Significant Influence of EC₅₀ Estimation by Model Choice and EC₅₀ Type

Abstract

The effective control to 50% growth inhibition (EC50) is a standard statistic for evaluating dose-response relationships. Many statistical software packages are available to estimate dose-response relationships but, recently, an open source package (“drc”) in R has been utilized. This package is highly adaptable, having many models to describe dose-response relationships and flexibility to describe both hormetic relationships and absolute and relative EC50. These models and definitions are generally left out of phytopathology literature. Here, we demonstrate that model choice and type of EC50 (relative versus absolute) can matter for EC50 estimation using data from Pythium oopapillum and Fusarium virguliforme. For some P. oopapillum isolates, the difference between absolute and relative EC50 was significant. Hormetic effects changed F. virguliforme EC50 distributions, leading to higher estimates than when using four- or three-parameter log-logistic models. Future studies should pay careful attention to model selection and interpretation in EC50 estimation and clearly indicate which model and EC50 measure (relative versus absolute) was used. We provide guidelines for model choice and interpretation for those wishing to set up experiments for accurate EC50 estimation.

Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal‐Based Nanomaterials

“…Various researchers have provided guidance for the proper derivation of EC50 values (see, e.g., Noel et al, 2018; Sebaugh, 2011). Puzyn et al (2011) used data generated according to this guidance to develop a model to predict effect levels for the cytotoxicity of 17 metal oxide nanoparticles to Escherichia coli cells based on the enthalpy of formation of a gaseous cation present in the same oxidation state as that in the metal oxide structure ($$\unicode{x02206}{H}_{\text{Me}+}$$).…”

“…Various researchers have provided guidance for the proper derivation of EC50 values (see, e.g., Noel et al, 2018;Sebaugh, 2011). Puzyn et al (2011) S1) and (B) shows model predictions for seven metalbased nanomaterials from 10 literature reports (Supporting Information, Table S2).…”

Soybean sudden death syndrome: Fungal pathogenesis and plant response

“…Most reports on disease treatments focus on the effect of fluopyram (Kandel et al, 2016a(Kandel et al, , 2018(Kandel et al, , 2019aWang et al, 2017;Noel et al, 2018;Sang et al, 2018;Sjarpe et al, 2020). value is the concentration of fungicide that reduces mycelial growth or conidial germination rate by 50%.…”

“…value is the concentration of fungicide that reduces mycelial growth or conidial germination rate by 50%. The EC 50 can be estimated using the extension package drc for the statistical environment R (Ritz et al, 2015) and this was applied to SDS-causing species exposed to fluopyram (Noel et al, 2018). The mean EC 50 value for inhibition of mycelial growth was 3.35 µg/ml (1.53-9.28 µg/ml) and that for inhibition of conidial germination was 2.28 µg/ml (0.81-5.57 µg/ml) for almost all F. virguliforme isolates (87% and 97% for mycelial growth and conidial germination, respectively) exposed to fluopyram (Wang et al, 2015a).…”

“…Most reports on disease treatments focus on the effect of fluopyram (Kandel et al ., 2016a, 2018, 2019a; Wang et al ., 2017; Noel et al ., 2018; Sang et al ., 2018; Sjarpe et al ., 2020). Fluopyram specifically inhibits fungal respiration by binding strongly to the ubiquinone‐binding site of the mitochondrial succinate dehydrogenase.…”