Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution.

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes.

Process economics evaluation and optimization of adeno-associated virus downstream processing.

Adeno-associated virus (AAV) manufacturing has traditionally focused upon lab-scale techniques to culture and purify vector products, leading to limitations in production capacity. The tool presented in this paper assesses the feasibility of using non-scalable technologies at high AAV demands and identifies optimal flowsheets at large-scale that meet both cost and purity targets. The decisional tool comprises (a) a detailed process economics model with the relevant mass balance, sizing, and costing equations for AAV upstream and downstream technologies, (b) a built-in Monte Carlo simulation to assess uncertainties, and (c) a brute-force optimization algorithm for rapid investigation into the optimal purification combinations. The results overall highlighted that switching to more scalable upstream and downstream processing alternatives is economically advantageous. The base case analysis showed the cost and robustness advantages of utilizing suspension cell culture over adherent, as well as a fully chromatographic purification platform over batch ultracentrifugation. Expanding the set of purification options available gave insights into the optimal combination to satisfy both cost and purity targets. As the purity target increased, the optimal polishing solution moved from the non-capsid purifying multimodal chromatography to anion-exchange chromatography or continuous ultracentrifugation.

Decisional tool for cost of goods analysis of bioartificial liver devices for routine clinical use.

BACKGROUND AIMS: Bioartificial liver devices (BALs) are categorized as advanced therapy medicinal products (ATMPs) with the potential to provide temporary liver support for liver failure patients. However, to meet commercial demands, next-generation BAL manufacturing processes need to be designed that are scalable and financially feasible. The authors describe the development and application of a process economics decisional tool to determine the cost of goods (COG) of alternative BAL process flowsheets across a range of industrial scales. METHODS: The decisional tool comprised an information database linked to a process economics engine, with equipment sizing, resource consumption, capital investment and COG calculations for the whole bioprocess, from cell expansion and encapsulation to fluidized bed bioreactor (FBB) culture to cryopreservation and cryorecovery. Four different flowsheet configurations were evaluated across demands, with cell factories or microcarriers in suspension culture for the cell expansion step and single-use or stainless steel technology for the FBB culture step. RESULTS: The tool outputs demonstrated that the lowest COG was achieved with microcarriers and stainless steel technology independent of the annual demand (1500-30 000 BALs/year). The analysis identified the key cost drivers were parameters impacting the medium volume and cost. CONCLUSIONS: The tool outputs can be used to identify cost-effective and scalable bioprocesses early in the development process and minimize the risk of failing to meet commercial demands due to technology choices. The tool predictions serve as a useful benchmark for manufacturing ATMPs.

End-to-end continuous bioprocessing: Impact on facility design, cost of goods, and cost of development for monoclonal antibodies.

This article presents a systematic approach to evaluate the business case for continuous processing that captures trade-offs between manufacturing and development costs for monoclonal antibodies (mAbs). A decisional tool was built that integrated cost of goods (COG) with the cost of development models and new equipment sizing equations tailored to batch, hybrid, and end-to-end continuous processes. The COG analysis predicted that single-use continuous facilities (sized using a dedicated downstream processing train per bioreactor) offer more significant commercial COG savings over stainless steel batch facilities at annual demands of 100-500 kg (~35%), compared to tonnage demands of 1-3 tons (~±10%) that required multiple parallel continuous trains. Single-use batch facilities were found to compete with continuous options on COG only at 100 kg/year. For the scenarios where batch and continuous facilities offered similar COG, the analysis identified the windows of operation required to reach different COG savings with thresholds for the perfusion rate, volumetric productivity, and media cost. When considering the project lifecycle cost, the analysis indicated that while end-to-end continuous facilities may struggle to compete on development costs, they become more cost-effective than stainless steel batch facilities when considering the total out-of-pocket cost across both drug development and commercial activities.

A common framework for integrated and continuous biomanufacturing.

There is a growing application of integrated and continuous bioprocessing (ICB) for manufacturing recombinant protein therapeutics produced from mammalian cells. At first glance, the newly evolved ICB has created a vast diversity of platforms. A closer inspection reveals convergent evolution: nearly all of the major ICB methods have a common framework that could allow manufacturing across a global ecosystem of manufacturers using simple, yet effective, equipment designs. The framework is capable of supporting the manufacturing of most major biopharmaceutical ICB and legacy processes without major changes in the regulatory license. This article reviews the ICB that are being used, or are soon to be used, in a GMP manufacturing setting for recombinant protein production from mammalian cells. The adaptation of the various ICB modes to the common ICB framework will be discussed, along with the pros and cons of such adaptation. The equipment used in the common framework is generally described. This review is presented in sufficient detail to enable discussions of IBC implementation strategy in biopharmaceutical companies and contract manufacturers, and to provide a road map for vendors equipment design. An example plant built on the common framework will be discussed. The flexibility of the plant is demonstrated with batches as small as 0.5 kg or as large as 500 kg. The yearly output of the plant is as much as 8 tons.

A roadmap for cost-of-goods planning to guide economic production of cell therapy products.

Cell therapy products are frequently developed and produced without incorporating cost considerations into process development, contributing to prohibitively costly products. Herein we contextualize individual process development decisions within a broad framework for cost-efficient therapeutic manufacturing. This roadmap guides the analysis of cost of goods (COG) arising from tissue procurement, material acquisition, facility operation, production, and storage. We present the specific COG considerations related to each of these elements as identified through a 2013 International Society for Cellular Therapy COG survey, highlighting the differences between autologous and allogeneic products. Planning and accounting for COG at each step in the production process could reduce costs, allowing for more affordable market pricing to improve the long-term viability of the cell therapy product and facilitate broader patient access to novel and transformative cell therapies.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations.

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analyzing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions, and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behavior during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF. Biotechnol. Bioeng. 2017;114: 2043-2056. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Perodicals, Inc.

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion.

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (AmbrTM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up. Biotechnol. Bioeng. 2017;114: 2222-2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

A scale-down mimic for mapping the process performance of centrifugation, depth and sterile filtration.

In the production of biopharmaceuticals disk-stack centrifugation is widely used as a harvest step for the removal of cells and cellular debris. Depth filters followed by sterile filters are often then employed to remove residual solids remaining in the centrate. Process development of centrifugation is usually conducted at pilot-scale so as to mimic the commercial scale equipment but this method requires large quantities of cell culture and significant levels of effort for successful characterization. A scale-down approach based upon the use of a shear device and a bench-top centrifuge has been extended in this work towards a preparative methodology that successfully predicts the performance of the continuous centrifuge and polishing filters. The use of this methodology allows the effects of cell culture conditions and large-scale centrifugal process parameters on subsequent filtration performance to be assessed at an early stage of process development where material availability is limited. Biotechnol. Bioeng. 2016;113: 1934-1941. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Allogeneic cell therapy bioprocess economics and optimization: single-use cell expansion technologies.

For allogeneic cell therapies to reach their therapeutic potential, challenges related to achieving scalable and robust manufacturing processes will need to be addressed. A particular challenge is producing lot-sizes capable of meeting commercial demands of up to 10(9) cells/dose for large patient numbers due to the current limitations of expansion technologies. This article describes the application of a decisional tool to identify the most cost-effective expansion technologies for different scales of production as well as current gaps in the technology capabilities for allogeneic cell therapy manufacture. The tool integrates bioprocess economics with optimization to assess the economic competitiveness of planar and microcarrier-based cell expansion technologies. Visualization methods were used to identify the production scales where planar technologies will cease to be cost-effective and where microcarrier-based bioreactors become the only option. The tool outputs also predict that for the industry to be sustainable for high demand scenarios, significant increases will likely be needed in the performance capabilities of microcarrier-based systems. These data are presented using a technology S-curve as well as windows of operation to identify the combination of cell productivities and scale of single-use bioreactors required to meet future lot sizes. The modeling insights can be used to identify where future R&D investment should be focused to improve the performance of the most promising technologies so that they become a robust and scalable option that enables the cell therapy industry reach commercially relevant lot sizes. The tool outputs can facilitate decision-making very early on in development and be used to predict, and better manage, the risk of process changes needed as products proceed through the development pathway.

Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture.

BACKGROUND: This paper considers a real-world optimization problem involving the identification of cost-effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time-consuming fitness evaluations. RESULTS: An industrially-relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process. Finally, a data-visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting. CONCLUSION: This work demonstrates that closed-loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost-effective purification strategies. © 2013 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Evaluating end-to-end continuous antibody manufacture with column-free capture alternatives from economic, environmental, and robustness perspectives.

Process intensification efforts have renewed interest in the potential of end-to-end continuous manufacture with column-free capture alternatives. This article describes a decisional tool that encompasses mass balance and design equations, process economics, stochastic simulation and multi-criteria decision-making and enables the evaluation of different batch, and continuous flowsheets for monoclonal antibody (mAb) manufacture. The traditional batch process was compared with end-to-end continuous bioprocesses with either protein A capture or column-free capture employing aqueous two-phase extraction or precipitation from economic, environmental, and robustness perspectives. The cost of goods analysis predicted that continuous flowsheets could offer substantial cost savings (~20%-40%) over the batch process at low and medium annual commercial demands (100-500 kg); however, at tonnage demands they resulted in either comparable or higher costs. Comparing the continuous options, the continuous flowsheets with protein A or precipitation yielded similar COG/g values, while aqueous two-phase extraction presented higher costs. The analysis of overall process mass intensities accounting for water and consumables suggested that the continuous flowsheet with protein A would result in the lowest environmental burden. When the economic, environmental, and operational criteria were reconciled using multi-criteria decision-making analysis, the continuous protein A-based flowsheet was found to be the most favorable. A target analysis highlighted the need for process improvements in the following parameters to reduce the manufacturing costs of the continuous column-free capture options below that of protein A: the perfusion volumetric productivity, the harvested cell culture fluid percentage in column-free operations, the column-free step yields along with the implementation of buffer concentrates.

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification.

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.

Fed-batch and perfusion culture processes: economic, environmental, and operational feasibility under uncertainty.

This article evaluates the current and future potential of batch and continuous cell culture technologies via a case study based on the commercial manufacture of monoclonal antibodies. The case study compares fed-batch culture to two perfusion technologies: spin-filter perfusion and an emerging perfusion technology utilizing alternating tangential flow (ATF) perfusion. The operational, economic, and environmental feasibility of whole bioprocesses based on these systems was evaluated using a prototype dynamic decision-support tool built at UCL encompassing process economics, discrete-event simulation and uncertainty analysis, and combined with a multi-attribute decision-making technique so as to enable a holistic assessment. The strategies were compared across a range of scales and titres so as to visualize how their ranking changes in different industry scenarios. The deterministic analysis indicated that the ATF perfusion strategy has the potential to offer cost of goods savings of 20% when compared to conventional fed-batch manufacturing processes when a fivefold increase in maximum viable cell densities was assumed. Savings were also seen when the ATF cell density dropped to a threefold increase over the fed-batch strategy for most combinations of titres and production scales. In contrast, the fed-batch strategy performed better in terms of environmental sustainability with a lower water and consumable usage profile. The impact of uncertainty and failure rates on the feasibility of the strategies was explored using Monte Carlo simulation. The risk analysis results demonstrated the enhanced robustness of the fed-batch process but also highlighted that the ATF process was still the most cost-effective option even under uncertainty. The multi-attribute decision-making analysis provided insight into the limited use of spin-filter perfusion strategies in industry. The resulting sensitivity spider plots enabled identification of the critical ratio of weightings of economic and operational benefits that affect the choice between ATF perfusion and fed-batch strategies.

Impact of ethanol on continuous inline diafiltration of liposomal drug products.

Liposomal drug products are playing an increasing role in the field of drug delivery. With this increased demand comes the need to increase the capabilities and capacity of manufacturing options. Continuous manufacturing techniques present a significant opportunity to address these needs for liposomal manufacturing processes. Liposomal formulations have unique considerations that impact translation from batch to continuous process designs. This article examines aspects of converting to a continuous design that were previously viewed as inconsequential in a batch process. The batch process involves the removal of ethanol (EtOH) through tangential flow filtration (TFF). EtOH was found to reduce the permeability of the hollow fibers used for TFF. This effect was determined to have minimal impact on the overall batch process design but considerable influence on the design of continuous TFF such as inline diafiltration (ILDF). Using a pilot scale setup, EtOH was found to decrease permeability in an inverse manner to EtOH concentration. Further assessment found that dilution of the EtOH levels prior to diafiltration can significantly reduce the amount of ILDF stages needed and that a continuous design requires less buffer to the commensurate batch design.

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics.

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD 4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MI CL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD 4 takes into account the available metadata and summaries all relevant statistics generated in steps 1-3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD 4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD 4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD 4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

Machine learning application in personalised lung cancer recurrence and survivability prediction.

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

Process economics evaluation of cell-free synthesis for the commercial manufacture of antibody drug conjugates.

Continuous improvements of cell-free synthesis (CFS) systems have generated interest in adopting the technology for the manufacture of biologics. This paper provides an evaluation of the manufacturing cost-effectiveness of CFS for the commercial production of antibody-drug conjugates (ADCs). The evaluation was performed using an advanced techno-economic engine (TEE) built in Python. The TEE is programmed in an object-oriented environment capable of simulating a plethora of process flowsheets and predicting size and cost metrics for the process and the facility. A case study was formulated to compare the economics of whole bioprocesses based on either a CFS system or a mammalian cell system (CHO) for the manufacture of an ADC at a range of product demands. The analysis demonstrated the potential of CFS for the commercial manufacture of biologics and identified key cost drivers related to the system. The CFS system showed an approximately 80% increase in the cost of goods compared to CHO with a significant cost attributed to the in-house manufacture of the bacterial cell extract, necessary for the CFS reaction step in the process. A sensitivity and target analysis highlighted the need for further process improvements especially in the titer for the CFS process to become more competitive against well-established systems.

Machine learning reveals hidden stability code in protein native fluorescence.

Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.