Design of Greener Drugs

Aligning parameters in pharmaceutical R&D and drivers for environmental impact


Vidaurre, Rodrigo et al. (2024): Design of greener drugs: aligning parameters in pharmaceutical R&D and drivers for environmental impact. Drug Discovery Today, Volume 29, Issue 7, doi: 10.1016/j.drudis.2024.104022.

The article "Design of greener drugs: aligning parameters in pharmaceutical R&D and drivers for environmental impact" published in Drug Discovery Today explores the challenges and opportunities in developing pharmaceuticals that are not only safe and effective for patients but also environmentally sustainable. This open access article – written by a cross-disciplinary team with representatives of universities, regulators, research-based pharmaceutical companies and SMEs – identifies the most promising areas for action in pharmaceutical research and development. It specifically aims to bridge the scientific concepts used by the expert communities in both environmental science and pharmaceutical R&D.

Environmental Concerns and R&D Integration

Currently, in pharmaceutical R&D environmental characteristics are not typically considered. The article discusses four main drivers of environmental concern (persistence, mobility, bioaccumulation, and ecotoxicity) and their interlinkages with parameters relevant for pharmaceutical R&D such as metabolic stability and lipophilicity. The compound properties that are sought in pharmaceutical R&D and those that determine their behaviour in the environment are mutually interrelated, presenting both challenges and opportunities to the design of greener pharmaceuticals.

Opportunities and Implementation

Implementing greener drug design requires the development of predictive in silico tools and high-throughput screening methods to assess environmental impacts early in the R&D process. The article suggests that focusing on reducing environmental persistence is a practical starting point. Improvement in environmental degradability can reduce environmental exposure, and with that it also reduces the environmental concerns related to the other three drivers of environmental concern (mobility, bioaccumulation, ecotoxicity).

Additionally, the paper emphasizes the potential benefits for pharmaceutical companies of including environmental considerations in their R&D processes, including proactive management of potential reputational risks, better market positioning in a changing society, and compliance with evolving environmental regulations. The article can be accessed here.

Developing greener drugs can reduce ecological damage from pharmaceutical residues without compromising patient efficacy and safety. Sustainability in pharmaceutical research is achievable and necessary.

More content from this project

Irene Bramke (AstraZeneca)
Neele Puhlmann (Leuphana University of Lüneburg)
Stewart F. Owen (AstraZeneca)
Daniela Angst (Novartis Pharma)
Caroline Moermond (National Institute for Public Health and the Environment (RIVM))
Bastiaan Venhuis (National Institute for Public Health and the Environment (RIVM))
Anna Lombardo (University of Helsinki)
Klaus Kümmerer (Leuphana University of Lüneburg)
Tiina Sikanen (University of Helsinki)
Jim Ryan (GSK)
Andreas Häner (F. Hoffmann-La Roche)
Gemma Janer (Novartis Pharma)
Silvio Roggo (Novartis Pharma)
Alison Nimrod Perkins (Eli Lilly and Company)
Published in
Drug Discovery Today
Published by
1878-5832 (online)
Project ID
green chemistry, pharmaceutical pollution, environmental sustainability, biodegradable drugs, eco-friendly pharmaceuticals, drug development, environmental impact, regulatory compliance, bioaccumulation reduction, persistent organic pollutants, sustainable healthcare, pharmaceutical R&D, environmental risk assessment, chemical safety, drug design innovation, environmental science, pollution prevention, ecological footprint, patient safety, drug efficacy
In Silico Tools, BIOWIN, VEGA, OCHEM, enviPath, experimental approaches, low- to medium-throughput assays, empirical evaluation, ecotoxicological tests, high-throughput screening, In Vitro Models, machine learning, models for predicting bioaccumulation, High-Throughput Methods, Zebrafish assays

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