Real-time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality

Real time on-line amino acid analysis to explore amino acid trends and link to glycosylation outcomes of a model monoclonal antibody

Bioreactor parameters can have significant effects on the quantity and quality of biotherapeutics. Monoclonal antibody products have one particularly important critical quality attribute being the distribution of product glycoforms. N-linked glycosylation affects the therapeutic properties of the antibody including effector function, immunogenicity, stability, and clearance rate. Our past work revealed that feeding different amino acids to bioreactors altered the productivity and glycan profiles. To facilitate real-time analysis of bioreactor parameters and the glycosylation of antibody products, we developed an on-line system to pull cell-free samples directly from the bioreactors, chemically process them, and deliver them to a chromatography-mass spectroscopy system for rapid identification and quantification. We were able to successfully monitor amino acid concentration on-line within multiple reactors, evaluate glycans off-line, and extract four principal components to assess the amino acid concentration and glycosylation profile relationship. We found that about a third of the variability in the glycosylation data can be predicted from the amino acid concentration. Additionally, we determined that the third and fourth principal component accounts for 72% of our model's predictive power, with the third component indicated to be positively correlated with latent metabolic processes related to galactosylation. Here we present our work on rapid online spent media amino acid analysis and use the determined trends to collate with glycan time progression, further elucidating the correlation between bioreactor parameters such as amino acid nutrient profiles, and product quality. We believe such approaches may be useful for maximizing efficiency and reducing production costs for biotherapeutics.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Process analytical technology for on-line monitoring of quality attributes during single-use ultrafiltration/diafiltration

Process analytical technology (PAT) is a fast-growing field within bioprocessing that enables innovation in biological drug manufacturing. This study demonstrates novel PAT methods for monitoring multiple quality attributes simultaneously during the ultrafiltration and diafiltration (UF/DF) process operation, the final step of monoclonal antibody (mAb) purification. Size exclusion chromatography (SEC) methods were developed to measure excipients arginine, histidine, and high molecular weight (HMW) species using a liquid chromatography (LC) system with autosampler for both on-line and at-line PAT modes. The methods were applied in UF/DF studies for the comparison of single-use tangential flow filtration (TFF) cassettes to standard reusable cassettes to achieve very high concentration mAb drug substance (DS) in the order of 100-200 g/L. These case studies demonstrated that single-use TFF cassettes are a functionally equivalent, low-cost alternative to standard reusable cassettes, and that the on-line PAT measurement of purity and excipient concentration was comparable to orthogonal offline methods. These PAT applications using an on-line LC system equipped with onboard sample dilution can become a platform system for monitoring of multiple attributes over a wide dynamic range, a potentially valuable tool for biological drug development and manufacturing.

Productivity improvement and charge variant modulation for intensified cell culture processes by adding a carboxypeptidase B (CpB) treatment step

The goal of cell culture process intensification is to improve productivity while maintaining acceptable quality attributes. In this report, four processes, namely a conventional manufacturing Process A, and processes intensified by enriched N-1 seed (Process B), by perfusion N-1 seed (Process C), and by perfusion production (Process D) were developed for the production of a monoclonal antibody. The three intensified processes substantially improved productivity, however, the product either failed to meet the specification for charge variant species (main peak) for Process D or the production process required early harvest to meet the specification for charge variant species, Day 10 or Day 6 for Processes B and C, respectively. The lower main peak for the intensified processes was due to higher basic species resulting from higher C-terminal lysine. To resolve this product quality issue, we developed an enzyme treatment method by introducing carboxypeptidase B (CpB) to clip the C-terminal lysine, leading to significantly increased main peak and an acceptable and more homogenous product quality for all the intensified processes. Additionally, Processes B and C with CpB treatment extended bioreactor durations to Day 14 increasing titer by 38% and 108%, respectively. This simple yet effective enzyme treatment strategy could be applicable to other processes that have similar product quality issues.

Spectroscopy integration to miniature bioreactors and large scale production bioreactors-Increasing current capabilities and model transfer

Spectroscopy techniques are being implemented within the biopharmaceutical industry due to their non-destructive ability to measure multiple analytes simultaneously, however, minimal work has been applied focussing on their application at small scale. Miniature bioreactor systems are being applied across the industry for cell line development as they offer a high-throughput solution for screening and process optimization. The application of small volume, high-throughput, automated analyses to miniature bioreactors has the potential to significantly augment the type and quality of data from these systems and enhance alignment with large-scale bioreactors. Here, we present an evaluation of 1. a prototype that fully integrates spectroscopy to a miniature bioreactor system (ambr®15, Sartorius Stedim Biotech) enabling automated Raman spectra acquisition, 2. In 50 L single-use bioreactor bag (SUB) prototype with an integrated spectral window. OPLS models were developed demonstrating good accuracy for multiple analytes at both scales. Furthermore, the 50 L SUB prototype enabled on-line monitoring without the need for sterilization of the probe prior to use and minimal light interference was observed. We also demonstrate the ability to build robust models due to induced changes that are hard and costly to perform at large scale and the potential of transferring these models across the scales. The implementation of this technology enables integration of spectroscopy at the small scale for better process understanding and generation of robust models over a large design space while facilitating model transfer throughout the scales enabling continuity throughout process development and utilization and transfer of ever-increasing data generation from development to manufacturing.

Bigdata analytics identifies metabolic inhibitors and promoters for productivity improvement and optimization of monoclonal antibody (mAb) production process

Recent advances in metabolite quantification and identification have enabled new research into the detection and control of titer inhibitors and promoters. This paper presents a bigdata analytics study to identify both inhibitors and promoters using multivariate data analysis of metabolomics data. By applying multi-way partial least squares (PLS) model to metabolite data from four fed-batch bioreactor conditions where feed formulation and selection agent concentrations varied, metabolites which exhibited the most significant impact on titer during cultivation were ranked from highest to lowest. The model outputs were then constrained to reduce the number of statistically relevant inhibitors or promoters to the top ten, which were used to conduct metabolic pathway analysis. Furthermore, a method is presented for identifying amino acids that prevent the accumulation of the inhibitors and/or enhance the formation of promoters during production. Finally, the metabolomics and pathway analysis results were integrated and validated with transcriptomics data to characterize metabolic changes occurring among different growth conditions. From these results, new feeding strategies were implemented which resulted in increased fed-batch production titer. Methodology from this work could be applied to future process optimization strategies for biotherapeutic production.

Real-time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality

Real‐time monitoring of cell cultures in bioreactors can enable expedited responses necessary to correct potential batch failure perturbations which may normally go undiscovered until the completion of the batch and result in failure. Currently, analytical technologies are dedicated to real‐time monitoring of bioreactor parameters such as pH, dissolved oxygen, and temperature, nutrients such as glucose and glutamine, or metabolites such as lactate. Despite the importance of amino acids as the building blocks of therapeutic protein products, other than glutamine their concentrations are not commonly measured. Here, we present a study into amino acid monitoring, supplementation strategies, and how these techniques may impact the cell growth profiles and product quality. We used preliminary bioreactor runs to establish baselines by determining initial amino acid consumption patterns, the results of which were used to select a pool of amino acids which gets depleted in the bioreactor. These amino acids were combined into blends which were supplemented into bioreactors during a subsequent run, the concentrations of which were monitored using a mass spectrometry based at‐line method we developed to quickly assess amino acid concentrations from crude bioreactor media. We found that these blends could prolong culture life, reversing a viable cell density decrease that was leading to batch death. Additionally, we assessed how these strategies might impact protein product quality, such as the glycan profile. The amino acid consumption data were aligned with the final glycan profiles in principal component analysis to identify which amino acids are most closely associated with glycan outcomes.