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.

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.

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.

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.

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.

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.

A decade in review: use of data analytics within the biopharmaceutical sector.

There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.

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.

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.

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products.

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market.

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.

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.

Benchmarking biopharmaceutical process development and manufacturing cost contributions to R&D.

This study aims to benchmark and analyze the process development and manufacturing costs across the biopharmaceutical drug development cycle and their contribution to overall research and development (R&D) costs. This was achieved with a biopharmaceutical drug development lifecycle cost model that captured the costs, durations, risks and interdependencies of the clinical, process development and manufacturing activities. The budgets needed for process development and manufacturing at each phase of development to ensure a market success each year were estimated. The impact of different clinical success rate profiles on the process development and manufacturing costs at each stage was investigated, with a particular focus on monoclonal antibodies. To ensure a market success each year with an overall clinical success rate (Phase I to approval) of ~12%, the model predicted that a biopharmaceutical company needs to allocate process development and manufacturing budgets in the order of ~$60 M for pre-clinical to Phase II material preparation and ~$70 M for Phase III to regulatory review material preparation. For lower overall clinical success rates of ~4%, which are more indicative of diseases such as Alzheimer's, these values increase to ~$190 M for early-phase and ~$140 Mfor late-phase material preparation; hence, the costs increase 2.5 fold. The costs for process development and manufacturing per market success were predicted to represent 13-17% of the R&D budget from pre-clinical trials to approval. The results of this quantitative structured cost study can be used to aid decision-making during portfolio management and budget planning procedures in biopharmaceutical development.

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-Up Model Validation and Missing Data on a Micro-Bioreactor System.

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a "small data" and a "big data" challenge. In the "small data" example, a large-scale data set is compared to data from a scale-down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one-sided test with principal component analysis. In the "big data" example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off-line missing values and the on-line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre-processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.

High throughput process development workflow with advanced decision-support for antibody purification.

Chromatography remains the workhorse in antibody purification; however process development and characterisation still require significant resources. The high number of operating parameters involved requires extensive experimentation, traditionally performed at small- and pilot-scale, leading to demands in terms of materials and time that can be a challenge. The main objective of this research was the establishment of a novel High Throughput Process Development (HTPD) workflow combining scale-down chromatography experimentation with advanced decision-support techniques in order to minimise the consumption of resources and accelerate the development timeframe. Additionally, the HTPD workflow provides a framework to rapidly manipulate large datasets in an automated fashion. The central component of the HTPD workflow is the systematic integration of a microscale chromatography experimentation strategy with an advanced chromatogram evaluation method, design of experiments (DoE) and multivariate data analysis. The outputs of this are leveraged into the screening and optimisation components of the workflow. For the screening component, a decision-support tool was developed combining different multi-criteria decision-making techniques to enable a fair comparison of a number of CEX resin candidates and determine those that demonstrate superior purification performance. This provided a rational methodology for screening chromatography resins and process parameters. For the optimisation component, the workflow leverages insights provided through screening experimentation to guide subsequent DoE experiments so as to tune significant process parameters for the selected resin. The resulting empirical correlations are linked to a stochastic modelling technique so as to predict the optimal and most robust chromatographic process parameters to achieve the desired performance criteria.

Potential of Continuous Manufacturing for Liposomal Drug Products.

Over the last several years, continuous manufacturing of pharmaceuticals has evolved from bulk APIs and solid oral dosages into the more complex realm of biologics. The development of continuous downstream processing techniques has allowed biologics manufacturing to realize the benefits (e.g., improved economics, more consistent quality) that come with continuous processing. If relevant processing techniques and principles are selected, the opportunity arises to develop continuous manufacturing designs for additional pharmaceutical products including liposomal drug formulations. Liposome manufacturing has some inherent aspects that make it favorable for a continuous process. Other aspects such as formulation refinement, materials of construction, and aseptic processing need development, but present an achievable challenge. This paper reviews the current state of continuous manufacturing technology applicable to liposomal drug product manufacturing and an assessment of the challenges and potential of this application.

On-Line Control of Glucose Concentration in High-Yielding Mammalian Cell Cultures Enabled Through Oxygen Transfer Rate Measurements.

Glucose control is vital to ensure consistent growth and protein production in mammalian cell cultures. The typical fed-batch glucose control strategy involving bolus glucose additions based on infrequent off-line daily samples results in cells experiencing significant glucose concentration fluctuations that can influence product quality and growth. This study proposes an on-line method to control and manipulate glucose utilizing readily available process measurements. The method generates a correlation between the cumulative oxygen transfer rate and the cumulative glucose consumed. This correlation generates an on-line prediction of glucose that has been successfully incorporated into a control algorithm manipulating the glucose feed-rate. This advanced process control (APC) strategy enables the glucose concentration to be maintained at an adjustable set-point and has been found to significantly reduce the deviation in glucose concentration in comparison to conventional operation. This method has been validated to produce various therapeutic proteins across cell lines with different glucose consumption demands and is successfully demonstrated on micro (15 mL), laboratory (7 L), and pilot (50 L) scale systems. This novel APC strategy is simple to implement and offers the potential to significantly enhance the glucose control strategy for scales spanning micro-scale systems through to full scale industrial bioreactors.