Exponential toxicology

The chemical universe to which humans and ecosystems are exposed is expanding at a rate that traditional toxicology cannot match. Estimates of the commercial global chemical inventory have moved from tens of thousands in the 1980s to roughly 350,000 distinct substances registered for commercial use in 2019, and exposures now arrive as mixtures, transformation products, and engineered materials whose number grows faster than any list can capture. Against this curve, con-ventional regulatory testing has at best advanced linearly, and in some jurisdictions it has stagnated outright. The mismatch is not a regulatory failure of will; it is a structural impossibility, because animal-based assays cannot be made exponentially cheaper, faster, or more numerous. The escape from the bottleneck has come from a different family of methods entirely. New approach methodologies (NAMs), and in particular high-throughput in vitro screening, computational toxicology, microphysiological systems, and most recently artificial intelligence, are themselves on exponential growth curves. This article argues that the only coherent response to exponential exposure is exponential testing, and that the field must now think and plan in exponential rather than linear terms. I examine why this is hard, drawing on cognitive science and on Hartmut Rosa's diagnosis of social acceleration, and propose how an exponential toxicology can be built without sacrificing scientific rigor.