As data volumes and expectations for fast scientific discovery continue to increase, laboratory-based research organizations can no longer rely on a siloed approach to data management. To remain competitive, scientific organizations need to connect all their data, from discovery through manufacturing, in a unified informatics platform.
Today, data management is on everyone’s minds. With modern analytical instrumentation and advanced experimentation, scientists are producing larger and larger volumes of data (1). Across an organization, those data volumes multiply, often resulting in much potential value being wasted.
The pace of business, meanwhile, keeps increasing, even for science-based organizations. The lightning-fast development of the COVID-19 vaccines, for example, created new expectations about the speed of innovation.
Faced with these challenges, leaders at competitive scientific organizations seek modern informatics solutions, to get more from their data. As they do, they may consider tools designed for various aspects of their business, such as an electronic laboratory notebook (ELN), a laboratory execution system (LES), or a scientific data management system (SDMS). Increasingly, however, these leaders realize that deploying isolated software applications is short sighted. A more strategic approach is to implement a single enterprise laboratory informatics platform that integrates and leverages data across the entire organization—from research and development (R&D) to manufacturing.
Unlike siloed software, a single enterprise laboratory informatics platform creates a fully connected ecosystem that enables increased efficiency, greater collaboration, faster innovation, and, ultimately, better profitability. It also creates the necessary infrastructure to fully take advantage of evolving artificial intelligence (AI) capabilities—for today and for the future.
With a comprehensive informatics solution, organizations can fully harness data, regardless of where it is produced in the organization. In this unified data repository, data become integrated and interoperable, to facilitate workflows and dataflows, with flexibility and scalability to meet evolving business needs.
Taking the example of a biopharmaceutical company, an enterprise laboratory informatics platform, based on an advanced laboratory information management system (LIMS), manages scientific data from drug discovery, all aspects of drug development, manufacturing, quality control (QC), and even from the supply chain. The platform brings together data from all the systems used in those operations, including software that directly controls analytical instruments, an ELN that captures, stores, and shares experimental data, an LES that enforces compliance with regulations, standards, and internal standard operating procedures, and an SDMS that ensures compliance and data integrity. It can also connect with sample management tools and consumables management systems for supply chain oversight, as well as business platforms such as an enterprise resource planning platform. This unified enterprise laboratory informatics can also readily integrate other third-party software.
This platform then uses powerful analytics—employing machine learning (ML) and AI—to extract, analyze, and visualize the scientific data from multiple sources, to provide insights and inform decision-making.
Implementing a single enterprise laboratory informatics platform enables a scientific organization to exploit its data for maximum advantage, to increase efficiency, facilitate collaboration, and enhance data integrity and security.
An enterprise laboratory informatics platform enables scientists to quickly find data from past experiments. If a scientist needs to formulate a new compound, for example, she can quickly bring up results from past formulation studies of similar compounds—even if she was not aware of those experiments or if they were carried out in another division or on another continent. The system can also access public information from scientific literature.
This ease in finding and using past work is enhanced by leveraging semantic search capabilities. Unlike lexical search, which only searches for exact terms and their close synonyms, semantic search uses natural language processing to improve the accuracy of results by considering intent and the contextual meaning of the words used in a query.
With the capability to find past research results, researchers like this formulation scientist avoid repeating that work, saving time and more quickly identifying the right combination of ingredients. Some LabVantage customers, for example, have experienced a 30% reduction in repeated experiments by implementing a unified platform powered by semantic search.
By reducing repeat work, scientists throughout an organization can shift time and effort from low-value work to high-value science, boosting work satisfaction and productivity at the same time.
An enterprise laboratory informatics platform also facilitates cross-functional data exchange and collaboration. In the example discussed above, the formulation scientist can get feedback in real time from colleagues in different roles or departments, who may have useful experience about the stability or manufacturability of different formulations. As companies continue to share data and insights, new opportunities for multidisciplinary collaboration will emerge—within the company and with external partners.
An enterprise laboratory informatics platform also increases data integrity and data security and facilitates regulatory compliance.
With a modern LIMS, access is secure, and every action is traceable, whether the action is pulling materials into an experiment, approving results, viewing data, or printing reports. Such traceability facilitates compliance with regulations or standards relevant to any industry, such as 21 CFR part 11, export administration regulations, or ISO 17025:2017.
The connected nature of an end-to-end system also facilitates the preparation of regulatory documentation and compliance audits. All relevant data and reports can be brought together quickly and easily, from all departments, say from drug characterization studies through manufacturing QC results.
An integrated LIMS also greatly increases cybersecurity compared to isolated data management systems, where weaknesses may be overlooked and not updated in a timely fashion. With a cloud-based software-as-a-service (SaaS) LIMS, software updates are rolled out regularly, and new cyber threats can be addressed quickly. With a single platform, that protection is deployed throughout all the connected software, simplifying management and avoiding risky gaps.
Leading LIMS providers also follow best-in-class practices, such as “security by default,” where the default settings of a software product are the most secure possible, with constant monitoring for potential vulnerabilities and third-party security testing and verification (2).
When an organization implements an enterprise laboratory informatics platform, the benefits are experienced at all levels, from the individual scientist to the business.
Individual scientists gain from a single platform in many ways. Some of those benefits were described above, in the example of a formulation scientist who saves time and effort by quickly finding past experiments and collaborating with colleagues having complementary knowledge.
Individuals can also benefit from the way a unified platform enables easy hand-off between departments. An R&D scientist who develops and validates an analytical method for a new product can easily migrate that method, along with the supporting data, to the QC team overseeing manufacturing quality. Although staff can access the information through different interfaces—the R&D scientist may track the original experiments in the ELN, whereas QC personnel may pull it into a protocol in the LES—they all access the same data, without a need to copy or transfer information. Data does not get lost, and the transfer process is smooth and simple, reducing headaches and stress for staff.
Managers also benefit from an enterprise laboratory informatics platform. With the system’s AI-powered analytics, managers can assess how their laboratories are operating and how resources are being used. For example, data visualization may enable them to see that an instrument is being overutilized, and that adding another instrument (or possibly organizing work in a slightly different way) would avoid having scientists wait for instrument availability. This type of analysis also helps the organization make strategic, informed investments.
A single platform also makes it easy to get updates into the status of a wide range of work. If a laboratory manager needs to report to the director about the status of a particular project, he can quickly log into the system, pull up the relevant dashboard, and see the recent progression of experiments.
At a business level, visualization tools enable faster gap analysis, to see where more resources are needed or where inefficient processes can be improved for cost savings. More broadly, the improved coordination and productivity resulting from an integrated system make it possible to get products to market faster, making the company more profitable.
There is one more critical benefit to implementing an integrated informatics solution: It makes it possible to effectively harness the AI of today, while also readying an organization for all the opportunities of evolving AI capabilities. Advanced AI algorithms and ML will continue to improve the precision and speed of scientific discovery (3).
With separate IT systems for different business units and departments, data sits in discrete, siloed compartments, unstructured and largely inaccessible. An integrated LIMS system, in contrast, leverages advanced analytical tools, such as ML and AI, to process both structured and unstructured data. This capability enables the system to identify patterns and outliers, extract meaningful insights, and present them through dashboards, reports, and advanced data visualization tools, ultimately facilitating better-informed and faster decision making.
In an enterprise laboratory informatics platform, those analytics and insights are not limited to unit operations or business units. The algorithms can draw on data from across operations to reveal new opportunities and connections that siloed informatics cannot access.
Going forward, the power of AI may provide insights and predictive power that we can only begin to imagine today. Perhaps it will make a connection to a macro trend that points to a business need. For example, it could detect reports of bacteria growth within a certain area of the country and suggest that new testing needs be done on a company’s products. Or perhaps it will consider the impact of an event like intense wildfires to propose new particulate testing for agricultural products or incoming materials.
Powerful AI can find patterns, connections, and insights that humans cannot identify as quickly. The concerns or opportunities that arise might affect just one aspect of the business, or they might affect many. By having a system in place that connects data from R&D through manufacturing and the supply chain, organizations will have the infrastructure in place to take advantage of whatever new AI capabilities become available.
For scientific organizations to compete, they need effective data management. The volume of data, the complexity of decision-making, and expectations for speed and agility all keep increasing. In this demanding scenario, a siloed approach to laboratory data management is no longer sufficient. A competitive strategy requires a single enterprise laboratory informatics platform that connects data from R&D through manufacturing, to manage projects from ideation to commercialization. With a fully connected platform, scientific organizations can take full advantage of all their data, for greater productivity, increased collaboration, and enhanced data integrity. With a unified system, companies can exploit current AI tools to deliver powerful insights for informed decision making, while creating the necessary infrastructure to harness the AI tools of the future. A single enterprise laboratory informatics platform empowers scientific organizations for success—today and tomorrow.
(1) Milman, B. L.; Zhurkovich, I. K. Big Data in Modern Chemical Analysis. J. Anal. Chem. 2020, 75, 443–452. https://doi.org/10.1134/S1061934820020124
(2) U.S. Cybersecurity and Infrastructure Security Agency, Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Security-by-Design and -Default, April 13, 2023. https://www.cisa.gov/sites/default/files/2023-04/principles_approaches_for_security-by-design-default_508_0.pdf (accessed 2024-08-09).
(3) Rial, R. C. AI in Analytical Chemistry: Advancements, Challenges, and Future Directions. Talanta, 2024, 125949. https://doi.org/10.1016/j.talanta.2024.125949
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