Stepping beyond hormesis modelling and sub-NOAEL predictions in plant biology

Authors: DUKE, STEPHEN - University Of Mississippi; BELZ, REGINA - University Of Mississippi

Submitted to: Current Opinion in Environmental Science & Health
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2022
Publication Date: 5/18/2022
Citation: Duke, S.O., Belz, R.G. 2022. Stepping beyond hormesis modelling and sub-NOAEL predictions in plant biology. Current Opinion in Environmental Science & Health. https://doi.org/10.1016/j.coesh.2022.100366.
DOI: https://doi.org/10.1016/j.coesh.2022.100366

Interpretive Summary: The hormesis phenomenon by which low doses of chemicals or other stressors, which are harmful at higher doses, exert a stimulatory effect on exposed organisms is widely established in various scientific disciplines as a fundamental and commonly occurring dose-response relationship, provided that the experimental design permits its detection. Hormesis is now well recognized, detectable and quantifiable through modeling. The generalizability of hormetic or biphasic dose-response relationships is expressed in particular in the quantitative sub-NOAEL (No-Observable-Adverse-Effect-Level) features of hormesis, which make hormesis reports comparable across studies. The modeling of a single curve and its sub-NOAEL quantities is fundamental, but stepping beyond this by multiple curve fittings and sub-NOAEL predictions under different biasing objectives can provide clues to important mechanisms of hormesis which cannot be assessed without modeling. The study of fundamental bias factors allows us to better understand the phenomenon of hormesis and deduce universally valid characteristics. This is a prerequisite for targeted use of hormesis in various areas and for risk assessment, especially when judged beyond the laboratory. This review demonstrates the basic value of stepping beyond a single-curve hormesis modelling by focusing on three fundamental influencing factors relevant to plant biology.

Technical Abstract: The hormesis phenomenon is now well recognized, detectable and quantifiable through modeling. The modeling of a single curve and its sub-NOAEL quantities is fundamental, but stepping beyond this by multiple curve fittings and sub-NOAEL predictions under different biasing objectives can provide clues to important mechanisms of hormesis which cannot be assessed without modeling. The study of fundamental bias factors allows us to better understand the phenomenon of hormesis and deduce universally valid characteristics. This is a prerequisite for targeted use of hormesis in various areas and for risk assessment, especially when judged beyond the laboratory. This review demonstrates the basic value of stepping beyond a single-curve hormesis modelling by focusing on three fundamental influencing factors relevant to plant biology.