A robotic liquid handler with chemical tubes is one example of a lab instrument that can be connected in an automated, cloud-based, data-logging workflow. Source: BIOVIA

Contract Research Organizations (CROs) across numerous industries are increasingly turning to the cloud for robust scientific collaboration workspaces that can increase efficiency, improve margins and enhance competitiveness. This is especially true of CROs providing drug discovery services such as medicinal chemistry, biological screening, drug metabolism/pharmacokinetics studies, process research and analytical R&D for biopharmas. Collaboration networks in the Life Sciences often combine numerous partners with diverse objectives involving single or multiple research projects that, in some cases, can tie up more than 50 percent of a commissioning organization’s IT budget. Non-cloud, Internet-based collaboration solutions such as email, SharePoint, VPN, Citrix and other data exchange mechanisms can result in security challenges, incompatible data formats and the need to prepare and curate files manually—jeopardizing data quality, lengthening project timelines, putting IP at risk and reducing productivity.

Contract Research Presents Numerous Challenges

A typical biopharma CRO network includes chemistry, screening and safety scientists at multiple pharmaceutical and biotechnology companies, government organizations, universities and even other CROs—many using their own project and data management platforms. A diverse and disconnected informatics environment creates project management, reporting and scientific data exchange challenges that must be addressed if the team is to collaborate effectively and meet its business goals. How do teams spin projects up and down quickly? How do partners access project-related reports and documents? How do they communicate and share experimental data? How do they manage different types of data and data formats such as chemical structure and quality data, calculated and measured physicochemical properties, biological assay data and pre-clinical data describing the characteristics of active pharmaceutical ingredients? When projects end, how is the data distributed among multiple partners and archived? How is IP managed properly?

The immensity of the data management and technology exchange challenge becomes apparent when one considers that a typical CRO network can process tens of thousands of compounds annually in the chemistry space, while generating hundreds of thousands of data points during bioassay testing.

With these challenges in mind, many CRO networks are turning to cloud-based solutions offering a scalable, secure, state-of-the-art informatics environment that also reduces the informatics footprint. Working in a cloud-based collaboration workspace provides a level of business agility and security that is not available with server-based, on-premises infrastructure. Operating in the cloud means organizations can get up and running in days rather than weeks or months with minimal IT involvement. Cloud systems are available anywhere, anytime—and organizations only pay for what they use.

Some scientists state that it can take up to two days per week to clean up and reformat scientific data received from collaborators. Cloud systems minimize time lost on data transformation and interpretation by ensuring that collaborative data complies with proprietary business rules established by sponsoring organizations. Cloud systems also support data annotation, providing critical context for other project team members using the data. Working with a dedicated cloud collaboration system, scientists can spend less time performing mundane data preparation and manipulation tasks and focus more closely on science.   

Finally, the cloud-based collaboration workspace protects each organization’s intellectual property (IP), controlling access and ensuring that each participant sees only the information they are authorized to access. The system protects IP by holding it in a secure neutral zone until ownership is clear. This is important because many collaboration models do not contractually resolve IP ownership until the project ends.

Cloud-based Collaboration Workspace Supports Workflow Automation

With contract research often bogged down by different types of data, dissimilar data formats and incompatible data transfer/sharing platforms across the partner ecosystem, the cloud offers a unifying virtual workspace where scientists, samples, robotic lab equipment, experimental data and analytical results are connected in a cohesive discovery workflow.

An interesting use case involves instrument data exchange with the cloud which allows automated, real-time data capture during drug metabolism/pharmacokinetics and other biological assay studies. The data exchange is performed by a local software agent acting as liaison between the instruments and the cloud. The connection is always initiated from the premises to the cloud for security reasons. In this workflow, chemists upload information to the cloud specifying the sample containers to be processed by robotic liquid handlers. Biologists access the information in the cloud and run the requested assays. Results generated by the assay readers are read and returned to the cloud where scientists analyze the data and select a smaller number of compounds for further screening. The compound management team then receives this new information from the cloud in a format readable by their liquid handlers. New containers are automatically prepared and new information sent to the cloud for the next round of screening as the optimization workflow progresses.

Automated, real-time data from liquid handling equipment and assay readers can help scientists in a cloud-based collaboration network make better and faster decisions about advancing drug discovery—all through the cloud. At the same time, networked scientists download instructions from the cloud in machine-readable formats consumable by the equipment (e.g., XML format representing the number of containers to be prepared, containers to be combined, quantities to be updated, etc.). The entire discovery workflow is made possible by scientifically-aware, on-premises (server-based) data processing tools along with the cloud solution’s open API. Members of the network with the appropriate permissions can access all real-time project information and track the workflow via the cloud.

In summary, connecting people, applications, equipment and data in a cloud-based collaboration workspace provides research organizations in the Life Sciences and other industries with significant business benefits—reducing errors resulting from the geographic translocation typically encountered in non-cloud research networks. Lab instruments like robotic liquid handlers and assay readers can be connected in an automated, cloud-based, data-logging workflow that speeds scientific decision making.  Exchanging scientific data and protocols securely and in real time improves agility, reduces costs and supports end-to-end collaboration dynamics.