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Digital Solutions for Sustainable Chemistry

How to reduce environmental impact with software

by
Jesse Harris

Jesse Harris is a digital marketing coordinator at ACD/Labs, focusing on supporting Luminata, ChemSketch, and Name. He has master’s degrees in both chemistry and chemical engineering and has been creating...

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The chemical and pharmaceutical industries have an enormous environmental footprint. Between energy use, chemical waste, and non-biodegradable products, it is undeniable that these companies will need to play a role in achieving global sustainability goals. As concerns about the environment grow, the pressure for these industries to reduce their impact is higher than ever.

While no individual or organization can transform an entire business sector, systemic change often comes through the accumulation of many smaller improvements. Nowadays, there are a variety of software and technology solutions that can mitigate, or even eliminate, wasteful processes, enabling organizations to become more sustainable. Furthermore, it is possible to reduce environmental impact without sacrificing productivity or performance.

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Here are five examples of how software is supporting sustainable chemistry:

Predictions over experiments

The “greenest” experiment is the one that you did not have to run. Researchers can prevent waste by using software predictions instead of running real-life experiments. Modern software can accurately predict the solubility, pKa, and logP of chemicals without experimental measurement.

Predictions are also helpful for assessing the environmental toxicity of potential products. This is particularly important in consumer goods categories, which tend to be produced on a much larger scale than pharmaceutical ingredients. Toxicity prediction software allows you to assess environmental toxicity without synthesizing the material and informs your decisions of what materials to synthesize.

Optimizing chromatography to reduce solvent footprint

As chemists, we sometimes do not put much thought into our solvent usage. We select a solvent that meets our experimental needs but can easily overlook sustainability. Unfortunately, this mindset ignores one of the best opportunities for reducing environmental impact.  

Researchers can prevent waste by using software predictions instead of running real-life experiments.

By weight, solvents are the main ingredient in almost all organic chemistry reactions, making them the primary waste source. While some solvents (such as short-chain alcohols) are relatively innocuous, others (such as halogenated solvents) are hazardous to the environment. Software can help select environmentally friendly solvents with comparable physicochemical properties.  

However, even if the most innocuous solvent is chosen, environmental concerns may still arise when using large volumes. Liquid chromatography often uses a massive amount of organic solvent. Method development software can be used to find methods that avoid hazardous solvents and reduce overall solvent usage. Not only will this avoid environmental impact, but it can also accelerate method development, saving time and resources.

Identify better synthetic routes

Many chemicals used in organic synthesis, such as strong acids and bases, hydride donors, Grignard reagents, and heavy metal catalysts, are chosen because they are highly reactive. That reactivity helps to get the transformation we want, but these materials are also environmentally toxic, making it difficult to dispose of this chemical waste safely. While using these environmentally hazardous substances is unavoidable in many cases, chemists should find alternatives when possible. This may mean substituting reagents or developing better synthetic routes, often requiring testing a broader range of conditions.

High throughput experimentation supported by analytical testing software allows research teams to quickly investigate a broader range of synthetic routes. This can lead to more sustainable synthetic methods that avoid using environmentally harmful reagents.

Avoid repeating experiments with data management

Every laboratory scientist has been in the position where they cannot find the results from a critical experiment. Try as they might, they cannot locate the chromatogram or NMR spectrum that is essential to their scientific findings. Unfortunately, they must attempt to recreate the experiment to get the necessary data.

While this situation is frustrating to the scientist, it is also environmentally and fiscally wasteful. Every chemistry experiment is resource-intensive, so repeating them is a significant loss. Of course, once the data has been lost, there is no alternative but to repeat the experiment, but how can organizations avoid this situation? Better analytical data management is the answer. By storing data in a centralized, chemically intelligent platform, it is possible to:

  • Search for analytical data based on meaningful parameters, such as structure, retention time, metadata, and more
  • Add contextual information to your analytical data to help with future interpretation
  • Ensure all files are stored in a format that is findable and accessible

Analytical data management systems are especially useful in pharmaceutical research, where projects run for several years. Results become increasingly hard to find over time due to hand-offs between teams, changes in personnel, and the growing complexity of the project. Implementing an analytical data management system will help avoid the need to repeat experiments when it comes time to complete regulatory filings.

What’s more, once this analytical data is curated and standardized, it can be used in artificial intelligence (AI), machine learning, and data science applications, which offer more opportunities for improving sustainability.

Method development software can be used to find methods that avoid hazardous solvents and reduce overall solvent usage. Not only will this avoid environmental impact, but it can also accelerate method development, saving time and resources.

AI and sustainability

Pharmaceutical discovery involves a significant amount of trial and error. Large numbers of novel molecules are synthesized and then tested against a drug target to assess efficacy. As described above, synthesis is environmentally taxing. With the high failure rate of molecules at this stage, the entire process is inherently wasteful.

Fully enabled AI technologies could reduce the environmental impact of drug discovery. Applications using a combination of quantitative structure-activity relationship predictions and in-house data will be able to better identify potential drug candidates. Of course, chemists will still need to synthesize chemicals, test active pharmaceutical ingredients, and formulate drug products, but more work will be done in silico, which means less waste and more efficiency.

Of course, the power of AI technology will be directly proportional to the quality of data used to build the models. Companies must now implement analytical data management solutions to meet tomorrow’s business and environmental objectives.

Reducing the environmental impact of chemistry

Ultimately, these solutions must be part of a broader effort to implement environmentally responsible measures across the chemical and pharmaceutical industries. Sources and usage of electricity, supply chains, packaging, and physical infrastructure must be evaluated.

Considering the scale of the work can feel overwhelming, but we should also remember that this challenge offers a chance to innovate. In fact, the solutions described above will improve efficiency and save money while reducing environmental impact. The environmental crisis is one of the greatest challenges, and greatest opportunities, of our time.