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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The scientific workplace is evolving with an increasing focus on collaboration, inclusivity, and interdisciplinary research. Scientists from different fields and specialties are coming together to solve complex problems, necessitating a new type of lab that promotes teamwork and idea-sharing. This has led to the rise of open lab spaces with flexible, modular designs that can quickly be adjusted to meet the constantly changing needs of science.

The post White Paper – DIGITAL BY DESIGN: Next Generation Workplaces For The Lab Of The Future appeared first on Astrix.

LIMS implementations usually demand a substantial investment of time, money and resources, typically costing hundreds of thousands to millions of dollars and requiring hundreds of person days to accomplish. Failure of a LIMS project can be a huge waste of time and resources, and a financial disaster for the organization involved. As such, it is critical to get a LIMS implementation right the first time in order to preserve your return on investment.

One important facet of any successful LIMS implementation and/or migration is the design and configuration of master data. In our experience, many companies involved in LIMS implementations tend to focus on software testing and configuration and put off dealing with master data until the end of the project. This is a huge mistake. Master data design and configuration is typically a much bigger job than anticipated and has multimillion-dollar impacts down the road on things like operational efficiency, time to market and LIMS return on investment (ROI).

In an effort to help organizations understand the importance and implications of master data and avoid project delays and cost overruns, we’ve put together a series of articles to highlight LIMS master data best practices. Some of the topics that will be covered in future articles in this series include:

• Master data configuration pitfalls
• Extrapolation of master data from your current paper records
• Master data naming conventions
• Strategies for handling master data in mergers and acquisitions (M&As)
• Designing your master data for maintainability and scalability
• Evolution of master data and change management
• Master data quality control
• Master data harmonization

In this part 1 article of our LIMS master data series, we’ll define master data and discuss the importance of developing a master data plan for your LIMS implementation. Without further ado, let’s dive into our LIMS master data series!

What is Master Data?

Master data can be thought of as the information that needs to be in place in the LIMS for users to be able to use the system as intended. Master data is core, top-level, non-transactional, static data that will be stored in disparate systems and shared across the enterprise, and possibly even beyond to external partners. As master data establishes a standard definition for business-critical data, its accuracy is very important, because it collectively represents a common point of reference and “single source of truth” for your organization. As such, everyone across the organization must agree on master data definitions, standards, accuracy, and authority. ​

Within most LIMS applications, there are two types of data that come into play – static (defined) and dynamic (transactional data). Dynamic data is the data users enter into the system as part of their daily activities such as test results, samples, batches or lots of a product. The master data is typically the static data that defines the structure of the system.

Master data and dynamic data are connected in the sense that the only way that dynamic data can be created is if master data already exists in the system. For example, in order to record a sample of a product for testing (transactional data) in a LIMS, the product name (master data) must exist in the LIMS so that the sample can be associated with a particular product in the system.

In most LIMS applications, various templates provide the ability to house the master data as lists/tables of values that will be used throughout the system. Master data typically includes core data entities like products, materials, specifications, sample types, analyses, lists, locations, reagents, instruments, environmental monitoring schedules, stability protocol templates and users. That said, universal specifications of master data items are not possible, as different laboratory types and/or LIMS will typically have different objects/entities identified as the master data.

Master data is foundational to business success. Even minor issues with master data can cause significant operational problems, and these problems will only be magnified as the organization scales, or reintroduced anytime new products or facilities are implemented. In order to avoid project delays and cost overruns for a LIMS implementation, it is critical to design and configure the master data properly. Towards this end, every LIMS implementation project should include a comprehensive Master Data Plan to ensure success.

Creating a Master Data Plan

In order to ensure a successful LIMS implementation, it is important to create a well thought out Master Data Plan that includes collecting all the master data that needs to be entered, deciding on a testing strategy to verify that the data has been entered accurately, creating a proper naming convention for your master data, and having an appropriate amount of time scheduled for entering the data into the system and testing it.

A Master Data Plan is a formal document that identifies the following:

• The rational for different aspects of the plan (e.g., why you have a specific naming convention)
• List of the organization’s master data that needs to be put into the system
• Schedule for when specific tasks need to be done
• The place(s) where the master data is created
• The people who will be doing the work of entering and testing the data. Note that these people need to have appropriate training for the job.
• How data is transferred into the system (e.g., the data migration plan)

One of the most important aspects of the Master Data Plan is determining what data needs to go into the system. This will involve scheduling an assessment of your data to determine what needs to be classified as master data, and also the master data entities in the LIMS being implemented to know which ones you will use and how. Note that this assessment may be utilized as an opportunity for you to do some housecleaning on your data. For example, you may decide not to add in master data for any test older than 5 years.

Another important feature of the Plan should be deciding on a naming convention. Here, it is important to get agreement on master data naming conventions amongst your user base so that they will be able to easily search for the data they need. Additionally, in organizations with multiple sites, using naming conventions that allow users to find their site-specific master data is crucial.

In a regulated environment, testing and documentation of testing may need to be included as part of your validation package. Towards this end, it is important that the person who tests the master data be different than the person who creates it. The person who does the testing must also have an understanding of the data they are testing and be trained in both the testing procedure and how the test results need to be documented. In addition, the Master Data Plan should document the procedure for updating master data in the system when necessary.

Conclusion

Your organization’s master data should serve to reduce the cost and time to integrate new facilities and enhance your organization’s flexibility to comply with regulations or enter new markets successfully. Over time, the master data contained in your LIMS will likely expand as your business expands with new products, facilities and regulatory bodies. Efficient master data management (MDM) will thus become critical to your operations. Be sure to tune in for the remaining parts of our master data series, where we will discuss the important best practices necessary to ensure your master data is designed and configured to deliver maximum business value for your organization.

Astrix is a laboratory informatics consulting firm that has been serving the scientific community since 1995. Our experienced professionals help implement innovative solutions that allow organizations to turn data into knowledge, increase organizational efficiency, improve quality and facilitate regulatory compliance. If you have any questions about our service offerings, or if you would like have an initial, no obligations consultation with an Astrix informatics expert to discuss your master data strategy or LIMS implementation project, don’t hesitate to contact us.

The post LIMS Master Data Best Practices Part 1: Defining the Terms appeared first on Astrix.

Many organizations combine numerous partners in diverse ways across multiple research projects. Even in simpler models, any collaboration with an external partner is typically not static, but evolves over time. Therefore, sponsoring organizations are often changing the business processes around the collaboration frequently and rapidly.

While external collaboration can provide many benefits including improved flexibility, enhanced innovation, and reduced time-to-market, externalized R&D activity introduces unique data and IT challenges. Some of these include:

• Synchronization of partner and in-house data across different transactional systems
• Maintaining secure and appropriately partitioned data
• Harmonizing master data to facilitate high quality data flow
• Developing appropriate long-term plans for data management, including potential data repatriation in an efficient manner
• Protecting intellectual property (IP) and managing joint IP

These challenges can result in additional costs and be potential limitations on the benefits of external collaborations. At the least, they introduce risks for sponsoring organizations. All too often, unfortunately, these data and information management aspects of a collaboration are not fully considered until problems arise. In this blog, we discuss key characteristics of informatics systems for collaborations, along with best practices for implementing a collaboration platform.

Approaches to R&D Collaboration Data Management

Nearly all R&D informatics systems are designed and implemented only to meet internal R&D requirements. Also, organic growth of internal R&D activities often leads to a tangled web of processes and systems with significant assumptions incorporated into the ecosystem. These latent aspects of systems frequently become impactful when considering the flow of data outside of the R&D organization, and how to open internal systems and/or their data to external collaborators. Some examples of system characteristics important in collaborative data flows are:

• User identity and access management processes and technology
• Data access control models
• Processes that require multiple systems with human-only integrations (“sneakernets”)

Sometimes these limitations make it infeasible to use the internal systems and processes with an external collaborator. Although it may seem more efficient to design our systems with external collaborations in mind, the reality of delivering informatics capabilities on budget and in time almost always means this does not happen. When faced with supporting external collaborations, this leaves the following choices:

• Use the collaborator’s system. If the collaborator is in the business of collaborations, they are potentially more likely to have systems that would meet the challenges above.
• Transfer data in email attachments. This lowest common denominator approach and unfortunately tends to be the status quo.
• Implement a new informatics capability.

There are important sub-aspects to the implementation of a new capability. First is the relationship to the existing system(s). If the current system is meeting requirements and is only insufficient for use in a collaboration, then considering how the system might be extended is an appropriate course of action. If the current system is lacking, or there’s a likelihood of long-term multiple collaborations, then a strategic assessment with the development of a roadmap to a solution architecture that meets all needs is essential.

If either a significant extension of an existing system or a new system is needed, then a cloud-first solution architecture should be considered. Cloud-first systems have several distinct qualities that make them a logical choice to meet the needs for R&D collaboration data management. Specifically, these qualities are:

• Configurable by intent
• Based on a tenant model for data and configuration
• Built for automated deployment

Key Characteristics of a Cloud Collaboration Platform

Some important characteristics of potential candidates for a cloud-based R&D collaboration data management solution are:

Configuration. An ideal platform is highly configurable, allowing organizations to define sites, projects and user roles and, along with the access permissions for each. The user authorization mechanism should be able to incorporate company-specific identity and directory systems for ease of use by scientists, rather than having separate identify and password management for the collaboration system. The platform should also support a range of core R&D capabilities, potentially including some of the functionality of:

• A flexible, multi-disciplinary ELN
• A portal that allows scientists for sharing reports, protocols, and other documents
• Data capture, analysis and visualization capabilities

Deployment. An effective cloud-based platform allows quick creation of separate collaboration environments for use with specific partners. Each environment should represent a secure data  partition. Data from an environment should be extractable and be able to be merged into other environments.

Security. Cloud providers should have ISO accreditation for their systems, technology, processes, and data centers. Data should be encrypted at rest and during transit using well-defined current best practice encryption techniques. The collaboration platform data architecture should have strong isolation across tenants and include logging of all system access and use.

Integration. The cloud platform should have a complete and robust programming interface (API) for integration with the internal systems of either organization in the collaboration. The platform must support bi-directional integration and data syncing between on-premise systems and cloud applications.

Best Practices for Implementing a Cloud Collaboration Platform

There are several best practices that should be followed to successfully implement an effective cloud collaboration platform. These include:

Strategic Planning. One of the most important steps in successfully implementing a cloud collaboration platform is the planning necessary to ensure project success. Towards this end, the first steps in the project should include a thorough workflow and business analysis in order to develop the optimized future-state requirements that guide the technology selection process. In addition, an end-state solution architecture should be developed, along with a strategic roadmap to deployment. Good strategic planning helps ensure the deployment effectively and efficiently meets business and technical needs.

Change Management. It is important to carefully consider the cultural impact, employee training, and new policies that necessary to ensure success of the new collaborative environment. Since collaborative R&D informatics systems by definition involve employees of multiple organizations, attention to change management for these systems is of paramount importance to success.

Efficient Testing. Bandwidth requirements for cloud computing are significant, and load and volume testing are important to ensure that system performs acceptably. Waiting until late in the project to discover that your system is not capable of handling the data transport requirements causes unnecessary scrambling to meet implementation goals.

Effective Validation. As some vendors claim prevalidation for their cloud-based software, it is important to understand exactly the scope of the Install/Operational/Performance Qualifications this covers. Compliance requirements mandate validation in the user’s environment, so prevalidation does not suffice to fully satisfy regulations. Working with the vendor to clearly establish individual and joint responsibilities for validation prevents unnecessary duplication and establishes an overall credible approach.

A Detailed SLA. Working with the vendor to create a detailed SLA is one of the most important things you can do to ensure a successful implementation. Without a well-written SLA, your organization could be in for many unpleasant surprises and additional expenses down the road. In addition to system change management processes and requirements to maintain compliance, an important and often overlooked aspect of the SLA is data storage, including controls of underlying data replication related to availability and disaster recovery.

Conclusion

In today’s increasingly collaborative R&D landscape, creating and managing informatics systems to help scientists handle, analyze and share information is critical for organizations to enhance innovation and remain competitive. Cloud- based collaborative platforms can provide a secure, scalable and flexible approach for handling the wide array of data types, sources and partnerships which are involved in modern collaborative research. These systems allow organizations to spin up robust collaborative environments easily with minimal IT support.

When implemented properly, cloud-based research informatics systems as a complement to R&D collaborations can provide important benefits to your organization:

• Effective use of data produced from the collaboration
• Increased scientist productivity
• Enhanced organizational flexibility and agility
• Reduced IT costs per user

There are attractive benefits to a cloud-based collaboration research informatics system, but implementation of the platform can be a difficult endeavor that requires much skill and planning to execute successfully. The project team should follow a proven, comprehensive methodology in order to ensure that the implementation provides significant business value for your organization.

Astrix Technology Group has over two decades of experience in the laboratory informatics domain. Our professionals bring together the technical, strategic and content knowledge necessary to help you efficiently select, configure, implement and validate a cloud collaboration platform that best meets your needs and keeps your total cost of ownership low. Whether your deployment utilizes public, private, or a hybrid cloud architecture, our experienced and skilled professionals can make the process of implementing or migrating to a cloud collaboration platform far more cost effective and efficient. Contact us today for more information on leveraging the cloud to improve agility, reduce cost and advance collaboration when working on new scientific discoveries and technological innovation.

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Because of their potential for impacting product quality, laboratory instruments are key targets of FDA inspections. During an inspection, the FDA will expect to see definitive evidence that instrument qualification schedules effectively control your manufacturing and testing processes. Lack of (or insufficient) qualification procedures and/or documentation are in fact frequently cited deviations in FDA inspectional observations and warning letters. In order to ensure your organization is compliant with regulations, let’s explore the relevant details regarding analytical instrument qualification and documentation.

Types of Instruments

The term “instrument” in this blog refers to any apparatus, equipment, instrument or instrument system used for analyses. The USP General Chapter <1058> Analytical Instrument Qualification classifies instruments into three categories to manage risk in the instrument qualification process:

Group A: Standard laboratory apparatus with no measurement capability or usual requirement for calibration (e.g., evaporators, magnetic stirrers, vortex mixers, centrifuges, etc.). Proper function of instruments in this group can be determined through observation, and thus no formal qualification activities are needed for this group.

Group B: Instruments providing measured values as well as equipment controlling physical parameters (such as temperature, pressure, or flow) that need calibration. Examples of instruments in this group are balances, melting point apparatus, light microscopes, pH meters, variable pipets, refractometers, thermometers, titrators, and viscometers. Examples of equipment in this group are muffle furnaces, ovens, refrigerator-freezers, water baths, pumps, and dilutors. Often, Group B instruments will only require calibration, maintenance or performance checks to verify proper function. The extent of activities necessary may depend on the criticality of the instrument for ensuring product quality.

Group C: Computerized laboratory systems that typically consist of an analytical instrument that is controlled by a separate workstation running instrument control and data acquisition, and processing software. Group C instruments will require proper calibration protocols, often including software validation, to ensure their proper functioning.

Examples of instruments in this group include the following:

• atomic absorption spectrometers
• differential scanning calorimeters
• dissolution apparatus
• electron microscopes
• flame absorption spectrometers
• high-pressure liquid chromatographs
• mass spectrometers
• microplate readers
• thermal gravimetric analyzers
• X-ray fluorescence spectrometers
• X-ray powder diffractometers
• densitometers
• diode-array detectors
• elemental analyzers
• gas chromatographs
• IR spectrometers
• near-IR spectrometers
• Raman spectrometers
• UV/Vis spectrometers
• inductively coupled plasma-emission spectrometers

While categorizing laboratory instruments into three categories provide a useful starting point when discerning what kind of qualification protocol is necessary for an instrument, it should be noted that the same type of instrument can fit into one or more categories depending on its intended use.

In addition, due to the wide diversity of laboratory instruments in use, and the different ways these systems are used, a single prescriptive approach for instrument qualification would be neither practical nor cost-effective. In general, a risk-based approach to instrument qualification should be followed to determine the extent of qualification activities necessary for any instrument. Generally speaking, the more critical an instrument is to product quality, and the more complex the instrument, the more work will be required to ensure adequate qualification status.

Software Validation with Instrument Qualification

It is becoming more and more difficult to separate the hardware and software parts of many modern analytical instruments. The software part of the more complex analytical instruments can be divided into different groups:

Firmware: Many instruments contain integrated chips with low-level software (firmware) that is necessary for proper instrument functionality. In most cases, the firmware cannot be altered by the user and is therefore considered a component of the instrument itself. Thus, no qualification of the firmware is needed – when the instrument hardware is qualified at the user-site, the firmware is also essentially qualified. Group B instruments fall into this category. In this case, the firmware version should be recorded as part of Installation Qualification (see below) activities, and any firmware updates should be tracked through change control of the instrument.

Some instruments come with more sophisticated firmware that is capable of fixed calculations on the acquired data, or even firmware that enables users to define programs for the instrument’s operation. Any firmware calculations need to be verified by the user, and firmware programs need to be defined and verified by the user to demonstrate that they are fit for intended purpose. User-defined programs need to be documented in change control and access to them should ideally be restricted to authorized personnel.

Instrument Control, Data Acquisition, and Processing Software: More complex analytical instruments are typically controlled by a separate workstation computer running instrument control and data acquisition, and processing software. Since the software is needed for data acquisition and calculations, both the hardware and software are essential for obtaining accurate analytical results from the instrument.

The software in these more complex instruments can be classified into three different types:

• non-configurable software that can’t be modified to change the business process
• configurable software that includes tools from the vendor to modify the business process
• configurable software with customization options (i.e., custom software or macros to automate the business process)

In these cases, the software is needed to qualify the instrument and instrument operation is necessary when validating the software. As a result, the software validation and analytical instrument qualification (AIQ) can be integrated into a single activity to avoid duplication.

Qualification Process and Required Documentation

AIQ is not an isolated event, but instead consists of interconnected activities that occur over the lifetime of the instrument. The first step involves the creation of user requirements, which effectively specify the operational and functional requirements that the instrument is expected to fulfill. The user requirements must define every requirement relating to safety, identity, strength, purity, and quality of the product.

The next steps in qualifying an instrument and establishing fitness for purpose proceed as follows:

Design Qualification (DQ): The DQ seeks to demonstrate that the selected instrument has all the capabilities necessary to satisfy the requirements. As such, the DQ will document the requirements, along with all decisions made in selecting an instrument vendor. This information will help ensure that the instrument can be successfully implemented for the intended purpose. Verification that instrument specifications meet the desired requirements may be sufficient for commercial off the shelf (COTS) instruments. However, it may be wise for the user to verify that the supplier has adopted a robust quality system that serves to ensure the vendor specifications are reliable. If the use of the instrument changes, or if it undergoes a software upgrade, it is important for the user to review and update DQ documentation.

Installation Qualification (IQ): The IQ documents the activities necessary to establish that the instrument was received as designed and specified, is correctly installed in the right environment, and that this environment is suitable for the proper use of the instrument. Depending on the results of a risk assessment, IQ may apply to any new instrument, pre-owned instrument, onsite instrument that has not been previously qualified (or qualified to industry standards), or a qualified instrument that is being moved to another location.

Operational Qualification (OQ): The OQ documents the activities necessary to verify that the instrument functions according to its operational specifications in the user environment. OQ demonstrates fitness for selected use and should reflect the user requirements. OQ activities should simulate actual testing conditions, including worst-case scenarios, and be repeated enough times to assure reliable testing results.

Testing activities in the OQ phase should include the following parameters:

• Fixed Parameters – These tests measure the instrument’s non-changing parameters (e.g., length, height, weight, voltage inputs, acceptable pressures, loads). These parameters will not change over the life of the instrument and therefore do not need to be retested. If the user trusts the manufacturer supplied specifications for these parameters, these tests may be waived.
• Software Functions – When applicable, OQ testing should include critical elements of the configured software to show the instrument works as intended. Functions applicable to data acquisition, analysis, security and reporting, along with access control and audit trails, should be tested under actual conditions of use.
• Secure data storage, backup, and archiving – When applicable, secure data storage, backup and archiving should be tested at the user’s site.
• Instrument Function Tests – Instrument functions that are required by the user should be tested to confirm that the instrument is operating as the manufacturer intended. Supplier information can be used to identify specifications for this testing, as well as to design tests that verify that the instrument meet’s specifications in the user environment.
• Software configuration and/or customization – Configuration or customization of instrument software should be documented and occur before any OC testing.

Performance Qualification (PQ): Also sometimes called user acceptance testing (UAT), PQ testing intends to demonstrate that an instrument consistently performs according to specifications appropriate for its intended use. PQ should be performed under conditions simulating routine sample analysis. As consistency is important in PQ, the test frequency is usually much higher than in OQ. Testing can be done each time the instrument is used, or scheduled at regular intervals.

Preventative Maintenance, Periodic Reviews and Change Control

Preventative maintenance (e.g., calibration), periodic reviews, repairs and other changes should be documented as part of instrument qualification requirements. When an instrument malfunctions, the cause should be investigated and documented. Once maintenance activities, changes, upgrades, moves and reinstallation at another location, or a repair is complete, relevant IQ, OQ and PQ tests should be run to verify the instrument is operating satisfactorily.

Critical instruments should undergo periodic review to confirm that the system is still under effective control. Areas for review may include: qualification/validation status, change control records, backup and recovery systems for records, correctness and completeness of records produced by the instrument, change control records, test result review and signoff, user procedures.

A change control process should be established in order to guide the assessment, execution, documentation and approval of any changes to laboratory instruments, including firmware and software. All details of the change should be documented, and users should assess the effects of the changes to determine if requalification (IQ, OQ or PQ) activities are necessary. It is important to note that, depending on the nature of a change, qualification tests may need to be revised in order to effectively evaluate the instrument’s qualification status after the change.

Conclusion

Analytical instruments can provide a high level of confidence in the quality of finished product through scientific data if they are qualified properly. Instrument qualification is an important part of compliance for laboratories in regulated industries and is important to ensure product quality and safety in any industry. Failure to qualify instruments properly can lead to serious consequences for an organization as a result of compliance violations and poor product quality.

Instrument qualification plans should be documented in the Validation Master Plan (VMP) and implemented by documenting user requirements and following the DQ/IQ/OQ/PQ protocols outlined in this blog. In order to demonstrate a commitment to manufacturing quality products, organizations should work to:

• develop a VMP that encompasses the entire validation
• implement well thought out AIQ qualification programs.
• implement a regular requalification plan for critical instruments (annually at minimum).
• maintain current SOP documentation.
• take appropriate steps to ensure data integrity and data security

While a commitment to quality requires much effort and focus, industry leading organizations understand that good quality practices and culture lead to a more efficient work environment, improved employee satisfaction, and ultimately increased profitability.

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