Chromatography plays a central role in pharmaceutical research and development, but much of its value is lost when data is trapped in proprietary formats scattered across instruments, labs, and outdated storage systems. Scientists often spend hours manually extracting, aligning, and validating results, hindering efficiency, delaying decisions, and driving up costs. To address this, a new generation of scientific data platforms is emerging to unify chromatography data, making it accessible, contextualized, and ready for advanced analysis.

Drug Discovery News spoke with Anthony Edge, a veteran chromatographer and Scientific Business Analyst at TetraScience, who has spent over 30 years advancing separation science and analytical chemistry. Edge specializes in transforming siloed chromatography data into actionable insights that reduce operational inefficiencies, prevent costly rework, and extend the life of instruments and consumables.

Why is chromatography such a fundamental technique in pharmaceutical research and development and quality control?

Chromatography is essential because it excels at separating the individual components of complex mixtures, a task that’s central to both research and development and quality control. Few other techniques offer the same combination of precision, versatility, and compatibility with powerful detectors like mass spectrometry. It stands at the very core of pharmaceutical development, with chromatographic techniques accounting for over half of all analytical data generated within biopharmaceutical organizations. 

From early-stage research, where novel compounds are identified and isolated, to late-stage quality control, where ensuring product purity and consistency is paramount, chromatography provides the essential insights. In all its forms, the technique generates a vast amount of data, especially with the advent of sophisticated detectors like mass spectrometry. This wealth of information is critical for decision-making. But there’s a catch: despite its importance, this data often ends up scattered across different systems and departments, making it harder to fully harness its potential. Fixing that disconnect is key to getting the most out of this cornerstone technique.

How has the role of chromatography data evolved with the increasing complexity of pharmaceutical products?

The evolution has been quite profound, moving in lockstep with the increasing sophistication and variety of new therapeutic modalities. As pharmaceutical products have become more complex, from small molecules to biologics and now cell and gene therapies, the demands on chromatography data have grown just as quickly. It’s no longer just about confirming purity or identity. We now rely on chromatography to uncover fine details like charge variants, glycan profiles, and protein aggregates — all of which can affect a drug’s safety and efficacy.

Thanks to advanced detectors like high-resolution mass spectrometers, a single chromatographic run can generate tens of gigabytes of data. That’s a goldmine of information, but it also introduces challenges. When data is scattered across different systems, it becomes much harder to compare results or spot trends. That’s why integrated platforms or unified dashboards are becoming so important, they help scientists make sense of the data and use it to drive better decisions.

What are some common challenges laboratories face in managing chromatography data across multiple instruments and systems?

A critical bottleneck in this field is data harmonization. One of the biggest hurdles labs face is managing data from different instruments that just don’t talk to each other. Most labs use equipment from multiple vendors, each with its own software and data format. As a result, chromatography data ends up scattered across systems, projects, and sites, making it difficult to access, compare, or analyze efficiently.

This fragmentation doesn’t just slow scientists down; it also complicates compliance, audits, and root cause investigations, especially in quality controlled environments that rely on multiple chromatography data systems. While efforts like the Allotrope Foundation’s Simple Model are promising steps toward standardization, getting the entire industry to adopt a single format is a long and complicated process.

Even when data is technically available, it’s often missing important context: the metadata that gives it meaning. That’s a real problem when data is being fed into AI or advanced analytics tools. If the inputs aren’t consistent or well-organized, the outputs won’t be reliable. Indeed, the principle of 'garbage-in-garbage-out' is particularly pertinent in this context; without meticulous data curation, the insights derived from machine learning can be unreliable or misleading.

How can effective chromatography data management impact drug development timelines and costs?

The impact is incredibly significant when you zoom out and look at the price of fragmented chromatography data. It extends far beyond mere operational inefficiency; each delay in drug development can cost millions in lost revenue. Effective data management, by contrast, offers a powerful lever for improvement. By breaking down these data silos through solutions like universal dashboards and integrated data management strategies:

• Productivity and data integrity see a marked increase. Automating data collection and analysis minimizes error-prone manual handling and boosts traceability. It also means scientists can access compliance data in minutes and focus on science, not system workarounds.
• Quality improves. We've seen organizations report tangible results such as reducing out-of-specification events and deviations by up to 75 percent by tracking performance trends across samples, methods, and instruments.
• Costs go down. Detecting potential issues before they become critical problems helps maintain product quality and prevent costly rework. We’ve seen firms reduce standard operating procedure violations by 80 percent by flagging repeat injections. This directly translates to significant cost savings and, crucially, accelerated development timelines. 

How can centralized data analysis improve the accuracy and reproducibility of chromatography results?

There are several ways centralizing data strengthens both accuracy and reproducibility. First, it creates a technically controlled environment where critical processing parameters such as integration algorithms, calibration models, and sequence-wide commands are applied uniformly across datasets. This consistency removes variability introduced by different users or chromatography data system defaults, meaning the data more accurately reflects true analyte concentrations. If a sample is processed later, the same numerical result will be reached.

Automated, centralized data capture also generates immutable audit trails, drastically cutting down compliance risk. When manual steps, like typing peak areas from reports into spreadsheets, are eliminated, transcription and calculation errors are reduced outright. Even when manual input is necessary, knowing that every action is logged tends to make users more careful, leading to less haste and fewer mistakes.

Centralization also ensures clear data provenance. That’s critical for reproducibility: it allows results to be precisely reconstructed and proves to regulators that processing was done exactly as specified. Consistent system performance monitoring is another major benefit. Centralized platforms can track key metrics like theoretical plates, peak asymmetry, and resolution using standardized calculations, making it easier to catch column degradation or hardware issues before they impact results.

And there’s a forward-looking angle: centralized systems lay the groundwork for AI and machine learning. Harmonized, contextualized datasets are ideal training material for models that can improve peak deconvolution, predict retention shifts, or flag subtle method instability. Altogether, centralization makes chromatography more accurate day-to-day and reliably reproducible across instruments, sites, and studies.

How are AI and data intelligence platforms used to identify patterns and predict outcomes from chromatography data?

This is where we see a genuine paradigm shift in separation science. Traditionally, the field has been highly deductive, forming hypotheses and testing them. AI flips that script. Machine learning thrives in large, complex datasets like those from chromatography, revealing patterns and relationships that are too subtle or multidimensional for human intuition alone. It’s a more abductive approach: trust the algorithm to find hidden peaks, deconvolute overlapping signals, and expose insights we might never have thought to look for.

In the OMICS space, there is already a clear pattern of discovery through tools like Principal Component Analysis (PCA), and it's starting to bring forward novel drug candidates. Beyond that, AI is accelerating method development and optimization. It can predict the best separation conditions, reduce solvent use, and boost method robustness. It’s also being used to monitor instrument performance in real-time, flag anomalies, and adjust parameters dynamically, leading to better uptime and lower operational costs. Predictive maintenance is a major win here.

In compound modification, AI can analyze spectral data from detectors and compare it to libraries for faster, more accurate compound identification. And, by consolidating real-time and historical data, AI-driven models can predict instrument malfunctions or even method failures with high accuracy, potentially saving weeks of troubleshooting.

That said, none of this is possible without structured, accessible chromatography data. The first step is always the same: break down the silos. Only then can AI deliver its full potential.

What are the biggest opportunities for advancing chromatography and data integration in drug discovery?

In this domain, at the confluence of separation science, data management, and AI, the most significant untapped opportunities lie in creating fully autonomous, intelligent chromatographic systems and leveraging integrated data for predictive science at scale. Consider these frontiers:

• Autonomous chromatography: Moving towards systems that can perform method development, sample analysis, and initial data interpretation with minimal human intervention. This is centered on augmenting scientists, not replacing them, allowing them to focus on experimental design, interpreting the significance of complex data, or developing new theoretical frameworks.
• Discovery of novel separations with AI: Using AI algorithms to explore vast chemical spaces to design new stationary phases or mobile phase combinations, potentially leading to greener and more efficient separations.
• Personalized chromatography: AI could enable the development of methods tailored to definite and complex sample matrices, such as those in personalized medicine.
• Deep integration and predictive modeling: The real power will come from seamlessly integrating chromatography data with other OMICS, imaging, and clinical data, then applying AI to build predictive models for drug efficacy, safety, or manufacturability. Doing any of this modeling requires a solid foundation of accessible, well-structured data—the "AI readiness" emphasized.

Achieving this future requires a concerted effort in data harmonization and building platforms designed for today's analytical needs and tomorrow's AI-driven insights. It's about transforming data into a truly consolidated asset.