Biopharmaceutical Manufacturing Research Articles

Parylene C Coating Efficacy Studies: Enhancing Biocompatibility of 3D Printed Polyurethane Parts for Biopharmaceutical and CGT Applications.

Additive manufacturing, particularly Vat photopolymerization, presents a promising technique for producing complex, tailor-made structures, making it an attractive option for generating single-use components used in biopharmaceutical manufacturing equipment or cell culture devices. However, the potential leaching of cytotoxic compounds from Vat photopolymer resins poses a significant concern, especially regarding cell growth and viability in cell culture applications. This study explores the potential of parylene C coating to enhance the inertness of a polyurethane-based photopolymer resin, aiming to prevent cytotoxicity and improve biocompatibility. The study includes an analysis of extractables from the resin and photoinitiator to evaluate the resin's composition and to define selected marker compounds for investigating the coating efficiency. The time-dependent accumulation of relevant extractable compounds over a 70-day period are assessed to address the long-term use of the coated components. The impact of irradiation on the material and the coating was evaluated, along with an accelerated aging study to address the long-term performance of the coating. Biocompatibility in terms of in vitro cell growth studies is evaluated using Chinese hamster ovary cells, a standard cell line in biopharmaceutical manufacturing. Results demonstrate that parylene C coating significantly reduces the release of cytotoxic compounds, such as the photoinitiator diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide (TPO). Although accelerated aging indicates a reduction in the barrier properties of the coating over time, the parylene C coating still effectively slows the release of extractables and significantly improves cell compatibility of the 3D printed parts. The findings suggest that parylene C-coated components can be safely integrated into biopharmaceutical manufacturing processes, with recommendations to minimize storage times between coating application and use to ensure optimal performance.

Predictive modeling for cell culture in commercial manufacturing of biotherapeutics.

The biopharmaceutical industry continually seeks advancements in the commercial manufacturing of therapeutic proteins, where mammalian cell culture plays a pivotal role. The current work presents a novel data-driven predictive modeling application designed to enhance the efficiency and predictability of cell culture processes in biotherapeutic production. The capability of the cloud-based digital data science application, developed using open-source tools, is demonstrated with respect to predicting bioreactor potency from at-line process parameters over a 5-day horizon. The uncertainty in model's prediction is quantified, providing valuable insights for process control and decision-making. Model validation on unseen data confirms the model's robust generalizability. An interactive dashboard, tailored to process scientist's requirements is also developed to streamline biopharmaceutical manufacturing processes, ultimately leading to enhanced productivity and product quality.

Overcoming the inconsistent cell culture performance caused by single-use bioreactors during scaling-up the manufacturing process for a therapeutic bispecific antibody

Single use technology has been widely used in modern biopharmaceutical process development and manufacturing. However, there are concerns about the adverse effects of disposable materials on bioprocess, product quality and patient safety. Recently, we observed slow cell growth, increased lactate accumulation, and decreased production titer during scaling up a bispecific antibody (BsAb) upstream process from 3L to 500L bioreactor. To investigate the phenomenon, we conducted medium incubation and leachable spike-in experiments. We found that cell culture medium incubated in CX5-14 bag caused poor cell culture performance, which was correlated with the concentration of bis(2,4-di-tert-butylphenyl)-phosphate (bDtBPP), a breakdown product of the antioxidant Irgafos®168 in polyethylene film. In addition, when the media were spiked with 0.15μg/ml or a greater concentration of bDtBPP, slow cell growth and reduced BsAb expression level were observed. In contrast, Aegis5-14 film produced bDtBPP at a level of below detection during medium incubation. Thus, the bispecific-expressing CHO cell line restores normal cell culture performance and production titer when Aegis5-14 single-use bioreactor was employed in large-scale manufacturing. Our results reveal the importance of evaluating leachable profiles from disposable materials during process development of bispecifics and other novel therapeutic modalities.

Harnessing the potential of microalgae for the production of monoclonal antibodies and other recombinant proteins.

Monoclonal antibodies (mAbs) have become indispensable tools in various fields, from research to therapeutics, diagnostics, and industries. However, their production, primarily in mammalian cell culture systems, is cost-intensive and resource-demanding. Microalgae, diverse photosynthetic microorganisms, are gaining attention as a favorable option for manufacturing mAbs and various other recombinant proteins. This review explores the potential of microalgae as a robust expression system for biomanufacturing high-value proteins. It also highlights the diversity of microalgae species suitable for recombinant protein. Nuclear and chloroplast genomes of some microalgae have been engineered to express mAbs and other valuable proteins. Codon optimization, vector construction, and other genetic engineering techniques have significantly improved recombinant protein expression in microalgae. These accomplishments demonstrate the potential of microalgae for biopharmaceutical manufacturing. Microalgal biotechnology holds promise for revolutionizing the production of mAbs and other therapeutic proteins, offering a sustainable and cost-effective solution to address critical healthcare needs.

On the role of excipients in biopharmaceuticals manufacture: Modelling-guided formulation identifies the protective effect of arginine hydrochloride excipient on spray-dried Olipudase alfa recombinant protein

Biopharmaceuticals are labile biomolecules that must be safeguarded to ensure the safety, quality, and efficacy of the product. Batch freeze-drying is an established means of manufacturing solid biopharmaceuticals but alternative technologies such as spray-drying may be more suitable for continuous manufacturing of inhalable biopharmaceuticals. Here we assessed the feasibility of spray-drying Olipudase alfa, a novel parenteral therapeutic enzyme, by evaluating some of its critical quality attributes (CQAs) in a range of excipients, namely, trehalose, arginine (Arg), and arginine hydrochloride (Arg-HCl) in the sucrose/methionine base formulation. The Arg-HCl excipient produced the best gain in CQAs of spray-dried Olipudase with a 63% reduction in reconstitution time and 83% reduction in the optical density of the solution. Molecular dynamics simulations revealed the atomic-scale mechanism of the protein–excipient interactions, substantiating the experimental results. The Arg-HCl effect was explained by the calculated thermal stability and structural order of the protein wherein Arg-HCl acted as a crowding agent to suppress protein aggregation and promote stabilization of Olipudase post-spray-drying. Therefore, by rational selection of appropriate excipients, our experimental and modelling dataset confirms spray-drying is a promising technology for the manufacture of Olipudase and demonstrates the potential to accelerate development of continuous manufacturing of parenteral biopharmaceuticals.

An in-silico approach towards multivariate acceptable ranges in biopharmaceutical manufacturing

Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application.Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Real-time multivariate statistical monitoring of biopharmaceutical processes with no prior product-specific history

Biologics drug substance manufacturing normally comprises several processing steps including cell culture, purification, etc. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) is an integral need in order to achieve early fault detection and prevention to guarantee robust and efficient process performance. As a part of RT-MSPM framework, Batch Evolution Models (BEMs), which take into account the transient phase of a batch prior to reaching full maturity (steady state), are the standard methods for real-time early fault detection in batch processes. Prior product-specific knowledge and training dataset are critical in order to model time (batch maturity) dependencies. Unlike traditional settings, modern biopharmaceutical manufacturing and clinical facilities use modular designs to enable simultaneous production of multiple medicines and quickly pivot from producing one medicine to another where no prior product-specific historical data might be available. In this article, first, a framework is proposed to detect the steady state operation of a manufacturing scale cell culture bioreactor. This is critical to achieve the ultimate goal to demonstrate feasibility of developing a continuous statistical process monitoring framework to monitor the steady state operation of a batch with no prior product specific history. Steady state time series data from previous step are processed following a systematic approach. The requirements for statistical properties of the processed time series in order to leverage data from different products to monitor a new product are outlined. The new framework establishes a guideline to develop models using datasets from other products with different recipes to monitor new products with no prior product-specific history. A novel software product is developed to test the framework. The framework is successfully applied for real-time monitoring of fast-rate sensors of a manufacturing cell culture bioreactor.

Status and future developments for downstream processing of biological products: Perspectives from the Recovery XIX yield roundtable discussions.

Governments and biopharmaceutical organizations aggressively leveraged expeditious communication capabilities, decision models, and global strategies to make a COVID-19 vaccine happen within a period of 12 months. This was an unusual effort and cannot be transferred to normal times. However, this focus on a single vaccine has also led to other treatments and drug developments being sidelined. Society expects the pharmaceutical industry to provide an uninterrupted supply of medicines. However, it is often overlooked how complex the manufacture of these compounds is and what logistics are required, not to mention the time needed to develop new drugs. The overarching theme, therefore, is patient access and how we can help ensure access and extend it to low- and middle-income countries. Despite unceasing efforts to make medications available to all patient populations, this must never be done at the expense of patient safety. A major fraction of the costs in biopharmaceutical manufacturing are for drug discovery, process development, and clinical studies. Infrastructure costs are very difficult to quantify because they often depend on whether a greenfield facility or an existing, depreciated facility is used or adapted for a new product. To accelerate process development concepts of platform process and prior knowledge are increasingly leveraged. While more traditional protein therapeutics continue to dominate the field, we are also experiencing the exciting emergence and evolution of other therapeutic formats (bispecifics, tetravalent mAbs, antibody-drug conjugates, enzymes, peptides, etc.) that offer unique treatment options for patients. Protein modalities are still dominant, but new modalities are being developed that can be learned from including advanced therapeutics-like cell and gene therapies. The industry must develop a model-based strategy for process development and technologies such as continuous integrated biomanufacturing must be adopted. The overall conclusion is that the pandemic pace was unsustainable, focused on vaccine delivery at the expense of other modalities/disease targets, and had implications for professional and personal life (work-life balance). Routinely reducing development time from 10 years to 1 year is nearly impossible to achieve. Environmental aspects of sustainable downstream processing are also described.

A deep learning-aided multi-objective optimization of a downstream process for production of monoclonal antibody products

Monoclonal antibodies (mAbs) have emerged as the dominant class of therapeutic moieties due to their superior therapeutic potential and safety profile. Downstream processing plays a pivotal role in ensuring the purity, yield, and quality of these therapeutic products. The biopharmaceutical industry generates a significant amount of data, making it an attractive target for creating machine learning (ML) and deep learning (DL) applications. This paper reports a DL-based 2D-convolutional neural network (2D-CNN) to predict: (i) Protein A mAb elute concentration, (ii) Cation exchange (CEX) mAb elute concentration, (iii) % Acidic variant, (iv) Aggregate (%), and (v) % Basic variant from process data that is routinely collected during biopharma manufacturing, including Protein A elution UV, CEX elution UV, CEX elution conductivity, and post viral inactivation pH profiles. Further, interpretation of predicted outputs using the SHAPLEY technique and optimization through multi-objective Bayesian optimization methods have been attempted so as to match the biosimilar to the reference molecule with respect to the critical quality attributes. It is observed that the proposed model outperforms other published approaches in prediction. The optimum results were experimentally validated by performing three consecutive runs, and a mean percentage deviation of less than 3 % was reported. The proposed approach demonstrates the feasibility of real-time prediction and optimization in biopharmaceutical product manufacturing using CNN-aided modeling and multi-objective weighted sum optimization.

Protein loss during membrane processes in biopharmaceutical manufacturing.

Maximizing product yield in biopharmaceutical manufacturing processes is a critical factor in determining the overall cost of goods, especially given the high value of these biological products. However, there has been relatively limited research on the quantitative analysis of protein losses due to adsorption and fouling during the different membrane filtration processes employed in typical downstream operations. This study aims to provide a comprehensive analysis of protein loss in the range of membrane systems used in downstream processing including clarification, virus removal filtration, ultrafiltration/diafiltration for formulation, and final sterile filtration, all using commercially available membranes with three model proteins (bovine serum albumin, human serum albumin, and immunoglobulin G). The correlation between protein loss and various parameters (i.e., protein type, protein concentration, throughput, membrane morphology, and protein removal mechanism) was also investigated. This study provides important insights into the nature of protein loss during membrane processes as well as a methodology for quantifying protein yield loss in bioprocesses.

Expediting online liquid chromatography for real-time monitoring of product attributes to advance process analytical technology in downstream processing of biopharmaceuticals

The application of Process Analytical Technology (PAT) principles for manufacturing of biotherapeutics proffers the prospect of ensuring consistent product quality along with increased productivity as well as substantial cost and time savings. Although this paradigm shift from a traditional, rather rigid manufacturing model to a more scientific, risk-based approach has been advocated by health authorities for almost two decades, the practical implementation of PAT in the biopharmaceutical industry is still limited by the lack of fit-for-purpose analytical methods. In this regard, most of the proposed spectroscopic techniques are sufficiently fast but exhibit deficiencies in terms of selectivity and sensitivity, while well-established offline methods, such as (ultra-)high-performance liquid chromatography, are generally considered as too slow for this task. To address these reservations, we introduce here a novel online Liquid Chromatography (LC) setup that was specifically designed to enable real-time monitoring of critical product quality attributes during time-sensitive purification operations in downstream processing. Using this online LC solution in combination with fast, purpose-built analytical methods, sampling cycle times between 1.30 and 2.35 min were achieved, without compromising on the ability to resolve and quantify the product variants of interest. The capabilities of our approach are ultimately assessed in three case studies, involving various biotherapeutic modalities, downstream processes and analytical chromatographic separation modes. Altogether, our results highlight the expansive opportunities of online LC based applications to serve as a PAT tool for biopharmaceutical manufacturing.

Past and future of an IMI-PharmaTrain (IMI-PhT)-initiated multinational pharmaceutical medicine course at the Semmelweis University in Hungary.

The pharmaceutical medicine course at the Semmelweis University of Budapest, Hungary, was initiated as part of the Innovative Medicines Initiative (IMI is the main program, IMI-PharmaTrain is one of the IMI projects) Pharmaceutical Medicine Training Programs (16 IMI Call 2008/1/16). The aim was to extend training in the development of pharmaceutical medicine to those EU member states where no such education was present. The final program envisaged the development of a cooperative education supported by universities located in Central and Eastern Europe. It was considered to be the economically and scientifically most viable approach to combine the expertise from these countries to form a united teaching staff and provide education jointly for young professionals of the region. Semmelweis University was selected to manage this coordinated program. In this report, we describe the organization and functioning of this international university-based pharmaceutical medicine education project called the Cooperative European Medicines Development Course (CEMDC) and evaluate its successes and shortcomings. During the pandemic, the educational course was interrupted. The follow-on program is reorganized as a postgraduate MSc course named "Semmelweis Pharma MBA" and will be started in 2025. It will continue the established PharmaTrain educational tradition. However, it will deal in more detail with the transition from basic pharmacological to industrial research, as well as biopharmaceutical formulation and manufacturing and marketing aspects of medicines development.

Comprehensive cell culture monitoring: Leveraging in-line Raman spectroscopy for enhanced biopharmaceutical manufacturing insights

Many biopharmaceutical manufacturers and researchers seek a comprehensive understanding and real-time monitoring of cell growth and metabolism to enhance the cell culture process. However, the biological complexity of cells and the intricacies of cell culture process have impeded the development of analytical and monitoring techniques. This study aims to present a complete picture of cell culture processes using in-line Raman spectroscopy. Initially, we proposed an efficient method for discriminating and removing anomalous spectra. Subsequently, Raman-based models for 27 components including amino acid, organic acid, lipid, organic alkali, alkaloid, alcohol, saccharide, biomass, physicochemical parameter, ion, and protein titer were established. The Q2 obtained by these models was greater than 0.8, and the relative percent difference (RPD) from external validation was greater than 2.0 except for glucose. Additionally, the proposed control charts effectively monitored normal batches and abnormal conditions such as bacterial contamination and insufficient feeding. In cases of unexplained culture abnormalities, the proposed workflow offered explanations.

Spatiotemporal Control of Protein Refolding through Flash-Change Reaction Conditions.

Recombinant protein production is an essential aspect of biopharmaceutical manufacturing, with Escherichia coli serving as a primary host organism. Protein refolding is vital for protein production; however, conventional refolding methods face challenges such as scale-up limitations and difficulties in controlling protein conformational changes on a millisecond scale. In this study, we demonstrate the novel application of flow microreactors (FMR) in controlling protein conformational changes on a millisecond scale, enabling efficient refolding processes and opening up new avenues in the science of FMR technology. FMR technology has been primarily employed for small-molecule synthesis, but our novel approach successfully expands its application to protein refolding, offering precise control of the buffer pH and solvent content. Using interleukin-6 as a model, the system yielded an impressive 96% pure refolded protein and allowed for gram-scale production. This FMR system allows flash changes in the reaction conditions, effectively circumventing protein aggregation during refolding. To the best of our knowledge, this is the first study to use FMR for protein refolding, which offers a more efficient and scalable method for protein production. The study results highlight the utility of the FMR as a high-throughput screening tool for streamlined scale-up and emphasize the importance of understanding and controlling intermediates in the refolding process. The FMR technique offers a promising approach for enhancing protein refolding efficiency and has demonstrated its potential in streamlining the process from laboratory-scale research to industrial-scale production, making it a game-changing technology in the field.

Predictive Ppk calculations for biologics and vaccines using a Bayesian approach - a tutorial.

In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15-30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre-licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data-driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian-based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre-defined commercialization stage-gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.

Digital application for drug product potency target evaluation in biopharmaceutical manufacturing.

Biopharmaceutical manufacturing entails a series of highly regulated steps. The manufacturing of safe and efficacious drug product (DP) requires testing of critical quality attributes (CQAs) against specification limits. DP potency concentration, which measures the dosage strength of a particular DP, is a CQA of great interest. In order to minimize the DP potency out-of-specification (OOS) risk, sterile fill finish (SFF) process adjustments may be needed. Varying the potency targets can be one such process adjustment. To facilitate such evaluation, data acquisition and statistical calculations are required. Regularly conducting the OOS risk assessment manually using commercial statistical software can be tedious, error-prone, and impractical, especially when several alternate potency targets are under consideration. In this work, the development of a novel framework for OOS risk assessment and deployment of cloud-based statistical software application to facilitate the risk assessment are presented. This application is intended to streamline the assessment of alternate potency targets for DP in biologics manufacturing. The major aspects of this potency targeting application development are presented in detail. Specifically, data sources, pipeline, application architecture, back-end and front-end development as well as application verification are discussed. Finally, several use cases are presented to highlight the application's utility in biologics manufacturing.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

Prediction models for the flux decay profile and initial flux of microfiltration for therapeutic proteins.

Microfiltration (MF) is an essential step during biopharmaceutical manufacturing. However, unexpected flux decay can occur. Although the flux decay profile and initial flux are important factors determining MF filterability, predicting them accurately is challenging, as the root cause of unexpected flux decay remains elusive. In this study, the methodology for developing a prediction model of flux decay profiles was established. First, the filtration profiles of different monodisperse polystyrene latex and silica beads of various sizes were evaluated. These results revealed that the size and surface electrostatic properties of the beads affect the flux decay profile. Taking the size and surface electrostatic properties of protein aggregates into account, we constructed a predictive model using model bead filtration profiles. We showed that this methodology was applicable to two different MF filters to predict the flux decay profile of therapeutic proteins. Because our proposed prediction model is based on normalized flux, the initial flux is required to predict the overall filtration profile. Then, we applied the Hagen-Poiseuille equation using sample viscosity values to estimate the initial flux. The developed prediction models can be used for effective MF scale-up assessment during the early stages of process development.

From Fluids to Pharmaceuticals: How Computational Fluid Dynamics (CFD) Transforms Drug Design

Computational Fluid Dynamics (CFD) is a powerful tool that helps scientists and engineers understand how fluids behave and move, whether in the air, liquids, or biopharmaceutical manufacturing processes. This technology, which uses computer simulations to replicate real-world scenarios, plays a critical role in various industries, including medicine and drug development. By creating virtual experiments on computers, CFD allows researchers to gain insights into the interaction of biological components for drug production at different scales, from small laboratory experiments to large-scale manufacturing processes and fast-track process optimisation. The strategy and implementation of CFD for a bioreactor system has been explained in this article.

A modular and multi-functional purification strategy that enables a common framework for manufacturing scale integrated and continuous biomanufacturing.

Biopharmaceutical manufacture is transitioning from batch to integrated and continuous biomanufacturing (ICB). The common framework for most ICB, potentially enables a global biomanufacturing ecosystem utilizing modular and multi-function manufacturing equipment. Integrating unit operation hardware and software from multiple suppliers, complex supply chains enabled by multiple customized single-use flow paths, and large volume buffer production/storage make this ICB vision difficult to achieve with commercially available manufacturing equipment. Thus, we developed SymphonX™, a downstream processing skid with advanced buffer management capabilities, a single disposable generic flow path design that provides plug-and-play flexibility across all downstream unit operations and a single interface to reduce operational risk. Designed for multi-product and multi-process cGMP facilities, SymphonX™ can perform stand-alone batch processing or ICB. This study utilized an Apollo™ X CHO-DG44 mAb-expressing cell line in a steady-state perfusion bioreactor, harvesting product continuously with a cell retention device and connected SymphonX™ purification skids. The downstream process used the same chemistry (resins, buffer composition, membrane composition) as our historical batch processing platform, with SymphonX™ in-line conditioning and buffer concentrates. We used surge vessels between unit operations, single-column chromatography (protein A, cation and anion exchange) and two-tank batch virus inactivation. After the first polishing step (cation exchange), we continuously pooled product for 6 days. These 6 day pools were processed in batch-mode from anion exchange to bulk drug substance. This manufacturing scale proof-of-concept ICB produced 0.54 kg/day of drug substance with consistent product quality attributes and demonstrated successful bioburden control for unit-operations undergoing continuous operation.