Dataset Information

Estimating the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships in meta-data evaluations.

ABSTRACT: The number of studies reporting hormetic responses is rapidly increasing, and quantitative evaluations are needed to improve the understanding of hormetic dose responses. However, there is no standardized methodology to estimate the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships developed using data mined from the published literature. Here, we propose a protocol that can be followed to estimate NOAEL, a process that is illustrated using a specific example. This protocol can be used for maintaining a mutual language (since NOAEL can be defined in different ways), permitting comparisons among different studies, and facilitating cumulative science.

SUBMITTER: Agathokleous E

PROVIDER: S-EPMC8720840 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships in meta-data evaluations.

Agathokleous Evgenios E Moore Michael N MN Calabrese Edward J EJ

MethodsX 20211106

The number of studies reporting hormetic responses is rapidly increasing, and quantitative evaluations are needed to improve the understanding of hormetic dose responses. However, there is no standardized methodology to estimate the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships developed using data mined from the published literature. Here, we propose a protocol that can be followed to estimate NOAEL, a process that is illustrated using a specific example. This ...[more]

PMID: 35004202

Similar Datasets

Project description:BackgroundTwo hormetic modifications of a monotonically decreasing log-logistic dose-response function are most often used to model stimulatory effects of low dosages of a toxicant in plant biology. As just one of these empirical models is yet properly parameterized to allow inference about quantities of interest, this study contributes the parameterized functions for the second hormetic model and compares the estimates of effective dosages between both models based on 23 hormetic data sets. Based on this, the impact on effective dosage estimations was evaluated, especially in case of a substantially inferior fit by one of the two models.Methodology/principal findingsThe data sets evaluated described the hormetic responses of four different test plant species exposed to 15 different chemical stressors in two different experimental dose-response test designs. Out of the 23 data sets, one could not be described by any of the two models, 14 could be better described by one of the two models, and eight could be equally described by both models. In cases of misspecification by any of the two models, the differences between effective dosages estimates (0-1768%) greatly exceeded the differences observed when both models provided a satisfactory fit (0-26%). This suggests that the conclusions drawn depending on the model used may diverge considerably when using an improper hormetic model especially regarding effective dosages quantifying hormesis.Conclusions/significanceThe study showed that hormetic dose responses can take on many shapes and that this diversity can not be captured by a single model without risking considerable misinterpretation. However, the two empirical models considered in this paper together provide a powerful means to model, prove, and now also to quantify a wide range of hormetic responses by reparameterization. Despite this, they should not be applied uncritically, but after statistical and graphical assessment of their adequacy.

Dataset Information

Estimating the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships in meta-data evaluations.

Publications

Estimating the no-observed-adverse-effect-level (NOAEL) of hormetic dose-response relationships in meta-data evaluations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets