Animal testing, long a mainstay response to the requirement for a standard set of laboratory studies for each active chemical and formulated product in chemical regulation, has been on the out as the world of toxicology and regulatory science has tasked New-Approach Methodologies, or NAMs, with sourcing scientifically-robust alternatives. While the guidelines and requirements depend on the type of chemical, product, and regulatory agency, animal testing had remained a laboratory staple because similar types of toxicological endpoints could be derived using representative species from various taxonomic groups: aquatic and terrestrial plants, invertebrates, and vertebrates.

However, recent European requirements under OECD and ECHA have been pushing for vertebrate animal testing as a last resort for informing risk. The US has followed suit by the EPA calling for NAMs to reduce or replace the use of vertebrate animal testing to the degree practical and scientifically justifiable.

Today, it seems as though the field of toxicology is changing before our eyes based on these NAMs. Toxicologists and data scientists are developing some amazing alternatives to animal testing! Receptor-based laboratory assays are certainly one existing method to replace in vivo testing. These assays can provide similar toxicological information focusing on a cellular and molecular level rather than a whole organism.

Webtools, databases and algorithms geared towards toxicological characterization are expected to become the norm. For example:

• Effect characterization, while minimizing animal testing, will likely include as much read-across approaches as scientifically defensible.
• Existing structure-based models (e.g., QSAR) will continue to draw similarities to similar chemicals with known toxicological profiles.
• A novel tool, GenRA (general read-across), intends to fill in toxicological data gaps and reduce uncertainty on a greater scale.
• GenRA is now available as a web application and also integrated with the EPA’s CompTox Chemicals Dashboard to start predictive read-across modeling right after a chemical search.

It looks like these NAMs will become the tools of the future used to characterize toxicology while we aim to reduce animal testing.