Every industry has embraced the idea that digital technologies need to be fully integrated in the business processes to ensure successful competition. In the era of Industry 4.0, your organization’s data (digital assets) are now recognized as your most valuable assets. Within the life sciences, however, there are many constraints of the scientific operating environment, such as regulatory oversight, evolving digital technologies, the data integrity demanded by decision makers and regulators, and the remaining legacy technologies. All of these constraints, and more, have made it difficult for the life sciences industries to leverage the automation and efficiency that would come with modernizing their data lineages.
An organization’s data inputs could include the manufacturing execution system (MES), a laboratory information management system (LIMS), an electronic laboratory notebook (ELN), the enterprise resource planning (ERP) system, the quality management system (QMS), multiple instrument classes, and many others. So how do you optimize your data and information sources and uses when they are spread across multiple, often proprietary, platforms, databases, systems, applications, workflows, devices, and networks—on premises, in the cloud, or both?
The answer is Integration Platform as a Service (IPaaS). The Gartner Glossary defines this offering as follows:
“Integration Platform as a Service (iPaaS) is a suite of cloud services enabling development, execution and governance of integration flows connecting any combination of on premises and cloud-based processes, services, applications and data within individual or across multiple organizations.”
At its core, iPaaS provides the interfacing mechanisms, tools, workflows, techniques, and functions to create an integration layer for your data sources across your enterprise. This is most easily achieved when your underlying data has been standardized using findable, accessible, interoperable, and reusable (FAIR) data principles. FAIR data standardization in a scheme specific to your industry like those of the Allotrope Foundation or analytical information markup language (AnIML) should be adopted before iPaaS implementation to make the integrations easier to achieve. The iPaaS software providers are not in the business of troubleshooting each connection.
Although it is possible to build your own integrated scientific platform using existing tools (some of which you may already have, such as the Microsoft Power Platform), it is often considered more efficient and economical to take advantage of the myriad third-party iPaaS suppliers that have recently cropped up. This frees your in-house developers (if you have them) to work on other software projects for your business. Several iPaaS providers specialize in the life sciences, such as Scitara, Ganymede, and Sapio Sciences.
If you have been in scientific operations for some time, you will know that the iPaaS concept is not a new one. Its latest iteration focuses on the advantages of a cloud-based architecture. Some of the vendor-stated pros of this approach include the following:
A fictionalized global contract manufacturing organization (CMO) for multiple pharmaceutical organizations deals with regulatory bodies such as the U.S. Food and Drug Administration (FDA), Health Canada, and the European Medicines Agency (EMA). Additionally, the CMO has a multitude of products; scores of specifications, analyses, and Certificates of Analysis (CoAs); and hundreds of analytes depending on product and/or customer needs. Its ERP system manages most of the material, product, and distribution life cycles for events, but not all of them. There are multiple LIMS; hundreds of instrument systems; an MES; a QMS; and document management, training, and inventory concerns, among other pieces of the puzzle.
So, iPaaS X is selected by this life sciences CMO. This product will provide integration across all the aforementioned systems, as advertised. During the vendor demonstration, the CMO representatives were convinced the product could simplify their data footprint. Now come the challenges.
Let’s take a closer look at the instrumentation systems, specifically. These typically provide interim, calculated, and final data and results. Let’s narrow the view down to just high-performance liquid chromatography (HPLC) instruments to keep it even simpler. However, there are multiple brands, components, and software versions that exist today at this CMO. To make matters more challenging for the iPaaS implementation, these systems are shared across multiple pharmaceutical companies’ product lines, so methods, testing, and result reports vary accordingly. For example, think about the reported significant figures, e.g., 3 vs. 5, required by each product or governing agency for a purity test. This is why the data standardization piece is critical to iPaaS success.
A data transformation solution is large and complex, but feasible to accomplish using iPaaS X. As with any product, tool, application, or database, there must be a strategy and approach for the solution set. It will take effort to configure each unique combination of instruments, methods, analytes, output, and reporting integrations. Detailed plans, prioritizations, budgets, schedules, and risk management criteria are excellent starting points for this project.
However, this CMO has never undertaken a project of this magnitude before. The staff aren’t sure which data standardization scheme would be easiest to adopt or what systems should be targeted for integration and in what order to bring them the fastest return on investment. This is where the industry expertise of CSols can be leveraged to ensure success. If you find yourselves unsure of where or how to begin with platform integration, as this life sciences CMO has, our team of scientific advisors can help you choose a data standardization scheme, define your platform integration needs, and guide your selection of a product aligned to your business model, platforms, risk tolerance, and budget.
iPaaS facilitates the integration of the multitude of data sources required to improve decision making. Configurable tools provided with the iPaaS software enable a self-service approach to integration and the possibilities of data analysis.
The data and technology infrastructure that can be achieved with iPaaS creates digital processes and generates analytics that improve your organization’s quality and speed. Whether you choose an off-the-shelf option or build a platform yourself, the first step should be properly structured and integrated FAIR data. Data standardization will enable workflow automation and the ability to derive cutting-edge insights. With a more accessible data lineage, you can take advantage of artificial intelligence (AI) to inform your design of experiments (DoE) and molecule optimization, which may reduce the time to drug candidate identification. Increasingly accurate digital twins (in silico analytical models) can improve predictions and validation decisions with respect to pharmacokinetics, pharmacodynamics, efficacy, and toxicology during drug candidate selection. Biotechnological and analytical advances (such as lab-on-a-chip and bioluminescent imaging) are giving life sciences companies the opportunity to create digital platforms that make rapid progress in drug development, reduce the number of validations needed, and optimize the available technology while leaving room for future advancements.
What could you do with an integrated platform? If you’d like to discuss your options, reach out to us.