Lonza (United Kingdom)

Recent advancements in biologics production using CHO cells have been partly driven by improved understanding of how variations in the cell culture environment influence cellular metabolism, productivity, and the attributes of the final product. In-silico models serve a valuable role in mapping the effects of various process parameters and media changes on cellular response. Advances in technologies such as data-driven analysis, self-learning systems, and digital twins are reinforcing progress toward smart manufacturing, enabling the real-time control of production processes. Furthermore, kinetic, and constraint-based mechanistic modeling, combined with omics approaches, are becoming increasingly incorporated into the bioprocess development and manufacturing innovation ecosystem. In this review, we cover CHO central metabolism as a foundation for mechanistic modeling and extend the discussion to include various mechanistic modeling approaches, highlighting the incorporation of glycosylation and secretory pathways. Multi-omics approaches provide a deeper understanding of intracellular processes and the dynamic interactions between product quality and pathways. In parallel, to achieve the Industry 4.0 vision of digitalization and machine learning techniques are finding wider adoption in biopharmaceutical development. We discuss the potential applications of these techniques for predictions, inference, optimization, and control. The role of big data analytics and artificial intelligence methods in reinforcing progress towards smart manufacturing and enabling real-time control of production processes is discussed. Finally, we summarize the application of machine learning and hybrid models to CHO bioprocesses, aiming to develop and manufacture drugs more efficiently and at a lower cost for patients.

Chinese hamster ovary (CHO) cells are the leading mammalian cell host employed to produce complex secreted recombinant biotherapeutics such as monoclonal antibodies (mAbs). Metabolic selection marker technologies (e.g. glutamine synthetase (GS) or dihydrofolate reductase (DHFR)) are routinely employed to generate such recombinant mammalian cell lines. Here we describe the development of a selection marker system based on the metabolic requirement of CHO cells to produce proline, and that uses pyrroline-5-carboxylase synthetase (P5CS) to complement this auxotrophy. Firstly, we showed the system can be used to generate cells that have growth kinetics in proline-free medium similar to those of the parent CHO cell line, CHOK1SV GS-KO™ grown in proline-containing medium. As we have previously described how engineering lipid metabolism can be harnessed to enhance recombinant protein productivity in CHO cells, we then used the P5CS selection system to re-engineer lipid metabolism by over-expression of either sterol regulatory element binding protein 1 (SREBF1) or stearoyl CoA desaturase 1 (SCD1). The cells with re-engineered proline and lipid metabolism showed consistent growth and P5CS, SCD1 and SREBF1 expression across 100 cell generations. Finally, we show that the P5CS and GS selection systems can be used together. A GS vector containing the light and heavy chains for a mAb was super-transfected into a CHOK1SV GS-KO™ host over-expressing SCD1 from a P5CS vector. The resulting stable transfectant pools achieved a higher concentration at harvest for a model difficult to express mAb than the CHOK1SV GS-KO™ host. This demonstrates that the P5CS and GS selection systems can be used concomitantly to enable CHO cell line genetic engineering and recombinant protein expression.

Hair proteins are significantly affected by environmental pH. This impact tends to increase with prior hair damage. To understand how pH affects bleached hair properties, we utilized a number of techniques allowing for the determination of hair thermal properties, swelling and water sorption, and dry and wet tensile properties. At pH 5, hair proteins had the best structural integrity, as determined by differential scanning calorimetry and the highest tensile modulus. At pH 10, protein cross‐linking density decreased, water content and hair cross‐sectional diameter increased. Alkaline treatment, when compared with pH 5, did not reduce intermediate filament conditions (evaluated via enthalpy measurement) nor mechanical property performance in the wet state. In contrast to alkaline‐treated hair, bleached hair equilibrated at pH 3 behaved very differently: it contained two different crosslink density zones, was the least stiff in dry and stiffest in wet conditions. Additionally, it absorbed less water and had the lowest diameter because of reduced water binding by protonated carboxyl groups. The pH 3 to 10 did not affect the mechanical strength of bleached hair in dry or wet conditions.

The monitoring and control of bioprocesses is of the utmost importance in order to provide a consistent, safe, and high-quality product for consumers. Current monitoring and control schemes rely on infrequent and time consuming offline sampling methods, which inherently leads to some variability in the process which may impact the product quality profile. As part of Lonza's dedication to process analytical technology (PAT) initiatives this study evaluated the ability to generate generic calibration models, which are independent of the cell line, using Raman probes to monitor changes in glucose, lactate, glutamate, ammonium, viable cell concentration (VCC), total cell concentration (TCC) and product concentration. Calibration models were developed from cell culture using two different CHOK1SV GS-KOTM cell lines producing different monoclonal antibodies (mAbs). Developed predictive models, measured changes in glucose, lactate, ammonium, VCC, and TCC with average prediction errors of 0.44, 0.23, 0.03 gL-1 , 1.90 × 106 cellsmL-1 , and 1.85 × 106 cellsmL-1 , respectively over the course of cell culture with minimal cell line dependence. The development of these generic models allows the application of spectroscopic PAT techniques in clinical and commercial manufacturing environments, where processes are typically run once or twice in GMP manufacturing based on a common platform process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:730-737, 2018.

Errors are a part of life. With human errors accounting for approximately 50% of quality incidents and related problems within the pharmaceutical industry, the need to improve human performance in manufacturing operations is obvious. The purpose of this article is to describe error-proofing ways of structuring and writing knowledge documents, procedures, batch records, as well as practices for structuring, conducting, and documenting training to assure competence. These practices are recommended for adoption to shift the current 'training for compliance' paradigm to a 'training for competence' paradigm. It will also be demonstrated that a training for competence focus achieves GMP compliance. Results at Lonza have been encouraging, with human error-related quality deviations and non-conformities reduced by more than 40% across 13 sites globally within the first two years of the implementation of its Error Prevention System.

Increases in mammalian cell culture titres and densities have placed significant demands on primary recovery operation performance. This article presents a methodology which aims to screen rapidly and evaluate primary recovery technologies for their scope for technically feasible and cost‐effective operation in the context of high cell density mammalian cell cultures. It was applied to assess the performance of current (centrifugation and depth filtration options) and alternative (tangential flow filtration (TFF)) primary recovery strategies. Cell culture test materials (CCTM) were generated to simulate the most demanding cell culture conditions selected as a screening challenge for the technologies. The performance of these technology options was assessed using lab scale and ultra scale‐down (USD) mimics requiring 25–110mL volumes for centrifugation and depth filtration and TFF screening experiments respectively. A centrifugation and depth filtration combination as well as both of the alternative technologies met the performance selection criteria. A detailed process economics evaluation was carried out at three scales of manufacturing (2,000L, 10,000L, 20,000L), where alternative primary recovery options were shown to potentially provide a more cost‐effective primary recovery process in the future. This assessment process and the study results can aid technology selection to identify the most effective option for a specific scenario.