Summary
The R package rbioacc is dedicated to the analysis of experimental data collected from bioaccumulation tests. It provides ready-to-use functions to visualise a data set and to estimate bioaccumulation metrics to be further used in support of environmental risk assessment, in full compliance with regulatory requirements. Such metrics are classically requested by standardised regulatory guidelines on which national agencies base their evaluation of applications for marketing authorisation of chemical active substances.
Package rbioacc can be used to get estimates of toxicokinetic (TK) parameters (uptake and elimination rates) and bioaccumulation metrics (e.g., BCF, BSAF, BMF) by fitting a one compartment TK model on exposure-depuration test data. The bioaccumulation metrics estimates as well as the parameters and the predictions of the internal concentrations are given with the quantification of their uncertainty.
This paper illustrates some classical uses of rbioacc with internal concentrations collected over time possibly at several exposure concentrations, analysed with a generic TK one-compartment model. These examples can be followed step-by-step to analyse any new data set, as long as the data set format is respected.
Statement of need Package rbioacc (Baudrot et al. 2021) has been tested using R (version 4.1.0 and later) on Linux and Windows machines. Regarding the particular case of TK models, package rbioacc was compared with published results considering other TK implementations under different software platforms. Giving very similar results than the other implementations, package rbioacc was thus confirmed as fit-for-purpose in fitting TK models on bioaccumulation test data. All functions in package rbioacc can be used without a deep knowledge of their underlying probabilistic model or inference methods. Rather, they were designed to behave as well as possible, without requiring the user to provide values for some obscure parameters. Nevertheless, models implemented in rbioacc can also be used as a first step to create specially new models for more specific situations. Note that package rbioacc benefits from a web interface, MOSAICbioacc, from which the same analyses can be reproduced directly on-line without needs to invest in R programming. MOSAICbioacc is freely available on the MOSAIC platform at https://mosaic.univ-lyon1.fr/ (Charles et al. 2021) or directly at https://mosaic.univ-lyon1.fr/bioacc (Ratier et al. 2021).
Availability Package rbioacc is available as an R package (with R >= 4.1.0); it can be directly downloaded from CRAN https://CRAN.R-project.org/package=rbioacc, where package dependencies and system requirements are also documented.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version of the manuscript has been revised to update the use of the rbioacc package in a web application: MOSAIC_bioacc