Redefining Excellence: The Future of Metrics & Data for Green Chemistry Evaluations in Pharma

Around the world, pharmaceutical companies and their industrial and academic partners stand firm in their commitment to the application of Green Chemistry principles, tools, and metrics. However, new challenges continue to emerge as the industry seeks to compress development timelines and extend portfolios to newer and more complex modalities. In this presentation, we share perspectives from across the industry (Abbvie, Bristol Myers-Squibb, Eli Lilly, and Merck) on establishing science-based targets and building laboratory culture that empowers sustainability-forward process development decisions. Effective direction of development decisions requires selecting appropriate metrics for the stage and scope of the process, and we explore solutions and current challenges to addressing this problem, including the extension of sustainability concepts beyond small molecules and into therapeutic peptides, oligonucleotides and biologics.

With the climate emergency demanding urgent action from all industries, pharmaceutical companies are increasingly focused on reducing greenhouse gas (GHG) emissions across the value chain. The manufacture of Active Pharmaceutical Ingredients (APIs) is often the dominant contributor to a medicine’s carbon footprint, thus route design and process optimisation represent a key opportunity to deliver meaningful climate benefits.

Traditionally, the industry has used indicative metrics such as Process Mass Intensity (PMI) as a measure for resource efficiency during API development. While valuable, PMI provides limited insight into the broader environmental consequences of processes, and fails to highlight raw materials with high environmental impact. As a result, it can be challenging to link PMI to organisational sustainability targets or to guide strategic decision making across the value chain.

Life Cycle Assessment (LCA) offers a holistic method for quantifying environmental impacts and identifying hotspots; however ISO and PAS2090-compliant LCAs are often too complex, costly, and time intensive for rapidly evolving development programmes.

To address this gap, AstraZeneca has developed LCA Lite, a streamlined tool enabling scientists to approximate the carbon footprint, water usage and PMI of processes efficiently. Although not as comprehensive as a full LCA, LCA Lite provides meaningful insight into routes and processes, enabling scientists without LCA expertise to make informed decisions with confidence. The tool not only supports identification of high impact raw materials and chemical steps but also facilitates earlier, data driven decision making. By embedding life cycle thinking into routine development, LCA Lite is already influencing route selection, supporting business cases, and informing the prioritisation of sustainability improvement opportunities across the portfolio.

This presentation will share examples of how LCA Lite has been applied in development projects and the sustainability insights it has generated.

Reducing the environmental impacts associated with active pharmaceutical ingredient (API) synthesis is essential for improving sustainability within the pharmaceutical sector. This study uses chloramphenicol—whose annual production reaches several thousand metric tons—as a representative case to compare two fundamentally different manufacturing approaches. An 11 step traditional route from benzaldehyde (Path A) and a modern 5–step route from 4–nitrobenzaldehyde featuring asymmetric organocatalysis (Path B) were evaluated using green chemistry metrics and Life Cycle Assessment (LCA). Laboratory scale data were combined with scale–up frameworks enhanced by machine learning predictions of key thermodynamic properties to accurately estimate process energy demands. Path A showed its primary hotspot in the catalytic hydrogenation of the aromatic nitro group, while impacts in Path B were dominated by organocatalyst production. Transitioning from laboratory to industrial scale reduced impacts by 18–80% for the classical route and 52–92% for the modern route. At industrial scale, Path B demonstrated superior environmental performance in most categories, surpassing Path A by 20–95%. Monte Carlo uncertainty analysis supported the reliability of these results. These findings underscore the critical influence of synthetic strategy and scale–up on the environmental footprint of API manufacture, and highlight the potential of contemporary catalytic methods to enable cleaner pharmaceutical processes.

As a small-molecule CDMO company, we keenly understand that this field is characterized by urgent development cycles, diverse synthetic routes and process options, but generally very high product carbon footprints. We have a strong commitment to continuously reducing our product carbon footprint through process optimization and lean production management. However, in practice we encountered two major challenges.
Large computation cost. We execute more than ten thousand chemical steps in production plant each year. If we were to rely on traditional lifecycle carbon footprint calculation methods, it would require enormous human and material resources, making the approach impractical to implement;
Poor timeliness. Traditional LCA can usually provide reasonably accurate carbon footprint figures only after massive production, and therefore cannot guide researchers in optimizing direction during the development phase.

To address these pain points, we build CBCI tool in our internal information platform, which provides two core functions.
Carbon footprint estimation for production batches: Taking structured electronic batch records as input, it retrieves two categories of standard carbon intensity parameters (equipment catagory such as reactors, centrifuges, dryers; and chemical catagory such as raw materials and solvents) to quickly output the estimated carbon footprint for that batch.
Carbon footprint prediction at the R&D stage: Using the tech package from the process development stage as input, and applying the same standard database and algorithms, it predicts the carbon footprint once the process moves into scaleup production.

The CBCI tool was deployed this year. In numerous cases where it has been applied, the margins of error for the two scenarios are no more than 10 and 5% respectively, fully meeting our business requirements. Overall, we have built a fit-for-purpose and handy tool for carbon footprint estimation & prediction. It not only enables rapid, precise carbon footprint assessment across our operations, but also integrates directly into the process development workflow. Overall it empowers every stakeholder in decarbonization and sustainability.

Sustainability-driven decision-making in complex molecule synthesis is often hindered by two fundamental limitations: the lack of production-relevant life cycle inventory data and the widespread use of simplified metrics that fail to capture holistic environmental burdens. As a result, critical environmental trade-offs in multistep synthetic routes may remain hidden during route selection and optimization. Consequently, there is a need for assessment frameworks that are quantitative, comprehensive, and applicable from early route design through manufacturing-scale evaluation. Our study reports an iterative, closed-loop framework that integrates life cycle assessment (LCA) directly into multistep synthetic route design. The approach relies on retrosynthesis-based extrapolation from basic chemical precursors to construct a comprehensive cradle-to-gate environmental inventory. Environmental performance is assessed across multiple life cycle impact categories, including climate change, human health, ecosystem quality, and natural resource use, with process mass intensity included as a reference metric to contextualize mass-based efficiency. The commercial antiviral drug Letermovir serves as a case study, reflecting contemporary challenges in pharmaceutical synthesis due to its structural complexity and industrial relevance. Using the integrated LCA framework, a de novo synthetic route was developed and evaluated relative to a literature-reported industrial route. The assessment enables a holistic comparison of environmental performance across impact categories and reveals differences that are not consistently captured by mass-based metrics alone.
Across both routes, key environmental contributions are traced to asymmetric catalysis and metal-mediated coupling steps, identifying critical leverage points for sustainability improvement. Overall, this work demonstrates how embedding life cycle assessment into synthetic design complements classical green chemistry metrics and enables informed decision-making in complex multistep synthesis. Beyond the specific case study, the framework offers a transferable blueprint for integrating life cycle thinking into synthetic chemistry, positioning sustainability as a proactive design principle rather than a retrospective evaluation.

The innovation Green Aspiration Level (iGAL) metric has become a foundational industry benchmark for assessing the environmental performance of API manufacturing. While iGAL 2.0 successfully standardized waste based greenness evaluation, its reliance on molecular size (FMW) limited its ability to account for structural complexity. iGAL 3.0 addresses this gap by integrating machine learning-derived molecular features to generate more accurate, complexity adjusted waste targets. This enhanced model delivers improved predictive performance, higher R-sq values, and more realistic greenness benchmarks across diverse APIs. In addition, a new open access web-based Scorecard tool allows users to input API structures, process waste (cEF), and development phase to rapidly benchmark Relative Process Greenness (RPG). Together, iGAL 3.0 and its digital implementation provide a more nuanced assessment framework, supporting alignment with global sustainability goals and advancing standardized green chemistry metrics across the Pharmaceutical industry.