Recent innovations surrounding advanced therapeutic modalities have catalyzed a paradigm shift in the biopharmaceutical industry. Today, entire drug development pipelines may be dedicated to biologic drugs, and continued focus on data and technologies supporting development of novel biologics will likely serve to further streamline their development, validation, and scale up in the future.
Even among more advanced therapeutic modalities, such as monoclonal antibodies (mAbs), a need for process standardization exists. Many cell line development (CLD) workflows used today are highly manual and repetitive. The complexity and importance of many CLD processing steps leave little room for inconsistencies. These challenges, coupled with the industry’s increasing demand for greater output while maintaining constant budget and FTE workflows, have made accurate, comprehensive cell line characterization and cell culture screening a more critical consideration than ever.
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More than ever, CLD is crucial to enabling successful process development and downstream scaling. To optimize and standardize the CLD workflows necessary to benefit downstream work, organizations must focus on the technology and expertise needed to innovate in three key areas: automation, analytics, and digitalization. In doing so, operators can surmount or ameliorate challenges related to personnel turnover, resourcing, human error, turnaround times, redundancies, and data inconsistencies.
Driving streamlined, standardized CLD through innovation
Revamping a CLD workflow to enable greater workflow automation, optimized analytical methods, and improved data management can represent a distinct challenge for biopharmaceutical companies and CRDMOs. Outdated technologies and disparate instruments making up a workflow can hamper efforts to implement laboratory automation, as can outdated methods ill-equipped for automation and integration. While certain instruments have been targeted by suppliers for automation in recent years, there exists a further opportunity to automate other process steps.
Similar opportunities exist for improving laboratory analytics through both automation and digitalization. By incorporating technologies that automate analytical sample preparation and enable detailed analyses, operators can incorporate faster, more informative analytics into their workflow. Finally, connecting analytical workflows with software and data management platforms needed to collect, interpret, and report these data will enable more streamlined tech transfer between labs and support later phases of drug development. The Life Sciences companies of Danaher are dedicated to pioneering solutions in the drug discovery and bioprocessing spaces with a portfolio of automation, analytics, and data solutions that will serve to transform these workflows by addressing the pain points of cell line development.
The first pillar: Increasing automation to transform CLD workflows
Automation can take many different forms in a biopharmaceutical workflow, typically centering on reducing the hands-on time necessary for processing a given step. For a stability study, an organization may enable a point solution such as automated sample processing by integrating a liquid handler with an incubator, eliminating the need to split wells manually every three or four days. In other cases, organizations may work to pursue “total lab automation,” establishing a workflow where a process is managed by an interconnected system, automating processes as diverse as cell culture or analytical sample preparation. While this level of automation still requires hands-on work for certain process steps, it serves to increase reproducibility and consistency, which in turn can increase laboratory capacity.
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Offerings from Beckman Coulter’s Access Solutions group or Molecular Devices’ Advanced Workflow Engineering Solutions enable this more individualized approach, establishing a workflow with instruments more tailored to an application and capable of the complexity necessary for efficient CLD. Other state-of-the-art instruments that can help laboratories achieve a fit-for-purpose solution can include cell viability analyzers, automated workstations, and single-cell dispensers.
The second pillar: Improving analytics to support faster, more informed decision-making
Achieving analytical insights to enable decision-making is a key challenge for CLD laboratories. For many organizations, the technologies and expertise required to drive these insights means outsourcing the majority of their analytics to an external laboratory. While CLD labs are able to leverage certain analytics in-house to measure IgG titer or count cells, these techniques are used for quick screening of cell lines. Other critical quality attributes (CQAs), such as product glycosylation or protein charge variants typically require outsourcing. Once clones are identified, many of the outsourced analytics needed to determine the best clones can take days to return results. Flexibility is key to enabling an optimized analytics approach.
When it comes to achieving right-sized analytical methods for a CLD workflow, another core challenge relates to securing enough purified drug substance to perform necessary assays. Many of the most common assays performed in CLD, such as glycan analysis or charge variant analyses, are undertaken using liquid chromatography/mass spectrometry (LC/MS), which requires sample purification steps prior to testing. Volumes inevitably lost during purification and preparation mean operators must prioritize testing based on what insights are needed most critically at various points in a process.
Marrying higher accuracy with greater flexibility for the assays needed in-house can reduce the hands-on time needed and increase lab capacity. For the Life Sciences companies of Danaher, these efforts are combined with a push to enable integration between analytical instruments and liquid handlers to establish more automated processes, connecting these, in turn, to digital solutions that can offer operators faster, more readily interpretable data. Systems such as SCIEX’s Intabio ZT System combine automated icIEF sample preparation and UV detection with a high-resolution mass spectrometer for simplified CQA identification. Beckman Coulter Life Sciences' Valitatiter can measure IgG in minutes, giving labs better insights faster. Likewise, Molecular Devices’ configurable plate readers can allow laboratories more latitude in measuring titer without sending samples for offline analysis, providing data insights as valuable as those afforded by outsourced HPLC analysis.
The third pillar: Enabling greater digitalization and data integration
For many CLD laboratories, ensuring data integrity is a complex undertaking. The specificity and sensitivity of instruments employed in a modern CLD workflow have resulted in more data points, more extended analyses, and more workflow considerations than ever before. This has created a bottleneck for data analysis and storage as housing and managing increasingly large and diverse datasets becomes the norm for these applications. Additionally, laboratories may not sufficiently communicate these data to other CLD labs within an organization, forming data silos that perpetuate organizational inefficiencies. Transitioning from legacy systems to cloud-based platforms is both crucial and challenging.
The idea of transitioning from a system that may work in the present to one that offers greater data insights is tough. However, complex data interpretation and the pain points created by the potential miscommunications and rework that data silos between disconnected systems create require the adoption of a different solution. Having data solutions that are streamlined, interoperable, and easily used can improve both short-term decision-making and later-stage regulatory interactions.
Tech transfer between laboratories during drug development can be a complex and delicate endeavor. By ensuring that data is complete and unambiguous, organizations can avoid the need for rework between labs and the potential for regulatory intervention when audits are performed. The solutions that the Life Sciences companies of Danaher have pursued offer users interconnected, comprehensive, and valuable data interfaces—platforms such as IDBS Polar Biopharma Lifecycle Management platform can enable a solution that securely manages drug progression while rapidly integrating a development ecosystem and software like Genedata Expressionist and Genedata Selector can offer users custom-built data processing, analysis, and reporting for mass spectrometry and next-generation sequencing.
Conclusion
Ultimately, the CLD process is a long and complicated one, with companies investing years and billions to traverse from drug discovery, development, and clinical trials to release. From vector construction to transfection, screening and characterization, scale-up, and establishment of a master cell bank, the Life Sciences companies of Danaher have a technology portfolio designed to improve the cohesion of a CLD process across workflow steps, tying instruments, analytical methods, and data management platforms together to create an end-to-end, bespoke solution. The result is a CLD workflow that keeps pace with the rapidly evolving drug development landscape while generating more accurate and accessible analytical data, all supported by data management systems that generate and curate meaningful, timely insights.