From Early Biological Signals to Prevention: Inferential Non-Equivalence in Regulatory Toxicology
Year: 2026
Type: article
Abstract: Regulatory toxicology is increasingly asked to draw on early signals (external exposures, internal chemicals, and molecular responses detectable before overt disease) for biomarker qualification, mechanistic evidence, and the steps of chemical risk assessment. Yet the evidence generated by these approaches supports different, non-interchangeable scientific claims: a measurable chemical can indicate internal exposure without indicating toxicity; a biological response can indicate biological activity without indicating adversity; and a predictive signature can forecast an outcome without identifying a modifiable cause of it. This inferential gap, between what early evidence can detect and what it can be taken to establish, is not always made explicit in regulatory interpretation. The hypothesis examined here is that the preventive and regulatory value of early biological evidence depends not simply on signal strength or predictive performance, but on whether the evidence resolves the specific competing explanations relevant to the inference being made. This article examines the hypothesis using six recurring scientific questions (exposure, biological-state, predictive, causal, intervention, and population inference), landmark human cases treated as inferential stress tests, and the biological dynamics of timing, persistence, and reversibility that constrain interpretation. For regulatory toxicology, the implication is that biomarker qualification, adverse outcome pathway (AOP) evidence, and risk-assessment evidence integration should be judged against the specific inference each decision requires. Each inference is also mapped to the step of chemical risk assessment in which it is needed.
Source: Toxics
Author: Abdullah M. Alnuqaydan
Institution: Qassim University
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225646248
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