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

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.

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.

Robust method for chromatogram shape analysis to improve early detection of performance drifts and adverse changes in process parameters during purification column operations

The biopharmaceutical industry is under increased pressure to maximize efficiency, enhance quality compliance, and reduce the cost of drug substance manufacturing. Ways to reduce costs associated with manufacturing of complex biological molecules include maximizing efficiency of chromatography purification steps. For example, process analytical technology (PAT) tools can be employed to improve column resin life, prevent column operating failures, and decrease the time it takes to solve investigations of process deviations. We developed a robust method to probe the shape of the chromatogram for indications of column failure or detrimental changes in the process. The approach herein utilizes raw data obtained from manufacturing followed by a pre-processing routine to align chromatograms and patch together the different chromatogram phases in preparation for multivariate analysis. A principal component analysis (PCA) was performed on the standardized chromatograms to compare different batches, and resulted in the identification specific process change that affected the profile. In addition, changes in the chromatogram peaks were used to create predictive models for impurity clearance. This approach has the potential for early detection of column processing issues, improving timely resolution in large-scale chromatographic operations.

Impact of Trisulfide on the Structure and Function of Different Antibody Constructs

Trisulfide is a post-translational modification (PTM) commonly found in recombinant antibodies. It has been demonstrated that trisulfide had no impact on the bioactivity of mono-specific antibodies (MsAbs). However, the impact of trisulfide on multi-specific antibodies has not been evaluated. In this study, two mass spectrometric methods were developed for comprehensive trisulfide characterization. The non-reduced peptide mapping method combined with the unique electron activated dissociation (EAD) provided signature fragments for confident trisulfide identification as well as trisulfide quantitation at individual sites. A higher throughput method using Fab mass analysis was also developed and qualified to support routine monitoring of trisulfide during process development. Fab mass analysis features simpler sample preparation and shorter analysis time but provides comparable results to the non-reduced peptide mapping method. In this study, a bi-specific (BsAb) and a tri-specific antibody (TsAb) were compared side-by-side with a MsAb to evaluate the impact of trisulfide on the structure and function of multi-specific antibodies. Results indicated that trisulfide dominantly formed at similar locations across different antibody constructs and had no impact on the size heterogeneity, charge heterogeneity, or bioactivities of any assessed antibodies. Together with the in vitro stability under heat stress (25 °C and 40 °C for up to four weeks) and rapid conversion from trisulfide to disulfide during in vivo circulation, trisulfide could be categorized as a non-critical quality attribute (non-CQA) for antibody products.

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.

Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics

Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for the production of monoclonal antibodies (mAbs). However, a challenge of analyzing large cell culture data is the high correlation between regressors (particularly media composition), which makes traditional analyses, such as analysis of variance and multivariate linear regression, inappropriate. Instead, partial least-squares regression (PLSR) models, in combination with machine learning techniques such as variable importance metrics, are an orthogonal or alternative approach to identifying important regressors and overcoming the challenge of a highly covariant data structure. A specific workflow for the retrospective analysis of cell culture data is proposed that covers data curation, PLS regression, model analysis, and further steps. In this study, the proposed workflow was applied to data from four mAb products in an industrial cell culture process to identify significant process parameters that influence the afucosylation levels. The PLSR workflow successfully identified several significant parameters, such as temperature and media composition, to enhance process understanding of the relationship between cell culture processes and afucosylation levels.

Data Augmentation to Support Biopharmaceutical Process Development through Digital Models—A Proof of Concept

In recent years, monoclonal antibodies (mAbs) are gaining a wide market share as the most impactful bioproducts. The development of mAbs requires extensive experimental campaigns which may last several years and cost billions of dollars. Following the paradigm of Industry 4.0 digitalization, data-driven methodologies are now used to accelerate the development of new biopharmaceutical products. For instance, predictive models can be built to forecast the productivity of the cell lines in the culture in such a way as to anticipate the identification of the cell lines to be progressed in the scale-up exercise. However, the number of experiments that can be performed decreases dramatically as the process scale increases, due to the resources required for each experimental run. This limits the availability of experimental data and, accordingly, the applicability of data-driven methodologies to support the process development. To address this issue in this work we propose the use of digital models to generate in silico data and augment the amount of data available from real (i.e., in vivo) experimental runs, accordingly. In particular, we propose two strategies for in silico data generation to estimate the endpoint product titer in mAbs manufacturing: one based on a first principles model and one on a hybrid semi-parametric model. As a proof of concept, the effect of in silico data generation was investigated on a simulated biopharmaceutical process for the production of mAbs. We obtained very promising results: the digital model effectively supports the identification of high-productive cell lines (i.e., high mAb titer) even when a very low number of real experimental batches (two or three) is available.

Data integrity within the biopharmaceutical sector in the era of Industry 4.0

Data Integrity (DI) in the highly regulated biopharmaceutical sector is of paramount importance to ensure decisions on meeting product specifications are accurate and hence assure patient safety and product quality. The challenge of ensuring DI within this sector is becoming more complex with the growing amount of data generated given increasing adoption of process analytical technology (PAT), advanced automation, high throughput microscale studies and managing data models created by machine learning (ML) tools. This paper aims to identify DI risks and mitigation strategies in biopharmaceutical manufacturing facilities as the sector moves towards Industry 4.0. To achieve this, the paper examines common DI violations and links them to the ALCOA+ principles used across the FDA, EMA and MHRA. The relevant DI guidelines from the ISPE's GAMP5® and ISA-95 standards are also discussed with a focus on the role of validated computerised and automated manufacturing systems to avoid DI risks and generate compliant data. The paper also highlights the importance of DI whilst using data analytics to ensure the developed models meet the required regulatory standards for process monitoring and control. This includes a discussion on possible mitigation strategies and methodologies to ensure data integrity is maintained for smart manufacturing operations such as the use of cloud platforms to facilitate the storage and transfer of manufacturing data, and migrate away from paper-based records. This article is protected by copyright. All rights reserved.

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.

Red cell manufacturing using parallel stirred-tank bioreactors at the final stages of differentiation enhances reticulocyte maturation

The aim of this study was to develop a robust, quality controlled, and reproducible erythroid culture system to obtain high numbers of mature erythroblasts and red blood cells (RBCs). This was achieved using a fully controlled stirred-tank bioreactor by the design of experiments (DOE) methods in the serum-free medium by defining the appropriate culture parameters. Human cord blood CD34+ cells were first cultured in static flasks and then inoculated to stirred-tank bioreactors. Cell diameter was gradually decreased and final RBC yields were significantly higher when cells were inoculated at sizes smaller than 12 μm. The larger immature cells in the basophilic stage did not survive, while smaller mature erythroid cells were successfully expanded at high agitation speeds, demonstrating that appropriate seeding timing is critical. A high inoculation cell density of 5 × 10⁶  cells/ml was achieved reaching 1.5 × 10⁷  cells/ml. By using DOE analysis fitted to precise stages of erythropoiesis, we were able to acquire the optimal culture parameters for pH (7.5), temperature (37°C), dissolved oxygen, agitation speed (500 rpm), inoculation timing (cell diameter 12-13 μm), media feeding regimen, and cell seeding density (5 × 10⁶  cells/ml). The final pure RBCs showed appropriate functions compared with fresh donor RBCs, confirming that manufacturing mature RBCs with reproducibility is possible.

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.

Scale-Down Model Development in ambr systems: An Industrial Perspective

High-Throughput (HT) technologies such as miniature bioreactors (MBRs) are increasingly employed within the biopharmaceutical manufacturing industry. Traditionally, these technologies have been utilized for discrete screening approaches during pre-clinical development (e.g., cell line selection and process optimization). However, increasing interest is focused towards their use during late clinical phase process characterization studies as a scale-down model (SDM) of the cGMP manufacturing process. In this review, the authors describe a systematic approach toward SDM development in one of the most widely adopted MBRs, the ambr 15 and 250 mL (Sartorius Stedim Biotech) systems. Recent efforts have shown promise in qualifying ambr systems as SDMs to support more efficient, robust and safe biomanufacturing processes. The authors suggest that combinatorial improvements in process understanding (matching of mass transfer and cellular stress between scales through computational fluid dynamics and in vitro analysis), experimental design (advanced risk assessment and statistical design of experiments), and data analysis (combining uni- and multi-variate techniques) will ultimately yield ambr SDMs applicable for future regulatory submissions.