Lab of the Future: Trends and Technologies

Credit: iStock ADAPTING TO CHANGE IN MODERN CLINICAL LABS HOW AI AND MACHINE LEARNING CAN IMPROVE SCIENTIFIC DATA HANDLING REDEFINING LAB PRACTICES TO PRIORITIZE SUSTAINABILITY LAB OF THE FUTURE Trends and Technologies SPONSORED BY CONTENTS 5 How AI and Machine Learning Can Improve Scientific Data Handling 8 The New Technologies Changing the Face of Data Integrity in Drug Discovery 11 Top Ten Tips for Data Compliance 16 Adapting to Change in Modern Clinical Labs: An Interview With Sean Tucker 19 How AI Is Revolutionizing Scientific Research 23 Redefining Lab Practices To Prioritize Sustainability 28 The Adoption of AI: Critical Concerns in the Life Sciences 31 The Museum of Analytical Antiquities LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 3 TECHNOLOGYNETWORKS.COM FOREWORD The “Lab of the Future” is a key topic across industries, including the pharmaceutical sector, with discussion of what Pharma 4.0 will be. The insights in this eBook will help you on your journey into this transformative landscape. Components of the Lab of the Future are many and varied. A future-ready lab integrates automation, comprehensive data capture, streamlined data management and proactive insights. In regulated industries, this work must be in compliance with applicable regulations and guidance. Coupled with these approaches to automation are the elimination of paper printouts and transcription error checking. Data must be acquired electronically at the beginning of the process and remain electronic until the final result is generated. Artificial Intelligence (AI) and machine learning (ML) are essential to the Lab of the Future, driving advancements in fields such as cancer research, materials science and drug discovery. Laboratories generate vast amounts of data and AI can be used to help extract essential information and knowledge, however, there are major concerns around the implementation of AI in the pharmaceutical industry and questions remain on how best to validate these tools for compliance. New technologies support compliance, but fostering a quality-first culture is essential, starting with senior management and extending across teams. Despite fears around revalidation costs, updating computerized systems is necessary to prevent obsolescence. Another consideration for any Lab of the Future is sustainability and environmental impact. How can scientists adapt or introduce changes to meet these twin requirements? Finally, although the focus so far has been on technologies, the missing part of the equation are the scientists. Introducing any new working practices or automation will result in change to working practices. How can this be managed effectively across diverse, multigenerational teams? We hope this eBook inspires and equips you with the knowledge to advance toward a Lab of the Future. Bob McDowall, PhD PREPARE YOUR LAB FOR THE FUTURE TRADE UP to the Latest Technologies from Waters Now is the perfect time to future-proof your lab and replace your obsolete and aging LC or MS systems. Don’t let budget restrictions stand in the way We o er finance options and flexible financing solutions to match payments to funding, bridge budget cycles and optimize your budget. Contact us today to learn more about Waters eligible trade-in o ers. We’ll help you create a replacement plan that is right for your lab.  Ensure reliable operation  Conform to regulatory guidelines  Meet evolving business requirements  Benefit from Waters service and support expertise New Technology you can rely on Stay Compliant Avoid Risk Improve Your Productivity waters.com/TradeUpNow Waters is a trademark of Waters Technologies Corporation. All other trademarks are the property of their respective owners. ©2024 Waters Corporation 5 LAB OF THE FUTURE Artificial intelligence and machine learning (AI/ML) have become widespread in scientific research. A subset known as generative AI (GenAI) that can produce detailed text and images with just a few human prompts was thrust into the general public’s consciousness with the November 2022 release of ChatGPT. A year later, ChatGPT became the first nonhuman entity to make the journal Nature’s annual list of the 10 most important contributors to science. A 2023 survey by the same journal found that 30% of postdoctoral researchers were employing AI chatbots to generate and edit code, manage literature and refine the text of their scientific papers. More recently, an Elsevier survey of corporate scientific R&D professionals indicated that 96% believe AI will accelerate knowledge discovery and 93% expect AI to lower business costs. More than 85% said that these technologies would improve work quality and free up time to pursue higher-value projects. However, similarly high numbers expressed concerns about misinformation, errors and reduced critical thinking. GenAI is merely one type of AI/ML, though the lines between GenAI, predictive AI and machine learning — that must be trained on specialized datasets — have been blurring. While forecasts can vary widely, the AI/ ML field as a whole has been exploding. A 2023 analysis suggested that the global AI market share would grow by about 19% annually to more than $2.575 trillion by 2032. Another analysis predicted a 14% annual growth in the market size of AI just for clinical trials, to $4.4 billion, in 2032. How AI and Machine Learning Can Improve Scientific Data Handling Neil Versel Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 6 Nvidia, which makes high-performance computing chips that fuel AI development, briefly surpassed Microsoft and Apple in June 2024 as the world’s most valuable company based on market capitalization. The technology is not without growing pains, however, as evidenced by an Nvidia sell-off that shaved $500 billion in value less than two weeks later. Another Nature article suggested that ChatGPT may be “corrupting” scientific peer review. In a Harvard Data Science Review study, researchers found that ChatGPT creator OpenAI’s GPT-4 large language model became less accurate between March and June 2023, while its supposedly less advanced predecessor, GPT-3.5, improved. As developers work through these issues, scientific researchers are finding all kinds of applications for AI/ ML. This listicle will explore how AI/ML is being used in different areas of scientific research to accelerate discoveries and improve efficiency. Applications and benefits of AI/ML in scientific data handling Data collection and management AI can automate data collection and curation for biomedical research, as well as streamline analysis at speed and scale no human could achieve. Notably, predictive AI has been shown to improve the detection of anomalies and outliers, particularly from medical images. Generative AI might be useful for upscaling images to “super-resolution” by creating simulated data to fill in gaps. It can also summarize massive datasets to accelerate hypothesis development. Writing code As ChatGPT is less than two years old, digital health developers are just beginning to understand its utility. Recent literature has found that ChatGPT and other GenAI technologies can act as a “coding copilot” to fill in technical expertise gaps at medical and scientific institutions and allow for more rapid development, testing and troubleshooting. Investigators from China and South Korea reported in the journal iMeta that GenAI systems, “excel in identifying and correcting errors in code, quickly pinpointing syntactical and logical issues, and providing explanations, thereby accelerating debugging and serving as an educational resource for developers.” Target discovery In 2021, researchers from India’s National Institute of Pharmaceutical Education and ResearchAhmedabad stated that AI had already begun to “revolutionize” the pharmaceutical industry. According to their paper, which appeared in Drug Discovery Today, AI-based predictive models can quickly recognize hit and lead compounds by predicting molecular structures, accelerating drug target discovery and validation. Similar models can also be applied to drug repurposing. A more recent paper from researchers at Insilico Medicine in Hong Kong discussed how AI’s strength in analyzing massive multiomic datasets and biological networks — including GenAI-derived synthetic data — is helping to shorten discovery timelines by mitigating the historical trade-off between high-confidence and novel targets. There are even specific AI tools that can quantify both novelty and confidence, helping investigators choose more promising drug targets. Clinical trial matching AI is starting to show its utility in clinical trial design and setup, particularly when it comes to recruiting subjects within the confines of complex inclusion and exclusion criteria by analyzing patient medical records to predict likely outcomes. It can also help extend trials to historically underserved populations. So far, the application of AI for trial matching has been most prominent in oncology. Notably, the US National Institutes of Health (NIH) and its affiliated centers including the National Library of Medicine (NLM) are testing several AI tools for trial matching. TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 7 Notably, NLM created a prototype large language model called TrialGPT specifically to match patients with trials with similar accuracy but more rapidly than a panel of physicians. Genomics Genomics is a natural application for AI and ML because of the sheer size of the datasets involved. In variant calling, there has been scientific evidence around the efficacy of Google’s DeepVariant since at least 2018, but that is far from the only genomics-related application of predictive and generative technologies. “Diagnostic rates in hereditary disease, particularly involving neurodevelopmental disability, have improved substantially in recent years thanks to AIbased approaches,” a team from molecular laboratory company Invitae reported in a 2023 review article in the American Journal of Medical Genetics. Of note, a group called the Genomic Testing Cooperative has implemented AI-based algorithms to assist pathologists in producing molecular profiles of their patients in pursuit of differential diagnoses of hematologic and solid tumors. Another paper, from 2022, went so far as to say that AI plus genomic and precision medicine “have the potential to improve patient healthcare.” Neuroscience There is an emerging field that Cold Spring Harbor Laboratory in New York has dubbed NeuroAI, which focuses on the nexus between AI and neuroscience. “The upcoming generation of scientists will need to possess fluency in both domains,” a recent editorial in Nature Machine Intelligence argued. A 2023 review article in the journal Sensors explained how brain-computer interface software can help detect diseases, predict progression and even control prosthetic devices. By automating data analysis, the technology can also lighten the workload of overtaxed radiologists, rendering them more productive and less likely to err in image interpretation. Environmental science AI can help design more energy-efficient buildings, monitor emissions of greenhouse gases and other pollutants, as well as predict shifting weather and climate patterns. This is not a new concept, as the American Meteorological Society is preparing to host its 24th annual conference on AI for environmental science. It should be noted, however, that the data centers behind large AI models in medicine and other industries can have massive carbon footprints due to their high energy demands. The future of AI/ML in scientific data handling Not quite two years after the introduction of ChatGPT, masses of scientific researchers are embracing AI/ML, including GenAI, for data collection and management, writing computer code, sorting through unprecedentedly large genomic datasets, identifying new drug targets, expanding clinical trial pools and addressing existential challenges such as climate change. But many are doing so with a wary eye, as these emerging technologies make mistakes, as evidenced by the prevalence of GenAI “hallucinations.” Indeed, in the Elsevier survey of corporate R&D professionals, 71% of respondents expect results from GenAI tools to be based on “high-quality trusted sources only.” A majority said that training an AI/ML model to be factually accurate and not cause harmful mistakes would “strongly increase their trust” in this technology. • 8 LAB OF THE FUTURE Automation and drug development are a perfect pair. Improved reliability and increased efficiency are the most obvious benefits of automation, which can overcome inconsistency and wasteful processes that have been the biggest hurdles in the pharmaceutical industry. The well-worn figures of the field’s failures are stark evidence for the need for updated technology – 90% of drugs fail during development and as much as 60% of research and development costs are attributable to attrition. Bringing in technology that can improve these grim statistics is a business imperative. That’s why the introduction of Industry 4.0 - shorthand for the application of technologies like automation, robotics and machine learning to industrial processes – has been rapid and redefining in drug discovery. One important benefit of these innovations is their effect on the robustness and quality of drug development information or data integrity. In this article, we’ll look at how data integrity can be enhanced in a digitalized pharmaceutical field and the nascent risks posed by embracing these technologies. The benefits of Industry 4.0 Filipa Mascarenhas-Melo, an assistant professor at the Polytechnic Institute of Guarda in Portugal, says that Industry 4.0 innovations offer “unprecedented opportunities” to pharmaceutical companies. The New Technologies Changing the Face of Data Integrity in Drug Discovery RJ Mackenzie Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 9 Enhanced tracking is one of the central promises of the switch from analog to digital data. While handwritten experimental reports or assay readouts can be lost, damaged or forged, automated processes can be calibrated to record and save each step of a process, making analysis far easier for companies and regulators. Dr. Stephen Goldrick, an associate professor of digital bioprocess engineering at University College London, called these changes “pivotal”. “This evolution mitigates concerns related to manual handling errors and enhances the ease of managing, storing, retrieving and querying electronic batch records,” he added. Mascarenhas-Melo points to systems such as electronic data management systems (EDMS) and laboratory information management systems (LIMS), which enable the tracking of electronic documents and samples within a workflow. Blockchain-based systems, which provide fixed ledgers that track data entries, can be used to follow the whole pharma supply chain. This boosts “transparency, authenticity and integrity of data related to drug manufacturing, distribution and sales,” says Mascarenhas-Melo. Industry 4.0 tools have also simplified data recovery after system failures, facilitated secure communication and data encryption protocols and made quality management protocols that ensure data integrity easier to implement. As digitalization improves workflows for pharmaceutical companies, regulators in the space must adapt their own processes to keep on top of these new technologies. Mascarenhas-Melo co-authored a review of good automated manufacturing practices, or GAMP, a series of recommendations for the design of digital systems published by the International Society for Pharmaceutical Engineering (ISPE). The first GAMP was created in response to the emergence of digital workflows in 1991 and the latest version – GAMP 5’s second edition, released in July 2022 – bears little resemblance to its 33-yearold predecessor. “The latest edition of GAMP 5 acknowledges the non-linear, agile and cyclical nature of modern software development, reflecting the industry’s shift towards continuous iteration and innovation,” comments Mascarenhas-Melo. Regulators, she says, have to prioritize “agility and responsiveness”. Despite this, the ISPE took 14 years to release an update to GAMP-5’s first edition, published in 2008. During this period, many companies’ digital infrastructure fundamentally changed – whether you consider the rise of cloud software offerings, which can be updated and scaled more easily, or the proliferation of AI tools in the field. Evergreen principles guide regulation Even in the face of these fundamental changes, there are data integrity practices that have proved to be enduring cornerstones of regulation. The ALCOA acronym, which emphasizes that good data should be Attributable, Legible, Contemporaneous, Original and Accurate, was first coined by the FDA’s Stan W. Woollen in the early 1990s. ALCOA (albeit in a modified form that also considers data’s completeness, consistency, endurance and availability) still “stands out for its effectiveness and widespread recognition within the industry,” says Goldrick. By using these agnostic principles as a guide, regulators are trying to make their guidance more adaptable, even in the face of changing “This evolution mitigates concerns related to manual handling errors and enhances the ease of managing, storing, retrieving and querying electronic batch records,” TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 10 technologies. “The percentage of violations requiring action has shown a decline, possibly indicating an improvement in compliance efforts by pharmaceutical companies,” Mascarenhas-Melo explains. While regulators and companies have proved nimble enough to navigate these changing technologies while ensuring data integrity, Goldrick suggests making sure staff and users come along for the ride is an essential factor that risks being ignored. “The implementation and maintenance of these systems demand significant training for users, entailing considerable costs and time dedicated to training,” he says. Mascarenhas-Melo highlights new GAMP-5 guidelines which encourage end users to liaise extensively with suppliers to make use of their expertise in maintaining digital systems. In turn, suppliers will have to adapt to a new, continuous support model where they work closely with end users long after a sale has been completed. The risks of Industry 4.0 Completely digitalized processes have security vulnerabilities that suppliers and pharmaceutical companies must prepare for, says Goldrick. “The reliance on cloud storage elevates the industry’s exposure to cybersecurity threats, including the potential for unauthorized access to sensitive or patientspecific information,” he says. These risks are already evident. A recent ransomware attack on UnitedHealth, the world’s largest healthcare company by revenue, involved threats to release patient data and saw pharmacy operations seize up for days. This attack happened concurrently with a strike on pharmaceutical giant Cencora. Ensuring critical healthcare data’s integrity, and training those asked to steward that data, is the only way to protect against such attacks. How to ensure data integrity in an evolving landscape Pharma’s match-up with Industry 4.0 technologies is here to stay. The questions are no longer whether the field will be changed by the technologies but rather who will be first to leverage the new state of play to their advantage: industry players, regulators or cybercriminals. To make systems secure and maximize data integrity, pharmaceutical companies will have to invest in both hardware and proper training for their staff. The costs involved may make some hesitate, but MascarenhasMelo has a warning for companies who drag their feet: “Those slow to adopt may find themselves at a competitive disadvantage, struggling to catch up as their more digitally mature counterparts forge ahead.” • ABOUT THE INTERVIEWEES: Dr. Filipa Mascarenhas-Melo is an assistant professor in the Higher School of Health at the Polytechnic Institute of Guarda, Portugal and an integrated researcher at the Institute’s BRIDGES - Biotechnology Research, Innovation and Design for Health Products – program. She is also a collaborating researcher in the Department of Pharmaceutical Technology at the University of Coimbra. Dr. Stephen Goldrick is a lecturer and associate professor in Digital Bioprocess Engineering at University College London’s Department of Biochemical Engineering. He specializes in the application of mathematical modelling and advanced data analytics to processes in the biotechnology field. REFERENCES: 1. van der Graaf PH. Probability of Success in Drug Development. Clin Pharmacol Ther. 2022;111(5):983–985. doi: 10.1002/cpt.2568 2. Pedro F, Veiga F, Mascarenhas-Melo F. Impact of GAMP 5, data integrity and QbD on quality assurance in the pharmaceutical industry: How obvious is it? Drug Discov Today. 2023;28(11):103759. doi: 10.1016/j. drudis.2023.103759 3. Bongiovanni S, Purdue R, Kornienko O, Bernard R. Quality in Non-GxP Research Environment. In: Bespalov A, Michel MC, Steckler T, eds. Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Springer International Publishing; 2020:1-17. doi: 10.1007/164_2019_274 11 LAB OF THE FUTURE Data compliance in both regulated and non-regulated laboratories includes data integrity (can you trust the numbers) and data quality (can you make a decision). Implicitly it also includes compliance with applicable regulations or quality standards.1-3 Although there are others, these are my “Top Ten Tips” for data compliance, as shown in Figure 1, which illustrates the relationships and interactions between them. 1. Data compliance starts at the top Any organization’s senior management is responsible for the quality management system (QMS)1–6 and therefore data compliance. Lack of senior management involvement in the QMS will directly impact the overall approach required by an organization – e.g., quality culture and ethos, the ability to admit mistakes without blame, compliance procedures and training as well as Gemba walks by senior management. Senior management is also responsible for ensuring that systems are current and comply with applicable regulations and standards, non-compliant systems need to be assessed and remediated. 2. Data integrity and compliance is everybody’s job Following on from Tip 1 is the message that data compliance is everybody’s job. It’s an ongoing task that demands constant attention. To keep data compliance at the forefront, senior management, laboratory management and supervisors need to reinforce it. One way to do this is a Gemba walk, where managers talk with staff directly to understand their challenges and emphasize the importance of data compliance. Part of Top Ten Tips for Data Compliance Bob McDowall, PhD Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 12 the Gemba walk involves talking with analysts who work with analytical processes regularly, this can help management understand where there are compliance issues; which can be raised as improvement ideas. Linked to Tip 1 is the ability to own up to mistakes and then understand the root cause – perhaps there is a problem with an SOP or workflow that is too complex and needs to be modified. 3. Have you qualified the instruments and validated application software? Analytical instruments and systems must be qualified and/or validated for the intended use against operating parameters defined in a laboratory user requirements specification (URS) to show they meet their intended use.7 Coupled with point of use or system suitability test checks performed on the day of analysis (e.g., analytical balance or pH meter). This means that the laboratory can rely on the data generated by the instrument. Many instruments are controlled by an application installed on a separate computer that acquires, processes, stores and reports results. This software needs to be validated for intended use requiring a URS and configuration of the software before testing against the user requirements. Documenting software configuration is important and will cover: • System settings: Use electronic signatures and include the reason for a change in audit trail entries • Workflows: Define workflows that enforce procedures rather than relying on procedural controls. This is discussed in more detail in Tip 7 • User roles: Confirm who can do what by defining different user roles and their access privileges • Account management: Ensure users are allocated to roles with no conflicts of interest (e.g., users with administrator privileges) TOP TEN TIPS FOR DATA COMPLIANCE Data Compliance Starts at the Top Managing the Human Factor OOS Investigations Complete Data Means Complete Data Implement Technical Controls Know Your Data Lifecycles Proble Processes & Procedures Validated Analytical Procedures Qualified Instruments & Validated Software Data Integrity and Compliance Is Everyone's Job 10 9 8 7 6 5 4 3 2 1 Figure 1: Interaction of the Top Ten Tips for Data Compliance. Credit: Technology Networks. TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 13 Both the URS and the configuration specification must be placed under change control and updated when the application is updated. An earlier article on how to handle software updates is available.8 4. Validated analytical procedures under actual conditions of use With trained staff using qualified and validated analytical instruments and systems, analytical procedures must be developed and validated under actual conditions of use.1,9,10 This includes procedures transferred from another laboratory or pharmacopoeial procedure. Ideally, experimental design software should define the design space and identify critical parameters that need to be controlled. The procedure is then validated and used to deliver reliable results in operational use. 5. Problem processes and procedures? Are your business processes, both manual and computerized, overly complex and difficult? Are your computer systems hybrid (electronic records with signed paper printouts)? Are calculations performed in spreadsheets with manual data input? These are signs that your processes and systems are slow and inefficient. A second-person review of an analysis could likely take longer than the analysis itself. Paper printouts coupled with manual input to computer systems result in multiple transcription error checks which are slow and error prone. It is far better to have an electronic and vavlidated process to streamline both the analysis and review. Are your SOPs and analytical procedures overly complex? This could make it difficult for an analyst to follow. On the flip side, if they’re overly simplistic an analyst might be forced to make assumptions. Don’t wait until there is a document review scheduled, get feedback from the analysts who work with the procedures and make any changes as necessary. 6. Know your data lifecycles Each analytical procedure will generate data and metadata but not all analytical procedures are created equal. Some simpler procedures are observation tests such as color, appearance or odor. Here the data would be a single observation along with the associated metadata such as analyst, date/time, batch or lot number, etc. For more complex spectroscopic and chromatographic analytical procedures, the data lifecycle is more complex with on-the-day checks of instruments, sample preparation, instrumental analysis, interpretation and generation of the reportable result. Knowing the data to be collected for each procedure is critical for ensuring that they are captured, interpreted and reported correctly.11 7. Implement technical controls As mentioned in Top Tip 2 about workflow clarity, there are two options for controlling a process: procedural controls (via an analytical procedure or SOP) or technical controls (enforcing an electronic workflow in an application). Of the two it is preferable to implement and validate technical controls. Although there may be an overarching procedure, it is the technical controls that will enforce it. Have you ever forgotten to initial and date a document or missed a signature off a record? Technical controls will ensure that after a user has logged on, all work will be attributed to them via their user identity and date/ time stamped. If a record requires a signature, then the workflow will ensure that this occurs at the correct place in the workflow. A review cannot occur until the performer of the work has electronically signed the record set, furthermore, if the correct control is enabled the performer cannot review and approve their own work. This approach, prioritizing technical over procedural controls, has been identified for inclusion in the update of EU GMP Annex 11.12 TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 14 8. Complete data means complete data FDA GMP 211.194(a) requires complete data from laboratory tests1 to ensure integrity and compliance. This is simple to interpret: everything captured during an analysis including all problems, instrument failures, etc. If a procedure or data system does not specify all the data and metadata to collect during an analysis, how does a reviewer or quality assurance specialist know how the data are complete? In contrast, GLP requires raw data which is defined as original observations and activities necessary to reconstitute the study report which is the same expressed differently.2,13 It also means that analysts can’t be selective in what is being reported – e.g., if one set of results is out of specification you can’t ignore them and repeat the work until a passing result is obtained. This would require an investigation to determine the root cause, which leads us to Top Tip 9. 9. Mishandling deviations including OOS investigations Laboratories are required to document and investigate deviations,1,14 however, many laboratories label these as incidents which is not a GMP term and is a way of hiding deviations. The FDA has published two versions of the Out-of-Specification (OOS) guidance for the investigation of aberrant results15,16 that outlines the process for laboratory investigations to find an attributable root cause for the deviation. The major problem with many investigations is that they are either not scientifically sound or that the root cause is attributed to “human error”. Too many of the latter raise questions about the quality and training of analytical staff. 10. Managing the human factor Even if you have Top Tips 1–9 implemented effectively in a laboratory, it’s important to remember that all processes and computerized systems are operated by humans, who can make mistakes. Technical controls can reduce but not eliminate problems caused either deliberately or in error. This is where Top Tip 1 comes into play and comes full circle back to how a laboratory trains its staff for integrity and compliance. Staff must be educated on what constitutes prohibited actions (e.g., reporting selective results, deleting data, etc.) and what actions are permitted. In addition, when mistakes are made, they must be reported and documented. In a non-blame culture, the reason for the mistake must be established and looked at as an opportunity to improve. Summary As can be seen from Figure 1, data compliance is not a single item but a series of interrelated activities that is predicated by active senior management involvement. Data compliance is everybody’s job and that must be reinforced by management throughout the organization. However, it comes down to how individuals work and act, which is the key to data compliance. REFERENCES 1. United States Food and Drug Administration. 21 CFR 211 - Current Good Manufacturing Practice for Finished Pharmaceuticals. Silver Spring, MD: United States Food and Drug Administration. 1978. https://www.ecfr.gov/current/ title-21/chapter-I/subchapter-C/part-211. 2. United States Food and Drug Administration. 21 CFR 58 - Good Laboratory Practice for Non-Clinical Laboratory Studies. Silver Spring, MD: United States Food and Drug Administration. 1978. https://www.ecfr.gov/current/title-21/ chapter-I/subchapter-A/part-58 3. International Organization for Standardization. ISO/IEC 17025:2017 - General requirements for the competence of testing and calibration laboratories. Geneva: International Organization for Standardization. 2017. https://www.iso.org/ obp/ui/#iso:std:iso-iec:17025:ed-3:v1:en 4. United States Food and Drug Administration. FDA Guidance for Industry Data Integrity and Compliance With Drug CGMP Questions and Answers. Silver Spring, MD: United States Food and Drug Administration. 2018. https://www.fda.gov/ regulatory-information/search-fda-guidance-documents/ data-integrity-and-compliance-drug-cgmp-questions-andanswers 5. Pharmaceutical Inspection Co-operation Scheme. PIC/S PI-041 Good Practices for Data Management and Integrity in Regulated GMP / GDP Environments. Geneva: Pharmaceutical Inspection Co-operation Scheme. 2021. https://picscheme.org/docview/4234 6. World Health Organisation. WHO Technical Report Series No.996 Annex 5 Guidance on Good Data and Records Management Practices. Geneva: World Health Organisation. 2016. http://academy.gmp-compliance.org/ guidemgr/files/WHO_TRS_996_ANNEX05.PDF TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 15 7. United States Pharmacopoeia. USP General Chapter <1058> Analytical Instrument Qualification. Rockville, MD: United States Pharmacopoeia Convention Inc. https://doi.usp.org/ USPNF/USPNF_M1124_01_01.html 8. McDowall RD. How to future-proof your LIMS: handling software updates. Technology Networks. https://www. technologynetworks.com/tn/how-to-guides/how-tofuture-proof-your-lims-handling-software-updates-370757. Published 2023. Accessed April 2024 9. United States Pharmacopoeia. USP General Chapter <1220> Analytical Procedure Lifecycle. Rockville, MD: United States Pharmacopoeia Convention Inc. 2022. https://www.uspnf. com/sites/default/files/usp_pdf/EN/USPNF/usp-nf-notices/ gc-1220-pre-post-20210924.pdf 10. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Q2(R2) Validation of Analytical Procedures, Step 4 Final. Geneva: ICH. 2023. 11. McDowall RD. Data Integrity and Data Governance: Practical Implementation in Regulated Laboratories. The Royal Society of Chemistry; 2018. doi: 10.1039/9781788013277 12. European Medicines Agency. Concept Paper on the Revision of Annex 11 of the Guidelines on Good Manufacturing Practice for Medicinal Products – Computerised Systems. 2022. https://www.ema.europa.eu/ en/documents/regulatory-procedural-guideline/conceptpaper-revision-annex-11-guidelines-good-manufacturingpractice-medicinal-products_en.pdf 13. Organisation for Economic Co-operation and Development (OECD). OECD Series on Principles of Good Laboratory Practice and Compliance Monitoring Number 1, OECD Principles on Good Laboratory Practice. Paris: OECD. 1998. 14. European Commission. EudraLex - Volume 4 Good Manufacturing Practice (GMP) Guidelines, Chapter 1 Pharmaceutical Quality System. Brussels: European Commission. 2013. https://health.ec.europa.eu/document/ download/e458c423-f564-4171-b344-030a461c567f_ en?filename=vol4-chap1_2013-01_en.pdf 15. United States Food and Drug Administration. FDA Guidance for Industry Out of Specification Results. Rockville, MD: US Food and Drug Administration. 2006. https://www.fda.gov/ media/71001/download 16. US Food and Drug Administration. FDA Guidance for Industry, Investigating Out-of Specification (OOS) Test Results for Pharmaceutical Production. Silver Spring. MD: US Food and Drug Administration. 2022. https://www.fda.gov/ media/158416/download 16 LAB OF THE FUTURE As the technological advancements of the Lab of the Future unfold, laboratory leaders are tasked not only with implementing innovation but also with fostering a flexible, multi-generational workforce in a rapidly evolving field. Sean Tucker, laboratory director of North Kansas City Hospital, was invited to answer your questions about the complexities of being a modern lab leader in Technology Networks’ Ask Me Anything session. Alex Beadle (AB): How can communication methods be adapted to effectively engage with all generations entering the lab? Sean Tucker (ST): We don’t all fit in a box, but we all have our strengths. It’s important to understand that there are individuals who receive information differently. As a leader, you must be able to respond to these differences. If you strictly communicate via email, it’s not going to be successful, and it’s not going to create relationships between each of your employees. Email is obviously a central component of an organizational structure, so our expectation is that every employee looks at their inbox at least once every single day. We use daily huddles in our core clinical laboratory, microbiology laboratory, transfusion medicine department and in our anatomic pathology department. These huddles involve meeting as a small team for 5 to 10 minutes. If there are any concerns or issues that take additional time, resources or commitment, these are set Adapting to Change in Modern Clinical Labs: An Interview With Sean Tucker Alex Beadle Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 17 aside for a conversation outside of the huddle. In a 24/7 operation, staffing and staffing issues are constant, and so we have had to adapt how we communicate with individuals, because we can’t expect them to check work emails at home. One way of working around this is implementing group text options. AB: What skills are essential for lab leaders today, and what are the best ways to develop them? ST: You have to be able to be flexible, and you cannot get discouraged when there are challenges ahead of you. As a leader, you’re in the position to be able to support individuals, but you’re not responsible for coming up with every solution. We are scientists. We are highly trained and capable individuals. Not everyone is able to manage complex situations or projects on their own, but that’s where leadership comes in. I think an integral component of leadership is to allow everyone to seize opportunities and help guide you as a leader. I have used the DISC personality test and StrengthsFinder, which are great to better understand an individual’s strength components. It is also helpful to find out what your leadership style is. I have found that a servant leadership style has worked for me the best. Scientists are quick to understand who is authentic and who is not authentic, and so we have to be cautious about being overconfident as we approach complex situations. But, you have to be confident enough to manage a project and make sure that it’s completed on time. AB: What does the Lab of the Future mean for clinical labs? ST: We have already seen the integration of automation within our laboratories over the last several decades and we are going to continue to see automation in clinical and research laboratories. In the United States, we are expecting approximately 30,000 retirements in the next few years, with about 10,000 graduates replacing them, so staffing shortages are going to be one of the top two major issues in the upcoming decades, and automation will likely be part of the solution. I imagine that in upcoming years we will see the introduction of robotic phlebotomists (ultrasound guided robotics that are experts in obtaining blood through a venipuncture procedure). We’re also starting to see the use of drones for laboratory specimen transport, specifically for organ donation. People want their health care delivered to them, so making things adaptable and more convenient is going to be integral moving forward. Artificial intelligence (AI) is also going to help us become more efficient. AI will not replace scientists, pathologists or cytologists but it will allow us to be more efficient and increase safety. In the United States, hospitals are using different types of systems that do not communicate with each other. This means a patient’s record can be different from one I imagine that in upcoming years we will see the introduction of robotic phlebotomists (ultrasound guided robotics that are experts in obtaining blood through a venipuncture procedure). TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 18 state to the next. I think we’re going to be able to use AI to be able to mine that data from their previous hospital. I think right now, we’re too isolated. By integrating data and optimizing AI to help us mine patient data, we’ll start to see some really positive outcomes in the next 10 to 20 years. AB: What advice would you have for early career scientists who are aspiring to become lab leaders themselves? ST: One thing I have always done is try to put myself in an uncomfortable situation. Too often we don’t detour from our comfortable positions to explore new opportunities. Try to detour as often as you possibly can within reason, both in your professional experience as well as in your personal life. I have received some of the best leadership advice through my experience as a husband and father, and learning what communication works well. It doesn’t have to always be in the laboratory where you develop your leadership skills. You have to be genuine, truthful and as transparent as you possibly can be. Whether it’s through travel, meeting friends or dating, put yourself into situations that make you a little uncomfortable and see where that pushes you. I guarantee you’re going to learn from that, and then you’re going to be able to take these stories and skill sets into leadership. AB: What are the biggest challenges facing laboratories today, and how are you looking at addressing these moving forward? ST: Staffing and employment will continue to be a challenge. For the last 20 years, I have been going into schools and talking about my career to make sure that kids understand that it’s not just a nursing degree or a physician’s degree that are the only opportunities in healthcare. There is a whole world of health sciences that you can participate in. Once you get to the college level, it becomes challenging to change your degree, so you have to reach them at a younger age. The other challenge is how we reimburse testing. We have to be able to cover the cost per test, and employees, their benefits and electricity all have to be included in the cost analysis to see if we can afford to perform the test in our laboratories. What we get reimbursed drives what testing we can perform. We have to advocate to our government structures for the gold standard tests to help support patient outcomes, showing that investing in the laboratory can have incredible outcomes on long term patient healthcare impacts. One example that we’re doing here currently in the laboratory is looking at looking at new microbiology identification technology that allows us to diagnose an infection sooner, so that we can get patients on the appropriate antibiotic therapy and discharge them from the hospital as quickly as possible, safely. The quicker you can discharge a patient from the hospital safely, the sooner your opportunity to have a new bed open is available, and in the United States, we get reimbursed based upon patient stay. Managing those challenges with reimbursement is kind of the next thing that keeps me up at night. ABOUT THE INTERVIEWEE: Sean Tucker is the director of Laboratory Services at North Kansas City Hospital. He is also the Chair-Elect of the Council of Laboratory Management and Administration at the American Society for Clinical Pathology, where he was also awarded the “Forty under 40” award. 19 LAB OF THE FUTURE Artificial intelligence (AI) is advancing scientific research, transforming traditional methods and opening new avenues for discovery. From accelerating material development to providing insights into cancer risk, AI’s capabilities extend across diverse fields. As AI continues to evolve, its role in research is advancing the pace of discovery and setting the stage for innovations that were previously thought to be years away. Here, we explore recent research utilizing AI. AI Speeds Up the Discovery of Quantum and Energy Materials In a study published in Advanced Materials, researchers report on the development of an AI model that predicts a materials’ optical properties across a wide range of light frequencies. Understanding the optical properties of materials is essential for developing optoelectronic devices, such as LEDs, devices that are pivotal in the semiconductor industry’s current resurgence. However, traditional means of calculation prevent the rapid testing of a large number of materials. The new AI model is able to predict the optical properties of a material with the same accuracy as quantum simulations using only crystal structures. This makes it suitable for a wide variety of applications, such as screening materials for high-performance solar cells and detecting quantum materials. “Machine-learning models utilized for optical prediction are called graph neural networks (GNNs),” said Ryotaro Okabe, a chemistry graduate student at MIT. “GNNs provide a natural representation of molecules and materials by representing atoms as graph nodes and interatomic bonds as graph edges.” How AI Is Revolutionizing Scientific Research Kate Robinson Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 20 AI Reveals Sex-Specific Risks Associated With Brain Tumors By examining how brain tumors differ between men and women using AI, researchers found distinct patterns in tumor incidence, progression and response to treatment. Glioblastoma is one of the most aggressive forms of cancer, with a median survival of 15 months after diagnosis. However, pinpointing the characteristics that might help doctors forecast which tumors are likely to grow more quickly has previously proven elusive. In the study, researchers leveraged the computational power of AI models to probe large volumes of medical images and find patterns. The AI model was trained to recognize the unique characteristics of tumors using data from more than 250 studies of glioblastoma patients. The model was then trained to identify any patterns between these characteristics and patients’ survival time while accounting for their sex. “There’s a ton of data collected in a cancer patient’s journey,” said Pallavi Tiwari, an associate professor in the Department of Radiology at the University of Wisconsin. “Right now, unfortunately, it’s usually studied in a siloed fashion, and this is where AI has huge potential.” AI Helps Decode Infant Behavior In a study published in Scientific Reports, researchers used AI to reveal that foot movements are crucial for understanding how infants connect with their environment. The baby-mobile experiment uses a colorful mobile gently tethered to an infant’s foot. When the baby kicks, the mobile moves, linking their actions to what they see. This setup helps researchers understand how infants control their movements and discover their ability to influence their surroundings. In the study, researchers tracked infant movements with a 3D capture system and applied various AI techniques to examine which methods best captured the nuances of infant behavior across different situations and how movements evolved over time. Both machine and deep learning methods accurately classified clips of infant movements as belonging to different stages of the experiment. Foot movements showed the highest change and accuracy rates. “Adults can follow instructions and explain their actions, while infants cannot. That’s where AI can help. AI can help researchers analyze subtle changes in infant movements, and even their stillness, to give us insights into how they think and learn, even before they can speak. Their movements can also help us make sense of the vast degree of individual variation that occurs as infants develop,” said Nancy Aaron Jones, a professor in the Department of Psychology at Florida Atlantic University. AI Model ‘SPOT’ Accurately Predicts Substrate Transport An AI model has demonstrated impressive accuracy in predicting substrate transport across cellular membranes, a critical insight for drug development. Transport proteins are responsible for the ongoing movement of substrates into and out of a biological cell and therefore determine the effectiveness of drug delivery. To increase the ease of determining which substrates a specific protein can transport, bioinformaticians at Heinrich Heine University Düsseldorf (HHU) developed the model, named SPOT, to predict this with a high degree of accuracy. The model was trained on 8,500 transporter-substrate pairs converted into numerical vectors. This allowed the vector for a new transporter and potentially suitable substrates to be entered into the AI system. The model was then used to predict the likelihood that certain substrates will match the transporter. Discussing the applications of this model, Dr. Martin Lercher, a professor of computational cell biology at HHU said: “In biotechnology, metabolic pathways TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 21 can be modified to enable the manufacture of specific products such as biofuels. Or drugs can be tailored to transporters to facilitate their entry into precisely those cells in which they are meant to have an effect.” AI Model Can Reveal the Structures of Crystalline Materials Research published in the Journal of the American Chemical Society demonstrates the development of an AI model that can improve the determination of powdered crystal structures in X-ray crystallography. Crystalline materials are made of lattices that consist of many identical, repeating units. For materials that exist only as powdered crystal, solving these structures becomes much more difficult because the fragments don’t carry the full 3D structure of the original crystal. The new model, called Crystalyze, was trained on simulated X-ray diffraction patterns for thousands of materials produced by an existing AI model. Crystalyze determines the size and shape of the lattice and which atoms will go into it. It then predicts the arrangement of atoms within the lattice. For each diffraction pattern, the model generates several possible structures, which can be tested by feeding the structures into a model that determines diffraction patterns for a given structure. When tested, the model was accurate about 67% of the time and able to determine structures for over 100 previously unsolved patterns. “Our model is generative AI, meaning that it generates something that it hasn’t seen before, and that allows us to generate several different guesses,” said Eric Riesel, MIT graduate student. “We can make a hundred guesses, and then we can predict what the powder pattern should look like for our guesses. And then if the input looks exactly like the output, then we know we got it right.” AI and the Lab of the Future 58% 66% 23% 14% 17% 42% 48% 28% 30% 8% 28% Unstructured data Data silos/no access data FAIRification of data Inability to share/transfer analytical methods Ontology management Lack of metadata standardization Lack of infrastructure and tooling Resistance to data sharing/collaboration Lack of knowledge and expertize Lack of data governance within organizations Other The Challenges Facing AI in the Lab of the Future A Pistoia Alliance report looked at what their life science leaders thought were the greatest barriers to their adoption of AI and other lab digitization. The graph below shows the percentage of respondents that identified issues as one of the top three obstacles. Unstructured Data Even if data is available to train and power AI systems, it still has to be in an appropriate format for the AI to parse. Messy datasets, like unclear handwritten notes, compromise the usefulness of tools like NLP. Data Silos/No Access to Data Data silos and access problems are common within bulky company structures. Maximizing AI’s potential requires different sources of data to be made available for training and analysis; cracking open these silos is essential work. Resistance to Data Sharing and Collaboration Data systems can be upgraded overnight; changing attitudes towards data sharing can be a generational task. Academics and researchers taught to preserve their IP and afraid of being scooped can block the availability of data for AI systems. Lack of Metadata Standardization AI systems are useful because they analyze not just data, but also the data that provides details about that data – metadata. By investing in informatics solutions like laboratory information management systems (LIMS), metadata can be organized and mandated into lab practices. Click here to view the full infographic 23 LAB OF THE FUTURE Scientists around the world are becoming increasingly conscious of the environmental impact of their research, however, the path to adopting sustainable practices can be complex. While some scientists may be uncertain about how to initiate or prioritize changes to counteract this footprint, others may not fully appreciate the broader advantages of implementing such changes. Challenges such as a lack of understanding, insufficient accountability among staff, and a need for more encouragement and support can hinder progress. Addressing these barriers is key to fostering a culture where sustainable practices are the norm – not the exception. In this article, we explore ways scientists can adapt their day-to-day practices to reduce the environmental footprint of their labs. We also highlight expert advice, initiatives and case studies that support these changes and showcase their effectiveness. Practical steps towards a sustainable lab Laboratories typically use five to 10 times more energy per square meter compared to office spaces – with fume hoods and ultra-low-temperature freezers being two main energy-intensive culprits commonly found in labs. While scientists may not be aware of the energy and water usage that their research warrants, they are likely to notice their consumption of materials, such as single-use plastics in biological labs. It is estimated that laboratories worldwide generate approximately 5.5 million metric tons of plastic waste each year – the equivalent of filling 67 cruise liners Redefining Lab Practices To Prioritize Sustainability Laura Elizabeth Lansdowne Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 24 The University of Colorado Boulder (CU Boulder) Green Labs Program, founded by Kathryn Ramirez-Aguilar in 2009, has found ways to effectively engage scientists and lab personnel in sustainable practices. “While I was working in labs, I began to wonder if my research was truly helping more than it was hurting because of the large resource consumption. For the most part… solutions had not yet been developed to help researchers take action to reduce the environmental footprint of their research. I left the lab bench and gained the support of CU Boulder to start the program to try to address this issue,” said Ramirez-Aguilar. The CU Boulder Green Labs Program aims to reduce the consumption of multiple resources, including energy, water, materials and hazardous chemicals in the university’s laboratories. It also advocates for the efficient and effective use of research equipment and lab space. “Over the years, scientists have repeatedly expressed interest in diverting their waste streams from the landfill,” noted Ramirez-Aguilar. Given that this area has garnered a lot of interest from researchers, it has fueled the demand for eco-friendly plastic labware which some companies are stepping up to try to address. Since the beginning, the CU Boulder Green Labs Program has also focused on enabling discussions with scientists on other key topics related to efficiency, including energy and water savings, and in more recent years, efforts have included the benefits of sharing equipment and the importance of optimized use of laboratory space. Sharing instruments saves resources “When scientists choose to share research equipment between labs then there are fewer instruments to purchase and maintain with research funding, thus saving researchers’ money. At the same time, less electricity is consumed and it’s a more efficient use of lab space – which is expensive space to build and particularly energyintensive because of ventilation needs,” said RamirezAguilar. Figure 1: Key reasons why managed, shared research equipment benefits institutions. Adapted from a figure created by CU Boulder. Credit: Technology Networks. TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 25 She continues: “If facilities with directors are set up to manage the shared research equipment and users, then there is also significant time savings (which also equates to financial savings) and other benefits to be realized by researchers because now there is a knowledgeable director to help researchers with training and troubleshooting problems.” In 2018, CU Boulder launched the BioCore Facility to streamline laboratory equipment sharing for three science departments on the campus. The program manages 90 shared pieces of equipment and over 60 researchers are utilizing the services across 18 laboratories. Since the program began, it has led to savings of approximately USD 3 million, attributed to the sharing and redistribution of resources. Many other universities and research institutes have implemented similar initiatives. For example, the University of Cambridge’s Equipment Sharing Project allows members of staff and students to share > 4000 items across various universities, including the University of Oxford, Imperial College London, University College London and the University of Southampton. Identifying quick wins to cut emissions Lisa O’Fee, sustainability advisor at The Institute of Cancer Research (ICR), reiterates the importance of initiatives similar to those implemented at UC Boulder. She is working to embed sustainability in everything the ICR does, in its mission to “defeat cancer”. Launched in December 2022, the ICR sustainability action plan “Sustainable Discoveries” sets out how the ICR will respond to the environmental crises we face such as climate change and biodiversity loss. ICR is committed to achieving net zero by 2040, with an interim reduction of 42% in carbon emissions by 2030. She describes some “quick wins” to reduce energy and carbon emissions: “Waste audits can identify if the correct waste segregation process is being adhered to and raise awareness. It’s important to look for opportunities for improvement, for example, new recycling routes and improved signage/training of staff. Sustainable procurement training equips scientists with the knowledge to select sustainable products – those that can be reused, recycled, or are manufactured from recycled content.” Energy monitoring to identify pieces of equipment that consume the highest amount of power is good to raise awareness and reinforce switching off if applicable. “Traffic light-coded switch-off stickers are also a good way to prompt scientists,” noted O’Fee. She highlights one of the two energy-intensive culprits mentioned above – the ultra-low temperature freezer: “Good practice in cold temperature storage is key to reducing energy consumption within the lab.” Programs like the International Laboratory Freezer Challenge promote optimal use and upkeep of cold storage equipment. This contest, organized by two nonprofit entities – the International Institute for Sustainable Laboratories (I2SL), where RamirezAguilar serves on the board, and My Green Lab – is free to enter and is designed to encourage laboratories to adopt best practices. Labs are scored on areas including: preventative maintenance, materials management, temperature tuning, retirements and upgrades, and cutting-edge practices. The 2023 Freezer Challenge, in which almost 2,000 labs participated worldwide, resulted in an energy saving of 20.7 million kWh, equivalent to approximately 14,663 metric tons of CO2, which is over twice the CO2 savings achieved in the previous year’s challenge. Based on the United States Environmental Protection Agency’s Greenhouse Gas Equivalencies Calculator, this amount is equivalent to offsetting greenhouse gas emissions from driving 36.9 million miles in an average gasoline-powered passenger vehicle or the annual CO2 emissions from 2,854 homes’ electricity use. Calculating carbon emissions Laboratories consume a significant amount of energy, so decreasing energy consumption can lead to proportional TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 26 reductions in CO2 emissions. But, considering the diverse range of emissions associated with equipment, consumables and supply chain activities (Figure 2), it can be difficult to know how best to calculate, assess and alter a lab’s carbon footprint. “It is much easier to calculate carbon emissions from Scope 1 and 2. Scope 3 emissions that are associated with purchased goods and services are more difficult to calculate and for the most part are calculated on a spend basis. A hybrid methodology is a much more accurate way to calculate emissions for the majority of Scope 3,” explained O’Fee. Resources, such as the Greenhouse Gas Protocol’s Technical Guidance for Calculating Scope 3 Emissions, are designed to help facilities evaluate their Scope 3 emissions. Adopting a life cycle thinking approach to lab equipment While sharing and maintaining equipment is important, when it’s time to replace instruments, it’s vital to consider how energy-efficient they are. O’Fee believes researchers shouldn’t be afraid to engage with equipment suppliers and challenge them about the sustainability practices associated with their products. She offers the following advice: “You could put together a sustainable supplier list and an accompanying questionnaire for the suppliers, requesting information on their carbon emission data, responsible procurement policy, and if they have any procurement framework or standard that they work to, for instance, EcoVardis or ISO 20400. Increasingly suppliers of lab consumables like plastics are giving information as to the type of plastic and amount within a product.” Using the associated emission factors, it is therefore possible to calculate the amount of carbon emitted. There are clear connections between resource use efficiency in scientific research and cost savings or cost Figure 2: Scope 1, 2 and 3 emissions. Credit: Technology Networks. “If lab members are buying more energy-efficient equipment or conducting their research in a way that reduces the use of energy or water, then there will be less energy and water consumption for the institutions to pay for,” noted Ramirez-Aguilar. TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 27 avoidance. Financial benefits can be achieved for both researchers and institutions. “If lab members are buying more energy-efficient equipment or conducting their research in a way that reduces the use of energy or water, then there will be less energy and water consumption for the institutions to pay for,” noted Ramirez-Aguilar. The CU Boulder Green Labs Program works with scientists on financial incentives. Funding can be applied towards the cost of research equipment purchases if efficient equipment options exist and if lab members are selecting efficient equipment. Ramirez-Aguilar elaborates: “We have been able to obtain funding to contribute toward the purchase of top energy-efficient ultra-low temperature (ULT) freezers and energy-efficient biosafety cabinets, the addition of eco-modes to glove boxes and the purchase of waterless condensers to use in chemical synthesis.” Promoting sustainable practices and sharing lessons learned The insights and initiatives highlighted in this article underscore a pivotal shift towards sustainable laboratory practices. By embracing shared equipment, diligent maintenance and strategic energy management, laboratories not only reduce their carbon footprint but also pave the way for a future where research is synonymous with resource mindfulness. The journey towards sustainability shouldn’t be focused solely on reducing emissions or saving costs – it’s also important to cultivate a mindset where every decision, from equipment procurement to daily operations, is made with sustainability at its core. “We are not limited by the number of engaged laboratory scientists that want to do more. After all, I have found that researchers care and typically go into science because they want to contribute to society or the world in a way that will have a positive impact,” concluded RamirezAguilar. USEFUL RESOURCES 1. Million Advocates for Sustainable Science 2. Freezer Challenge 3. International Institute for Sustainable Laboratories Best Practices 4. International Institute for Sustainable Laboratories Working Groups 5. International Institute for Sustainable Laboratories Sustainable Lab Awards 6. My Green Lab 7. Greenhouse Gas Protocol Technical Guidance for Calculating Scope 3 Emissions ABOUT THE INTERVIEWEES: Kathryn Ramirez-Aguilar completed her PhD in analytical chemistry in 1999. She gained 15 years of research experience before shifting her focus away from the bench, dedicating her efforts toward enhancing the environmental sustainability of scientific research and addressing its influence on climate change more broadly. As well as managing the CU Green Labs Program at the University of Colorado Boulder, she serves on the board of the International Institute for Sustainable Laboratories (I2SL), acts as chair of the I2SL University Alliance Group (UAG), and heads the Bringing Efficiency to Research Grants initiative under the I2SL UAG, aiming to integrate efficiency and sustainability into US research funding. Lisa O’Fee, a biochemist specializing in drug discovery with a focus on oncology, has been part of the Institute of Cancer Research (ICR) since 2013. In 2023, Lisa transitioned from the ICR’s Division of Cancer Therapeutics to assume the role of sustainability advisor at the institute. In her new capacity, she has been pivotal in developing and implementing the institute’s sustainability strategy. She coordinates several sustainability initiatives at the ICR, including the freezer challenge and My Green Lab certification for the laboratories. 28 LAB OF THE FUTURE Artificial intelligence (AI) is continuing to expand and develop across many industries, including the life sciences. A survey conducted by the Pistoia Alliance at its annual conference uncovered that most life sciences experts acknowledge AI’s potential, but concerns remain about its trustworthiness and implementation at scale. Technology Networks spoke with Dr. Becky Upton, president of the Pistoia Alliance, to learn more about the key findings of the survey and their significance. In this interview, Dr. Upton also discusses some of the main hurdles preventing the wider adoption of AI in pharma and how these challenges may be overcome. Anna MacDonald (AM): Can you give us an overview of the aims of the survey conducted at the Pistoia Alliance conference and the key findings? Becky Upton (BU): The Pistoia Alliance conducted its recent survey to gauge the extent of AI adoption in the life sciences sector. Our conference was the perfect place to conduct this research, gathering nearly 300 experts from across the pharmaceutical, technology and regulatory fields. Key findings revealed that while 70% of life sciences experts acknowledge AI’s potential, many struggle with its implementation at scale, largely due to issues like data integrity and interoperability. The survey also uncovered concerns about the trustworthiness of AI. A notable number (63%) of respondents expressed worries that poor data quality could lead to incorrect conclusions and even potentially harmful clinical decisions. These insights underscore how urgent it is for more industry-wide collaboration to address these challenges, including by standardizing data practices and supporting the ethical use of AI. The Adoption of AI: Critical Concerns in the Life Sciences Anna MacDonald Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 29 The survey results are instrumental in shaping our ongoing and future initiatives aimed at fostering innovation through collaboration. AM: Did any of the survey findings surprise you? BU: The survey findings also echo trends uncovered by the Alliance’s Lab of the Future (LoF) research from September 2023. The LoF report equally cited issues related to data management, including once again that low-quality and poorly curated datasets are the number one barrier (58%) to implementing AI. Privacy and security concerns around data were also raised as a challenge by 34% of respondents in the report. It’s no surprise to see these issues still persist more than half a year later. While AI technology is advancing rapidly – with iterations to consumer models like ChatGPT dropping monthly – it will take longer for the highly regulated life sciences industry to adopt AI at scale. Working habits must change, and the industry must ensure AI is being adopted ethically and safely, with relevant regulatory guidance in place. Ultimately, we are dealing with patients’ health, so decisions about the technology’s use cannot be taken lightly. This is why bodies like the Pistoia Alliance are so important – to create a shared space to tackle challenges related to emerging technologies together and to ensure that collaboration delivers. AM: What are the main hurdles that are preventing wider adoption of AI in pharma? BU: The adoption of AI in pharma is hindered by a combination of technical and cultural challenges. Underpinning the successful application of AI is the need to first establish a robust foundational data backbone. Yet, data quality and management clearly remain a significant barrier in the life sciences industry. Many organizations struggle with data silos, unstructured data and a lack of metadata standardization, all of which prevent data from being easily accessible and interoperable. This fractured data environment slows down workflows and diminishes the potential benefits of AI. Cultural resistance to new approaches and to sharing expertise is another hurdle. Many companies have been using the same processes and toolsets for years, so there is often pushback to shaking workflows up by introducing AI, even if overall it would speed up R&D. This is why it’s so important to build user-friendly tools and for technology champions to share best practice across their organization and the industry as a whole. One final barrier is the lack of proven business cases for AI adoption. Senior stakeholders often need tangible evidence of AI’s benefits to justify further investment, such as time saved or the number of new drug targets identified. Without clear success stories, it’s challenging to secure the necessary buy-in for further AI programs from decision-makers and budget-holders. AM: Can you tell us about some of the projects that the Pistoia Alliance has launched and how they will help to address some of the challenges highlighted? BU: Since its inception, the Pistoia Alliance has been fueling the successful adoption of emerging technologies and acting as champions of science through its portfolio of projects and communities. The Alliance is committed to ensuring collaboration delivers tangible outcomes – from producing best practice guides to developing new data models and standards. Here are just a few of our initiatives. AI Community of Experts: This community provides a safe space for organizations to share ideas and best practices using our pre-competitive legal framework. The community is looking to develop dedicated projects in several important areas, including AI in regulatory compliance, ethics in AI, information security for AI and benchmarking for AI models and recently established a new large language models in biological R&D project. Identification of Medicinal Products (IDMP) Ontology: The Alliance created a common data model in collaboration with Bayer, Novartis, GSK, Roche, Merck KGaA, Boehringer Ingelheim, Johnson & Johnson, AstraZeneca, Amgen, AbbVie and Pfizer. This ontology sets data standards to improve substance TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 30 identification, cross-border prescriptions, regulatory process integration, supply-chain analytics and pharmacovigilance. Clinical Trial Environmental Impact Community: In partnership with more than a dozen Pistoia Alliance members from pharma, service and technology providers and the Sustainable Health Coalition, Pistoia Alliance is developing a standard methodology for measuring the carbon footprint of clinical trials. This will allow companies to quantify the environmental impact of their clinical development programs against an industry-wide standard, including an evaluation of the impact of the use of decentralized components. AM: How does the Alliance plan to strengthen ties with global regulatory agencies? BU: The Alliance plans to deepen our involvement with regulatory agencies in our projects and communities, to decrease the amount of friction in regulatory submissions. The partnership is mutually beneficial; via the Alliance, regulators can speak to multiple companies under one roof about new standards and processes for companies to adopt. At the same time, pharma companies can share their existing standards and processes, allowing regulators to identify potential issues and requirements accordingly. This collaborative approach saves time, money and, crucially, speeds up the delivery of new treatments to patients. We have already seen success with this strategy, notably in our work with the FDA on the In Vitro Pharmacology Group and Global Substance Registration System (GSRS) Consortia. AM: 70% of the life science experts surveyed recognize AI’s potential. Did the survey uncover reasons why the remaining 30% do not share these views? Is the Alliance planning to investigate this further? BU: Of the remaining respondents, 13% said that practical AI applications in the life sciences industry are limited, saying the technology is too theoretical at the moment. This further reiterates stakeholders needing tangible evidence of AI’s benefits to justify further investment. On the other hand, 10% said that AI has already led to a lot of breakthroughs in R&D. It’s true that some areas have seen AI success, such as accelerating existing workflows in small molecule discovery and lead optimization for new drug candidates – shaving months off time-intensive processes. The final 7% said that only cutting-edge startups and tech-led companies can use AI effectively, but we don’t believe this is the case at the Alliance. Established pharma companies possess substantial data science expertise and a wealth of data that have the potential to build robust AI models. The key lies in implementing the right data standards, developing a clear plan for AI use cases and actively collaborating with and learning from the successes of other companies. Facilitating this level of collaboration is exactly the reason why the Pistoia Alliance exists. ABOUT THE INTERVIEWEE: Dr. Becky Upton was appointed as the Pistoia Alliance’s first female President in June 2022. She is a long-time supporter of pre-competitive collaboration in R&D and the critical role it plays in advancing science. Becky currently leads the Pistoia Alliance’s strategy, defining its future within areas of increasing importance to the industry, such as data standards, emerging technologies, diversity and inclusion, sustainability, and precision medicine. Becky has worked across business development and scientific services for companies including VWR (now part Avantor) and Pion. She has a PhD in Biochemistry from Imperial College and an MBA from Cranfield University. 31 LAB OF THE FUTURE Most labs have them. Working analytical systems purchased back in the mists of time but with obsolete operating systems and/or instrument applications that have not been updated since initial qualification and validation. Welcome to your Museum of Analytical Antiquities. We explore the reasons for this and suggest ways to keep your analytical systems current and compliant. In this article we discuss why it is important to keep current with software updates; it is complementary to last year’s article on how to keep your LIMS current.1 Here, the focus is on analytical systems consisting of an instrument controlled by an application installed on a network or workstation for acquiring, processing, reporting results and storing data. Typically (depending on intended use), these are classified as United States Pharmacopoeia (USP) <1058> Group C systems.2 We will look at the Museum of Analytical Antiquities from two perspectives. The first is from the business rationale and why software should be kept up to date, and the second is from the regulatory expectations of keeping current and meeting regulatory requirements. These are your front-line analytical systems, and not just those retained to read legacy data – although, that is another discussion altogether. The complete version of this article is available here. Why do labs create a Museum? In GMP-regulated laboratories, there is the requirement to validate software to demonstrate that it is fit for its intended use.3–7 This is perceived, incorrectly, as a major bottleneck. So once validated, the company mantra is DON’T TOUCH IT. The rationale is that revalidation The Museum of Analytical Antiquities Paul Smith and Bob McDowall Credit: iStock TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 32 is perceived as a major cost. This is not the case as we will demonstrate in this article. This results in laboratories failing to upgrade their application software and operating systems as well as failing to fix known cybersecurity flaws which could be exploited if, for example, uncontrolled USB devices are used carelessly.8 Unlike a LIMS, which is a network solution, most computerized analytical systems are designed for standalone use and hence are not connected to the IT infrastructure. Partly this is due to IT not wanting to manage laboratory systems or laboratories not wanting to be controlled by IT. Ideally, these systems should be connected to the IT network at least for backup and recovery, time synchronization and user account management, etc. However, the usual situation is that most sit in majestic isolation in your laboratory. This provides some degree of protection from external cybersecurity threats but introduces other competing risks, like individual data backup/restore procedures (especially if USB devices are used), as well as business continuity, disaster recovery and data governance. Instrument issues and obsolescence Hardware issues are ways of providing candidates for the Museum as shown in Figure 1. If an analytical instrument is obsoleted by a supplier, the availability of replacement parts and consumables may prevent the use of the instrument. If instrument firmware is either not supported or updated, then software drivers may not work. In addition, if an old computer system fails, is the new replacement workstation able to run an outdated operating system or a new instrument not supported by the old application? The focus of this article is on software updates and the consequences of not being current. Software = change Unlike an analytical instrument with moving parts software does not wear out. However, software is not static but changes over time for a variety of reasons until it reaches its end of life, as shown in Figure 1: 1. Application enhancements: Adding new features or functions for useability and better performance driven mainly by market forces. 2. Compliance enhancements: Incorporation of new or updated GXP regulatory compliance, data integrity or pharmacopoeial requirements. 3. Error fixes: Fixing software errors in the application software through a specific hotfix Instrument Workstation Obsolete Operating System Application Can’t Run On New Hardware Instrument Obsoleted Failure to Install Security Patches Lack of or Poor Compliance Functions Firmware Not Updated Application not Updated Application Obsoleted Instrument Computer Platform Application Failure to Implement Error Fixes Key Obsolete Database & Middleware Software Drivers Not Compatible Parts and Supplies Not Available New Instrument Not Supported by Old Application Figure 1: Areas for failing to update an analytical computerized system. Credit: R D McDowall. TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 33 or minor release and keeping compatibility with technology. 4. Security fixes: Remediating cybersecurity vulnerabilities of an operating system, software component or application. 5. Outdated application or software: Obsolescence or discontinuation of the application software with no supplier support. Obsolescence or discontinuation of an operating system or middleware components with no supplier support or a database has reached the maximum size the older versions can support. Instrument firmware can also be a reason for obsolescence, as failure to update firmware can prevent interfacing to the application software. Software drivers for the instrument may not be compatible with a new application. When faced with one or more of these situations, what should a laboratory do? Please note, that the ostrich option is not one of the choices here. Update of software As noted above updates of the software can occur for one of the reasons given in the section above. Let us take them in turn: 1. Application enhancements: Depending on the business process being automated by the system as well as the system’s intended use, some of these features may or may not be of interest in a release. It is important to read the release notes to see what is new in each version. If an upgrade has features that a laboratory wants to use, often it is not installed as the validation is perceived as too difficult or the perception that the whole system must be revalidated. This will be discussed later in this article. 2. Compliance enhancements: For example, when a Pharmacopeia mandatory general chapter changes, the requirements will be incorporated in the latest version of the software. Therefore, the new version is required to comply with the Pharmacopeial procedure. If you don’t upgrade, how will you comply? A spreadsheet, like the ostrich option, is not an effective solution. 3. Error fixes: If a laboratory is impacted by a software error, it is advisable to install the hotfix to resolve the problem. There may also be application updates to keep compatible with changes in other system components. 4. Security fixes: Vulnerabilities in operating systems, databases and middleware are identified and patches are released to prevent exploitation by malware. The advice is to keep current to prevent loss of data. 5. Outdated application of software: Application support isn’t just a tech support phone call to help solve a problem or resolution only on a known bugonly support. It may also include the lack of a piece of paper that says the vendor supports your system and the inability of a vendor to provide hardware qualification on a regular basis if the firmware/ software set is not compatible with their qualification tool. This can be especially risky after instrument preventative maintenance (PM) is performed. An additional factor not listed above is the contractual agreement with an application supplier.3, 9 The quality of the software support you get, from training to helping you answer questions during a regulatory inspection, can depend on your risk management process, the supplier knowledge, their QMS and the infrastructure of the company. Here, a laboratory pays for ongoing software support, and as part of the agreement, the application must be kept on current or the previous major release, otherwise support lapses or the company must upgrade to the latest version before support is provided. Obsolescence of system components All components, including the instrument and the computerized system, can be impacted by this. The instrument supplier may decide that the instrument TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 34 and or the application has reached the end of life and is declared obsolete. Ideally, the supplier will manage this process well ahead of time through an EGS (end of guaranteed support) framework. There may be limited software support offered by a supplier, where remediation of known bugs is the only option (for up to two years) to give users the opportunity to move to a new system. In some instances, the supplier may be able to provide extended professional services such as computerized system validation (CSV) to speed up the implementation of updates or upgrades. More important is that Windows 10 operating system becomes obsolete in October 2025, after which there will not be any updates unless you pay for them. This option will last only for three years. What are you going to do? Again, the ostrich option is not very helpful. The scale of recent global cybersecurity attacks has highlighted that some of the most at-risk organizations are those managed by governments – where Windows XP or other obsolete operating system software may still be in use! Regulatory expectations and keeping current The formal title of FDA manufacturing regulations is Current Good Manufacturing Practice for Finished Pharmaceutical Products or cGMP. The focus here is on the word current. There is an explanation of the term in preamble comment 17 in the Federal Register of September 197810 where the GMPs were published, but a better explanation is found on the FDA’s website11: Accordingly, the “C” in CGMP stands for “current,” requiring companies to use technologies and systems that are up-to-date in order to comply with the regulations. Systems and equipment that may have been “top-of-theline” to prevent contamination, mix-ups, and errors 10 or 20 years ago may be less than adequate by today’s standards. Note, the last sentence: obsolete systems may be less than adequate by today’s standards. Those working in Europe who do not make products for the US market should stop laughing now as an equivalent regulation is found in the European Directive 2001/83/EC Article 23 §112: … the authorisation holder must, in respect of the methods of manufacture and control provided for in Article 8(3)(d) and (h), take account of scientific and technical progress and introduce any changes that may be required to enable the medicinal product to be manufactured and checked by means of generally accepted scientific methods. So, both FDA regulations and the EU directive state that companies must keep current, and this includes computerized systems. Therefore, how will laboratories handle questions from an inspector that their systems are not current or up to date? Two possibilities are that the company policy of not upgrading the system or a supplier’s support is inadequate. Here we can look at periodic reviews of computerized systems. Role of periodic reviews To ensure that computerized systems remain in a validated state, EU GMP Annex 11 clause 11 requires periodic evaluations: Computerised systems should be periodically evaluated to confirm that they remain in a valid state and are compliant with GMP. Such evaluations should include, where appropriate, the current range of functionality, deviation records, incidents, problems, upgrade history, performance, reliability, security and validation status reports.3 Whilst not explicitly stated, obsolescence of software components is a part of a periodic review as a system not maintained by software updates may have security, backup and recovery flaws and data may be vulnerable to loss from malware. In the unlikely event that there have been several software changes during the period covered by the review, there should be an assessment of the cumulative impact of the changes on the system. However, PIC/S PI-041 in section 9.3.5 as part of a TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 35 periodic review states that data integrity limitations of software should be addressed in a timely manner.13 Given the extremely close alignment of PIC/S and EU GMP, this may be an addition to the draft Annex 11 update although it is not mentioned explicitly in the 2022 proposal.14 Equally so, as hardware ages, the bathtub curve shows that the failure rate increases and the system and the data it holds becomes more vulnerable. Increasingly, instrument and application suppliers can provide some level of instrument monitoring services, so that instruments that break down more are prioritized for replacement. Following Sod’s Law, any such failure will always come at the worst possible time. Therefore, one role of a periodic review should highlight to management the risks posed by obsolete systems and simplified through a monitoring service. The example GMP regulatory citations for obsolete systems will be found in the complete article Role of the supplier A key support role for any supplier of analytical systems is to provide accurate and reliable release notes for each version or hotfix that they issue, e.g., new features and bug fixes. The aim is to inform users of changes and their impact on the system: e.g., is a change localized or does it impact the entire system? This information permits any laboratory whether to upgrade or not and also the level of revalidation required from nothing to a full system revalidation. The process owner is accountable for the decision and extent of validation in conjunction with the validation team and QA. Typically, for most laboratory computerized systems, these updates are not frequent unless there is a critical software bug that requires urgent resolution. This is unlike a SaaS LIMS where software is upgraded every 3–4 months whether you like it or not as this is in your supplier contract.1 Other topics discussed in detail in the complete article are: • Application upgrades • Application replacement • Operating system obsolescence • Does your old app run on Windows 11? • Revalidating the system Summary As noted in the summary of the LIMS article, it is much easier to have small incremental upgrades which are easier to handle, rather than major projects if left too long.1 The same applies to computerized analytical systems. Many citations for older systems focus on their failure to have audit trail functionality. With the update of Annex 11, audit trail functionality will not be a regulatory expectation but mandatory. To avoid potential technical and regulatory issues, start upgrading the systems in your Museum of Analytical Antiquities NOW. In October 2025 Windows 10 will be obsolete. Many laboratory systems run on this operating system – what will you and your supplier do? This appears as déjà vu all over again as there are still systems with Windows XP and Windows 7 that are currently entries in the Museum. Will your Museum of Analytical Antiquities increase in size? ACKNOWLEDGEMENTS We would like to thank Mahboubeh Lotfinia, Mike Korbel, Simon Mitchell and Steffan Nielsen for their review and comments in preparation of this article. REFERENCES 1. McDowall RD. How To Future-Proof Your LIMS: Handling Software Updates. Technology Networks. https://www. technologynetworks.com/tn/how-to-guides/how-tofuture-proof-your-lims-handling-software-updates-370757. Published March 3, 2023. Accessed September 5, 2024. 2. United States Pharmacopeia. General Chapter, –1058– Analytical Instrument Qualification. USP-NF. Rockville, MD: United States Pharmacopeia. Published 2024. 3. European Commission. EudraLex - Volume 4 Good Manufacturing Practice (GMP) Guidelines, Annex 11 Computerised Systems. European Commission: Brussels. Published June 30, 2011. 4. Food and Drug Administration. FDA Guidance for Industry General Principles of Software Validation. 2002,: Rockville, MD. 5. 21 CFR Part 11; Electronic Records; Electronic Signatures TECHNOLOGYNETWORKS.COM LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 36 Final Rule. Federal Register, 1997. 62(54): p. 13430 - 13466. 6. Good Automated Manufacturing Practice (GAMP) Guide 5, Second Edition. 2022, Tampa, FL: International Society of Pharmaceutical Engineering. 7. GAMP Good Practice Guide A Risk Based Approach to GXP Compliant Laboratory Computerised Systems, Second Edition 2012, Tampa, FL: International Society for Pharmaceutical Engineering. 8. RD McDowall. Consigning SneakerNet to the Graveyard of Technology. Spectroscopy. 2021. 36(4): p. 14-17. 9. EudraLex - Volume 4 Good Manufacturing Practice (GMP) Guidelines, Chapter 7 Outsourced Activities. 2013, European Commission: Brussels. 10. 21 CFR 211 - Current Good Manufacturing Practice for Finished Pharmaceuticals, in Federal Register. 1978. p. 45014 - 45089. 11. Facts About the Current Good Manufacturing Practices (CGMPs). FDA. https://www.fda.gov/drugs/ pharmaceutical-quality-resources/facts-about-currentgood-manufacturing-practices-cgmp. Published 2021. Accessed September 5, 2024. 12. 12. European Union Directive 2001/83/EC on Medicinal Products for Human Use. 2001, European Commission: Brussels. 13. IC/S PI-041 Good Practices for Data Management and Integrity in Regulated GMP / GDP Environments. 2021, Pharmaceutical Inspection Convention / Pharmaceutical Inspection Cooperation Scheme: Geneva. 14. Concept Paper on the Revision of Annex 11 of the Guidelines on Good Manufacturing Practice for Medicinal Products – Computerised Systems. 2022; Available from: https://www.ema.europa.eu/en/ documents/regulatory-procedural-guideline/conceptpaper-revision-annex-11-guidelines-good-manufacturingpractice-medicinal-products_en.pdf. LAB OF THE FUTURE: TRENDS AND TECHNOLOGIES 37 TECHNOLOGYNETWORKS.COM CONTRIBUTORS Alex Beadle Alexander is a science writer and editor for Technology Networks. He writes news and features for the Applied Sciences section, leading the site’s coverage of topics relating to materials science and engineering. He holds a master’s degree in Materials Chemistry from the University of St Andrews, Scotland. Anna MacDonald Anna is a senior science editor at Technology Networks. She holds a first-class honors degree in biological sciences from the University of East Anglia. Before joining Technology Networks she helped organize scientific conferences. Bob McDowall Bob is an analytical chemist with over 50 years’ experience and has been involved with process redesign, specifying and implementing laboratory informatics solutions for over 40 years and has nearly 35 years’ experience of computerized system validation in regulated GXP environments. Kate Robinson Kate Robinson is a science editor for Technology Networks. She joined the team in 2021 after obtaining a bachelor’s degree in biomedical sciences. Laura Lansdowne Laura is the managing editor at Technology Networks, she holds a first-class honors degree in biology. Before her move into scientific publishing, Laura worked at the Wellcome Sanger Institute and GW Pharma. Neil Versel Neil is a healthcare and life sciences journalist, specializing in bioinformatics, information technology, genomics, patient safety, healthcare quality and health policy. Versel has been covering healthcare since 2000, across a wide range of publications in the US, Canada and Europe. Paul Smith Paul is a laboratory compliance consultant helping regulated laboratories understand instrument qualification, data integrity and the latest regulatory trends. RJ Mackenzie RJ is a freelance science writer based in Glasgow. He covers biological and biomedical science, with a focus on the complexities and curiosities of the brain and emerging AI technologies. RJ was a science writer at Technology Networks for six years, where he also worked on the site’s SEO and editorial AI strategies.