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Advances in high-throughput, next-generation technologies mean that pharmaceutical R&D now demands the management of vast quantities of data. This information is increasingly diverse and comes from multiple sources, spanning the entire drug discovery and development process.

Connecting laboratory instruments and systems remains a challenge as organizations look to streamline data sharing at every stage. Digital technology is recognised as a strategic enabler of innovation and business capability across the drug development process. Increasingly, many companies are turning to the latest digital solutions, with modern informatics platforms delivering the improved integration, process efficiency and productivity that is necessary to drive scientific advancement.

Here, we look at how the latest extensible lab informatics platforms are transforming pharmaceutical R&D, explore the challenges they are overcoming, and consider their current applications and future potential.

New challenges in R&D

When it comes to developing innovative biotherapeutics and small molecule therapeutics, analytical approaches and experimental design are evolving at great speed. Consequently, pharma and biotech companies are faced with the ongoing challenge of managing new types of multi-dimensional data generated in large volumes by increasingly automated workflows. The sector must be capable of adapting quickly as more advanced technologies come on stream and, given the move towards more collaborative working practices that encompass external expertise and organizations, there is a heightened need for seamless and secure data sharing too.

Given these requirements, businesses need an effective solution that allows data to be carefully managed throughout the drug development pipeline, from concept and research through to scale-up and manufacture. However, R&D workflows frequently rely on multiple disparate systems across different departments, using tools that were built as point solutions. The architectures that connect these systems may be both brittle and intricate. As a result, greater integration has become a priority.

The need for advanced digital solutions

If complex workflows are to function efficiently, it is essential that informatics solutions are both robust and integrated. Cloud-based data management platforms are becoming the preferred option for many organizations, due to their ability to deliver a complete and open digital ecosystem for pharmaceutical R&D. These systems also provide the flexibility and extensibility needed in an environment where continuous change is a reality. By adopting these platforms, data can be gathered, collated and secured in one place, with instruments and devices configured to automatically upload information to secure user accounts and informatics pipelines. This promotes efficient data analysis and well-managed knowledge sharing within and between teams and sites, as well as with external collaborators.

Of course, transitioning to such a solution must come with minimal disruption to existing processes. For example, many laboratories have adopted systems such as electronic laboratory notebooks (ELN) and databases to capture and organize data. These are often dedicated to specific workflows; however, they do not necessarily have to be discarded when putting in place new informatics platforms. Modular Platform-as-a-Service (PaaS) solutions can be incorporated seamlessly into an organization’s existing systems. Where the preference is for a complete replacement of existing systems, data are simply moved across to the new platform. If an organization needs only specific features that will, for example, add new capabilities, modular PaaS solutions can integrate additional parts without replacing current infrastructure.

Delivering customized R&D workflows

Customized workflows are a key component within complete digital solutions, achieved through data-driven or decision-point approaches. Data-driven criteria are used to automate the movement of samples and associated data to the next assay in the chain, while taking a decision-point approach allows users to decide on next steps according to variable conditions. Both contribute to the construction of complete workflows.

Workflow-specific applications offered as part of complete digital solutions provide templates and incorporate industry best practice to ensure compliance with current regulatory guidelines. They operate on top of data management solutions such as ELN, laboratory information systems (LIMS) and scientific data management systems (SDMS) and help standardize workflow steps. With the ability to combine pre-configured, modular applications, these solutions are very effective at driving the development of workflows that meet the needs of individual laboratories. As a result, the latest digital solutions can support the full range of life science disciplines, from small molecule discovery and development through to genomics and biobanking.

Accommodating different data types and the potential for AI

One characteristic of pharmaceutical workflows is the wide variety of structured, unstructured and reference data they generate and use. Data management platforms integrate all types of data into a single system, associating unstructured (and therefore difficult to search) data with structured data for easier data mining and workflow analysis. Mining and cross-referencing data with other information in a workflow then makes it easy to link data from different sources for more informed decision making, while analysis and trending capabilities add a further dimension to process management.

Comprehensive integration into a single digital platform builds the ideal environment in which to apply artificial intelligence (AI) systems for more extensive data mining and analysis. Accessing vast data reserves in this way has the potential to rapidly deliver new insights, with the latest AI programs capable of analysing even unstructured biological data. Using AI to extract useful information from workflow data, for example, may pave the way for improved processes, reduced risk and enhanced R&D outcomes.

Cloud versus on-site

Although the latest digital solutions tend to be based on a cloud-first approach, most also offer on-site options. Cloud-based systems do, however, deliver a number of significant benefits in terms of improved performance, stability and scalability, and are often more affordable than on-site systems. Importantly they are designed to be flexible and extensible to easily accommodate evolving workflows and regulatory guidelines, with some even including system validation and Health Insurance Portability and Accountability Act (HIPAA) compliance. And because upgrades are delivered by the service provider, fewer internal resources are required to keep these services up to date.

For most organizations, security is a major consideration. With on-site systems, security measures are usually developed and implemented in-house, and include features to identify and mitigate risks in each facility, as well as ensure compliance with regulations across all servers and sharing functions. Cloud service providers such as Amazon Web Services (AWS) have security built in, including secure VPN access, firewalls, data backup and recovery, all handled with encryption. All of these security benefits reduce the burden on individual organizations.

Accelerating therapeutics development

One company looking to adopt an integrated approach to managing their R&D pipeline was Amgen, one of the world’s largest biotechnology companies. Having multiple complex molecular biology workflows, they needed an extensible digital ecosystem to bring these processes together. Thermo Fisher Scientific’s Platform for Science provided the ideal solution.

Amgen wanted to achieve end-to-end visibility across all processes in order to enhance decision-making in their biologics discovery and development programs. The ability to easily add and change capabilities in their processes and existing IT architecture was important, and they needed to automate workflows and integrate instrumentation. Marrying transactional data with reference data was also a priority.

These goals were achieved by bringing workflow and data management together on a single platform that enabled Amgen to join the dots between their full range of systems and tools (Figure 1). After implementing their new integrated platform, data could be shared across sites worldwide with information flowing seamlessly within the platform, streamlining workflows and working practices. Their new system facilitated the high-throughput generation of mutations of target biologics, with dashboards and graphical visualization of data workflows and any bottlenecks helping them to better manage their resources.

Figure 1

Mapping the digital platform against the whole process allowed users to follow data through the system. Not only did this level of connectedness benefit the Amgen’s individual research sites, it facilitated working with external partners. Colleagues and partners with different methods and workflows could combine and view data in a controlled manner that met their needs, and subsequently bring it back together in a way that ensured support for the overall goal of the company.  

Importantly, with such a vast pool of data associated with each workflow, the digital platform provided Amgen with the ability to look back at an individual sample and determine its history and all of the actions and users that had been involved – just like having a highly organized and easily searchable laboratory notebook. With such powerful audit trail functionality available at the click of a button, the company was not only able to comply with the latest guidelines around data integrity, but easily demonstrate this to auditors too.

Conclusion

The unprecedented pace of change in pharmaceutical R&D is putting pharma and biotech companies under pressure to manage enormous volumes of often new and increasingly complex data from many different sources. Connectivity is a pressing need and organizations are looking increasingly to the latest digital technologies, and especially cloud-based data management platforms, to create a comprehensive digital ecosystem for their organization. Flexible, extensible platforms that can evolve as needs continue to change are becoming the norm.

Integrated digital platforms not only improve efficiency by enabling instruments and devices to upload data directly to secure accounts, but also increase productivity by managing data for R&D pipelines, experiments and inventory logs, and by providing end-to-end visibility of processes. This transformative approach is enabling pharmaceutical organizations to overcome the constraints of disparate and non-communicative systems, providing a clear path to greater control, innovation and business success.  

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