Tag: Artificial intelligence

The Intelligent Lab: How AI and Advanced Metabolomics are Redefining Scientific Discovery

The pace of scientific discovery is no longer governed solely by the physical limits of manual experimentation. We are currently witnessing a shift that is as transformative as the invention of the microscope itself. Artificial Intelligence (AI) and advanced metabolomics are reshaping how science is conducted, moving research from a “trial-and-error” model to a predictive, data-driven discipline. By combining high-resolution analytical hardware with machine learning, laboratories can now solve complex biological challenges – such as developing animal-free culture media – with unprecedented speed.

The complexity of the modern workflow

In the fast-evolving landscape of biopharmaceuticals and cell biology, the reliance on traditional methods often leads to significant hurdles. For decades, the industry has relied on fetal bovine serum (FBS) to supplement cell culture media, despite its high costs, ethical concerns, and inherent inconsistency.

Many lab teams find themselves buried under mountains of raw data from complex matrices, struggling to identify which specific molecular components actually drive performance.

When dealing with undefined raw ingredients, such as plant and microbial extracts, understanding chemical composition is critical to ensuring batch-to-batch reproducibility and process continuity when scaling.

From raw peaks to actionable insights

The challenge in modern labs isn’t a lack of data; it is the complexity of interpreting high-dimensional datasets. Manual analysis of thousands of formulations is no longer feasible. As regulatory requirements for biologics become more stringent, the demand for defined, reproducible, and regulatory-compliant media has grown.

Advanced metabolomics provides the molecular profiling required to qualify raw materials, while AI handles the broad combinatorial screening. This synergy allows researchers to tailor media composition to specific cell lines, improving yield and efficiency across the drug development lifecycle.

Optimising media with LC/Q-TOF

To solve the media development challenge, Chemetrix supports the implementation of untargeted metabolomic workflows. By utilising the Agilent 6545 LC/Q-TOF, labs can perform detailed molecular characterisation of both raw materials and finished formulations.

How Chemetrix assists:

Our specialists help your team establish metabolomic workflows that provide detailed molecular information for R&D. We assist in identifying “critical component targets” – biomarkers of performance – that become your QC benchmarks. By linking these molecular features to cellular outcomes, we help you replace inconsistent serums with precise, scalable,
animal-free alternatives.

Agilent 6545 LC/Q-TOF

Predictive productivity

Efficiency in the modern lab is increasingly driven by smart automation. The Agilent Infinity III LC Series is designed to address the operational risks that lead to downtime and lost samples through integrated AI-powered solutions.

How Chemetrix assists:

Chemetrix provides the technical expertise to integrate these platforms into your existing regulatory-ready environment. The Infinity III offers predictive analytics and real-time alerts to pre-empt operational failures. We assist in configuring these advanced informatics platforms so that your lab can handle complex workflows with greater precision. This shift to an automated, AI-enabled system allows your staff to focus on high-value data interpretation rather than routine manual monitoring.

Compressing development from years to months

The shift toward AI-guided development marks a new paradigm in biological optimisation. By continuously training algorithms with high-quality experimental data, each project makes the platform more intelligent. This iterative process has the power to compress development cycles that once took years into just a few months. When molecular characterisation is linked directly to cellular performance, the result is a more resilient supply chain and a faster time-to-market for novel therapies.

Optimising the path to discovery

The integration of AI and separation science is no longer a luxury; it is the foundation for the next generation of bioprocess innovation. At Chemetrix, we provide the local application expertise and technical support required to navigate these digital transformations.

Your action plan

Identify a workflow in your lab that currently relies on undefined ingredients or manual screening. Contact a Chemetrix specialist today for a workflow audit. We will help you leverage advanced metabolomics and AI-powered instrumentation to ensure your processes are reproducible, compliant, and ready for the future of biomanufacturing.

AI Technology and the Lab of the Future

In 2022, Agilent announced its acquisition of advanced artificial intelligence (AI) technology developed by Virtual Control, an AI and machine learning software developer that creates innovative analysis solutions in lab testing. Agilent will integrate the software, known as ACIES, into its industry-leading gas chromatography and mass spectrometry (GS/MS) platforms to improve the productivity, efficiency and accuracy of high-throughput labs the company serves around the world.

ACIES automates the labour-intensive task of gas chromatography/mass spectrometry data analysis improving efficiency in the laboratory workflow, from sampling to reporting. Agilent will integrate the technology into its MassHunter software package for LC/MS and GC/MS instruments.

 

Digital labs

This move by Agilent signals that the digital age is very much here for laboratories. Science has always driven the world forward and now it will do the same for laboratories.

The lab of the future is a concept built on the foundation of digitalised labs. It encompasses smart technological workflow systems that are connected and capable of collecting vast amounts of data via integrated automation.

A digitalised lab should be considered a more advanced lab as it has more access to data. With data being key to transforming science, increasing amounts of data generated in any lab, let alone a digitally connected lab, could be a game-changer – but only if it’s collected and synthesised into information and knowledge that is useful.

The digital environment (i.e., paperless work in an electronic format) capitalises on digitalisation. It incorporates all of the necessary instrumentation for complete data analysis and enables the full value of the data for decision-making. The ability to monitor operations and provide more sophisticated insights is a core reason for introducing AI into the operational lab environment.

 

 

Transforming science

Artificial intelligence (AI) is often defined as the ability of a machine to learn how to solve cognitive challenges. However, in the context of scientific methodology and laboratory interconnectivity, AI is starting to be used for capturing data to model human observation and decision-making processes.

Taken forward, connecting all instruments in a lab via AI enables the opportunity for an even more astute understanding of the interactions between technology and also users, potentially providing an all-inclusive view of all laboratory operations.

Accessing this powerful source of information will become a necessary component of scientific productivity. This is an inevitable next step in creating lab management systems that are so efficient and provide knowledge that is so valuable that only AI will be able to produce them.

AI, coupled with universal sensing capabilities to detect and monitor a range of variables, e.g., an instrument’s power draw, enables companies to realise certain operational and financial benefits to their business and plan for the future. Through high-quality and readily available insights, AI enables the simultaneous monitoring of all equipment usage in the lab and holistic capacity tracking.

Watch our webinar on Industrialising High-Throughput Glycoproteomics Using AI for Clinical Use

 

Staying competitive in a competitive world

Globally, scientific innovation is accelerating, so labs need to consider the technology investments required to become digitally enabled in order to keep up and stay competitive. We live in a data-driven world, so scientific laboratories must fundamentally transform how they create, manage, and effectively use all the data that is generated in their lab ecosystem. Achieving and sustaining a competitive edge in a world of constant change will require the continual transformation of lab operations and scientific data management. This will be the first and most important step toward becoming a truly digitalised lab.

 

Standardising honey fingerprinting methods

Although previous work has been done developing case studies for fingerprinting foodstuffs, including honey, the approaches among laboratories have been different regarding sample preparation and instrumental conditions. There are also differences in terms of data processing and analysis. As a result, two laboratories analysing the same sample may obtain slightly different results. Ideally, developing a standardised fingerprinting method that could be used across all LC/MS-based workflows, enabling the same testing technique to be used across multiple laboratories, would be optimal and where future work is aimed.

Read our article on Fingerprinting Honey to Ensure Purity

When addressing the issues of food safety, product quality, and authenticity, each may be governed by separate sets of regulations. For example, looking at the residues of contaminants in honey, such as pesticides, there may be differences globally. Countries may have their restrictions for the maximum limit for specific compounds. Contaminants are a part of the picture when considering fingerprinting for honey, but permitted levels may vary between countries.

Additionally, as samples come from the field to the lab for testing, there is potential interest in reversing this and bringing the lab out into the field instead. This interesting but not yet recognised capability would enable regulators and the global food industry to respond more quickly to honey contamination and food fraud.

Step into the future, elevate your business and talk to our team of experts about how you can improve the productivity, efficiency and accuracy of your lab.

Enhancing Labs With Digitalisation

This article was originally published by Agilent

The topic of optimising laboratory efficiencies is at the forefront of discussions for many lab managers. With the support of new and improved smarter technologies, previous efficiency- and productivity-related challenges are beginning to dissipate as manual processes are starting to be replaced with automated and integrated applications, helping to pave the way towards a fully digitalised lab as part of the internet of things (IoT) movement.

According to the global advisory firm Gartner, a digitalised lab is one that is using digital technologies to change the way they operate their lab, optimise their business model, and ultimately provide new revenue and value-producing opportunities. In a nutshell, it is the process of moving to a digital business.

The results from a survey of pharma lab leaders support this observation. Responses highlighted the urgency to improve and update laboratory processes. Survey takers said that they:

  1. Wanted to achieve quicker results (55%)
  2. Saw a demand for superior quality (44%)
  3. Wanted to improve data integrity (43%)
  4. Found that their current workflow requires optimisation (83%)

Additional survey results showed that only 4% of lab managers are using utilisation data (a tool to understand how all instrumentation in labs is performing) for decision-making. More astonishingly, on average, some lab instruments were only being used 35% of the time.

 

Goodbye Laborious Systems, Hello Smart Technology

To combat some of the key challenges often faced with existing lab workflows, smart technology is increasingly at the core of change. By helping transform ordinary labs into smart technological labs, companies such as Chemetrix can provide better instrumentation and services to their customers without compromising the quality of results, cost-effectiveness, or laboratory space.

The lab of the future is a concept built on the foundation of digitalised labs. It encompasses smart technological workflow systems that are connected and capable of collecting vast amounts of data via integrated automation. At the Lab of the Future 2020 congress in Cambridge, UK, a keynote speaker at the event was quoted as saying “The lab of the future won’t be bound by walls,” suggesting that the digitalisation of labs will enable more fluidity and interconnectivity between assays and other procedures.

 

Transforming Science With Digitally Connected Labs

A digitalised lab should be considered a more advanced lab as it has more access to data. With data being key to transforming science, increasing amounts of data generated in any lab, let alone a digitally connected lab, could be a game-changer – but only if it’s collected and synthesized into information and knowledge that is useful.

The digital environment (i.e., paperless work in an electronic format) capitalizes on digitalisation. It incorporates all of the necessary instrumentation for complete data analysis, and enables the full value of the data for decision making.

Artificial intelligence (AI) is often defined as the ability of a machine to learn how to solve cognitive challenges. However, in the context of scientific methodology and laboratory interconnectivity, AI is starting to be used for capturing data to model human observation and decision-making processes. Taken forward, connecting all instruments in a lab via AI enables the opportunity for an even more astute understanding of the interactions between technology and also users, potentially providing an all-inclusive view of all laboratory operations.

By monitoring and identifying inefficiencies and making recommendations, AI goes beyond data interpretation to the level of suggestive intelligence, which could be used to more effectively manage lab operations, and ultimately accelerate research and discovery.

 

Ai Technology Will Augment Digitalisation Of The Lab

The ability to monitor operations and provide more sophisticated insights is a core reason for introducing AI into the operational lab environment. Accessing this powerful source of information will become a necessary component of scientific productivity. This is an inevitable next step in creating lab management systems that are so efficient and provide knowledge that is so valuable that only AI will be able to produce them.

AI, coupled with universal sensing capabilities to detect and monitor a range of variables, e.g., an instrument’s power draw, enables companies to realize certain operational and financial benefits to their business and plan for the future. Through high-quality and readily available insights, AI enables the simultaneous monitoring of all equipment usage in the lab and holistic capacity tracking.

 

Providing Digitalised Innovations To Address Customers’ Key Challenges

Chemetrix is proud to supply Agilent technologies and platforms that have pushed the boundaries in providing solutions that support the needs of its customers by enhancing the interconnectivity of its instrument products, services, and consumables through:

  • Integrated products and services that advance the digital lab
  • Faster, customer-preferred online interactions that improve the ease of doing business
  • Solutions that increase operational efficiencies

As an example, part of the Agilent CrossLab Group, the Digital Lab Program, is an ecosystem of products designed to complement one another by delivering enhanced digital capabilities to customer end-users, improving their laboratory experience. This initiative has brought certain technologies to life with industry-leading tools in data intelligence to enhance the scientific and economic outcomes of labs worldwide, such as:

  • Asset Monitoring – Agilent CrossLab Asset Monitoring combines advanced IoT sensor technology and data analytics to enable lab-wide visibility. It integrates sensor-based utilisation monitoring with business analytics, allowing you to capture lab-wide instrument utilisation data across all of your workflows, view analytics compiled in dashboards to drive insights for improvements and justify CapEx, OpEx, and productivity decisions using fact-based data.
  • Smart Alerts – Monitoring instrument health and providing email-based alerts, notifying lab operators when to consider replacing key consumables, when to perform preventive maintenance, and when an Agilent instrument stops running anywhere in the lab. Digital lab-wide connectivity lets users remotely monitor all of their Agilent instruments.
  • SLIMS – End-users can effectively track samples as they progress through the laboratory from sample receipt to automated result reporting. SLIMS combines the best of a laboratory information management system (LIMS) with an electronic laboratory notebook (ELN) to enable end-to-end solutions and manage the full content and context of your laboratory.
  • OpenLab Software/Cloud Storage – This has become a viable option for virtually every computing workload in the laboratory, from sample management to complex analytics to secure data storage.

 

Staying Competitive In A Competitive World

Globally, scientific innovation is accelerating, so labs need to consider the technology investments required to become digitally enabled in order to keep up and stay competitive. We live in a data-driven world, so scientific laboratories must fundamentally transform how they create, manage, and effectively use all the data that is generated in their lab ecosystem. Achieving and sustaining a competitive edge in a world of constant change will require the continual transformation of lab operations and scientific data management. This will be the first and most important step toward becoming a truly digitalised lab.

 

Industrialising High-Throughput Glycoproteomics Using AI for Clinical Use

Cancer is a leading cause of death worldwide and there is a great movement globally to develop new treatments and advance how cancer is diagnosed. Technology has been a great help, particularly in recent years, and now there’s new innovation that could take our cancer diagnosis and treatment to a new level.

According to an article published by The Guardian, doctors, scientists and researchers have built an artificial intelligence model that can accurately identify cancer in a development they say could speed up diagnosis of the disease and fast-track patients to treatment. This is but one of many new developments that include AI technology in cancer diagnosis as well as treatment.

In this webinar, we learn the predictive powers of artificial intelligence combined with cutting-edge mass spectrometry to discover clinically relevant biomarkers that can only be revealed by high-resolution analysis of the glycoproteome. This presentation is for all who are interested to learn more about the real-world clinical application of glycoproteomics on cancer diagnosis.

 

Speaker

Dr. Low Ley Hian
Director of Development
InterVenn Biosciences

 

Register and watch on demand >