The Role of AI, ML, and HPC in Federal Research

Federal research institutions and laboratories are at the forefront of scientific innovation, tackling some of the most complex challenges in healthcare, environmental science, national security, and more. The integration of AI, ML, and HPC is enhancing their capabilities in several key areas:

Data Analysis and Interpretation

• AI and ML: These technologies enable researchers to quickly and accurately analyze vast amounts of data. Machine learning algorithms can identify patterns and correlations that might be missed by traditional methods, providing deeper insights into research data.
• HPC: High-performance computing provides the computational power needed to process and analyze large datasets, facilitating complex simulations and models that are crucial for scientific research.

Accelerating Scientific Discoveries

• AI and ML: By automating repetitive tasks and processes, AI and ML free up researchers to focus on innovative and high-impact work. For example, AI-driven drug discovery platforms can screen thousands of compounds in a fraction of the time it would take using traditional methods.
• HPC: HPC systems allow researchers to perform large-scale simulations and experiments that would be impractical or impossible to conduct physically. The Department of Energy’s Advanced Scientific Computing Research (ASCR) program is dedicated to discovering, developing, and deploying computational and networking capabilities that analyze, model, simulate, and predict complex phenomena, which are crucial to the advancement of science.

Enhancing Operational Efficiency

• AI and ML: In federal labs, AI and ML can optimize resource allocation, manage laboratory equipment, and streamline administrative processes. Predictive maintenance powered by AI can reduce downtime and extend the lifespan of critical research infrastructure.
• HPC: HPC infrastructure supports the parallel processing of tasks, improving the efficiency of data-intensive research projects and enabling real-time data analysis and decision-making.

Applications of AI, ML, and HPC in Federal Research Labs

Healthcare and Biomedical Research

• AI/ML: AI and ML are revolutionizing healthcare research by enabling precision medicine, predictive analytics, and personalized treatment plans. For instance, in a recent study, the National Institutes of Health (NIH) used AI to interpret echocardiograms and measure incident outcomes.
• HPC: HPC systems are critical for processing genomic data and conducting large-scale biological simulations. These capabilities are essential for understanding complex diseases and developing new therapies.

Environmental Science

• AI/ML: Machine learning models are used to predict environmental changes, analyze satellite imagery, and monitor biodiversity. Federal agencies like NASA and the Environmental Protection Agency (EPA) leverage AI to assess the impact of climate change and develop mitigation strategies.
• HPC: HPC enables the simulation of climate models, providing detailed predictions of future environmental conditions. These simulations inform policy decisions and help in disaster preparedness and response.

National Security

• AI/ML: AI and ML enhance national security by providing advanced threat detection, cybersecurity, and intelligence analysis capabilities. The Department of Defense (DoD) and other federal agencies utilize AI to analyze vast amounts of data for national defense purposes.
• HPC: HPC supports the modeling and simulation of defense systems, cyber operations, and strategic planning. These capabilities are crucial for maintaining national security and developing advanced defense technologies.

Challenges and Future Directions

While the integration of AI, ML, and HPC in federal research and labs offers tremendous benefits, it also presents several challenges:

1. Data Management: Handling and processing the massive volumes of data generated by AI and HPC systems require robust data management strategies and infrastructure.
2. Security and Privacy: Ensuring the security and privacy of sensitive research data is paramount. Federal labs must implement stringent cybersecurity measures to protect against data breaches and cyber threats.
3. Skill Gaps: There is a growing need for skilled professionals who can develop, implement, and manage AI, ML, and HPC technologies. Outsourcing to strategic partners with the necessary expertise and investing in education and training programs are essential to address this skills gap.

Conclusion

The integration of AI, ML, and HPC in federal research and labs is transforming the landscape of scientific discovery and innovation. These technologies are enhancing data analysis, accelerating research, and improving operational efficiency across various fields. As federal agencies continue to adopt and refine these technologies, they will play a crucial role in addressing some of the most pressing challenges of our time and driving the future of science.

How Astrix Can Help

Astrix can provide expert support solutions for integrating AI, ML, and HPC technologies into federal research and laboratories. Our team of experienced professionals can help you navigate the complexities of these advanced technologies, ensuring that your organization stays at the cutting edge of scientific innovation. Contact us to learn more about how we can assist you in transforming your research capabilities and achieving your scientific goals

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1. Decentralized Clinical Trials (DCTs)

Decentralized clinical trials (DCTs) are transforming the traditional model by leveraging technology to conduct trials outside conventional clinical settings. This approach brings the study to the patient, utilizing digital health technologies, remote monitoring, and telemedicine to collect data.

Benefits:

• Increased Participation: DCTs reduce geographical barriers, enabling more diverse patient participation.
• Improved Patient Convenience: Participants can undergo assessments and provide data from the comfort of their homes, enhancing retention rates.
• Faster Recruitment: With broader reach, DCTs can accelerate participant recruitment, reducing trial timelines.

2. Advanced Data Analytics and Artificial Intelligence (AI)

The integration of advanced data analytics and artificial intelligence (AI) is revolutionizing how clinical trial data is collected, analyzed, and interpreted. These technologies enable researchers to uncover patterns and insights that were previously unattainable.

Applications:

• Predictive Analytics: AI can predict patient outcomes, optimize trial design, and identify potential risks early in the trial process.
• Real-Time Monitoring: Advanced analytics allow for continuous monitoring of patient data, ensuring timely intervention if adverse events occur.
• Data Integration: Combining data from various sources (e.g., electronic health records, wearable devices) provides a comprehensive view of patient health and trial progress.

3. Personalized Medicine and Precision Trials

The shift towards personalized medicine is driving the development of precision trials, where treatments are tailored to individual patient profiles based on genetic, biomarker, phenotypic, or psychosocial characteristics.

Key Aspects:

• Targeted Therapies: Trials are increasingly focusing on specific patient subgroups most likely to benefit from the intervention.
• Biomarker-Driven Studies: Identifying and utilizing biomarkers for patient selection and treatment efficacy assessment.
• Adaptive Trial Designs: These designs allow modifications based on interim results, improving trial efficiency and success rates.

4. Patient-Centric Approaches

A growing emphasis on patient-centric approaches ensures that trials are designed and conducted with the participant’s needs and experiences in mind. This trend recognizes patients as partners rather than subjects.

Strategies:

• Enhanced Communication: Transparent and ongoing communication with participants about trial progress and findings.
• Patient Advocacy Involvement: Involving patient advocacy groups in trial design to ensure relevance and improve patient engagement.
• Flexible Protocols: Designing protocols that accommodate patients’ lifestyles and reduce the burden of participation.

Blockchain for Data Security and Transparency

The use of blockchain technology in clinical trials offers enhanced security, transparency, and traceability of data. This technology addresses many challenges associated with data integrity and regulatory compliance.

Advantages:

• Immutable Records: Blockchain provides an unalterable ledger of all trial activities, ensuring data integrity and trustworthiness.
• Enhanced Privacy: Secure data sharing while maintaining patient confidentiality.
• Regulatory Compliance: Facilitates adherence to regulatory requirements by providing transparent and verifiable data trails.

6. Increased Focus on Rare Diseases and Orphan Drugs

There is a heightened focus on developing treatments for rare diseases and orphan drugs. Advances in genetics and molecular biology enable the identification of disease mechanisms and potential therapeutic targets for these conditions.

Developments:

• Collaborative Research: Partnerships between academia, industry, and patient organizations to pool resources and expertise.
• Regulatory Support: Incentives and support from regulatory bodies to expedite the development and approval of treatments for rare diseases.
• Innovative Trial Designs: Use of innovative trial methodologies, such as platform trials and basket trials, to evaluate multiple therapies simultaneously.

7. Virtual and Augmented Reality (VR/AR)

Virtual reality (VR) and augmented reality (AR) are emerging tools in clinical trials, offering novel ways to enhance patient engagement and training for clinical staff.

Applications:

• Patient Education: VR/AR can provide immersive educational experiences, helping patients understand trial procedures and expectations.
• Simulation Training: Training for clinical staff using VR simulations to improve protocol adherence and patient interactions.
• Pain and Stress Management: VR applications to manage patient pain and anxiety during procedures.

8. Regulatory Evolution

Regulatory agencies worldwide are adapting to the changing landscape of clinical trials by introducing flexible and adaptive regulatory frameworks. These changes aim to facilitate innovation while ensuring patient safety and data integrity.

Regulatory Trends:

• Guidance on DCTs: Development of guidelines to support the implementation of decentralized trials.
• Adaptive Design Acceptance: Increased acceptance of adaptive trial designs to accelerate development timelines.
• Real-World Evidence (RWE): Encouraging the use of real-world evidence to supplement traditional clinical trial data for regulatory decisions.

Conclusion

The next five years promise significant advancements in clinical trials, driven by technology, personalized medicine, patient-centric approaches, and regulatory evolution. These trends are poised to enhance the efficiency, accuracy, and inclusivity of clinical research, ultimately leading to the development of better treatments and improved patient outcomes. By embracing these innovations, the clinical trial industry is set to transform and meet the challenges of modern healthcare.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific 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 strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

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The Importance of Innovations in Life Science Research

Technological improvements have made research and treatment more efficient, benefiting experts and patients. Health professionals now leverage advanced technologies to interpret vast data quickly, leading to more accurate results and improved patient care. These innovations also facilitate safe global collaboration among researchers, making it easier to share knowledge and resources.

The Intersection of Life Sciences and Technology

Each year, life science researchers eagerly adopt innovations that promise to benefit patients and the entire sector. Let’s explore some of the most significant scientific breakthroughs in this field.

Bioinformatics

Bioinformatics combines biology and computer science to interpret complex biological data. This discipline is becoming increasingly exciting with advancing technology as researchers move toward automation and AI. Future challenges include improving single-cell omics and quantum computing, both pivotal for the sector.

Single-Cell Omics

Single-cell omics analyze individual cells and their molecular makeup, providing insights into genomics, metabolomics, and related fields. Technological advances allow experts to understand cellular heterogeneity better, enhancing research in areas like cancer. For example, a 2023 study found that single-cell omics could trace prostate cancer cells, significantly improving cancer research.

Quantum Computing

Quantum computing surpasses traditional machinery by providing the processing power needed for advanced systems. This technology can revolutionize molecular biology by understanding quantum foundations, such as protein folding. Quantum computing enables faster and more accurate simulations to accelerate research and development in life sciences.

Biotechnology

Biotechnology enhances care and diagnostics by developing drugs, vaccines, and treatments. It also impacts agriculture by preventing crop infestations and mitigating weather-related issues. Innovations in biotechnology are driving significant improvements in healthcare and food security.

3D Bioprinting

3D bioprinting uses bioink and cells to replicate organs and tissues, revolutionizing regenerative medicine and organ transplants. This innovation promises to reduce complications and tailor treatments to individual patient needs. In plastic surgery, for instance, 3D bioprinting can create custom implants that match the patient’s unique anatomy.

Electrochemical Biosensors

Biosensors detect chemical substances in the body and food, improving quality of life. Portable biosensors are crucial for point-of-care diagnostics, allowing individuals to monitor glucose levels and blood pressure at home. These devices provide real-time data, enabling timely interventions and better health management.

Genomics

Genomics, the study of genomes, helps healthcare providers predict medical futures. Technological advancements in this field lead to better diagnostics and treatment plans. Providers can offer personalized care tailored to their needs by understanding an individual’s genetic makeup.

Next-Generation Sequencing

Next-generation sequencing (NGS) provides a detailed look at DNA and RNA, enhancing the understanding of proteins and genetic information. This technology reduces costs and waiting times while increasing output, making advanced genetic testing more accessible.

Genome Editing

Genome editing, through technologies like CRISPR, allows precise genetic manipulation. Though primarily in research stages, it holds potential for significant advancements in genetic diseases. By correcting genetic mutations, genome editing could offer cures for previously untreatable conditions.

Expanding Technologies for Life Science Innovation

AI and blockchain are transforming life sciences, offering new research and patient care possibilities. Let’s explore some key innovations in these areas.

AI

AI benefits life sciences through drug discovery, data collection, and improved diagnostics. By analyzing large datasets, AI helps develop new drugs and personalize medical care. AI-powered tools can identify patterns and trends that may not be apparent to human researchers, leading to groundbreaking discoveries.

Drug Discovery

AI reduces the time and cost of drug development by processing vast amounts of data. It helps identify drug candidates and streamline the development process, accelerating the path to market. This efficiency allows pharmaceutical companies to bring life-saving medications to patients more quickly.

Personalized Medicine

AI enhances diagnostics by using historical data to make informed decisions, improving the accuracy of detecting illnesses like brain tumors. By analyzing a patient’s unique genetic and medical history, AI can recommend personalized treatment plans that are more likely to succeed.

Blockchain

Blockchain technology enhances transparency and security in healthcare, protecting patient data and tracking prescription drug manufacturing and shipments. Its decentralized nature makes it difficult for cyber thieves to access entire databases, ensuring patient records remain secure.

Cybersecurity

Blockchain’s decentralized structure makes it a robust solution for protecting sensitive information. By distributing data across multiple nodes, blockchain reduces the risk of data breaches and unauthorized access. This security is crucial for maintaining patient trust and complying with regulatory standards.

Pharmaceutical Supply Chain

Blockchain technology ensures transparency in drug production, reducing fraud risks and enhancing security. By providing an immutable record of each step in the supply chain, blockchain helps verify the authenticity of medications and prevents counterfeiting.

Machine Learning

Machine learning (ML) in healthcare helps professionals perform routine tasks more efficiently, leading to better health outcomes. ML algorithms can analyze large datasets to identify patterns, predict disease, and enhance diagnostics.

Support Vector Machine

This ML algorithm analyzes large datasets to identify patterns, aiding disease prediction and diagnosis. By recognizing subtle correlations within data, support vector machines can provide early warnings of potential health issues, enabling timely interventions.

Robot-Assisted Surgery

Robotic technology assists in surgery, reducing human error and improving precision. These systems can perform delicate procedures with greater accuracy, minimizing the risk of complications and improving patient outcomes. Robot-assisted surgery is becoming a viable substitute for traditional surgical methods.

Conclusion

Integrating technology and life sciences continues to drive advancements in fields such as bioinformatics, pharmaceuticals, and genomics. These innovations are transforming healthcare, improving research, diagnostics, and treatment, and ultimately enhancing patient care.

By staying informed about these ongoing innovations, your business can leverage the latest advancements to stay competitive and deliver exceptional results.

How Astrix Can Help

Astrix provides expert consultants who are instrumental in helping businesses navigate and leverage sophisticated technological solutions. Our team consists of highly skilled professionals with extensive experience in various technological domains.

By partnering with Astrix, businesses can access tailored consulting services that address their unique challenges and objectives. Our consultants work closely with clients to identify opportunities for technological integration, streamline operations, and enhance efficiency.

Through personalized guidance and strategic planning, Astrix empowers businesses to harness the full potential of cutting-edge technologies, ensuring they remain at the forefront of innovation in their respective industries.

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The revolutionary impact of artificial intelligence (AI) on research and development (R&D) labs is redefining the way discoveries are made and innovations are forged. AI’s ability to analyze complex data, uncover patterns, and accelerate experimentation fundamentally reshapes the scientific landscape, propelling labs into a new era of efficiency and productivity.

“Life science leaders will recognize AI for what it truly is — a highly potent tool that can facilitate significant progress in a field that continuously generates vast amounts of data that historically exceeded human capacity for comprehensive analysis.” – Gartner1

Organizations are investing more in digital infrastructure, prompting labs to integrate AI into current workflows for greater efficiency and faster drug discovery. AI-driven technologies help research labs automate routine tasks, analyze and interpret data, and create predictive models for drug discovery and development.

Practical Applications of AI in Scientific Discovery

AI’s role in scientific discovery is expanding rapidly due to the surge in data generated by labs worldwide. By analyzing extensive datasets, AI can uncover significant trends, forecast outcomes using existing data, and simulate detailed scenarios that are difficult to replicate in a lab setting.

The Importance of Effective Data Management in AI

AI models need quick and efficient access to vast data sets to identify patterns and make precise predictions. Enhancing data quality improves the accuracy and reliability of these models. Effective data management involves integrating information from various sources, including LIMS, ELN, databases, data lakes, and lab instruments, into a coherent dataset that can be used for training and validating AI models.

Ensuring accurate, complete, and consistent data quality begins with Master Data Management. This effort provides a solid framework for data governance and implements robust data security and privacy measures, all in compliance with relevant regulations.

Next Steps to Prepare for an AI-Ready Lab

In summary, AI enhances scientific discovery by identifying trends, predicting outcomes, and enabling simulation-based research. This opens up new avenues for research and innovation. However, establishing the optimal lab environment for effectively incorporating AI technology requires specialized knowledge and multi-disciplinary expertise. Partnering with experts with domain-specific knowledge and practical experience will provide valuable guidance for seamlessly integrating AI into your R&D strategy. This approach ensures scalability, maintainability, and operational excellence, driving advancements in science.

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:

¹Harwood, R., et al. “Predicts 2024: Generative AI Brings New Value to Life Sciences”, Gartner, January 10, 2024.

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Typically, you can find the SoA located on a few pages in a clinical trial protocol created on a word document. It appears in a table that creates a chronological visit outline and specific checklist of each assessment expected at that visit. Further detailed information about each visit and assessment not included in the table is often found in embedded in the protocol in numerous different sections, in lengthy footnotes or appendices. In a digital SoA, every assessment or measurement is defined alongside industry standards with inclusion of what would be written in these footnotes or other sections of the protocol. Creating a digital SoA, combines this information into a searchable format and eliminates manual review across different sections of the protocol, reducing the risk for missed required datapoints. Every assessment or measurement used in protocol design that was created in the digital SoA platform, would be defined universally enhancing the understanding of what is to be expected out of that requirement and laying the foundation for a Digital Protocol.

Furthermore, sponsors and study teams, even within an organization, often  refer to the same specific assessment by different names. Within a digital SoA, a standardized language is employed allowing for easier communication and shared understanding across key stakeholders, improved interoperability with downstream consumers of the SoA content, and streamlined updates for protocol amendments. For example, the digital SoA can quickly be updated from standardized information as amendments are approved, minimizing the room for error, protocol deviations, and missed system updates. Research staff and Clinical operations teams can refer to a single source of truth that holds all relevant information and rely on its accuracy to guide their practices.

This standardization also drives enhanced data management, one of the most prominent areas in clinical trials that can benefit from a digital SoA. Traditionally, electronic data capture (EDC) systems are created by a Data Manager that reviews the SoA document and translates it into defined system requirements and specific data standards³. This digitized information removes the need for translation and sets the stage for automation to associate the assessments listed in the SoA with the comparable data standards, system requirements and data collection forms³. EDC can be developed in direct accordance with the protocol requirements within every patient visit. Navigation within those EDC platforms would directly correlate with the protocol, in a user-friendly platform. Digital SoA takes a traditionally time consuming, tedious process and streamlines it to benefit data collection across the trial. Further, a digital SoA promotes a data driven design by its ability to provide real-time insights on decisions made by Data Managers or research staff. For example, a digital SoA can provide a snapshot of a what data would be impacted if a lab assessment is missed. Data snapshot abilities can reduce the burden on a Data Manager, allowing users togenerate queries showing the impact of different decisions and scenarios prior to them occurring. A study team can guide their decisions for each patient based upon real-time, real-world data and see the risks, safety information, patient overviews, data impacts, etc. that each decision may cause.

Additionally, data management teams need consistency. The information found within a digital SoA is reusable for future protocols and creates standardized repeatable workflows such as downstream integrations and document generation⁴. Interoperability is improved through standardization using a consistent data model such as USDM (Unified Study Definition Model) supported by industry standards (e.g., ICH M11: Clinical electronic Structured Harmonized Protocol (CeSHarP)⁵ , CDISC controlled terminology⁶, etc.)As each assessment and measurement is defined, this consistency in data model and terminology allows information to more easily flow downstream across platforms and documents like EDC, CTMS, IRT, lab manuals, etc. bridging the gap between systems and reducing manual effort for the study. All in all, a digital SoA supports the data management process, while maintaining improved efficiency and accuracy in trials.

The concept of turning a clinical trial “digital” is no stranger to researchers. Technology has continually changed the face of clinical trials and overcome challenges through its developments. The benefits that are seen can be endless – processes have been streamlined, costs are reduced, patient data is better obtained and protected, accuracy and ability to adhere to protocols are improved, etc. Implementing a digital SoA is just one of those impressive developments. The SoA carries the weight of success for researchers, therefore, its development into something more is important to consider as the field continues to further digitize and embrace technology.

References

1. Tufts Center for the Study of Drug Development. Impact Reports – Rising Protocol Design Complexity is Driving Rapid Growth in Clinical Trial Data. csddtuftsedu. 2021;23(1). https://csdd.tufts.edu/impact-reports.
2. Ken Getz, M.B.A. K. Shining a Light on the Inefficiencies in Amendment Implementation. wwwappliedclinicaltrialsonlinecom. 2023;32(12). https://www.appliedclinicaltrialsonline.com/view/shining-a-light-on-the-inefficiencies-in-amendment-implementation
3. Georgieff T. Navigating toward a Digital Clinical Trial Protocol. wwwappliedclinicaltrialsonlinecom. 2023;32(12). Accessed May 3, 2024. https://www.appliedclinicaltrialsonline.com/view/navigating-toward-a-digital-clinical-trial-protocol
4. XTalks – Faro Health Inc. Clinical Data Management Insights: Using Digital SoA to Solve Modern Clinical Trial Challenges. Xtalks. Published August 24, 2023. Accessed May 7, 2024. https://xtalks.com/webinars/clinical-data-management-insights-using-digital-soa-to-solve-modern-clinical-trial-challenges/.
5. International Council for Harmonisation of Technical Requirements. ICH M11: Clinical Electronic Structured Harmonised Protocol (CeSHarP) FDA and Health Canada Regional ICH Consultation.; 2023. Accessed May 7, 2024. https://www.fda.gov/media/167334/download.
6. CDISC Digital Data Flow for Clinical Trial Protocols. Accessed June 21, 2024 https://www.cdisc.org/ddf

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific 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 strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

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However, this can prove challenging for many science- and research-based organizations to tackle on their own. Dorsett argues that collaborative efforts between subject matter experts and technical leads lead to more effective outcomes.

“There’s a bit of work and experience that are required in the selection of the right problems, in order to actually get back any kind of return on a technical investment,” says Dorsett. “The process of producing the data or using the data or building the models requires balanced guidance that is informed by both the technology side and the science side of things.”

According to Dorsett, taking a collaborative approach to AI and machine learning applications will ultimately allow science-based organizations to achieve advancements more rapidly and better leverage the systems they already have in place.

“We have, as an industry, a tremendous amount of research and development data locked away in our systems – and one of the challenges is findability and usability of that information that we already have,” he says. “AI has the ability to help scientists find what they need when they actually need it.”

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Implementing a LIMS is a complex process that must align with current and future business needs. Careful planning, expertise across a wide range of technologies and disciplines, and a strategic distribution of resources are critical to ensuring a successful deployment.

Key Factors to Consider When Choosing a LIMS Implementation Partner

When it comes to implementing a LIMS, having expertise beyond the vendor’s standard offerings can be extremely beneficial. Since LIMS functionality spans numerous aspects of lab workflows and associated instrumentation, its deployment goes beyond the software. This process includes integrating and enabling unilateral and bilateral communication with various lab instruments, systems, and applications. Additionally, it often involves migrating data from other legacy LIMS or consolidating data from multiple sources.

Professional Service Organizations (PSOs), like Astrix, specializing in lab informatics, provide end-to-end solutions tailored for science-based businesses. Their expert consultants leverage extensive experience and proven methodologies to deliver vendor-agnostic strategies and customized configurations aligned with industry standards. These areas of expertise include:

• Comprehensive Scientific and Business Knowledge: Enables the analysis of business processes to thoroughly understand lab operations, instruments, and compliance regulations, ensuring that LIMS functionality is optimized to meet the specific needs of your lab.
• Lab-Wide Expertise: Diverse team of readily available specialists skilled in the latest technologies, applications, and security protocols.
• Unbiased Recommendations: Benefit from vendor-neutral strategies, recommendations, and configurations based on industry standards.
• Lab Informatics Strategy and Roadmap: Development of scalable, integrated, and automated solutions to maximize the efficiency of your LIMS deployment and transform lab operations.
• Industry Best Practices: Utilize experience with proven industry techniques and hands-on expertise with leading LIMS vendor platforms and Computer System Validation.
• Data Migration and Centralization: Expert consultants who specialize in data extraction, transformation, and security across a wide array of software applications and lab systems enable a holistic approach that streamlines your data migration process, reducing risk and enhancing efficiency.

Expert Professional Service Organizations deliver vendor-neutral strategies and customized configurations, providing comprehensive solutions through business process analysis to ensure the optimal LIMS deployment for your lab.

Cost-savings Through Reduced Downtime

The success of any LIMS implementation project hinges upon effective planning and budgeting, balancing costs without sacrificing quality. One of the most substantial cost-saving aspects of engaging an expert PSO is reducing downtime. Experienced professionals can implement LIMS quickly and correctly the first time, minimizing disruptions to your lab’s workflow. Reduced downtime means maintaining lab productivity and avoiding hidden costs associated with prolonged implementation periods.

By ensuring that the system is implemented correctly and efficiently from the start, you avoid the additional costs of correcting mistakes later on. A well-implemented LIMS enhances data accuracy, compliance, and overall operational efficiency, leading to long-term savings and a higher return on investment (ROI).

In Summary

Expert Professional Service Organizations bring a wealth of experience across various industries, enabling them to quickly identify and implement best practices. Their familiarity with LIMS software and implementation strategies can significantly reduce the time and resources needed to get your system up and running efficiently. Unlike vendor service teams and in-house teams that may have limited experience with different lab systems, Expert PSOs offer a broader perspective and innovative solutions. This flexibility allows for a more adaptable and scalable system that can grow with your lab’s changing demands.

Engaging a cost-effective Expert PSO for a LIMS implementation offers numerous advantages, from significant cost savings and reduced downtime to tailored solutions and long-term financial benefits. Expert PSOs can streamline the LIMS implementation process, ensuring your lab runs smoothly while keeping financial constraints in check. You can avoid common pitfalls and maximize your investment by leveraging their expertise, efficiency, and comprehensive support.

About Astrix

Astrix is the unrivaled market leader in creating & delivering innovative strategies, technology solutions, and people to the life science community. We are a globally recognized leading LIMS Implementation Partner. 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.

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What is LIMS data migration and why is it needed?

Data migration refers to the process of transferring data from one system or location to another. This could involve moving data from an old LIMS to a new one, upgrading to a newer version of the LIMS, or consolidating data from multiple sources into a single LIMS.

A myriad of factors driving business imperatives and technological evolution warrant the need for LIMS data migration. Primarily, the need for data migration arises from system upgrades or replacements, where laboratories seek to harness the capabilities of advanced LIMS platforms to improve efficiency and productivity. Similarly, vendor changes may prompt the migration, as laboratories move towards providers offering enhanced features or better support. Evolving compliance requirements may also necessitate data migration to avoid regulatory pitfalls. Moreover, as businesses expand, data centralization becomes imperative for streamlining operations, eliminating data silos, and facilitating collaboration across geographies. The adoption of cloud-based solutions not only enhances accessibility but also enables scalability and cost-effectiveness. Additionally, the demand for enhanced reporting and analytics drives laboratories towards platforms capable of delivering actionable insights from vast datasets. Legacy system decommissioning and security enhancements further propel the need for migration, as laboratories strive to mitigate risks and strengthen their data infrastructure against emerging threats.

Steps for a Successful Data Migration

Figure 1: Steps for a successful LIMS data migration (Figure courtesy of CloudLIMS)

Executing a data migration exercise well involves following a set of steps and stages thoroughly.

Planning

The first crucial step in a LIMS data migration project is planning. It begins with the allocation of essential resources within the laboratory, ensuring that sufficient personnel, expertise, and technical infrastructure are available to support the migration process. Collaborating with a reliable LIMS vendor can significantly streamline this phase, as they can provide guidance and assign a dedicated project manager to oversee the entire migration journey. During the planning stage, several key considerations must be addressed to ensure a smooth and effective migration process.

• Stakeholder alignment is paramount, with all parties involved in the project having clearly defined goals aligned with the overarching objective of achieving a seamless transition. This involves identifying data owners and assembling specialized teams or task forces to manage specific aspects of the migration.
• Legacy data evaluation is another critical aspect of the planning stage that requires a thorough assessment of the data housed within the existing system. Laboratories must determine which data is essential for migration to the new system and whether certain information can be archived or stored in alternative repositories, such as data warehouses, to streamline the migration process.
• Data suitability is also a crucial consideration, ensuring that the data earmarked for migration is compatible with the new LIMS platform and meets the necessary quality standards.
• Regulatory compliance is another vital aspect to address during the planning stage, ensuring adherence to stringent security and compliance guidelines to maintain accreditations and certifications.
• Moreover, establishing a clear migration timeline that outlines the proposed schedule for the migration exercise and identifies strategies to minimize disruption to laboratory operations is essential. This involves careful coordination of activities to ensure a seamless transition while mitigating the risk of operational disruptions.
• An integral decision during the planning stage is the selection of the migration approach. Three common methods include the parallel approach, incremental approach, and the big bang approach.
  • In the parallel approach, both the existing (legacy) LIMS and the new LIMS operate simultaneously for a defined testing period. This method is widely adopted by labs due to its ability to ensure consistency and accuracy in the new system. However, it’s crucial to note that this approach can be resource-intensive and complex. Deploying personnel to support two systems can incur significant expenses. Nevertheless, the benefits are notable, as this approach enhances the accuracy of the system. Additionally, having the old system available for reference during the transition period offers reassurance and allows data verification when needed.
  • In the incremental method, data migration occurs gradually, often in phased stages rather than all at once. This approach involves a step-by-step migration process, minimizing the risk of disruptions and allowing laboratory operations to continue uninterrupted. For instance, in the initial phase, system configurations such as calculations can be addressed. Subsequently, instrument integration can be tackled in the second phase, followed by the integration of third-party software in the third phase. By breaking down the migration process into stages, laboratories can seamlessly implement the new system while maintaining normal operations.
  • In the big bang approach, laboratory data and functionality are transferred from the existing system to the new system in a single, comprehensive operation. This transition occurs all at once, usually during a predefined cutover period. While this method offers speed, it is less commonly used due to its drawbacks such as significant downtime and disruption to customer service. Moreover, as data complexity and quantity increase, implementing the big bang approach becomes increasingly challenging.

Data Extraction

The data extraction step is an important step in the data migration process, involving the retrieval of data from the existing LIMS system in preparation for its transfer to a new LIMS or an updated version of the existing LIMS. This phase demands a comprehensive assessment to identify the specific data elements and records requiring migration, including diverse information ranging from sample details to test results and instrument data. Careful consideration is given to all data to be transferred, calling for a thorough audit of the source data. Labs need to identify the kind of data they have. For instance, transferring structured data, organized within tables and columns, is typically a smooth process, whereas transferring unstructured data, such as images, is more challenging. It’s important to have a LIMS that can support the export of large volumes of data in a simple and practical way. As the foundational step in the migration journey, proper data extraction lays the groundwork for a seamless transition to the new system.

Data Transformation

The data transformation or data mapping step in the data migration process is a crucial step where attributes from one database are matched to their counterparts in another using a predefined template. This process is essential for ensuring that data retains its integrity and structure during the transition to a new LIMS or a transformed version of an existing one. The complexity of data mapping can vary significantly, depending on factors such as the volume of data, the diversity of data types, and the disparities between the legacy data source and the new LIMS. Complex mappings may involve transformations to reconcile differences in data formats, field names, or data structures, while simpler mappings may entail straightforward one-to-one mappings. Regardless of complexity, extensive attention to detail is required to ensure accurate data transfer and minimize the risk of data loss or corruption.

Figure 2: Leverage a good LIMS for seamless data transformation using its attribute mapping capabilities.

Data Cleaning

The data cleaning step of the data migration process is a critical one aimed at enhancing the quality and reliability of datasets slated for transfer to a new LIMS. This phase involves an examination of datasets, tables, and databases to identify and rectify various anomalies, including unreliable, inaccurate, duplicated, or outdated information. Through rigorous error detection and rectification, data cleaning mitigates the risk of transferring errors and inaccuracies to the new system, safeguarding data integrity. Key tasks within the data cleaning process include identifying and rectifying errors such as spelling discrepancies, inaccuracies, or incomplete information within the dataset. Additionally, removing duplicate data and standardizing data formats, units, and structures to align with the specifications of the new LIMS are essential. Furthermore, data integrity checks and normalization procedures are performed to validate data consistency and adherence to predefined standards.

Data Validation

The data validation step is aimed at verifying the accuracy, consistency, and compliance of data transferred from the old system to the new system. This rigorous process ensures that the migrated data is devoid of potential errors and discrepancies. Through systematic validation procedures, the data is examined to identify any anomalies or inconsistencies that may have arisen during the migration process. Key aspects of data validation include verifying the completeness and correctness of transferred data, ensuring that all essential information has been accurately migrated. Additionally, consistency checks are conducted to ascertain that data formats, units, and structures conform to the specified requirements of the new LIMS.

Data Load

The data load step in the data migration process marks the culmination of the journey, where databases, tables, or structures of the new LIMS are populated with the extracted, transformed, and validated data from previous stages of migration. This phase represents the final bridge between the old and new systems. Through the execution of data loading procedures, the integrity and accuracy of the migrated data are preserved, ensuring that the new LIMS is equipped with a robust foundation of reliable information. Key considerations during this phase include optimizing data loading processes to minimize downtime and disruption to laboratory operations, as well as implementing mechanisms to monitor and verify data integrity post-loading.

Post-Migration Evaluation

Post-migration evaluation ensures that the migrated data aligns with expectations, meets stringent quality standards, and facilitates the effective operation of the new LIMS. This critical phase includes a comprehensive validation process to affirm migration success, preserve laboratory data integrity, and promptly address any emerging issues encountered or observed during the transition. Central to this evaluation is a data integrity check to verify the completeness and accuracy of all data transferred from the old system to the new system. Subsequently, the functionality of the new LIMS is examined to confirm its seamless operation, This is done with assessments such as user acceptance testing (UAT), performance monitoring, and validation of customizations. Custom configurations or modifications are particularly tested to ensure they enhance workflow efficiency without compromising overall system performance. A thorough post-migration evaluation instills confidence in the reliability and effectiveness of a lab’s new LIMS, resulting in improved operational efficiency and productivity.

Conclusion

As technology continues to evolve, laboratories increasingly seek more advanced LIMS solutions to streamline operations and drive innovation. Adopting these systems often requires a data migration exercise to transition from legacy platforms to modern, feature-rich LIMS. As outlined in the various steps of the LIMS data migration process, from planning to post-migration evaluation, it’s evident that data migration is a complex task that demands careful consideration and precise execution. Each stage, whether it’s data extraction, transformation, cleaning, or validation, plays a crucial role in ensuring a seamless data transfer while preserving integrity and accuracy. Moreover, the post-migration evaluation step serves as a final checkpoint, affirming migration success and validating the operational efficacy of the new LIMS. Ultimately, each step in the data migration process serves as a vital building block, fitting seamlessly together like pieces in a tangram, empowering laboratories on their digitization journey towards greater efficiency, innovation, and scientific discovery.

About CloudLIMS:

CloudLIMS.com is an ISO 9001:2015 and SOC 2-certified informatics company. Their SaaS, in-the-cloud Laboratory Information Management System (LIMS), CloudLIMS, offers strong data security, complimentary technical support, instrument integration, hosting and data backups to help biorepositories, analytical, diagnostic testing and research laboratories, manage data, automate workflows, and follow regulatory compliance such as ISO/IEC 17025:2017, GLP, 21 CFR Part 11, HIPAA, ISO 20387:2018, CLIA, ISO 15189:2012, and ISBER Best Practices at zero upfront cost. Their mission is to digitally transform and empower laboratories across the globe to improve the quality of living.

About the Author

Arun Apte, CEO, CloudLIMS

Arun Apte is a serial entrepreneur and laboratory research scientist specializing in bioinformatics. He founded CloudLIMS in 2014, bringing the benefits of a SaaS LIMS to the laboratory market. This has enabled hundreds of small labs including biorepositories, clinical research and diagnostic labs, and analytical testing labs, including food & beverage, cannabis, environmental, water, and material testing labs to gain the efficiency of a LIMS previously only affordable to large laboratories. Arun is an invited speaker at conferences all over the world. Recently, he was an invited speaker at the Cannabis Quality Conference 2023.

Prior to founding CloudLIMS, he founded PREMIER Biosoft forging strategic partnerships with Thermo, Agilent, SCIEX, and other mass spectrometry instrument companies. Apte holds a B.A. in molecular and cell biology and biophysics from the University of California at Berkeley. He has published extensively on bioinformatics.

About Astrix

Astrix partners with many of the industry leaders in the informatics space to offer state of the art solutions for all of your laboratory informatics needs. 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.

The post LIMS Data Migration: How to Ace the Journey from Planning to Post-Migration Evaluation appeared first on Astrix.

However, in November 2023, the International Council for Harmonisation (ICH) announced the adoption of its Q5A(R2) guideline on viral safety evaluation of biotechnology products and a guideline on validating analytical procedures. These guidelines encourage the biopharmaceutical industry to adopt new standards in biologics QC, including next-generation sequencing (NGS)-based assays.

The Need for Advanced QC Testing

Therapeutic biologics, such as monoclonal antibodies and recombinant proteins, treat various diseases, including cancer, autoimmune disorders, and infectious diseases. These biologics are highly complex molecules; even minor structural variations can affect their efficacy and safety. Therefore, rigorous QC testing ensures that each batch meets the required standards.

Challenges with Traditional Animal Testing

While animal testing has been the gold standard for QC biologics testing for decades, it has several limitations. Animal testing can be time-consuming and expensive and raises ethical concerns regarding the use of animals in research. Additionally, not all human pathogens can replicate in animals, limiting the effectiveness of these tests.

Embracing Next-Generation Sequencing (NGS) in QC Testing

NGS is a highly sensitive technology that is becoming widely adopted in biopharmaceutical drug development and manufacturing to assess biotherapies’ product characterization and biosafety. NGS allows researchers to analyze the entire genome or specific regions of interest quickly and accurately. In biologics QC, NGS can be used to sequence the DNA or RNA of the biologic, allowing researchers to detect and characterize any potential variations or impurities.

The Role of Regulatory Guidelines

Regulatory authorities are establishing guidelines for biopharmaceutical companies using NGS-based assays, including guidelines for computational systems that support NGS-based assay data analysis within a validated (GxP) environment to ensure product quality, data integrity, and patient safety.

Conclusion

As the biopharmaceutical industry continues to innovate and develop new therapeutic biologics, the need for advanced QC testing methods becomes increasingly important. By embracing NGS-based assays and moving away from traditional animal testing, the industry can ensure the quality, efficacy, and safety of therapeutic biologics while also addressing ethical concerns and reducing costs.

How Astrix Can Help

Astrix specializes in providing outsourced resources to support biopharmaceutical companies with their biologics quality testing needs. Our staffing and consulting solutions have the expertise you need in NGS-based assays and compliance with regulatory guidelines. We have decades of experience supporting companies in adopting new standards in biologics QC and ensuring the quality, efficacy, and safety of therapeutic biologics.

Contact us today to learn more about how we can help!

• Learn more about our strategic staffing solutions.
• Learn more about our consulting solutions.
• Learn more about our clinical and regulatory and regulatory services.

The post Advancing Quality Control Testing in Therapeutic Biologics appeared first on Astrix.

Informed consent is not only an ethical requirement but also a legal one, with specific guidelines outlined in the Common Rule and FDA regulations. Other informed consent regulations include: The Office of Human Research Protections (OHRP) regulation 45 CFR 46.116, General Data Protection Regulation (GDPR) in Europe, and the National Statement on Ethical Conduct in Human Research in Australia. These regulations ensure that participants are fully informed about the risks and benefits of the study, as well as any alternative treatments or procedures. The regulations also require researchers to minimize the risk of harm to participants and to protect their confidentiality and privacy.

However, creating an informed consent document can be a complex and challenging task. The guidelines for Informed Consent Forms (ICFs) can use technical jargon that may be difficult for participants to understand, and researchers may struggle to balance the requirements of the regulations with the need for clear and concise information. Preparing an informed consent document is a crucial step in conducting ethical and responsible research, and understanding the basics can help researchers navigate the process with greater ease. In this blog, we shall delve into the intricate art of crafting informed consent documents that comply with the exacting standards of the FDA and Common Rule guidelines.

FDA and Common Rule: Basic Elements for Informed Consent Writing

The FDA and Common Rule require nine fundamental elements to be included in informed consent documents:

1. Introduction: The introduction should identify the research project, explain its purpose, and describe the procedures that will be performed.
2. Purpose of the research: The purpose of the research should be clearly explained, including the scientific goals, the procedures involved, and the expected duration of the study.
3. Foreseeable risks and discomforts: The potential risks and discomforts associated with the research should be described in detail, including any physical, psychological, social, or economic harm that may result.
4. Possible benefits: Any potential benefits of participating in the study should be explained, including direct benefits to the participant, benefits to others, and benefits to society.
5. Possible alternatives: The participant should be informed of any available alternative treatments or procedures and the benefits and risks associated with each.
6. The Extent of confidentiality: The extent to which confidentiality will be maintained should be described, including any exceptions to confidentiality.
7. Terms for compensation for injury: The terms for compensation for any injury that may result from participation in the study should be explained.
8. Statement of voluntary participation: The participant should be informed that participation is voluntary and that they can withdraw from the study at any time without penalty.
9. Clinical trial disclosure statement for Phase II studies: If the study is a Phase II clinical trial, a statement disclosing whether the drug or device being tested has been approved by the FDA for any use should be included. A Phase II study must also include the following statement as mentioned on the FDA website:

“A description of this clinical trial will be available on http://www.ClinicalTrials.gov, as required by U.S. Law. This website will not include information that can identify you. At most, the website will include a summary of the results. You can search this website at any time.”

FDA and Common Rule: Additional Elements for Informed Consent

In addition to the 9 basic elements of informed consent required by the FDA and Common Rule, there are 10 additional elements that are essential to ensure that research participants are fully informed before deciding to participate in a study. These additional elements address specific concerns and considerations that may arise in certain types of research studies and provide important information for participants to make an informed decision about their participation.  In this section, we will discuss each of these 10 additional elements in detail.

1. Potential risks to pregnant subjects or developing fetuses: If the study involves pregnant women or women of childbearing age, this element informs them of the potential risks to their own health or that of their developing fetus.
2. Possibility of termination of participation: This element outlines the process for terminating participation in the study, including the participant’s right to withdraw at any time without penalty.
3. Costs to the participant: This element explains any costs that the participant may incur as a result of participating in the study, such as travel expenses or medical procedures that are not covered by insurance.
4. Procedure for terminating participation: This element explains the steps that the participant must take to terminate their participation in the study.
5. Commitment to update the subject on study findings: This element outlines the researcher’s commitment to keeping the participant informed about any significant findings that emerge during the course of the study.
6. Estimate of the total number of participants: This element provides an estimate of the total number of participants in the study, which can help the participant understand the scope of the research.
7. Description of the “Certificate of Confidentiality” for NIH-funded studies: This element explains the purpose and significance of the Certificate of Confidentiality, which is intended to protect the participant’s privacy and confidentiality.
8. Information on the Genetic Information Nondiscrimination Act (GINA) for genetic testing studies: This element explains the implications of GINA for genetic testing studies, including the participants’ right to privacy and protection against discrimination based on their genetic information. For infectious diseases or epidemics, this element requires including a statement that results are required by law to be reported to local health authorities.
9. Statement on reporting results for infectious diseases or pandemics: This element explains the researcher’s obligation to report any positive test results for infectious diseases or pandemics, such as HIV or COVID-19.
10. Additional statements for Common Rule studies: This element may include any additional statements or disclosures that are required under the Common Rule, which is a set of federal regulations governing research with human subjects.

In order to safeguard the rights of subjects in research that is governed by the Common Rule, it is crucial to incorporate certain essential statements. Firstly, a statement should be included regarding the possibility of removing identifiers from collected data for future research purposes without the need for further informed consent to maintain subject privacy and prevent unauthorized use of their personal information. Alternatively, a statement should be added that obtained biological samples cannot be utilized for future research without the explicit consent of the subject, particularly for sensitive or personal data such as genetic information, to protect their privacy. Thirdly, it is necessary to incorporate a statement that specifies whether the subject’s biological samples may be commercially reused. This is important to allow the subject to make an informed decision regarding the potential commercial usage of their samples. Fourthly, a statement must be included to explain the circumstances under which clinically relevant research findings will be disclosed to the subject. This is important to ensure that the subject understands the possible benefits and risks associated with participation and can make an informed decision. Finally, a statement should be added indicating if the investigator intends to perform whole genome sequencing to inform the subject about any associated risks and allow them to make an informed decision.

By including these statements in research governed by the Common Rule, researchers can ensure that they adhere to ethical and industry standards, protect the privacy of subjects, and respect their rights during the research process.

 3 Ways Laboratory Software for Clinical Research Supports Informed Consent Management

 Utilizing laboratory software for clinical research, also known as Laboratory Information Management System (LIMS), can aid clinical researchers in automating and simplifying the handling of informed consent procedures. This not only decreases the chances of human error but also eliminates the possibility of tampering with the Informed Consent Forms (ICFs).

Let’s look at 3 ways a LIMS helps support clinical researchers in consent management.

1. Manage Documents  A LIMS manages all internal and external documents including consent forms, standard operating procedures (SOPs), and quality management manuals. The system keeps track of each document’s revision history to ensure that employees only access the most recent version. Additionally, a LIMS assigns role-based access rights to staff, allowing controlled access to confidential documents. With the document management feature, users can manage the latest version of the consent form. A LIMS can also associate filled-out consent forms with individual participant records for proper tracking and consent management.
2. Protect the Privacy of Participants  A LIMS ensures the anonymity of sensitive participant data and grants role-based access rights to protected health information (PHI) of participants. Only authorized personnel can view the masked data, and any changes or views of PHI are logged in the audit trail. This feature is beneficial for audits as external auditors can request a PHI audit report at any time
3. Associate Participants with a Study  A LIMS allows users to create a clinical research study and associate participants with it. In this way, all participants are associated with the study. Thus, all the consent forms, participant data, and all SOPs that apply to a study can be organized in one place.

A clinical LIMS helps automate and streamline the management of informed consent. It minimizes human error associated with manual paper-based processes and also eliminates ICF tampering risks. What’s more, a clinical LIMS can be integrated with digital tools used for the collection of informed consent.

Conclusion

Creating an informed consent document is a complex and challenging task that requires balancing regulatory requirements with the need for clear and concise information. Laboratory software for clinical research streamlines the process of managing informed consent documents to help researchers comply with the standards of the FDA and Common Rule guidelines. Using a LIMS can be a differentiator for clinical research labs, as it can make the consent management process a breeze, leading to better participant satisfaction and improved research outcomes.

About CloudLIMS

CloudLIMS.com is an ISO 9001:2015 and SOC 2-certified informatics company. Their SaaS, in-the-cloud Laboratory Information Management System (LIMS), CloudLIMS, offers strong data security, complimentary technical support, instrument integration, hosting and data backups to help biorepositories, analytical, diagnostic testing and research laboratories, manage data, automate workflows, and follow regulatory compliance such as ISO/IEC 17025:2017, GLP, 21 CFR Part 11, HIPAA, ISO 20387:2018, CLIA, ISO 15189:2012, and ISBER Best Practices at zero upfront cost. Their mission is to digitally transform and empower laboratories across the globe to improve the quality of living.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific 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 strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

The post Navigating the Complexities of Informed Consent Writing: Tips and Strategies for Clinical Researchers appeared first on Astrix.

1 Recruitment

Subject recruitment within clinical trials is considered one of the most crucial determinants for a successful trial. There are many challenges faced in this area that can lead to failure in reaching recruitment goals and inaccurately recruiting the proper subject for the study protocol. Minimizing recruitment barriers is pertinent, therefore, this is where AI comes into play. Considerable efforts are put forth towards recruitment. For example, sites typically assess eligibility by conducting interviews, thorough EMR reviews, physical exams, calling potential patients, numerous outreach events, etc. which directly affects the amount of paperwork, employees needed, and clinic time to carry out this process. AI can be implemented to analyze large databases leading to more efficient and reliable processes and eliminate these common recruitment limitations¹. Defined inclusion and exclusion criteria, demographics, imaging parameters, and comorbidities can be identified and included in database searches performed by AI. AI is a trained system that can extract those ideal patients within an EMR system or other recruitment databases and match them with complex clinical trial criteria while minimizing the common risks faced within recruitment. Eligibility is validated, as well as the ability to predict patient retention through AI proving promising results for clinical research.

2 Data Collection

To produce results of drug efficacy and safety for eventual usage, the collection, cleaning, and management of high-quality data is necessary in the field of clinical research.  One way that AI is streamlining data collection in clinical trials is through the use of digital health technologies (DHTs). By relying on AI algorithms, automated data collection produces usable, real-time information through wearable devices, sensors, investigational product trackers, video capture, etc.² These features allow a site or sponsor to prioritize the safety of subjects, while obtaining actionable insights through data. Additionally, defining of biomarkers while continuously collecting data through AI, can validate patient drug responses, identify sudden changes, or predict patient health outcomes for the study.

3 Predictive Insights

Another key indicator for a successful clinical trial is proper study design. AI is being utilized to enhance the overall study design process through the prediction of trends in patient data, success rates, and outcomes, which leads to a reduction of the length and cost of a trial. The success rate of a trial can be predicted by AI through previous patterns, patient data, site specific data and related trials. Within patient outcome prediction, it is noted that AI is being used to simulate data that allows for a more efficient statistical outcome measure and identify patients who are progressing to reach endpoints quicker, which results in shorter trial durations². Predictive insights allow for sponsors, clinical research organizations, and research sites to make informed decisions on what trials are best suited for their needs. The risk of failure, time, and resources are reduced with this information and allow for transparency on the expected future of the trial. Additionally, this allows for design teams to make improvements upon the trial with the predictive insights provided.

AI in clinical trials is expected to continually be incorporated into the field of pharmaceutical and biotech research. The streamlining of the processes within clinical trials will be evolving over time with the help of AI. Innovation will continue to challenge the field and help grow in areas that were once unheard of. While there will be challenges that come alongside AI integration, the benefits are undeniable within clinical research and significant strides will be made towards enhancing their processes.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific 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 strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

References

1. Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023;5(1). doi: https://doi.org/10.1259/bjro.20220023.

2. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to Clinical trials: Opportunities and Challenges.Health Technol. Published online February 28, 2023. doi: https://doi.org/10.1007/s12553-023-00738-2.

The post Three Ways AI (Artificial Intelligence) is Being Used to Streamline Clinical Trials appeared first on Astrix.

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.

The post Laboratory Technology Trends to follow in 2024 appeared first on Astrix.

What is FHIR?

FHIR stands for Fast Healthcare Interoperability Resources and is a HL7  standard for simplified electronic healthcare information exchange across the industry. The adoption of a FHIR framework is the next step to improve upon data interoperability. FHIR  provides representation and the ability to share information, such as EHRs, across hospitals, organizations or between healthcare providers in a standardized way despite the differences in the way an EHR is housed and stored.¹ HL7 ’s goal is to leverage pre-existing “logical and theoretical models to provide a consistent, easy to implement, and rigorous mechanism for exchanging data between healthcare applications.” ²

What are the basic building blocks?

FHIR is built upon exchangeable information called a “Resource.” It utilizes HTTP-based REST application programming interfaces (APIs) to access, manage and use these specified Resources.³ Resources are comprised of specific data elements, a common set of metadata and a human readable part. For example, a FHIR Resource can represent categories such as patients, specified treatments, laboratory results, imaging, etc. and will be defined by common use data elements that result in exchangeable patient records. Resources take this data information and use the components necessary to connect it to other relevant information related to other Resources. Simplicity, adaptability, and efficiency are what FHIR is accomplishing for the field by making Resources easily understood and implemented for further exchange.

What is philosophy?

The philosophy of FHIR is to build a foundation of Resources, individually or combined, that are adequate for most common use cases, as defined by HL7 . The Resources are used to characterize core information that will be shared amongst the majority of implementations. As for the remaining content, a built-in extension mechanism can be to combine Resources in and Implementation Guide that addresses the specific use case, which leads to expansion of the capabilities set out by FHIR .²

How is it secure?

Protecting health data, such as sensitive patient information, is one of the biggest concerns found within healthcare information technology. It is imperative that stakeholders know that data exchange will be secure. FHIR uses modern security standards, such as authentication, encryption, and labelling sensitive information for authorized users, to protect its data.⁴

How has it evolved?

HL7 FHIR  was initially presented in May 2012, and has since evolved into what it is today through 4 different releases. What started as a true draft standard that included 49 Resources, has now reached 145 Resources, and counting. In 2013, the first release comprised of an emphasis on two use cases – creation of a Personal Health Record on mobile devices and the retrieval of documents to a mobile device – which stimulated the chatter, interest, and thoughts on how this could change the field of healthcare information technology. In 2015 for the second release, additionally non-clinical Resources were implemented, the structure of Resources were adjusted for ease of extensions and the FHIR Maturity Model (FMM) was created that established a 0 – 5 draft to final status set of levels for the Resources to achieve. In the third release in 2017, improvements were made to Resources (Clinical, Administrative, Financial, Clinical Decision Support and Clinical Quality Measure) and a new framework was established to enhance workflow. Currently, we are in release 4 that occurred in 2019. This release included 9 new Resources, Patient and Observation Resources named as normative content, RESTful API, XML and JSON formats, and removed the Trial Use name. Additionally, FHIR release 4 was published as the requirement standard for Health IT Certification in the Final Rule for the 21^st Century Cures Act. From 49 to 145 Resources, the standard has continually upgraded to meet the needs of the community and will continue to do so.⁵

Where are we currently?

In 2023, HL7 published FHIR  Release 5 which offered improvements in interoperability and data management abilities. There were numerous advancements that were made, but here are some of the tops to be explored. To create a more complete FHIR based healthcare data exchange across the field, they focused on the development of more supportive conditions and standardized framework for easier access to the data. Patient data is easier accessed and managed for a focus on care and minimizes the room for error within the data. Users now have the opportunity to use topic-based subscriptions, which enables FHIR notifications when there is a change to data in the system. Medication definition Resources were revised to better align with drug catalogs. Resources can now be managed in large sectors to be more efficient in data exchange, such as Groups and Lists. The number of Resources is now published to be 157. To find a comprehensive list of all that can be found in release 5 and what to know about FHIR , click the link here. FHIR looks to maintain its work and prominence in health IT and continue to integrate itself as the standard.

With healthcare data increasing in complexity, technology advancing and expectations rising amongst users, FHIR will continuously adapt and progress in its capabilities. FHIR is the future of healthcare technology and will continue to promote data interoperability by the use of its central operating system.

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References

1. eCQI Resource Center. FHIR – Fast Healthcare Interoperability Resources | eCQI Resource Center. ecqi.healthit.gov. Published January 31, 2024. https://ecqi.healthit.gov/fhir#:~:text=Fast%20Healthcare%20Interoperability%20Resources%20(FHIR).
2. HL7 FHIR. FHIR Specification (v5.0.0: R5 – STU). www.hl7.org. Published March 26, 2023. https://www.hl7.org/fhir/overview.html.
3. Ayaz M, Pasha MF, Alzahrani MY, Budiarto R, Stiawan D. The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities [published correction appears in JMIR Med Inform. 2021 Aug 17;9(8):e32869]. JMIR Med Inform. 2021;9(7):e21929. Published 2021 Jul 30. doi:10.2196/21929.
4. The Office of the National Coordinator for Health Information Technology.What Is HL7  FHIR ? https://www.healthit.gov/sites/default/files/page/2021-04/What%20Is%20FHIR%20Fact%20Sheet.pdf.
5. The Office of the National Coordinator for Health Information Technology.FHIR  Version History and Maturity. https://www.healthit.gov/sites/default/files/page/2021-04/FHIR%20Version%20History%20Fact%20Sheet.pdf.

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