Most major pharmaceutical companies have implemented comprehensive sustainability initiatives, driving a clear shift across the industry. The analytical community is moving towards practices that align with the principles of green chemistry. Green chemistry is key in minimizing the environmental impact of analytical labs, especially within separation science, which includes techniques that tend to be resource intensive and detrimental to the environment. In industries where methods can be in place for years, early method design decisions can have a lasting impact. Incorporating green principles alongside productivity and analytical goals from the start ensures methods are more sustainable throughout their lifecycle.
Designing Ideal Chromatographic Methods
Green chemistry is an area where practical actions can be taken to minimize the environmental footprint. Green chromatography involves designing methods that allow chemical activities to be carried out in an environmentally sustainable manner. Aligning closely with Anastas and Warner’s 12 Principles of Green Chemistry,1 the aim is to minimize hazardous substances harmful to the environment.
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One way to achieve this is to strive for an ideal chromatographic method. As Troy Handlovic (Scientist, Amgen) shares, balancing greenness with performance, productivity and practicality is critical for developing methods that are both effective and sustainable. Performance is easy to measure using familiar metrics like selectivity, resolution, retention, and efficiency. While productivity and practicality are routinely assessed by run time, loading capacity, robustness, and reproducibility. By contrast, greenness is a newer and less clearly defined concept, making it more difficult to evaluate and intentionally design for.2
Quantifying a Chromatographic Method’s Greenness
Without measurable criteria, sustainability can become subjective or easily overlooked during method development. Green analytical chemistry metrics provide a framework to quantify solvent use, toxicity, energy consumption, and waste. Despite the existence of many complementary metrics, the lack of a single, intuitive measure can create confusion.
To address this, the Analytical Method Greenness Score (AMGS) was introduced as an open access tool that combines multiple green chemistry considerations into one overall score. AMGS breaks greenness into three components (instrument energy, solvent energy, and solvent environmental health and safety (EHS))3—making it easy to see what drives a method’s impact and where improvements can be made. Lower scores indicate greener methods.
Digital Tools for Smarter, More Sustainable Methods
Improvements in sustainability need to be made without compromising method performance. Digital tools for retention modeling, in silico simulation, and multivariate optimization are crucial to enable chromatographers to explore method design and development before running experiments.
ACD/Labs
Predict First So You Can Experiment Less
Early identification of rational starting conditions helps design shorter, more robust methods, requiring fewer reinjections or redevelopment. Scientific expertise and a deep understanding of physicochemical properties are essential in rational selection of parameters. However, they are limited by factors such as time, inherent bias, and data complexity. Combining scientific insight with digital tools can help bridge this gap.
Predictive tools using quantitative structure–property relationship (QSPR) models and advanced algorithms can be used to accurately predict key properties such as logP, logD, and pKa and support decisions around solvent systems and pH. Further identification of strong starting conditions can be done with software tools for column selection, column comparison, and pH optimization. This is particularly useful to select appropriate column dimensions, particle size, and flow rate—parameters that can significantly reduce solvent use and instrument run time.
From Simulations to Sustainable Separations
Software tools enable scientists to optimize chromatographic methods and identify the most promising conditions before even entering the lab. In silico modeling allows scientists to investigate a broader separation space, without the added burden of time and consumables. Visualization tools such as resolution maps show how compounds will separate under modeled conditions and highlight regions of interest across the entire design space. Comparison between experimental and modeled results helps identify potentially problematic parameter changes and strategically apply targeted adjustments, before any experiments are conducted.
ACD/Lbs
Digital tools use 1D, 2D, or 3D models to optimize key separation parameters such as pH, temperature, gradient, and so on, for better peak separation. Software such as AutoChrom from ACD/Labs uses different mathematical parameters to rank the possible experiments and allow the most promising conditions to be identified and selected based on what best meets defined success criteria (run time, retention factor, and resolution). By fine-tuning these parameters in silico scientists can gain insight into their method design space with minimal experimentation and solvent use, saving time, and without compromising method performance.
Greater Method Robustness
In R&D organizations, methods are often transferred between teams, projects, sites, and partner organizations. As such, it is crucial for performance to be consistent and reliable, no matter where or how methods are run. A robust method is resistant to small variations in conditions, and therefore less prone to failed runs and reinvestigation. A method’s performance can be impacted by several factors and scientists can investigate this by using software tools to simulate variation of parameters, predicting chromatographic behavior within specified bounds.
In silico parameter changes can be applied to either individual or combinations of parameters including flow rate, temperature, gradient, pH, buffer concentration, and solvent ratio. Method development software automatically generates a list of iterative experiments for robustness testing and scientists can visualize the impact on separation quality. Visualization tools such as resolution maps and chromatogram overlays help compare experimental results with predicted outcomes and identify the most robust conditions for optimal separation. By doing this in silico, method performance can be explored across a broad design space, supporting transferability and longevity of the method—without any additional experimentation.
Visualizing Greenness Alongside Results
Sustainability maps offer a way to visualize a chosen greenness indicator, for example AMGS, alongside analytical chromatographic performance metrics, such as resolution. As demonstrated by Handlovic et al., who incorporated AMGS into a sustainability map, this approach provides a powerful framework for integrating environmental considerations into method development.4 These maps help identify where regions of optimal separation occur and where they overlap with the most sustainable regions. By highlighting regions that deliver acceptable resolution with lower environmental impact, it is possible to strategically select greener methods within the separation space without additional experimentation; ensuring that sustainability is designed from the start.
Smarter Data Management for Greener Methods
In modern day multi-instrument, multi-vendor labs, file incompatibility is a big challenge. Data is often unstructured and unharmonized resulting in data silos, which lead to loss of data and duplication of experiments. To reduce redundant experimentation and maintain knowledge obtained during the resource intensive process of method development, data standardization and storage are crucial.
Method development software that provides a solution to harmonize multiple file formats and data types into a single standardized data format is an invaluable tool. Being able to analyze, process, and store all analytical techniques within a single interface ensures data continuity. Storing live analytical and chemical information in easily searchable, centralized databases means that knowledge is consolidated, and the risk of repeating past mistakes is reduced.
Integrating green metrics into knowledge sharing systems allows labs to save greenness scores alongside the chemical and analytical information of methods, so that when methods are reused greenness scores are readily available. Furthermore, scientists can track sustainability gains over time and focus optimization where it matters most.
Building Sustainable Methods that Last
Chromatographers are uniquely positioned to drive sustainability in R&D laboratories by shaping decisions that influence greenness throughout a method’s entire lifecycle. Designing and developing methods that are more efficient, transferable, and resilient is made possible by considering performance, productivity, robustness, and greenness from the outset. Incorporating digital tools such as predictive software, in silico modeling, and computer-assisted method development help reduce waste, energy use, and rework over time. As green chemistry expectations continue to rise, the chromatographer’s role is no longer simply to deliver a robust separation, but to deliver one that is scientifically sound, operationally efficient, and sustainable by design.
References
- Anastas, P. T.; Warner, J. C. (1998). Green Chemistry: Theory and Practice. Oxford University Press.
- Handlovic, T. (2025, Nov. 7). In Silico Mapped Separation Spaces for Green Method Development. Advanced Chemistry Development, Inc. (ACD/Labs). In Silico Mapped Separation Spaces for Green Method Development
- ACS Green Chemistry Institute. (n.d.). About the AMGS Calculator—ACSGCIPR. ACS GCI Pharmaceutical Roundtable https://acsgcipr.org/tools/about-the-amgs-calculator/ (accessed 2026-01-27).
- Handlovic, T. T.; Roy, D.; Farooq, M. Q.; Leme, G. M.; Crossley, K.; Haidar Ahmad, I. A. (2025). In Silico Modeling Enables Greener Analytical and Preparative Chromatographic Methods. Green Chemistry, 27(1), 109–119. https://doi.org/10.1039/d4gc04300f
