The widespread use of Artificial Intelligence (AI) has propelled digital capabilities, leading to unprecedented scientific advancement. AI-powered lab enterprises are transforming vast data landscapes into innovative insights, intelligent automation allows businesses to achieve their full digital potential, and mobile technologies enable seamless remote interaction and collaboration that keeps teams connected.

This blog explores these transformative trends, redefining how labs work in 2024 and setting the stage for future research and development powered by AI-driven workflows.

Low-code/No-code LIMS platforms

Low-code/no-code platforms offer many advantages to organizations by streamlining the creation of digital solutions, substantially reducing the time required to build and deploy applications.

Components can be easily assembled into applications by simply dragging and dropping, speeding up the development process and allowing for rapid adjustments in response to market trends or customer needs for LIMS platforms. Since less manual coding is needed, organizations can lower the costs associated with application development, enabling users with minimal coding ability to build and customize applications.

These platforms often come with integrated tools designed to support compliance with industry standards and best practices, reducing the security risks associated with application development. Organizations should evaluate these tools case-by-case to decide the best fit for their specific needs.

Mobile access to lab technology

Lab functionality is breaking free from the constraints of physical locations, transforming our traditional approach to work. Smartphones and tablets offer direct connectivity to Laboratory Information Management Systems (LIMS), allowing for remote operation and monitoring of lab instrumentation, on-site mobile data collection and analysis, and immediate updates to Electronic Lab Notebooks (ELNs) from any location.

The portability of lab technology provides the flexibility to perform essential lab functions remotely, like reviewing and approving reports from any location, gathering health data from patients in their homes, overseeing continuous cell culture growth without the need for after-hours visits to the lab, and ensuring safety by reducing exposure to potentially hazardous substances or biological agents.

Advancements in mobile technologies and the Internet of Things (IoT) deliver real-time insights for many lab activities previously only possible onsite. For example, these innovations enable the continuous monitoring of air and water quality to aid in environmental safety and conservation efforts. Integrated tools like cameras, GPS, and QR/barcode scanners are improving on-site testing and sample tracking to ensure accurate data collection and improve the overall data gathering and review process.

Collaboration in the remote workplace

In an increasingly remote work environment, collaboration has become essential and challenging for organizations worldwide. Despite geographical distances, working together efficiently is crucial for fast-paced scientific innovation. Collaborative efforts between industry and academia are particularly significant, as they foster the exchange of knowledge, expertise, and innovative ideas, which drive research and development and practical applications. Such collaborations help academia understand real-world industry issues while giving industries access to the latest scientific discoveries.

The rise of digitalization and process automation, particularly cloud-based LIMS (Laboratory Information Management Systems), has made data more findable, accessible, interoperable, and reusable (FAIR), meeting key prerequisites for advanced analysis with AI and ML. These technological advances facilitate the efficient storage, retrieval, and interpretation of shared data, enhancing the ability to derive insights and make informed decisions.

For collaboration to be effective in a remote setting, employing the right digital tools is essential. Cloud computing and AI enable seamless information sharing among global teams. As we move into 2024, these technologies, alongside platforms supporting digital engagement and tools like Generative AI, are vital for unlocking global knowledge and resources, thereby accelerating scientific progress.

AI and data-powered lab enterprises

AI and data technologies are transforming laboratory enterprises, driving data-led decisions, and offering real-time insights and visualizations. With advanced algorithms analyzing large data sets, these AI-enhanced labs enhance research efficiency and resource management.

AI-driven tools accelerate drug discovery processes, forecast molecular interactions, and identify potential drug candidates faster. These advancements also promote enhanced collaboration through improved data visualization, making complex data more accessible and sharable among researchers.

The convergence of AI with data technologies redefines scientific research and healthcare, leading to a new era of innovation and discovery.

• Build and scale AI capabilities by raising AI competence in business teams to accelerate discovery, optimize trials and engage customers.
• Reinforce innovation areas in your organization with new technologies that drive market growth and energize development in new treatments.
• Drive toward digital excellence by scaling digital solutions, building the digital threads and KPIs needed to enable continuous innovation.

Source: 2024 Gartner CIO and Technology Executive Survey¹

Intelligent lab automation

Intelligent lab automation offers a future where lab equipment and processes are automated and made significantly smarter by AI, leading to a more efficient and innovative lab environment. This smart automation encompasses advanced robotics control, IoT features, and the ability to perform complex analyses.

Virtual assistants streamline training and operation, offering intuitive, voice-activated guidance for lab personnel. Standard lab procedures, along with the tracking of samples, can be efficiently automated through the use of smart robotics and application software. AI and ML-driven remote diagnostic tools and preventive maintenance protocols enable labs to take measures to reduce equipment downtime.

AI systems can adapt to new protocols and assays more easily than traditional automation. They can learn from new types of experiments and optimize the lab workflow. ML algorithms can be trained using role-based decision-making to recognize good results and to flag outliers or potential errors. This contributes to improved accuracy in quality control as unusual results can be automatically detected and reviewed.

With intelligent lab automation, data is not just collected; it is analyzed, sorted, and presented alongside recommended actions, offering unprecedented decision support and compliance. This shift paves the way for labs to run with optimized resource utilization and propels lab work into a future of innovation and streamlined efficiency.

Summary

By leveraging these trends and insights, organizations can adapt their strategies and operations to stay ahead in the rapidly evolving landscape of science and technology. Labs must update their digital infrastructure to support AI innovation, leading to smarter, more automated workflows that enhance lab efficiency. Ensuring the availability of high-quality data is vital for powering AI-driven analytics and fostering collaboration. Labs must move away from outdated LIMS and siloed data systems, investing instead in state-of-the-art LIMS that offer flexibility and are equipped with cutting-edge technologies. By embracing digital transformation and developing AI capabilities, labs will be positioned to drive continuous scientific progress and keep pace with the future of research and technology.

About Astrix

Astrix is the unrivaled market leader in creating & delivering innovative strategies, technology solutions, and people to the life science community. Through world-class people, process, and technology, Astrix works with clients to fundamentally improve business, scientific, and medical outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of fully integrated services designed to deliver value to clients across their organizations. To learn the latest about how Astrix is transforming the way science-based businesses succeed today, visit www.astrixinc.com.

References:

¹ Smith, J., “Infographic: 2024 Top Technology Investments and Objectives in Life Sciences”, Gartner, October 27, 2023.

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Research data from one or multiple lab data systems needs to be accessible for secondary analysis based on all information in a company. ZONTAL can continuously synchronize experiment data into a vendor neutral FAIR data layer without compromising integrity, audit trails, and retention policies. ELNs can also be decommissioned for a variety of reasons:

• Technological Obsolescence
• Vendor Discontinuation
• Mergers and Acquisitions
• Changing Business Needs

However, with ZONTAL the entire experiment information including the result data and links to other business systems remain accessible.
In this webinar, we will explore:

• A case study of a successful ELN decommissioning of +10 years of experiments
• A standardized approach to continuous consolidation of multiple LIMS systems
• A new way to deliver the interoperable Allotrope use case

The post On Demand Webinar – Laboratory Data Systems Consolidation appeared first on Astrix.

The post Partner Webinar With Scitara – Orchestrating Digital Transformation in the Scientific Laboratory appeared first on Astrix.

Scientific organizations that have recently undergone a merger, and oftentimes even those that have not, are frequently in the situation where different labs in different locations are using different LIMS technologies/solutions. This scenario serves to inhibit process efficiency, cross-organization data reporting, regulatory compliance, and can result in high IT demand.

Given the advanced capabilities of modern LIMS, and the competitive advantages gained through establishing digital continuity across the product lifecycle, there is a strong incentive for modern scientific organizations with disparate LIMS to harmonize their laboratory environment by integrating the multiple LIMS into a single system. In this blog, we will discuss best practices for a project of this nature.

Strategic Planning: Workflow Analysis

Migrating multiple LIMS into a single system is a significant challenge and should not be taken lightly. In order to accomplish a laboratory harmonization project in a way that creates significant business value for your organization, strategic planning is essential. A project of this magnitude is a fantastic opportunity to optimize your laboratory environment by aligning laboratory functional needs with the strategic needs of the business. Towards this end, the initial phase of the project should be a thorough workflow and business analysis.

In this phase of the project, meetings and interviews are conducted with key bench-level analysts across the multiple sites to develop a complete and accurate picture of the current-state workflows and systems. Additionally, meetings with the organization’s management team are conducted to flush out the goals and vison for the optimized future-state.

With current-state workflows in hand, the project team creates a model of the optimized future-state workflows. Once the future-state model is created, system functionality is constrained to only those functions bringing business benefit to the customer, and system requirements are extracted and documented.

Strategic Planning: Enterprise Architecture for the Lab

Integration requirements for LIMS projects involving multiple systems and sites include laboratory devices and instruments, as well as enterprise systems that need access to laboratory data. In order to create a fully-integrated laboratory environment, it is critical to design a laboratory informatics architecture that is aligned with business goals, along with a strategic roadmap to deployment.

Enterprise Architecture for the lab should follow a best practice Value Engineering methodology to create a strategy that maximizes value delivery and aligns business requirements, technology and people in your organization. This process begins with interviews with the IT personnel to determine the current-state architecture of the technology (applications, information systems, instruments, etc.) and how it supports the objectives of your business. This current-state assessment looks at functions and security, user-defined and core system entities, workflow events, customization, reports, and instrument and system interfaces.

Next, a future state architecture should be designed that is aligned with business goals utilizing the future-state user and business requirements that were developed in the Business Process Analysis stage discussed earlier. The end result of this process is a practical roadmap to implement the overall strategic vision for the laboratory informatics ecosystem architecture. In some instances, transitional architectures will need to be designed to provide business value in early stages, without having to wait until the full vision is implemented down the road.

LIMS Selection, Implementation and Integration

Whether you choose to harmonize and standardize laboratory operations on a new LIMS or a legacy system, the technical, business and user requirements from the strategic planning phase are utilized to guide the technology selection, implementation and integration process.

When integrating the multiple LIMS into a single system, there are a number of best practice recommendations that should be considered during the implementation phase:

Iterate Your Implementation. For a project of this size and complexity, it is wise to implement the future-state vision in stages, with the first iteration being the minimum viable solution to go into production with. Just because you can do something does not mean it is cost or time effective to do so. Once this minimum solution is operational, users can provide feedback as to the pain points that they are experiencing, and then decisions can be made as to whether it makes sense from a financial and time perspective to customize the system to provide further automation.

Have a Data Migration and Management Strategy. Data migration for a project of this nature can be a significant challenge. Much or all of the static (and sometimes dynamic) legacy data will need to be extracted, translated and loaded into a new location. Depending on the value of the data in question, it can be archived, transferred to a new repository where it can be consulted and used to create reports, or fully migrated into the new system to be actively used in the new LIMS.

Sometimes, data will be left in legacy systems (e.g., data for a clinical trial that is currently in process) that will be decommissioned over time. Questions to ask that help to determine your data migration/management strategy include: What is the best location to store all the different types of legacy data? How are we going to get the critical data out of the legacy system and into the new LIMS? How are we going to harmonize data across multiple sites?

Data management/migration for any informatics implementation almost always turns out to be a much bigger task than one might have imagined, but this is especially true when trying to migrate several legacy systems to a new LIMS. It is therefore important to formulate a Master Data Management/Migration Strategy at the beginning of the project in order to avoid significant time and cost overruns as the project proceeds, as well as minimal disruption to your lab operations during migration activities. Static data should be migrated as early as possible, while dynamic data migration should be done last to make sure the new system contains the most up-to-date data.

Once the data has been migrated, regulatory requirements mandate that it must be validated to make sure it is accurate and has been transferred properly. Even if your company is not in a regulated industry, validating migrated data is important to make sure your data is sound.

Think about Security Early. It is important to fully detail the desired user roles and the permissions associated with each role before you build the system. As with managing the static data aspects of the project, designing and implementing user permission layers can be a bigger task than expected. Waiting until the end of the project to implement a security framework is a recipe for cost and time overruns.

Don’t Go Overboard with Instrument Integration. While instrument integration can have wide-ranging benefits for your laboratory, some instrument integrations can be challenging and cause project delays. It is important to have a master instrument integration plan that details what, why and when instruments are to be integrated and also identifies which instruments are worth integrating. Don’t let your project get bogged down by trying to integrate instruments that do not provide significant ROI.

Designate Appropriate Internal and External Resources for Project Implementation. A variety of different skill sets are necessary to accomplish the strategic planning, configuration, integration and validation required to successfully execute a LIMS migration project. Unforeseen project challenges can also require outside specialists and/or subject matter experts to move the project forward.

Staffing requirements for a LIMS project of this nature can be extremely complex. Several key roles on the project team will likely need to be staffed by the LIMS vendor and/or an external consultant. Significant collaboration between external and internal resources is required for the success of any LIMS migration project.

The bottom line is that it is crucial for organizations to have a highly competent and skilled project team in place, along with a good project communication plan, to ensure project success. Make sure you plan on designated internal resources spending significant time on the project, which means they will be less available for their day jobs.

Avoid Customizations Unless They Are Really Required: Extensive customizations to satisfy requirements can dramatically extended project duration and make your system difficult to maintain, validate and upgrade. Best practice is to examine and understand all the configurable out-of-the-box features of the new LIMS, and utilize as many of these as you can in your implementation to meet your requirements and simplify implementation.

Change Management. The new system will be a failure if no one uses it. Users must be consulted and involved in the requirements development process in order to ensure that they will accept and use a new and unfamiliar system. In addition, change management activities should involve a comprehensive training program for users of the new system.

Conclusion

Scientific organizations that have recently undergone a merger can benefit from harmonizing their laboratory environment by integrating multiple legacy LIMS into a single system. Benefits of doing so include elimination of data siloes, improved process efficiency, simplified IT environment, and enhanced innovation and regulatory compliance. The complexity of such a project, however, makes having a highly competent and skilled project team a necessity.

If you would like to have an initial, no obligations consultation with an Astrix informatics expert to discuss your LIMS migration project or your laboratory informatics strategy, please feel free to contact us.

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