Simplifying stable CHO cell line generation with high probability of monoclonality by using microfluidic dispensing as an alternative to fluorescence activated cell sorting.

Single cell cloning is a critical step for cell line development (CLD) for therapeutic protein production, with proof of monoclonality being compulsorily sought in regulatory filings. Among the different single cell deposition technologies, we found that fluorescence activated cell sorting (FACS) offers high probability of monoclonality and can allow selective enrichment of the producer cells. However, FACS instruments are expensive and resource-intensive, have a large footprint, require highly skilled operators and take hours for setup, thereby complicating the cell line generation process. With the aim of finding an easy-to-use alternative to FACS, we identified a flow cytometry-based microfluidic cell dispenser, which presents a single cell sorting solution for biopharmaceutical CLD. The microfluidic cell dispenser is small, budget-friendly, easy-to-use, requires lower-cost consumables, permits flow cytometry-enabled multiparametric target cell enrichment and offers fast and gentle single cell dispensing into multiwell plates. Following comprehensive evaluation, we found that single cell deposition by the microfluidic cell dispenser resulted in >99% probability of monoclonality for production cell lines. Moreover, the clonally derived producer cell lines generated from the microfluidic cell dispenser demonstrated comparable or improved growth profiles and production capability compared to the FACS derived cell lines. Taken together, microfluidic cell dispensing can serve as a cost-effective, efficient and convenient alternative to FACS, simplifying the biopharmaceutical CLD platform with significant reductions in both scientist time and running costs.

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.

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.

Use of Stability Modeling to Support Accelerated Vaccine Development and Supply.

Stability assessment of pharmaceuticals in specific storage and shipment conditions is a key requirement to ensure that safe and efficacious products are administered to patients. This is particularly relevant for vaccines, with numerous vaccines strictly requiring cold storage to remain stable. When stability evaluation is exclusively based on real-time data, it may represent a bottleneck for rapid and effective vaccine access. Stability modeling for vaccines represents a key resource to predict stability based on accelerated stability studies; nevertheless, this approach is not fully exploited for these kinds of products. This is likely because of the complexity and diversity of vaccines, as well as the limited availability of dedicated guidelines or illustrative case studies. This article reports a cross-company perspective on stability modeling for vaccines. Several examples, based on the direct experience of the contributors, demonstrate that modeling approaches can be highly valuable to predict vaccines' shelf life and behavior during shipment or manipulation. It is demonstrated that modeling methodologies need to be tailored to the nature of the vaccine, the available prior knowledge, and the monitored attributes. Considering that the well-established strategies reported in ICH or WHO guidelines are not always broadly applicable to vaccines, this article represents an important source of information for vaccine researchers and manufacturers, setting the grounds for further discussion within the vaccine industry and with regulators.

Kinetic modeling as a tool to understand the influence of cell culture process parameters on the glycation of monoclonal antibody biotherapeutics.

Glycation, the nonenzymatic reaction between the reducing sugar glucose and the primary amine residues on amino acid side chains, commonly occurs in the cell culture supernatant during production of therapeutic monoclonal antibodies (mAbs). While glycation has the potential to impact efficacy and pharmacokinetic properties for mAbs, the most common undesirable impact of glycation is on the distribution of charged species, often a release specification for commercial processes. Existing empirical approaches are usually insufficient to rationalize the effects of cell line and process changes on glycation. To address this gap, we developed a kinetic model for estimating mAb glycation levels during the cell culture process. The rate constant for glycation, including temperature and pH dependence, was estimated by fitting the kinetic model to time-course glycation data from bioreactors operated at different process settings that yielded a wide range of glycation values. The parameter values were further validated by independently estimating glycation rate constants using cell-free incubation studies at various temperatures. The model was applied to another mAb, by re-estimating the activation energy to account for effect of a glycation "hotspot". The model was further utilized to study the role of temperature shift as an approach to reduce glycation levels in the manufacturing process for mAb2. While a downshift in temperature resulted in lowering of glycation levels for mAb2, the model helped elucidate that this effect was caused due to contribution from changes in glucose consumption, mAb secretion and temperature, instead of a direct impact of temperature alone on the kinetic rate of glycation.

Assurance of monoclonality in one round of cloning through cell sorting for single cell deposition coupled with high resolution cell imaging.

Regulatory authorities require that cell lines used in commercial production of recombinant proteins must be derived from a single cell progenitor or clone. The limiting dilution method of cell cloning required multiple rounds of low-density cell plating and microscopic observation of a single cell in order to provide evidence of monoclonality. Other cloning methods rely on calculating statistical probability of monoclonality rather than visual microscopic observation of cells. We have combined the single cell deposition capability of the Becton Dickinson Influx™ cell sorter with the microscopic imaging capability of the SynenTec Cellavista to create a system for producing clonal production cell lines. The efficiency of single cell deposition by the Influx™ was determined to be 98% using fluorescently labeled cells. The centrifugal force required to settle the deposited cells to the bottom of the microplate well was established to be 1,126g providing a 98.1% probability that all cells will be in the focal plane of the Cellavista imaging system. The probability that a single cell was deposited by the cell sorter combined with the probability of every cell settling into the focal plane of the imager yield a combined >99% probability of documented monoclonality.

Medium term planning of biopharmaceutical manufacture using mathematical programming.

Regulatory pressures and capacity constraints are forcing the biopharmaceutical industry to consider employing multiproduct manufacturing facilities running on a campaign basis. The need for such flexible and cost-effective manufacture poses a significant challenge for planning and scheduling. This paper reviews the problem of planning and scheduling of biopharmaceutical manufacture and presents a methodology for the planning of multiproduct biopharmaceutical manufacturing facilities. The problem is formulated as a mixed integer linear program (MILP) to represent the relevant decisions required within the planning process and is tested on two typical biopharmaceutical industry planning problems. The proposed formulation is compared with an industrial rule based approach, which it outperforms in terms of profitability. The results indicate that the developed formulation offers an effective representation of the planning problem and would be a useful decision tool for manufacturers in the biopharmaceutical industry particularly at times of limited manufacturing capacity.