Hormone receptors are not identical across mammals. Over millions of years of evolution, the genes encoding these proteins have accumulated small changes in each lineage. Those changes alter the receptor's shape — the three-dimensional structure that determines which molecules can dock onto it and how strongly they bind.

In plain terms, a receptor is like a lock, and both natural hormones and synthetic chemicals are keys. If evolution has slightly reshaped the lock in one species, a chemical key that fits snugly in a mouse receptor may simply not turn in the receptor of a related mammal — or may turn it far too easily. By comparing receptor evolution across mammalian species, researchers can begin to uncover where these lock-and-key mismatches arise and which species are likely to be unusually sensitive or unusually resistant to a given chemical.

Why it matters for risk assessment

If sensitivity to a chemical can be traced, at least in part, to specific features of a species' hormone receptors, then safety testing becomes less of a guessing game. Rather than assuming that results in one animal translate directly to others, scientists could predict — from receptor structure alone — which species warrant closer scrutiny.

That prospect is significant for environmental protection. Wildlife exposed to chemical runoff, pesticides, and industrial pollutants includes hundreds of mammalian species that will never be tested individually in a laboratory. An evolutionary framework offers a way to extrapolate from a few well-studied receptors to many unstudied ones.

Important caveats

The findings should be read as an early step rather than a finished answer. Hormone receptors are one of several ways chemicals interact with the body, and species-specific sensitivity could also stem from differences in how animals absorb, break down, and excrete compounds. Receptor evolution explains part of the picture, not necessarily all of it.

Researchers also caution that translating receptor-level differences into whole-animal outcomes — survival, reproduction, population health — requires careful validation. A mismatch observed at the molecular level does not automatically predict what happens in a living creature exposed to a chemical mixture in a real environment.

Still, the approach marks a shift. Instead of treating species-to-species variation as unexplained noise in toxicity data, it offers a mechanistic, evolution-based lens for understanding that variation. For a field long dependent on cross-species assumptions, that alone is meaningful progress.