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Managing High-Throughput Workflows

Digital platforms address the challenges of an expanding information pipeline

by Alec Westley
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The massive expansion in the volume of data generated by modern high-throughput techniques such as next-generation sequencing, quantitative polymerase chain reaction, mass spectrometry, and synthetic biology has opened up a wealth of opportunities for pharmaceutical and biotech R&D. Yet, alongside the enormous possibilities enabled by the “big data” revolution is an important challenge for laboratories: how to manage and maximize these complex data sets.

To enable fast and effective decision making—critical for translating fundamental data into tangible outcomes— the vast quantities of information produced by today’s R&D workflows must not only be organized in a meaningful way, but also be easily accessible, searchable, and sharable as well. Moreover, given the predicted future pace of change, it is vital that informatics platforms can evolve with changing workflows and new types of data.

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For many pharmaceutical companies, cloud-based informatics platforms are the most effective solution to this challenge. These flexible and scalable platforms can overcome data management challenges by integrating R&D streams and organizing the vast quantities of information they generate in a central hub. Platform-asa- service (PaaS) solutions are an extensible approach to cloud-based data management and offer a cost-effective route to achieving end-to-end R&D pipeline visibility.

The growing complexity of pharmaceutical R&D data

Advances in analytical techniques over the past two decades have pushed the limits of what’s possible in pharmaceutical R&D. Thanks to groundbreaking science, increased automation, and powerful informatics, multidimensional data can now be rapidly generated on a remarkable scale. What’s more, this pace of change is actually accelerating.

For pharmaceutical and biotech companies to fully realize the benefits of these new technologies, it is essential that the data management tools they use are sufficiently flexible and scalable to evolve with the changing R&D landscape. For businesses to make the best possible decisions based on all the available insight, these platforms must bring together workflows and offer comprehensive visibility over R&D streams. Furthermore, as the industry embraces more collaborative working practices, there is a growing need for digital ecosystems to facilitate seamless and secure data sharing.

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Despite this need, the current reality is that many laboratories employ fragmented data management platforms that do not easily accommodate expanding R&D pipelines. In recent years, large numbers of laboratories have transitioned from paper-based approaches to laboratory information management systems (LIMS) and electronic laboratory notebooks (ELNs). However, paperless systems on their own are unable to address the challenges associated with large-scale multidimensional data. Whether it’s due to rapid business growth or a fragmented approach to IT procurement, if these systems are implemented as point solutions or assigned to specific workflows in an unconnected manner, the potential benefits of digitization can be lost. A joined-up digital ecosystem is essential to drive operational efficiency, enhance innovation, and improve research quality.

Bringing R&D streams under one umbrella

Modern drug discovery is putting additional demands on pharmaceutical R&D. With laboratories under continued pressure to increase innovation and reduce rates of attrition while operating under tight research budgets, streamlined processes and joinedup thinking have never been so important. Moreover, with regulatory authorities increasingly putting the integrity of pharmaceutical data under the microscope, organizations of all sizes—from small biotechs to multinational giants—need data management platforms that offer a complete, open, and secure platform for their R&D workflows.

Cloud-based platforms bring R&D data under a single umbrella, offering a flexible and cost-effective solution to the challenge of managing an expanding information pipeline. While few R&D workflows are the same, most typically handle a combination of structured, unstructured, and reference data. Historically, one of the biggest challenges associated with managing this mix has been integrating these disparate data types into a single system. Cloudbased platforms break down pre-existing data silos to provide a single connected digital ecosystem. By associating previously difficult-to- search unstructured data with structured data such as analytical measurements and run sequences, all this information can now be easily searched, accessed, and mined for reporting and trending. Furthermore, because this data can be cross-referenced with information from other sources, cloud-based platforms offer comprehensive workflow visibility for smarter, faster decision making.

In today’s drug discovery landscape, morecollaborative working practices are becoming the new normal—whether that’s in the form of the expanding number of interorganizational partnerships or the growing appreciation of the benefits an integrated approach to R&D can bring. Seamless sharing of knowledge between colleagues is fundamental for successful teamwork, and this is especially important when that insight is in the form of large data sets. Cloud-based PaaS solutions make data retrieval and distribution straightforward by giving authorized users access to the information they need in real time. With supporting access from mobile devices such as smartphones and tablets, decisions can be made at any time and from any location. This strong framework for collaboration means partners can accelerate project timelines and unlock the potential from their research much more quickly.

With the trend toward increasingly multidimensional data on an ever-growing scale showing no signs of slowing, the data management solutions used for R&D workflows must be both flexible and scalable to meet the needs of tomorrow. Cloud-based PaaS systems are based on a robust framework that’s designed to support and evolve with changing technologies and regulatory requirements.

It’s this extensible nature that makes cloudbased informatics platforms a more costeffective solution than traditional on-site systems, especially when it comes to maintaining optimal performance. Because upgrades are managed by the cloud service provider, fewer internal resources must be spent ensuring systems are kept up to date. This is particularly useful when it comes to data integrity and security—factors that are frequently cited as top priorities for R&D laboratories.

In general, on-site systems for minimizing security risks rely on custom-built solutions that have been developed and installed in-house. While these are an important investment, they require substantial time and resource investment to implement. Furthermore, when regulatory guidelines change, such as those relating to data integrity best practices, manually reviewing every aspect of existing data workflows for compliance and bringing systems up to date can consume vast amounts of time and budget. Cloud-based platforms offer built-in security features, including firewalls, backup, encrypted VPN access, and disaster recovery solutions. And because updates and maintenance are managed by the service provider, the burden on internal departments is essentially eliminated—allowing businesses to invest resources into fundamental R&D.

Transition to scalable flexibility

Regardless of organizational size, implementing new IT infrastructure is never a trivial undertaking. Transferring large volumes of multidimensional data stored in LIMS and ELNs can be a complex affair, especially when workflow-critical information must remain accessible at all times. It’s therefore essential that any transition to a new platform comes with minimal disruption and does not compromise the integrity of laboratory data.

Cloud-based PaaS systems designed around modular frameworks make upgrading to the latest data management tools straightforward. Part of this streamlined approach is due to the way these modular solutions can be customized to meet an organization’s specific requirements. For laboratories looking to implement a full-scale overhaul of their data management systems, for example, data can be carried over from existing platforms in its entirety. On the other hand, if partial expansion is required, additional systems can be incorporated into their established framework without the need to replace fundamental infrastructure.

This level of flexibility extends even to the data management tools and functionality these platforms offer. Some PaaS providers offer a host of workflowspecific applications that allow organizations to extend their platforms as their R&D pipelines grow. And because these pre-configured modular applications are built around the latest industry best-practice and regulatory guidance and function on top of existing LIMS, ELNs, and other scientific data management tools, they are ready to support laboratories from day one.


Rapid advances in analytical capabilities are opening new opportunities for pharmaceutical R&D. Multidimensional data can now be produced on a scale and at a speed that few would have imagined a decade ago. However, to remain at the forefront of innovation, laboratories require extensible informatics platforms that can keep pace with the volume and complexity of this data. Many forward-looking organizations are turning to cloud-based data management systems to better integrate their research pipelines and provide a comprehensive overview of their processes for monitoring, trending, and sharing purposes. These modern digital ecosystems are overcoming the challenges associated with fragmented data management systems, enabling effective management of the high-throughput workflows of today—as well as those that are just around the corner.