliable scalability to commercial manufacturing. Improving productivity and providing robust estimates for manufacturing scale would significantly cut costs and reduce timelines for biologics development and facilitate patient access. CHO cells Monoclonal antibodies (mAbs) Ambr250 process mapping RSM optimization high throughput (HTP) bioreactor Figures Figure 1 Figure 2 Figure 3 1. Introduction The demand for biologics such as monoclonal antibodies (mAbs) has been gaining traction in recent years, as evident from a plethora of approved mAbs by the U.S. Food and Drug Administration (FDA) in the markets [ 1 ]. The biopharmaceutical market is rapidly growing and is estimated to cross $ 975 billion by 2030 [ 2 ]. With the surge in demand for biologics, there is an urgent need to develop and implement more efficient, optimized and scalable processes with improved product titer and quality attributes. A rapid process development and characterization phase can ensure bringing these mAb-based therapeutics to the patients quickly through the approval pipeline. Mammalian expression system using Chinese hamster ovary (CHO) cell line is still the favored route accounting for about 80% of mAbs approved for therapeutic use owing to being conducive to adequate post-translational modifications similar to those found in human proteins, such as glycosylation, which is considered to be a Critical Quality Attribute (CQA) in biopharmaceutical industries [ 3 ]. The CHO cells are also favored for their ease of genetic manipulation, suitability for large-scale industrial culturing, and history of regulatory approval. [ 4 ]. Despite these advantages, the production process in CHO cells is complex, and maintaining productivity, product quality, efficiency, and consistency remains challenging. Fed-batch culture is predominantly the preferred mode of operation at the manufacturing scale while the need for increased process efficacy has gained impetus due to surging market demands [ 5 ]. Inherent challenges remain pertaining to improving production titer of the fed-batch mode through better process mapping, optimization and minimizing the cost of goods for manufacturing. While additional production approaches are being investigated such as intensified fed-batch (IFB) and perfusion cultures to improve titer, logistical and economic challenges for their implementation at manufacturing scale exists [ 6 – 8 ]. Hence, accelerated technology transfer from cell line development and media screening to optimization of production parameters to improve titer, getting consistent quality attributes and culture performance is pivotal for the currently used fed-batch mode. Moreover, understanding the impact of various key process parameters on growth and metabolism of CHO cells fed-batch mode to boost productivity remains to be discerned [ 9 ]. Hence, it is imperative to build on the limited progress on the antibody production efficiency side, garner robust empirical data and reduce drug development timelines to meet high demand and exponential growth of biomanufacturing market. This study focuses on high-throughput (HTP) systems using Ambr250 parallel bioreactors, which allow for simultaneous experimentation across multiple conditions using Design of Experiments (DOE) to optimize critical process parameters (CPP). This scaled down automated reactor system with robust control over key operation parameters significantly reduces timelines for long haul CHO fed-batch process development while being representative of the commercial scale [ 10 ]. This entails better scalability and tech transfer from bench to pilot scale during the early stages of development pipeline much quicker, and at a reduced cost and resources. The automated 12-way ambr250 independently controls dissolved oxygen (DO), pH, temperature (T), agitation, gassing and feeding strategy for each individual vessel. However, comprehensive empirical data sets investigating various parametric impact on process efficacy from these high throughput bioreactor systems is still limited in the literature. A systematic mapping approach was employed to identify critical process parameters (CPP) through literature review and preliminary experiments [ 11 , 12 ]. While feeding strategy and initial seed density (SD) could play crucial roles in productivity, and overall process efficiency, little work is available in optimizing these variables and studying impacts through statistical approach in scaled down automated bioreactor systems such as Ambr250. Therefore, many industrial strategies for therapeutic protein production by CHO cells are still based on empirical results [ 13 , 14 ]. An optimal feeding strategy ensures a balanced supply of essential nutrients (e.g., glucose, and amino acids) required for catabolism/anabolism while preventing accumulation of toxic by-products such as lactate and ammonium. Likewise, optimal seed density promotes robust cell expansion, shorter lag phases, and in turn reduces the risk of nutrient depletion or excessive waste and by-product accumulation [ 15 – 17 ]. In this study, a statistical design of experiment (DoE) using JMP Pro 18 was used to evaluate the impact of initial seed density (SD) and feeding rate (FR) along with any potential interactions between identified parameters. Based on the DOE design, a model was developed and the optimal conditions for the highest mAb titer were predicted and further validated. Process was continuously monitored/controlled and dataset of the critical culture parameters such as viable cell density, cell viability, pH, mAb titer, and key metabolites production profile were collected daily for further data analysis. 2. Materials and Methods 2.1 Cell line, media used and seed train expansion A Chinese Hamster Ovary (CHO) cell line (ATUM miCHO) used for an IgG based mAb production was used in this study. Commercially available growth and expansion media (Ex-Cell, Advanced CHO medium, Sigma Aldrich, USA) were used for cell culture. Feed medium (Cellvento ModiFeed Comp, Sigma Aldrich, USA) was used to optimize feeding rate (FR) conditions. A 1mL Vial (10 7 cells/mL) was thawed and seed train expansion was carried out in the growth media supplemented with 4 mM L-Glutamine (Sigma Aldrich, USA) in non-baffled shake flasks (Corning, USA) in a humidified CO 2 incubator at 37°C, 120 rpm and 5% CO 2 (New Brunswick S41i, Eppendorf). Cells were passaged every 3–4 days at a seeding density of 0.4 (± 0.25%) x 10 6 cells/mL for up to 3 passages prior to inoculation in the ambr250 bioreactors. 2.2 Fed-batch cell culture in ambr250 bioreactors The 12-way ambr250 bioreactor system (Sartorius, Göttingen, Germany) was set up under controlled environmental conditions, cells were inoculated in 10 separate single use 250 mL bioreactors (Mammalian Vessels 001-5G25, Sartorius) as per the DoE Table 1 . Dissolved oxygen was maintained at 30% via Air/O 2 sparging. pH was controlled at a set point of 7 (± 0.2) using 7.5% sodium bicarbonate (Sigma-Aldrich, USA) as base and CO 2 gas flow. Initial temperature was maintained at 37°C followed by temperature shift (TS) at specific days (VCD of > 10 x 10 6 cells/mL or day 5, whichever is earlier) to 33°C coupled with initializing feed strategy during the production phase as outlined in Table (1). The agitation was set at 330 rpm. The ambr250 bioreactor was controlled through the Runtime software (Sartorius, Göttingen, Germany). Addition of feed, anti-foam (FoamAway™, Thermo Fisher, USA) and daily sampling were carried out by the automated liquid handler. Requisite feed volume was calculated by the ambr250 Runtime software. Glucose concentration was maintained at a target concentration of 3–6 g/L by calculated bolus addition of a 45% (D Glucose, Sigma-Aldrich, USA) stock solution. Pump lines were used for addition of base, glucose and the basal growth media. The culture time ranged from 14–16 days and samples were collected daily for offline mAb titer and total protein analysis during the production phase. 2.3 In-process culture control and analytical assays Bioreactors were sampled daily for total cell count, viable cell density (VCD), cell viability (%) and metabolite profile, automated through the liquid handler using the integrated Nova BioFlex2 (Nova Biomedical, USA). 16 cell culture test profile including Gluc, Glu, Lac, Gln, NH 4 + , K+, Na+, Ca++, pH, PO 2 , pCO 2 , viable cell density, total cell density, viability, cell diameter and osmolality were analyzed in Bioflex2 using the requisite test cartridges. Growth rates were calculated from natural logarithm of viable cell density data with culture time by linear regression. The product (mAb) titer and total protein analysis was carried out offline using the Cedex Bio (Roche, Switzerland) analyzer using the test kits and associated QC controls (IgG bio and Total Protein Bio, Roche). Daily collected culture samples were centrifuged at 5000 rpm for 6 minutes, and the supernatant were used for titer analysis and subsequently stored at – 80°C for any further analysis. 2.4 Design of Experiments (DoE) and Statistical analysis The central composite rotatable experimental design method (CCRD) was chosen to determine the effect of two operating variables, initial seed density (SD, x 10 6 cells/mL) and feeding rate (FR, % culture volume, Vc) addition during the mAb production phase as the main response variables. Selection of the factors and range of the variables were based on a few preliminary experiments and results previously reported in literature [ 11 , 12 ]. The successful optimization of these parameters can have a substantial impact on the biopharmaceutical industry, potentially reducing costs and enhancing mAb availability timelines for patients. The number of tests required for CCRD is the sum of 2k factorial runs with its origin at the center, 2k axial runs, and numbers of replicate tests at the center, where k is the number of the variables [ 18 ]. This design generated a total of 10 experimental runs with two tests at the center point. The summary of experimental runs for two variables is presented in Table 1 . The values of the variables are coded to lie at ± for factorial points, 0 for the center points and aA for axial points. A JMPⓇ Pro 18.0.2 (SAS Institute Inc., Cary, NC) software package was used for DoE and for evaluating and fitting the second order model to these two independent variables according to the following equation: $$\:Y={b}_{0}+\sum\:_{i=1}^{k}{b}_{i}{x}_{i}+\sum\:_{i=1}^{k}{b}_{ii}{x}_{i}^{2}+\sum\:_{i}^{i<j}\sum\:_{j}{b}_{ij}{x}_{i}{x}_{j}+e$$ 1 where Y is the dependent or response variable(s) to be modeled, xi and xj are the independent variables (factors), and b i , b ii and b ij are the measures of the linear, quadratic and interaction effects, respectively. The variable x i x j represents the first-order interactions between x i and x j variables, and e is the error. Upon collecting the response data from the experimental runs, a regression analysis using the least-squares method is carried out to determine the coefficients of the response model, standard errors, and significance. The effects were considered statistically significant when the p-value < 0.05 at 95% confidence level. The optimum values of the independent variables were obtained from the estimated variables in the model and by inspecting the response surface contour plots and JMP optimizer. Table 1 Coded Variables and Respective Actual Levels (SD and FR) in Experimental Design for mAb production using CCRD Method Bioreactor No Coded variables Seed Density (x 10 6 cell/mL) Feed Rate (Vc*,%) mAb titer (g/L) B1 0A 0.8 3.31 4.27 B2 ++ 1.2 3 4.40 B3 0 0.8 2.25 4.39 B4 A0 1.4 2.25 4.57 B5 +− 1.2 1.5 4.01 B6 −+ 0.4 3 3.45 B7 a0 0.2 2.25 3.46 B8 0 0.8 2.25 4.31 B9 −− 0.4 1.5 3.27 B10 0a 0.8 1.19 3.18 * Culture volume (Vc) 5. Results 5.1 Profile of CHO cells Growth The culturing of CHO cells was performed in ambr250Ⓡ parallel bioreactor according to the DoE presented in Table 1 . The cultures achieved peak viable cell density (VCD) between day 5–7 of culture days, with bioreactors (B1, B3, B6, B10) reaching > 15 x 10 6 cells/mL concentrations of VCD (Fig. 1 ). B6 achieved the highest VCD of 15.7 x 10 6 cells/mL on day 7. This value gradually dropped to ~ 10 x 10 6 cells/mL at harvest on day 14. The cell viability (%) remained relatively high throughout the production course, remaining > 85%, except B10 with the lowest feed rate (1.19%), which dropped to 72% at harvest in addition to having the lowest VCD simultaneously at 8.6 x 10 6 cells/mL. All the cultures were within the cell viability threshold of > 70% set as criteria for harvest or day 14. Temperature shift (TS) to 33°C was applied to reactors between day 3–5 upon reaching a VCD of 10 x 10 6 cells/mL along with the start of feeding rate (FR) at the specified conditions (Table 1 ) to initiate the production stage. Reactors with SD > 10 6 cells/mL met the TS conditions by day 3 while for reactors with low SD (0.2–0.4 x 10 6 cells/mL), TS was applied on day 5. As depicted in Fig. 1 D, growth rate during the exponential phase ranged from 0.72–0.90 day − 1 . Growth rates started declining after the TS period and eventually plateaued with no net growth during the production phase from day 5–6 onwards until harvest on day 14. Data indicated that variations in these process parameters (SD and FR) impacted cell proliferation and viability, illustrating its significant impact as critical process parameters. 5.2 Production of mAb/metabolites and CHO metabolism Figure 1 shows the time course profile for mAb and total protein titer along with other major metabolites/ by-products. The highest mAb concentration of 4.57 g/L was achieved in the B4 run operated with SD of 1.4 x 10 6 cells/mL, and FR of 2.25% (center point) (Table 1 , Fig. 1 F). The bioreactor runs with lower SD (0.2–0.4 x 10 6 cells/mL) and FR (1.19–1.5%) resulted in lower mAb titer below 3.5 g/L. The lowest mAb titer of 3.18 g/L was obtained in the B10 experimental run at 0.8 M x 10 6 cells/mL SD (center point), and 1.19% FR (axial point). The mean value of mAb titer at the center points with SD of 0.8 x 10 6 cells/mL and FR (2.25%) was 4.36 ± 0.06 g/L. The highest volumetric productivity of 0.33 g.L − 1 .day − 1 was observed for B4 which coincided with harvest day VCD of 11.5 x 10 6 cells/mL and 85% cell viability. The lactate profile peaked during the exponential growth phase to an average value of ~ 1.3 g/L before declining from day 7 onwards during the production phase (Fig. 1 C). The lactate values stabilized to around 0.1 g/L towards the last few days from Day 10–14 until harvest. The pCO2 profile increased during the production phase and the trend continued until harvest. The glutamate concentration varied substantially among the ten experimental runs as a function of FR as it was present in the feed medium (Fig S1 ). The glutamate concentrations were lower in the experimental runs with low FR (1.2–1.5%), i.e. B5, B9 and B10. The statistical analysis showed a significant positive correlation ( p < .0001* , R 2 = 0.97) between FR and glutamate concentration at harvest (Fig S1 ). B10 corresponds to the highest ammonium concentration of 7.7 mmol/L, simultaneously with the lowest mAb titer and cell viability/VCD at harvest. This indicates a negative impact on the process due to lower FR of 1.19% impacting overall cell growth and metabolism eventually affecting mAb titer. Higher SD and FR > 2.25% were more favorable for better cell culture performance and mAb titer. In the exponential growth phase, the cells generate energy through catabolism and utilize various substrates for biomass generation. Glucose is the major carbon source, which is mainly oxidized via glycolysis to form pyruvate. Pyruvate is further oxidized to Acetyl CoA to enter TCA cycle. However, CHO cells are known for their inefficient metabolism and substantial portions of pyruvate are converted to lactate as a main waste product. Table S1 summarizes the amount of lactate produced per amount glucose consumed (Y Lac/Glu ) for B1-B10. The results indicate that lactate metabolism is impacted by the SD. In CHO cells, glucose is mainly oxidized via glycolysis during the exponential phase [ 19 , 20 ]. The results showed that 30% -48% of pyruvate was converted to lactate in these set of experiments depending on the cultivation conditions. Those runs with lower initial SD (B6, B7 and B9) showed lower yields and rates of lactate production. The pyruvate concentration as a supplement in the base media remained unchanged or slightly increased during production phase (data not shown) indicating partial secretion of surplus pyruvate to the media. However, pyruvate was consumed and depleted from the media during the mAb production phase in a similar fashion to lactate consumption. 5.3 Process optimization and response surface model Multiple regression analysis was implemented to analyze and fit the linear and quadratic equation to the experimental dataset. All the estimated parameters including the values of coefficients, interactive terms, t Ratio, and p-values for the model were assessed for statistical significance (Table 2 ). Feeding rate (FR) in linear and quadratic form was highly significant for the yield of mAb concentration ( p < 0.05 ) whereas the SD was only significant in linear form. Additionally, no interaction was found between the two investigated independent variables, i.e. SD and FR (Table 2 ). The larger magnitude of the t-ratio and the smaller magnitude of the p-value indicate more significance of the corresponding coefficients [ 21 ]. The model adequacy and fit were evaluated through the analysis of variance data. The adjusted R 2 value of 0.92 for the model can be deemed favorable while allowing some variability for the model within the design space. The p-value for the lack of fit was not statistically significant (p = 0.19) illustrating the model adequacy for predicting the response variable. The actual values vs. predicted values obtained from the statistical model are in good agreement as evident from Fig. 2 A. Table 2 Model Coefficients Estimated by Multiple Linear Regressions for mAb Concentration Term Estimate Std Error t Ratio Prob>|t| Uncoded Estimate Intercept 4.295 0.147 29.24 < .0001* -0.400 SD (e6)(0.4,1.2) 0.391 0.073 5.32 0.006* 2.140 Feed Rate(1.5,3) 0.257 0.073 3.51 0.025* 2.722 SD (e6)*Feed Rate 0.063 0.104 0.60 0.580 0.208 SD (e6)*SD (e6) -0.163 0.097 -1.68 0.168 -1.020 Feed Rate*Feed Rate -0.318 0.097 -3.27 0.031* -0.566 Applying the uncoded coefficients in Eq. 1 , results in the following empirical equation for estimating the mAb titer: Y mAb = -0.4 + 2.14 SD + 2.72 FR + 0.221 (SD×FR)-1.02 SD 2 -0.57 FR 2 (2) The responses for yield of mAb production as three-dimensional surface plot of two factors (SD and feeding rate) and the corresponding contour plot are depicted in Fig. (2B, S2). Increase in FR showed to have a positive impact on the titer of mAb to a certain level and further increase seems to have no impact or even negative impact on the response variable. The surface plot for mAb concentration reached a peak at around 2.5% FR and then declined with further increase. SD showed to have a strong positive impact on the mAb concentration especially at initial densities up to 1M cell/mL and then this positive impact levels off around 1.2 x 10 6 cells/mL. Further analysis of the contour plot reveals that to reach a mAb concentration > 4.5 g/L, SDs greater than 1 x 10 6 cells/mL are required. The optimum region that yielded maximum production of mAb (4.53 g/L) corresponded to a SD of (1.1–1.2) x 10 6 cells/mL and FR between 2.5%-3% (Fig. 2 B). The optimal conditions for maximum response variable for mAb titer were determined using the desirability function profile in JMP pro. This analysis resulted in the highest mAb concentration of 4.57 g/L at 2.68% FR and 1.1 x 10 6 cell/mL SD, respectively (Fig. S3). 5.4 Model Validation Validation experimental runs at the optimized conditions, 1.1 x 10 6 cells/mL (SD) and 2.68% FR from the RSM models were conducted in duplicates in ambr250. Figure 3 depicts the culture profile data for VCD, cell viability, titer, pCO2, lactate, osmolarity, and ammonium profile for the validation runs at the optimized conditions. The cultures reached a peak VCD of ~ 14 x 10 6 cells/mL and cell viability remained high throughout the time course, reaching 84% at the 14-day period. Growth rate during the exponential phase reached 0.7 day − 1 . TS was applied on day 3 and the growth rate slowed down and remained stable with no net growth throughout the production phase. Culture period was extended to day 16 to investigate the mAb production and growth parameter trends as impacted by culture time. Cell viability dropped to 77% at harvest on D16. The lactate profile increased to ~ 1.2 g/L during the exponential growth phase on day 2 before briefly dropping and plateauing after TS was applied on day 3, indicating lactate shift from production to consumption state. It had a sharp decline from 1.3 g/L on day 6 to 0.1 g/L by day 9 during the production phase (Fig. 3 C). The lactate values stabilized to around 0.1 g/L and then started to progressively increase until harvest. After an initial spike during the exponential stage, ammonium values went on a downward trajectory before starting to increase from day 8 onward, briefly stabilized (6–7 mmol/L) for few days before decreasing to 5 mmol/L on day 16 (Fig. 3 C). Simultaneously, towards the later stages (Day 11), VCD and cell viability was slowly declining. Osmolarity was increasing (400–530 mOsm/kg), along with pCO2 values during this phase (Fig. 3 D), although volumetric productivity became stable at ~ 0.33 g.L − 1 .day − 1 from day 11–16 (Fig. 3 F). This trend has been reported, although some studies have even observed volumetric productivity continued to rise [ 22 , 23 ]. Having an increasing or stable trend for the volumetric productivity throughout the production stage under optimal operating parameters can lead to decrease in culture time allowing cost cutting [ 24 ]. The mAb titer reached 4.41 g/L and 5.01 g/L by day 14 and at harvest on day 16, respectively (Fig. 3 E). Total protein content in the culture supernatant also potentially comprising of host cell proteins (HCP) linearly increased through the culturing period reaching 5.6 g/L on day 16. Another key attribute of an efficient mAb production process is having reduced impurities such as HCPs/DNAs and associated cellular debris to minimize downstream processing costs [ 25 ]. The mAb titer of 4.41 g/L obtained from the optimized conditions at day 14 was in close agreement with the predicted value of 4.57g/L, thus validating the model predictions and adequacy. 6. Discussion The high-throughput Ambr250 bioreactors enabled robust control and rapid experimentation, leading to significant improvements regarding the interplay of critical process parameters. It entailed mapping key process parameters and inferred its critical role for process development and optimization to improve mAb titers. Ambr250 parallel mini bioreactors have been validated as a robust scale down model (SDM) of the commercial manufacturing process [ 10 , 26 ]. A DoE aided approach was applied in this study to optimize the operating range of two critical process variables- seed density (SD) and feeding rate (FR). It was observed from the DoE experiments that the variance in the SD and FR impacted the cell performance and mAb titer profile (Fig. 1 ). FR was a significant factor influencing culture performance in combination with SD > 1x 10 6 cells/mL range resulting in higher mAb titer. The lactate concentration spiked (> 1.2 g/L) during the exponential phase while initial supplemented glutamine was rapidly consumed simultaneously and depleted from the media with a spike in ammonia levels. This suggests glutamine might have been a part contributor to the lactate production while the majority coming from glycolysis during this initial phase [ 27 , 28 ]. Lactate values stabilized from day 3–6 as TS to 33°C was applied along with starting specified FR to reactors reaching ~ 10 x 10 6 cell/mL concentration, inducing the proliferative cells to production stage. Lactate shift was observed from day 6–8 onwards towards consumption until day 12. This switch is considered favorable and often serves as a key metabolic tuning characteristics, however the exact intrinsic causes driving this still needs more investigation [ 29 , 30 ]. A feeding strategy incorporating lactate feeds during stationary phase has led to 8% increase in titer, also linking this to reduced ammonia levels [ 31 ]. The increase in ammonium concentration across the culture conditions in the DoE experiments during the production phase correlated with the glutamate accumulation in the culture. The % FR addition directly contributed to the variance in glutamate concentration due to its presence in the added feed medium. Significant difference in glutamate levels was observed at harvest across the experimental conditions (Fig S1 ). The bioreactors with FR range between (1.2–1.5%) exhibited glutamate concentration that were 1.5 to 4 times lower, indicating a shift towards glutamate consumption, potentially for energy generation during the stationary phase. There was a strong correlation between the feed addition rate and glutamate accumulation during the production phase (Fig. S1 ). This fact indicates that even commercially available media may not be fully optimized for a specific CHO culture, leaving potential for further media refinement. Ammonium concentration for these bioreactors was at higher levels as well. These reactors also had the lowest cell viability (71.5–82%) comparatively. It seems that at lower feed rates, the CHO cells face an unbalanced nutrient supply that may impact the cell activities and metabolism. The highest accumulation of NH 4 + in the stationary phase was accompanied with the lowest glutamate concentration (B10-Fig. 1E, Fig S1 ). These phenomena are most likely due to an accelerated conversion of glutamate to α-ketoglutarate in TCA cycle through the oxidative metabolism. Similar trends under nutrient stress leading to glutamate consumption have been reported [ 32 ]. CHO cell growth inhibition was previously reported for an ammonium concentration greater than 5 mM [ 33 ]. Additionally, a 50% reduction in growth of CHO cells was observed at ammonium concentrations above 8 mM [ 34 ]. This is good agreement with results obtained in our experimental runs and it can explain the lowest viability observed in B9 and B10 where the NH 4 + concentration was higher than other bioreactors primarily resulting from lower FR. Pereira et al used C 13 metabolic flux analysis to reduce ammonia production by 40% while maintaining culture viability and titer by varying different amino acid composition of the culture media [ 35 ]. They specifically used glutamine, glutamate, asparagine, aspartate and serine to manipulate media composition for the investigation. These facts suggest that feeding strategy with tailored components which can favorably tune the metabolic flux can lead to good cell culture performance and potentially improve productivity. In the DoE runs, mAb production varied significantly as a function of process parameters. The highest mAb concentration of 4.57 g/L was achieved in the B4 run operated with SD of 1.4 x 10 6 cells/mL, and FR of 2.25% (center point). Bioreactors operated at SD > 1 x 10 6 cells/mL and FR of > 2% were more productive. Initial SD density beyond the sub-optimal range can enhance cell proliferation rate leading to higher VCD with adequate culture integrity, thus favoring volumetric productivity (Fig. 1 A). Our results showed lactate productivity is greater in bioreactor runs inoculated with higher SD. While lactate is considered as an inhibitory byproduct in the growth phase, it is consumed in the stationary (mAb expression) phase. In CHO cell cultivation, the metabolic shift from lactate production to lactate consumption is an indicator of metabolic efficiency. It seems that higher lactate production rate can lead to a faster metabolic shift and enhancement of the mAb titer in the stationary phase. The SD range investigated in the DoE provided empirical data to better map this parameter. However, FR was the more critical factor identified to have a significant impact on both culture performance and productivity in combination with optimal SD range. FR strategy is pivotal to control the production phase effectively for fed-batch operations. Lower FR (1.19–1.5%) had a deleterious impact on both growth and metabolism resulting in significantly lower mAb titer compared to titers in runs operated at higher FR (e.g B9, B10 vs. B3, B4 in Table 1 ). In addition, RSM model analysis entailed identifying optimal conditions and operating levels for these critical parameters. Validation experiments were in good agreement with the predicted mAb titer values for the RSM model. Additional culture time up to day 16 resulted in mAb titer reaching ~ 5 g/L. However, further investigation on the metabolic flux can help potentially optimize the production phase to boost volumetric productivity and reduce culture time while increasing titer. Addition of key precursors for the TCA cycle through adequate media and feeds can offer further scope for process optimization. Keeping the HCP and associated impurity flux in the culture to a minimum value, and maintaining a high-density culture with increased viability can also subsequently improve product quality while mitigating downstream processing costs and efforts [ 25 ]. Process development and gaining a mechanistic understanding of factors influencing culture performance, titer and product attributes is essential for aptly meeting the growing demands of biopharmaceuticals, providing better efficacy during commercial manufacturing. Overall, these findings contribute valuable knowledge that can be adopted by mAb manufacturers to streamline production processes. Furthermore, insights from this study can guide future research focusing on novel bioreactor technologies and process development. 7. Conclusion This research demonstrated that process mapping and optimization using HTP Ambr250 parallel bioreactors is a promising framework. Combination of automated bioreactor system such as ambr250 and statistical modeling via DoE provide a strong tool for studying and optimizing mAb production by using lower resources and significantly less time. This study highlights the potential for enhanced yields of mAb production process as CHO fed-batch is still the predominantly adopted route at commercial scale whilst allowing smoother tech-transfer to existing manufacturing infrastructure. The optimized process in Ambr250 is highly scalable and has a great potential for providing a process blueprint allowing reliable scalability to commercial stage while reducing drug development timeline. The results indicated that by fine-tuning of the mAb production process variables, especially SD and FR, maintaining cell viability and productivity is attainable beyond 14 days. Future work on this HTP ambr250 platform to optimize feed components during the growth and production phases offers significant potential for characterization and optimization of product titer, quality and efficacy. This can favorably impact screening and process development timelines, reliable tech transfer which would eventually allow quicker access of therapeutics to the patients. Declarations Conflict of Interest: The authors declare no competing interest. Author Contribution Author contributions:Farid Talebnia Rowshan: Conceptualization, Investigation, Methodology, Visualization, Writing - review & editing, Software, Formal analysis, Validation, Project administration, Supervision.Achinta Bordoloi: Conceptualization, Investigation, Writing - original draft, Methodology, Validation, Visualization, Software, Formal analysis, Data curation. Acknowledgement We would like to thank Wheeler Bio Inc. for providing the antibody-producing CHO cell line (ATUM miCHO) and their technical support throughout this study. We acknowledge the financial support received from the Economic Development Administration (EDA), USA under grant number #08_79_05677 for funding the OU bioprocessing Core Facility at The University of Oklahoma that enabled this research. The opinions, findings, and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the EDA, Wheeler Bio Inc. or University of Oklahoma. References Shukla AA, Wolfe LS, Mostafa SS, Norman C (2017) Evolving trends in mAb production processes. Bioengineering & Translational Medicine 2, 58–69. https://doi.org/10.1002/btm2.10061 Etit D, Meramo S, Ögmundarson Ó, Jensen MK, Sukumara S (2024) Can biotechnology lead the way toward a sustainable pharmaceutical industry? 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Manahan M, Nelson M, Cacciatore JJ, Weng J, Xu S, Pollard J (2019) Scale-down model qualification of ambr® 250 high-throughput mini-bioreactor system for two commercial-scale mAb processes. Biotechnology Progress 35, e2870. https://doi.org/10.1002/btpr.2870 Sandner V, Pybus LP, McCreath G, Glassey J (2019) Scale-Down Model Development in ambr systems: An Industrial Perspective. Biotechnology Journal 14, 1700766 .https://doi.org/10.1002/biot.201700766 Krumm TL, Ehsani A, Schaub J, Stiefel F (2023) An Investigation into the Metabolic Differences between Conventional and High Seeding Density Fed-Batch Cell Cultures by Applying a Segmented Modeling Approach. Processes . Luo Y, Kurian V, Ogunnaike BA (2021) Bioprocess systems analysis, modeling, estimation, and control. Current Opinion in Chemical Engineering 33, 100705. https://doi.org/10.1016/j.coche.2021.100705 Calmels C, McCann A, Malphettes L, Andersen MR (2019) Application of a curated genome-scale metabolic model of CHO DG44 to an industrial fed-batch process. Metabolic Engineering 51, 9–19. https://doi.org/10.1016/j.ymben.2018.09.009 Torres M, Zúñiga R, Gutierrez M, Vergara M, Collazo N, Reyes J, Berrios J, Aguillon JC, Molina MC, Altamirano C (2018) Mild hypothermia upregulates myc and xbp1s expression and improves anti-TNFα production in CHO cells. PLoS ONE 13.10.1371/journal.pone.0194510 Wang Z, Wang C, Chen G (2022) Kinetic modeling: A tool for temperature shift and feeding optimization in cell culture process development. Protein Expression and Purification 198, 106130.https://doi.org/10.1016/j.pep.2022.106130 Gonzalez-Rivera JC, Galvan A, Ryder T, Milman M, Agarwal K, Kandari L, Khetan A (2024) A high-titer scalable Chinese hamster ovary transient expression platform for production of biotherapeutics. Biotechnology and Bioengineering n/a. https://doi.org/10.1002/bit.28817 Montgomery DC, Runger GC, Hubele NF (2010) Engineering Statistics, Wiley. Nargund S, Qiu J, Goudar CT (2015) Elucidating the role of copper in CHO cell energy metabolism using (13)C metabolic flux analysis. Biotechnol Prog 31, 1179-1186.10.1002/btpr.2131 Wijaya AW, Ulmer A, Hundsdorfer L, Verhagen N, Teleki A, Takors R (2021) Compartment-specific metabolome labeling enables the identification of subcellular fluxes that may serve as promising metabolic engineering targets in CHO cells. Bioprocess Biosyst Eng 44, 2567-2578.10.1007/s00449-021-02628-1 Talebnia F, Pourbafrani M, Taherzadeh MJ, Lundin M (2008) Optimization study of citrus wastes Saccharification by dilute acid hydrolysis. BioResources 3, 108–122 Qin J, Wu X, Xia Z, Huang Z, Zhang Y, Wang Y, Fu Q, Zheng C (2019) The effect of hyperosmolality application time on production, quality, and biopotency of monoclonal antibodies produced in CHO cell fed-batch and perfusion cultures. Applied Microbiology and Biotechnology 103, 1217-1229.10.1007/s00253-018-9555-7 Saeki H, Fueki K, Maeda N (2025) Enhancing monoclonal antibody production efficiency using CHO-MK cells and specific media in a conventional fed-batch culture. Cytotechnology 77.10.1007/s10616-024-00669-4 Schwarz H, Lee K, Castan A, Chotteau V (2023) Optimization of medium with perfusion microbioreactors for high density CHO cell cultures at very low renewal rate aided by design of experiments. Biotechnology and Bioengineering 120, 2523–2541. https://doi.org/10.1002/bit.28397 Oh YH, Mendola KM, Choe LH, Min L, Lavoie AR, Sripada SA, Williams TI, Lee KH, Yigzaw Y, Seay A, Bill J, Li X, Roush DJ, Cramer SM, Menegatti S, Lenhoff AM (2024) Identification and characterization of CHO host-cell proteins in monoclonal antibody bioprocessing. Biotechnology and Bioengineering 121, 291–305. https://doi.org/10.1002/bit.28568 Alsayyari AA, Pan X, Dalm C, van der Veen JW, Vriezen N, Hageman JA, Wijffels RH, Martens DE (2018) Transcriptome analysis for the scale-down of a CHO cell fed-batch process. Journal of Biotechnology 279, 61–72. https://doi.org/10.1016/j.jbiotec.2018.05.012 Dean J, Reddy P (2013) Metabolic analysis of antibody producing CHO cells in fed-batch production. Biotechnology and Bioengineering 110, 1735–1747. https://doi.org/10.1002/bit.24826 Hong JK, Nargund S, Lakshmanan M, Kyriakopoulos S, Kim DY, Ang KS, Leong D, Yang Y, Lee D-Y (2018) Comparative phenotypic analysis of CHO clones and culture media for lactate shift. Journal of Biotechnology 283, 97–104. https://doi.org/10.1016/j.jbiotec.2018.07.042 Hartley F, Walker T, Chung V, Morten K (2018) Mechanisms driving the lactate switch in Chinese hamster ovary cells. Biotechnology and Bioengineering 115, 1890–1903. https://doi.org/10.1002/bit.26603 Torres M, Hawke E, Hoare R, Scholey R, Pybus LP, Young A, Hayes A, Dickson AJ (2025) Deciphering molecular drivers of lactate metabolic shift in mammalian cell cultures. Metabolic Engineering 88, 25–39. https://doi.org/10.1016/j.ymben.2024.12.001 Helfer A, Gros S, Kolwyck D, Karst DJ (2023) Tuning metabolic efficiency for increased product yield in high titer fed-batch Chinese hamster ovary cell culture. Biotechnology Progress 39, e3327 .https://doi.org/10.1002/btpr.3327 Nicolae A, Wahrheit J, Bahnemann J, Zeng A-P, Heinzle E (2014) Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC Systems Biology 8, 50.10.1186/1752-0509-8-50 Yang M, Butler M (2000) Effects of ammonia on CHO cell growth, erythropoietin production, and glycosylation. Biotechnol Bioeng 68, 370-380.10.1002/(sici)1097 – 0290(20000520)68:4 3.0.co;2-k Kurano N, Leist C, Messi F, Kurano S, Fiechter A (1990) Growth behavior of Chinese hamster ovary cells in a compact loop bioreactor. 2. Effects of medium components and waste products. J Biotechnol 15, 113-128.10.1016/0168–1656(90)90055-g McAtee Pereira AG, Walther JL, Hollenbach M, Young JD (2018) 13C Flux Analysis Reveals that Rebalancing Medium Amino Acid Composition can Reduce Ammonia Production while Preserving Central Carbon Metabolism of CHO Cell Cultures. Biotechnology Journal 13, 1700518. https://doi.org/10.1002/biot.201700518 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.docx floatimage1.jpeg Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Bioprocess and Biosystems Engineering → Version 1 posted Editorial decision: Revision requested 07 Jul, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 09 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6831589","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469244492,"identity":"c86ae359-013c-4729-a4b3-095236358457","order_by":0,"name":"Achinta Bordoloi","email":"","orcid":"","institution":"University of Oklahoma","correspondingAuthor":false,"prefix":"","firstName":"Achinta","middleName":"","lastName":"Bordoloi","suffix":""},{"id":469244493,"identity":"80dac32d-81bc-4df7-b8d2-a9cb5eeed028","order_by":1,"name":"Farid Talebnia Rowshan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC9gYg8YDhAAMbA/MBkABjAyEtPAeYGRgSwFrYEkjUAmQaEKmFgf/gh4SKO3l87Gc+PuZhsJHdcICgFmZmiYQzz4rZeHI3G/MwpBkT1GLPwMwgkdh2OLGNIXebNA/D4USibPmR+A+ohf/NM6CW/0RpYZNIbABqkchhA2o5QIQWZmYzi4Rjz4BanhkbzjFINp5JUAt74+MbH2ruJM7vT3744E2FnWwfIS1A3yMDA0LKR8EoGAWjYBQQBQBv+UBjD3UVXAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Oklahoma","correspondingAuthor":true,"prefix":"","firstName":"Farid","middleName":"Talebnia","lastName":"Rowshan","suffix":""}],"badges":[],"createdAt":"2025-06-05 18:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6831589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6831589/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00449-025-03229-y","type":"published","date":"2025-08-30T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84644511,"identity":"21343fd1-620f-4e92-867a-c6597e8e4515","added_by":"auto","created_at":"2025-06-15 16:14:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367133,"visible":true,"origin":"","legend":"\u003cp\u003eCell culture analysis profile for the DoE optimization experimental runs (B1-B10) with ambr250, shaded area represents temperature shift (TS) days (3-5) based on culture conditions. A) Viable cell concentration B) Cell viability\u0026nbsp; C) Lactate D) Growth rate E) NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u0026nbsp; \u003c/sup\u003eF) mAb titer G) Volumetric productivity H) pCO\u003csub\u003e2\u003c/sub\u003e I) Osmolarity\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/a7f5b996a6481117f2911cc3.jpeg"},{"id":84645109,"identity":"77cabeff-9035-4a80-a599-cf4dc809bcaa","added_by":"auto","created_at":"2025-06-15 16:30:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168419,"visible":true,"origin":"","legend":"\u003cp\u003eA) Profile of actual values vs. predicted values of mAb concentration B) Response surface plot showing the impact of seed density (SD) and feeding rate (FR) on the production of mAb\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/cb13d503a4dc48f7a5976798.png"},{"id":84644521,"identity":"fb25c3be-1ec0-49d1-b592-e3e2d21bf0ec","added_by":"auto","created_at":"2025-06-15 16:14:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233489,"visible":true,"origin":"","legend":"\u003cp\u003eValidation runs (n=2) cell culture profile at the optimized condition from the RSM model.\u0026nbsp; A) VCD and cell viability B) Growth rate C) Lactate/Ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e/) \u0026nbsp;D) pCO\u003csub\u003e2\u003c/sub\u003e/Osmolarity\u0026nbsp;E) mAb titer and total Protein F) Volumetric productivity\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/8fa932dc61b7cf76b8bea135.jpg"},{"id":93160092,"identity":"a0fbd8fa-effe-4821-b066-375a8d6b4904","added_by":"auto","created_at":"2025-10-09 16:31:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/f15bcc96-7535-4d04-8cda-2080c43a1165.pdf"},{"id":84644524,"identity":"131057e1-2d51-470a-a9c3-40bf974fd25f","added_by":"auto","created_at":"2025-06-15 16:14:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":145364,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/58386353cbd0e5671f520e9a.docx"},{"id":84644745,"identity":"7af4a761-d7bd-4fec-86cc-53c00c58061b","added_by":"auto","created_at":"2025-06-15 16:22:32","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":184158,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6831589/v1/cf3352ab3966e0cf71fa4f7c.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProcess Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr\u003csup\u003e®\u003c/sup\u003e 250 High-throughput Parallel Bioreactors\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe demand for biologics such as monoclonal antibodies (mAbs) has been gaining traction in recent years, as evident from a plethora of approved mAbs by the U.S. Food and Drug Administration (FDA) in the markets [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The biopharmaceutical market is rapidly growing and is estimated to cross \u003cspan\u003e$\u003c/span\u003e975\u0026nbsp;billion by 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the surge in demand for biologics, there is an urgent need to develop and implement more efficient, optimized and scalable processes with improved product titer and quality attributes. A rapid process development and characterization phase can ensure bringing these mAb-based therapeutics to the patients quickly through the approval pipeline. Mammalian expression system using Chinese hamster ovary (CHO) cell line is still the favored route accounting for about 80% of mAbs approved for therapeutic use owing to being conducive to adequate post-translational modifications similar to those found in human proteins, such as glycosylation, which is considered to be a Critical Quality Attribute (CQA) in biopharmaceutical industries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The CHO cells are also favored for their ease of genetic manipulation, suitability for large-scale industrial culturing, and history of regulatory approval. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite these advantages, the production process in CHO cells is complex, and maintaining productivity, product quality, efficiency, and consistency remains challenging.\u003c/p\u003e \u003cp\u003eFed-batch culture is predominantly the preferred mode of operation at the manufacturing scale while the need for increased process efficacy has gained impetus due to surging market demands [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Inherent challenges remain pertaining to improving production titer of the fed-batch mode through better process mapping, optimization and minimizing the cost of goods for manufacturing. While additional production approaches are being investigated such as intensified fed-batch (IFB) and perfusion cultures to improve titer, logistical and economic challenges for their implementation at manufacturing scale exists [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Hence, accelerated technology transfer from cell line development and media screening to optimization of production parameters to improve titer, getting consistent quality attributes and culture performance is pivotal for the currently used fed-batch mode. Moreover, understanding the impact of various key process parameters on growth and metabolism of CHO cells fed-batch mode to boost productivity remains to be discerned [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hence, it is imperative to build on the limited progress on the antibody production efficiency side, garner robust empirical data and reduce drug development timelines to meet high demand and exponential growth of biomanufacturing market.\u003c/p\u003e \u003cp\u003eThis study focuses on high-throughput (HTP) systems using Ambr250 parallel bioreactors, which allow for simultaneous experimentation across multiple conditions using Design of Experiments (DOE) to optimize critical process parameters (CPP). This scaled down automated reactor system with robust control over key operation parameters significantly reduces timelines for long haul CHO fed-batch process development while being representative of the commercial scale [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This entails better scalability and tech transfer from bench to pilot scale during the early stages of development pipeline much quicker, and at a reduced cost and resources. The automated 12-way ambr250 independently controls dissolved oxygen (DO), pH, temperature (T), agitation, gassing and feeding strategy for each individual vessel. However, comprehensive empirical data sets investigating various parametric impact on process efficacy from these high throughput bioreactor systems is still limited in the literature.\u003c/p\u003e \u003cp\u003eA systematic mapping approach was employed to identify critical process parameters (CPP) through literature review and preliminary experiments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While feeding strategy and initial seed density (SD) could play crucial roles in productivity, and overall process efficiency, little work is available in optimizing these variables and studying impacts through statistical approach in scaled down automated bioreactor systems such as Ambr250. Therefore, many industrial strategies for therapeutic protein production by CHO cells are still based on empirical results [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. An optimal feeding strategy ensures a balanced supply of essential nutrients (e.g., glucose, and amino acids) required for catabolism/anabolism while preventing accumulation of toxic by-products such as lactate and ammonium. Likewise, optimal seed density promotes robust cell expansion, shorter lag phases, and in turn reduces the risk of nutrient depletion or excessive waste and by-product accumulation [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, a statistical design of experiment (DoE) using JMP Pro 18 was used to evaluate the impact of initial seed density (SD) and feeding rate (FR) along with any potential interactions between identified parameters. Based on the DOE design, a model was developed and the optimal conditions for the highest mAb titer were predicted and further validated. Process was continuously monitored/controlled and dataset of the critical culture parameters such as viable cell density, cell viability, pH, mAb titer, and key metabolites production profile were collected daily for further data analysis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell line, media used and seed train expansion\u003c/h2\u003e \u003cp\u003eA Chinese Hamster Ovary (CHO) cell line (ATUM miCHO) used for an IgG based mAb production was used in this study. Commercially available growth and expansion media (Ex-Cell, Advanced CHO medium, Sigma Aldrich, USA) were used for cell culture. Feed medium (Cellvento ModiFeed Comp, Sigma Aldrich, USA) was used to optimize feeding rate (FR) conditions. A 1mL Vial (10\u003csup\u003e7\u003c/sup\u003e cells/mL) was thawed and seed train expansion was carried out in the growth media supplemented with 4 mM L-Glutamine (Sigma Aldrich, USA) in non-baffled shake flasks (Corning, USA) in a humidified CO\u003csub\u003e2\u003c/sub\u003e incubator at 37\u0026deg;C, 120 rpm and 5% CO\u003csub\u003e2\u003c/sub\u003e (New Brunswick S41i, Eppendorf). Cells were passaged every 3\u0026ndash;4 days at a seeding density of 0.4 (\u0026plusmn;\u0026thinsp;0.25%) x 10\u003csup\u003e6\u003c/sup\u003e cells/mL for up to 3 passages prior to inoculation in the ambr250 bioreactors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Fed-batch cell culture in ambr250 bioreactors\u003c/h2\u003e \u003cp\u003eThe 12-way ambr250 bioreactor system (Sartorius, G\u0026ouml;ttingen, Germany) was set up under controlled environmental conditions, cells were inoculated in 10 separate single use 250 mL bioreactors (Mammalian Vessels 001-5G25, Sartorius) as per the DoE Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Dissolved oxygen was maintained at 30% via Air/O\u003csub\u003e2\u003c/sub\u003e sparging. pH was controlled at a set point of 7 (\u0026plusmn;\u0026thinsp;0.2) using 7.5% sodium bicarbonate (Sigma-Aldrich, USA) as base and CO\u003csub\u003e2\u003c/sub\u003e gas flow. Initial temperature was maintained at 37\u0026deg;C followed by temperature shift (TS) at specific days (VCD of \u0026gt;\u0026thinsp;10 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL or day 5, whichever is earlier) to 33\u0026deg;C coupled with initializing feed strategy during the production phase as outlined in Table\u0026nbsp;(1). The agitation was set at 330 rpm. The ambr250 bioreactor was controlled through the Runtime software (Sartorius, G\u0026ouml;ttingen, Germany). Addition of feed, anti-foam (FoamAway\u0026trade;, Thermo Fisher, USA) and daily sampling were carried out by the automated liquid handler. Requisite feed volume was calculated by the ambr250 Runtime software. Glucose concentration was maintained at a target concentration of 3\u0026ndash;6 g/L by calculated bolus addition of a 45% (D Glucose, Sigma-Aldrich, USA) stock solution. Pump lines were used for addition of base, glucose and the basal growth media. The culture time ranged from 14\u0026ndash;16 days and samples were collected daily for offline mAb titer and total protein analysis during the production phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 In-process culture control and analytical assays\u003c/h2\u003e \u003cp\u003eBioreactors were sampled daily for total cell count, viable cell density (VCD), cell viability (%) and metabolite profile, automated through the liquid handler using the integrated Nova BioFlex2 (Nova Biomedical, USA). 16 cell culture test profile including Gluc, Glu, Lac, Gln, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, K+, Na+, Ca++, pH, PO\u003csub\u003e2\u003c/sub\u003e, pCO\u003csub\u003e2\u003c/sub\u003e, viable cell density, total cell density, viability, cell diameter and osmolality were analyzed in Bioflex2 using the requisite test cartridges. Growth rates were calculated from natural logarithm of viable cell density data with culture time by linear regression. The product (mAb) titer and total protein analysis was carried out offline using the Cedex Bio (Roche, Switzerland) analyzer using the test kits and associated QC controls (IgG bio and Total Protein Bio, Roche). Daily collected culture samples were centrifuged at 5000 rpm for 6 minutes, and the supernatant were used for titer analysis and subsequently stored at \u0026ndash; 80\u0026deg;C for any further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Design of Experiments (DoE) and Statistical analysis\u003c/h2\u003e \u003cp\u003eThe central composite rotatable experimental design method (CCRD) was chosen to determine the effect of two operating variables, initial seed density (SD, x 10\u003csup\u003e6\u003c/sup\u003e cells/mL) and feeding rate (FR, % culture volume, Vc) addition during the mAb production phase as the main response variables. Selection of the factors and range of the variables were based on a few preliminary experiments and results previously reported in literature [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The successful optimization of these parameters can have a substantial impact on the biopharmaceutical industry, potentially reducing costs and enhancing mAb availability timelines for patients.\u003c/p\u003e \u003cp\u003eThe number of tests required for CCRD is the sum of 2k factorial runs with its origin at the center, 2k axial runs, and numbers of replicate tests at the center, where k is the number of the variables [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This design generated a total of 10 experimental runs with two tests at the center point. The summary of experimental runs for two variables is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The values of the variables are coded to lie at \u0026plusmn;\u0026thinsp;for factorial points, 0 for the center points and aA for axial points. A JMPⓇ Pro 18.0.2 (SAS Institute Inc., Cary, NC) software package was used for DoE and for evaluating and fitting the second order model to these two independent variables according to the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Y={b}_{0}+\\sum\\:_{i=1}^{k}{b}_{i}{x}_{i}+\\sum\\:_{i=1}^{k}{b}_{ii}{x}_{i}^{2}+\\sum\\:_{i}^{i\u0026lt;j}\\sum\\:_{j}{b}_{ij}{x}_{i}{x}_{j}+e$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Y is the dependent or response variable(s) to be modeled, xi and xj are the independent variables (factors), and b\u003csub\u003ei\u003c/sub\u003e, b\u003csub\u003eii\u003c/sub\u003e and b\u003csub\u003eij\u003c/sub\u003e are the measures of the linear, quadratic and interaction effects, respectively. The variable x\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ej\u003c/sub\u003e represents the first-order interactions between x\u003csub\u003ei\u003c/sub\u003e and x\u003csub\u003ej\u003c/sub\u003e variables, and e is the error. Upon collecting the response data from the experimental runs, a regression analysis using the least-squares method is carried out to determine the coefficients of the response model, standard errors, and significance. The effects were considered statistically significant when the \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at 95% confidence level. The optimum values of the independent variables were obtained from the estimated variables in the model and by inspecting the response surface contour plots and JMP optimizer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoded Variables and Respective Actual Levels (SD and FR) in Experimental Design for mAb production using CCRD Method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioreactor No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoded variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeed Density (x 10\u003csup\u003e6\u003c/sup\u003e cell/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFeed Rate (Vc*,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emAb titer (g/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Culture volume (Vc)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Profile of CHO cells Growth\u003c/h2\u003e \u003cp\u003eThe culturing of CHO cells was performed in ambr250Ⓡ parallel bioreactor according to the DoE presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cultures achieved peak viable cell density (VCD) between day 5\u0026ndash;7 of culture days, with bioreactors (B1, B3, B6, B10) reaching\u0026thinsp;\u0026gt;\u0026thinsp;15 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL concentrations of VCD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). B6 achieved the highest VCD of 15.7 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL on day 7. This value gradually dropped to ~\u0026thinsp;10 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL at harvest on day 14. The cell viability (%) remained relatively high throughout the production course, remaining\u0026thinsp;\u0026gt;\u0026thinsp;85%, except B10 with the lowest feed rate (1.19%), which dropped to 72% at harvest in addition to having the lowest VCD simultaneously at 8.6 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL. All the cultures were within the cell viability threshold of \u0026gt;\u0026thinsp;70% set as criteria for harvest or day 14. Temperature shift (TS) to 33\u0026deg;C was applied to reactors between day 3\u0026ndash;5 upon reaching a VCD of 10 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL along with the start of feeding rate (FR) at the specified conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to initiate the production stage. Reactors with SD\u0026thinsp;\u0026gt;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e cells/mL met the TS conditions by day 3 while for reactors with low SD (0.2\u0026ndash;0.4 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL), TS was applied on day 5. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, growth rate during the exponential phase ranged from 0.72\u0026ndash;0.90 day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Growth rates started declining after the TS period and eventually plateaued with no net growth during the production phase from day 5\u0026ndash;6 onwards until harvest on day 14. Data indicated that variations in these process parameters (SD and FR) impacted cell proliferation and viability, illustrating its significant impact as critical process parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Production of mAb/metabolites and CHO metabolism\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the time course profile for mAb and total protein titer along with other major metabolites/ by-products. The highest mAb concentration of 4.57 g/L was achieved in the B4 run operated with SD of 1.4 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL, and FR of 2.25% (center point) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The bioreactor runs with lower SD (0.2\u0026ndash;0.4 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL) and FR (1.19\u0026ndash;1.5%) resulted in lower mAb titer below 3.5 g/L. The lowest mAb titer of 3.18 g/L was obtained in the B10 experimental run at 0.8 M x 10\u003csup\u003e6\u003c/sup\u003e cells/mL SD (center point), and 1.19% FR (axial point). The mean value of mAb titer at the center points with SD of 0.8 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and FR (2.25%) was 4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 g/L. The highest volumetric productivity of 0.33 g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was observed for B4 which coincided with harvest day VCD of 11.5 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and 85% cell viability. The lactate profile peaked during the exponential growth phase to an average value of ~\u0026thinsp;1.3 g/L before declining from day 7 onwards during the production phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The lactate values stabilized to around 0.1 g/L towards the last few days from Day 10\u0026ndash;14 until harvest. The pCO2 profile increased during the production phase and the trend continued until harvest. The glutamate concentration varied substantially among the ten experimental runs as a function of FR as it was present in the feed medium (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The glutamate concentrations were lower in the experimental runs with low FR (1.2\u0026ndash;1.5%), i.e. B5, B9 and B10. The statistical analysis showed a significant positive correlation (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.0001*\u003c/em\u003e, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.97) between FR and glutamate concentration at harvest (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). B10 corresponds to the highest ammonium concentration of 7.7 mmol/L, simultaneously with the lowest mAb titer and cell viability/VCD at harvest. This indicates a negative impact on the process due to lower FR of 1.19% impacting overall cell growth and metabolism eventually affecting mAb titer. Higher SD and FR\u0026thinsp;\u0026gt;\u0026thinsp;2.25% were more favorable for better cell culture performance and mAb titer.\u003c/p\u003e \u003cp\u003eIn the exponential growth phase, the cells generate energy through catabolism and utilize various substrates for biomass generation. Glucose is the major carbon source, which is mainly oxidized via glycolysis to form pyruvate. Pyruvate is further oxidized to Acetyl CoA to enter TCA cycle. However, CHO cells are known for their inefficient metabolism and substantial portions of pyruvate are converted to lactate as a main waste product. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e summarizes the amount of lactate produced per amount glucose consumed (Y\u003csub\u003eLac/Glu\u003c/sub\u003e) for B1-B10. The results indicate that lactate metabolism is impacted by the SD. In CHO cells, glucose is mainly oxidized via glycolysis during the exponential phase [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results showed that 30% -48% of pyruvate was converted to lactate in these set of experiments depending on the cultivation conditions. Those runs with lower initial SD (B6, B7 and B9) showed lower yields and rates of lactate production. The pyruvate concentration as a supplement in the base media remained unchanged or slightly increased during production phase (data not shown) indicating partial secretion of surplus pyruvate to the media. However, pyruvate was consumed and depleted from the media during the mAb production phase in a similar fashion to lactate consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Process optimization and response surface model\u003c/h2\u003e \u003cp\u003eMultiple regression analysis was implemented to analyze and fit the linear and quadratic equation to the experimental dataset. All the estimated parameters including the values of coefficients, interactive terms, t Ratio, and p-values for the model were assessed for statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Feeding rate (FR) in linear and quadratic form was highly significant for the yield of mAb concentration (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) whereas the SD was only significant in linear form. Additionally, no interaction was found between the two investigated independent variables, i.e. SD and FR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The larger magnitude of the t-ratio and the smaller magnitude of the p-value indicate more significance of the corresponding coefficients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The model adequacy and fit were evaluated through the analysis of variance data. The adjusted R\u003csup\u003e2\u003c/sup\u003e value of 0.92 for the model can be deemed favorable while allowing some variability for the model within the design space. The \u003cem\u003ep-value\u003c/em\u003e for the lack of fit was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.19) illustrating the model adequacy for predicting the response variable. The actual values vs. predicted values obtained from the statistical model are in good agreement as evident from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Coefficients Estimated by Multiple Linear Regressions for mAb Concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb\u0026gt;|t|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUncoded Estimate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (e6)(0.4,1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeed Rate(1.5,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (e6)*Feed Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (e6)*SD (e6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeed Rate*Feed Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eApplying the uncoded coefficients in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, results in the following empirical equation for estimating the mAb titer:\u003c/p\u003e \u003cp\u003e \u003cb\u003eY\u003c/b\u003e \u003csub\u003e \u003cb\u003emAb\u003c/b\u003e \u003c/sub\u003e \u003cem\u003e= -0.4\u0026thinsp;+\u0026thinsp;2.14 SD\u0026thinsp;+\u0026thinsp;2.72 FR\u0026thinsp;+\u0026thinsp;0.221 (SD\u0026times;FR)-1.02 SD\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e-0.57 FR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (2)\u003c/p\u003e \u003cp\u003eThe responses for yield of mAb production as three-dimensional surface plot of two factors (SD and feeding rate) and the corresponding contour plot are depicted in Fig.\u0026nbsp;(2B, S2). Increase in FR showed to have a positive impact on the titer of mAb to a certain level and further increase seems to have no impact or even negative impact on the response variable. The surface plot for mAb concentration reached a peak at around 2.5% FR and then declined with further increase. SD showed to have a strong positive impact on the mAb concentration especially at initial densities up to 1M cell/mL and then this positive impact levels off around 1.2 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL. Further analysis of the contour plot reveals that to reach a mAb concentration\u0026thinsp;\u0026gt;\u0026thinsp;4.5 g/L, SDs greater than 1 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL are required. The optimum region that yielded maximum production of mAb (4.53 g/L) corresponded to a SD of (1.1\u0026ndash;1.2) x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and FR between 2.5%-3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The optimal conditions for maximum response variable for mAb titer were determined using the desirability function profile in JMP pro. This analysis resulted in the highest mAb concentration of 4.57 g/L at 2.68% FR and 1.1 x 10\u003csup\u003e6\u003c/sup\u003e cell/mL SD, respectively (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Model Validation\u003c/h2\u003e \u003cp\u003eValidation experimental runs at the optimized conditions, 1.1 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL (SD) and 2.68% FR from the RSM models were conducted in duplicates in ambr250. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the culture profile data for VCD, cell viability, titer, pCO2, lactate, osmolarity, and ammonium profile for the validation runs at the optimized conditions.\u003c/p\u003e \u003cp\u003eThe cultures reached a peak VCD of ~\u0026thinsp;14 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and cell viability remained high throughout the time course, reaching 84% at the 14-day period. Growth rate during the exponential phase reached 0.7 day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. TS was applied on day 3 and the growth rate slowed down and remained stable with no net growth throughout the production phase. Culture period was extended to day 16 to investigate the mAb production and growth parameter trends as impacted by culture time. Cell viability dropped to 77% at harvest on D16.\u003c/p\u003e \u003cp\u003eThe lactate profile increased to ~\u0026thinsp;1.2 g/L during the exponential growth phase on day 2 before briefly dropping and plateauing after TS was applied on day 3, indicating lactate shift from production to consumption state. It had a sharp decline from 1.3 g/L on day 6 to 0.1 g/L by day 9 during the production phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The lactate values stabilized to around 0.1 g/L and then started to progressively increase until harvest. After an initial spike during the exponential stage, ammonium values went on a downward trajectory before starting to increase from day 8 onward, briefly stabilized (6\u0026ndash;7 mmol/L) for few days before decreasing to 5 mmol/L on day 16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Simultaneously, towards the later stages (Day 11), VCD and cell viability was slowly declining. Osmolarity was increasing (400\u0026ndash;530 mOsm/kg), along with pCO2 values during this phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), although volumetric productivity became stable at ~\u0026thinsp;0.33 g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e from day 11\u0026ndash;16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). This trend has been reported, although some studies have even observed volumetric productivity continued to rise [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Having an increasing or stable trend for the volumetric productivity throughout the production stage under optimal operating parameters can lead to decrease in culture time allowing cost cutting [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The mAb titer reached 4.41 g/L and 5.01 g/L by day 14 and at harvest on day 16, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Total protein content in the culture supernatant also potentially comprising of host cell proteins (HCP) linearly increased through the culturing period reaching 5.6 g/L on day 16. Another key attribute of an efficient mAb production process is having reduced impurities such as HCPs/DNAs and associated cellular debris to minimize downstream processing costs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The mAb titer of 4.41 g/L obtained from the optimized conditions at day 14 was in close agreement with the predicted value of 4.57g/L, thus validating the model predictions and adequacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThe high-throughput Ambr250 bioreactors enabled robust control and rapid experimentation, leading to significant improvements regarding the interplay of critical process parameters. It entailed mapping key process parameters and inferred its critical role for process development and optimization to improve mAb titers. Ambr250 parallel mini bioreactors have been validated as a robust scale down model (SDM) of the commercial manufacturing process [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A DoE aided approach was applied in this study to optimize the operating range of two critical process variables- seed density (SD) and feeding rate (FR). It was observed from the DoE experiments that the variance in the SD and FR impacted the cell performance and mAb titer profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). FR was a significant factor influencing culture performance in combination with SD\u0026thinsp;\u0026gt;\u0026thinsp;1x 10\u003csup\u003e6\u003c/sup\u003e cells/mL range resulting in higher mAb titer.\u003c/p\u003e \u003cp\u003eThe lactate concentration spiked (\u0026gt;\u0026thinsp;1.2 g/L) during the exponential phase while initial supplemented glutamine was rapidly consumed simultaneously and depleted from the media with a spike in ammonia levels. This suggests glutamine might have been a part contributor to the lactate production while the majority coming from glycolysis during this initial phase [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Lactate values stabilized from day 3\u0026ndash;6 as TS to 33\u0026deg;C was applied along with starting specified FR to reactors reaching\u0026thinsp;~\u0026thinsp;10 x 10\u003csup\u003e6\u003c/sup\u003e cell/mL concentration, inducing the proliferative cells to production stage. Lactate shift was observed from day 6\u0026ndash;8 onwards towards consumption until day 12. This switch is considered favorable and often serves as a key metabolic tuning characteristics, however the exact intrinsic causes driving this still needs more investigation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A feeding strategy incorporating lactate feeds during stationary phase has led to 8% increase in titer, also linking this to reduced ammonia levels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The increase in ammonium concentration across the culture conditions in the DoE experiments during the production phase correlated with the glutamate accumulation in the culture. The % FR addition directly contributed to the variance in glutamate concentration due to its presence in the added feed medium. Significant difference in glutamate levels was observed at harvest across the experimental conditions (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The bioreactors with FR range between (1.2\u0026ndash;1.5%) exhibited glutamate concentration that were 1.5 to 4 times lower, indicating a shift towards glutamate consumption, potentially for energy generation during the stationary phase. There was a strong correlation between the feed addition rate and glutamate accumulation during the production phase (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This fact indicates that even commercially available media may not be fully optimized for a specific CHO culture, leaving potential for further media refinement. Ammonium concentration for these bioreactors was at higher levels as well. These reactors also had the lowest cell viability (71.5\u0026ndash;82%) comparatively. It seems that at lower feed rates, the CHO cells face an unbalanced nutrient supply that may impact the cell activities and metabolism. The highest accumulation of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in the stationary phase was accompanied with the lowest glutamate concentration (B10-Fig.\u0026nbsp;1E, Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These phenomena are most likely due to an accelerated conversion of glutamate to α-ketoglutarate in TCA cycle through the oxidative metabolism. Similar trends under nutrient stress leading to glutamate consumption have been reported [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCHO cell growth inhibition was previously reported for an ammonium concentration greater than 5 mM [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, a 50% reduction in growth of CHO cells was observed at ammonium concentrations above 8 mM [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This is good agreement with results obtained in our experimental runs and it can explain the lowest viability observed in B9 and B10 where the NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e concentration was higher than other bioreactors primarily resulting from lower FR. Pereira et al used C\u003csup\u003e13\u003c/sup\u003e metabolic flux analysis to reduce ammonia production by 40% while maintaining culture viability and titer by varying different amino acid composition of the culture media [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. They specifically used glutamine, glutamate, asparagine, aspartate and serine to manipulate media composition for the investigation. These facts suggest that feeding strategy with tailored components which can favorably tune the metabolic flux can lead to good cell culture performance and potentially improve productivity.\u003c/p\u003e \u003cp\u003eIn the DoE runs, mAb production varied significantly as a function of process parameters. The highest mAb concentration of 4.57 g/L was achieved in the B4 run operated with SD of 1.4 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL, and FR of 2.25% (center point). Bioreactors operated at SD\u0026thinsp;\u0026gt;\u0026thinsp;1 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and FR of \u0026gt;\u0026thinsp;2% were more productive. Initial SD density beyond the sub-optimal range can enhance cell proliferation rate leading to higher VCD with adequate culture integrity, thus favoring volumetric productivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Our results showed lactate productivity is greater in bioreactor runs inoculated with higher SD. While lactate is considered as an inhibitory byproduct in the growth phase, it is consumed in the stationary (mAb expression) phase. In CHO cell cultivation, the metabolic shift from lactate production to lactate consumption is an indicator of metabolic efficiency. It seems that higher lactate production rate can lead to a faster metabolic shift and enhancement of the mAb titer in the stationary phase.\u003c/p\u003e \u003cp\u003eThe SD range investigated in the DoE provided empirical data to better map this parameter. However, FR was the more critical factor identified to have a significant impact on both culture performance and productivity in combination with optimal SD range. FR strategy is pivotal to control the production phase effectively for fed-batch operations. Lower FR (1.19\u0026ndash;1.5%) had a deleterious impact on both growth and metabolism resulting in significantly lower mAb titer compared to titers in runs operated at higher FR (e.g B9, B10 vs. B3, B4 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, RSM model analysis entailed identifying optimal conditions and operating levels for these critical parameters.\u003c/p\u003e \u003cp\u003eValidation experiments were in good agreement with the predicted mAb titer values for the RSM model. Additional culture time up to day 16 resulted in mAb titer reaching\u0026thinsp;~\u0026thinsp;5 g/L. However, further investigation on the metabolic flux can help potentially optimize the production phase to boost volumetric productivity and reduce culture time while increasing titer. Addition of key precursors for the TCA cycle through adequate media and feeds can offer further scope for process optimization. Keeping the HCP and associated impurity flux in the culture to a minimum value, and maintaining a high-density culture with increased viability can also subsequently improve product quality while mitigating downstream processing costs and efforts [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Process development and gaining a mechanistic understanding of factors influencing culture performance, titer and product attributes is essential for aptly meeting the growing demands of biopharmaceuticals, providing better efficacy during commercial manufacturing.\u003c/p\u003e \u003cp\u003eOverall, these findings contribute valuable knowledge that can be adopted by mAb manufacturers to streamline production processes. Furthermore, insights from this study can guide future research focusing on novel bioreactor technologies and process development.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis research demonstrated that process mapping and optimization using HTP Ambr250 parallel bioreactors is a promising framework. Combination of automated bioreactor system such as ambr250 and statistical modeling via DoE provide a strong tool for studying and optimizing mAb production by using lower resources and significantly less time. This study highlights the potential for enhanced yields of mAb production process as CHO fed-batch is still the predominantly adopted route at commercial scale whilst allowing smoother tech-transfer to existing manufacturing infrastructure. The optimized process in Ambr250 is highly scalable and has a great potential for providing a process blueprint allowing reliable scalability to commercial stage while reducing drug development timeline. The results indicated that by fine-tuning of the mAb production process variables, especially SD and FR, maintaining cell viability and productivity is attainable beyond 14 days. Future work on this HTP ambr250 platform to optimize feed components during the growth and production phases offers significant potential for characterization and optimization of product titer, quality and efficacy. This can favorably impact screening and process development timelines, reliable tech transfer which would eventually allow quicker access of therapeutics to the patients.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConflict of Interest:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributions:Farid Talebnia Rowshan: Conceptualization, Investigation, Methodology, Visualization, Writing - review \u0026amp; editing, Software, Formal analysis, Validation, Project administration, Supervision.Achinta Bordoloi: Conceptualization, Investigation, Writing - original draft, Methodology, Validation, Visualization, Software, Formal analysis, Data curation.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Wheeler Bio Inc. for providing the antibody-producing CHO cell line (ATUM miCHO) and their technical support throughout this study. We acknowledge the financial support received from the Economic Development Administration (EDA), USA under grant number #08_79_05677 for funding the OU bioprocessing Core Facility at The University of Oklahoma that enabled this research. The opinions, findings, and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the EDA, Wheeler Bio Inc. or University of Oklahoma.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShukla AA, Wolfe LS, Mostafa SS, Norman C (2017) Evolving trends in mAb production processes. \u003cem\u003eBioengineering \u0026amp; Translational Medicine\u003c/em\u003e 2, 58\u0026ndash;69.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/btm2.10061\u003c/span\u003e\u003cspan address=\"10.1002/btm2.10061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEtit D, Meramo S, \u0026Ouml;gmundarson \u0026Oacute;, Jensen MK, Sukumara S (2024) Can biotechnology lead the way toward a sustainable pharmaceutical industry? \u003cem\u003eCurrent Opinion in 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Effects of medium components and waste products. \u003cem\u003eJ Biotechnol\u003c/em\u003e 15, 113-128.10.1016/0168\u0026ndash;1656(90)90055-g\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcAtee Pereira AG, Walther JL, Hollenbach M, Young JD (2018) 13C Flux Analysis Reveals that Rebalancing Medium Amino Acid Composition can Reduce Ammonia Production while Preserving Central Carbon Metabolism of CHO Cell Cultures. \u003cem\u003eBiotechnology Journal\u003c/em\u003e 13, 1700518.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/biot.201700518\u003c/span\u003e\u003cspan address=\"10.1002/biot.201700518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bioprocess-and-biosystems-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bioprocess and Biosystems Engineering](https://www.springer.com/journal/449)","snPcode":"449","submissionUrl":"https://submission.nature.com/new-submission/449/3","title":"Bioprocess and Biosystems Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CHO cells, Monoclonal antibodies (mAbs), Ambr250, process mapping, RSM optimization, high throughput (HTP) bioreactor","lastPublishedDoi":"10.21203/rs.3.rs-6831589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6831589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe demand to accelerate monoclonal antibody (mAbs) process development timelines using Chinese hamster ovary (CHO) host cells to bring therapies to patients sooner is gaining momentum. The applicability of single use high throughput (HTP) bioreactor system such as ambr250 facilitating precise and automated control is very promising. This entails optimizing process parameters through design of experiments (DoE) using less resources and time, compared to traditionally employed large scale bench top reactors (2-5L). It is imperative to improve mAb productivity through robust process development to mitigate current manufacturing challenges. In this study, a systematic mapping approach was employed to identify critical process parameters\u0026nbsp;(CPP) and improve process efficacy. A central composite design (CCD) was used in ambr250 bioreactors to investigate the impact of initial seeding density (SD) and feeding rate (FR) on mAb production. Variance in the SD and FR impacted the cell performance and mAb titer profile based on which parameter optimization was done using response surface methodology. Significant impact of FR and SD was identified leading to improved mAb titer of up to 5 g/L. Bioreactors operated at SD \u0026gt; 1 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and FR of \u0026gt; 2 % were more productive, and respective optimal FR and SD were estimated at 2.68 % and 1.1 x10\u003csup\u003e6\u003c/sup\u003e cells/mL. The cell viability and productivity were well-maintained at optimal conditions allowing extended cultivation time for higher mAb titer. These findings optimizing operating range of CPPs to improve productivity by using HTP ambr250 scaled-down platform would provide a framework for quicker early phase process development, allowing reliable scalability to commercial manufacturing. Improving productivity and providing robust estimates for manufacturing scale would significantly cut costs and reduce timelines for biologics development and facilitate patient access.\u003c/p\u003e","manuscriptTitle":"Process Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr® 250 High-throughput Parallel Bioreactors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-15 16:14:28","doi":"10.21203/rs.3.rs-6831589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-07T12:58:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T18:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118812372342637902624740113136243758576","date":"2025-06-11T21:14:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325183330852420553493398088164715608116","date":"2025-06-10T07:05:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T02:52:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T01:10:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T00:50:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bioprocess and Biosystems Engineering","date":"2025-06-05T18:51:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioprocess-and-biosystems-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bioprocess and Biosystems Engineering](https://www.springer.com/journal/449)","snPcode":"449","submissionUrl":"https://submission.nature.com/new-submission/449/3","title":"Bioprocess and Biosystems Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eac3fa2d-2ec8-4eda-9592-c40dc89c781c","owner":[],"postedDate":"June 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T16:30:59+00:00","versionOfRecord":{"articleIdentity":"rs-6831589","link":"https://doi.org/10.1007/s00449-025-03229-y","journal":{"identity":"bioprocess-and-biosystems-engineering","isVorOnly":false,"title":"Bioprocess and Biosystems Engineering"},"publishedOn":"2025-08-30 00:00:00","publishedOnDateReadable":"August 30th, 2025"},"versionCreatedAt":"2025-06-15 16:14:28","video":"","vorDoi":"10.1007/s00449-025-03229-y","vorDoiUrl":"https://doi.org/10.1007/s00449-025-03229-y","workflowStages":[]},"version":"v1","identity":"rs-6831589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6831589","identity":"rs-6831589","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}