Chromatography
The future of chromatography: How AI and automation are driving laboratory effi ciency and decision-making
John Beary, Agilent Technologies
The pressure on labs to perform has never been greater. Recent reports show that 60% of lab professionals are experiencing signifi cant downtime due to equipment failures, missed calibration schedules and challenges in locating critical lab assets, highlighting the urgent need for smarter, more connected lab infrastructure. Compounding the challenge, 64% of labs have already implemented cost-cutting measures across Europe, with many also coming up against severe budget constraints and staff reductions.
However, balancing the mounting fi nancial and human resource pressure with client expectations, regulatory demands and the need for faster turnaround times isn’t easy. So how can labs improve effi ciency and productivity while also ensuring accuracy and compliance? The answer is the application of advanced automation tools, including robots, and artifi cial intelligence (AI).
The concept – getting smart systems to carry out tasks with minimal direct human intervention – is straightforward; turning it into a reality requires a good understanding of the available technologies and a clearly defi ned set of objectives. For the best results, it’s also important to be able to access siloed data and implement workfl ows capable of operating across a range of tools.
Turning concepts into reality
In the fi eld of chromatography, collaborative robots, or ‘cobots,’ are transforming effi ciency and precision. These advanced machines are engineered to work alongside human operators, enhancing their capabilities by taking on repetitive, time-consuming tasks.
The ability of cobots to execute complex analytical protocols that demand a high degree of accuracy and consistency enhances the reliability and precision of the processes. This is particularly critical in the pharmaceutical industry, where accuracy is paramount to ensure the safety and effi cacy of products.
Also, by performing routine and labour-intensive tasks, cobots enable labs to process a higher volume of work, without compromising on quality, freeing up human operators to focus on more valuable scientifi c and problem-solving tasks.
Cobots can also provide economic benefi ts as the effi ciencies gained from their use, and the consequent reduction in the need for manual labour, can result in signifi cant cost savings. Their adaptability and fl exibility – they can be easily reprogrammed and repurposed – enables them to seamlessly integrate into a variety of chromatography workfl ows, adapting as the demands of the industry evolve.
The power of AI
AI is also becoming an integral tool in chromatographic and mass spectrometric (MS) analysis, particularly in the fi elds of biopharmaceutical development and drug discovery. Its application not only enhances data processing, interpretation, and decision-making processes, it also makes them more effi cient and accurate.
For example, AI-powered systems can now handle routine tasks, such as sample processing, data analysis and report generation, while also reducing the risk of human error, leading to more reliable results. Importantly, it frees up lab staff to focus on higher-value tasks that support innovation and value creation.
Ensuring peak performance
Increasing peak detection and quantifi cation levels are one of the key areas where AI can make a substantial impact. Machine learning algorithms can be trained to
accurately identify and differentiate between peaks, even in complex chromatograms where peaks can co-elute.
This is further enhanced by AI’s ability to optimise integration parameters, such as baseline correction and peak width, ensuring more accurate quantifi cation results. It can also extract additional information from chromatography data, such as peak area, height and retention time, which further refi nes the accuracy of quantifi cations.
A collaborative environment
It’s very hard to extract the maximum value from siloed data, which can occur when there is insuffi cient integration between departments, systems, or applications. This is particularly prevalent in industries that use a diverse range of data sources and proprietary technology formats spread across a number of geographical locations. As a result, there is a concerted drive to move away from vendor specifi c/proprietary technologies.
The Allotrope Foundation, an international consortium of pharmaceutical, biopharmaceutical, and other scientifi c research-intensive industries, is involved in breaking down these silos to enable seamless data integration. Its open-access data initiatives aim to create a more collaborative environment for data sharing and integration, particularly within the scientifi c community.
The foundation continues to promote the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. By developing universal data formats and ontologies, Allotrope enables laboratories to enhance data integrity, improve quality, and enable the ability to provide real-time regulatory compliance – ultimately facilitating faster, more informed decision-making.
Through its work, Allotrope aims to make the intelligent analytical laboratory a reality – an automated environment where data, methods and hardware components are seamlessly shared among disparate platforms, and data integrity is built-in by design.
INTERNATIONAL LABMATE - SEPTEMBER 2025
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