Development of generic physiologically based kinetic models for four fish species: rainbow trout, zebra fish, fathead minnow and three-spined stickleback

The R codes with the DOI: 10.5281/zenodo.1414332 presented here constitute generic physiologically-based models for four fish species: rainbow trout (Onchorhynchus mykiss), zebrafish (Danio rerio), fathead minnow (Pimephales promelas), and three-spined stickleback (Gasterosteus aculeatus). In addition, the functions “ODE” in Desolve package and “soboljansen” were used to solve the PBK models numerically and carry sensitivity analysis using sobol plots. The full data collection and implementation of the models using case studies is described in Grech et al., 2018.

The  R codes are provided for:

1. Generic R code for PB-K modelling and partition coefficient Quantitative Structure Activity Relationship (QSAR) models
2. Generic codes for each species and sex
3. Parameterisation of models for each males and females of each species separately:

- Rainbow trout (Onchorhynchus mykiss)

-Zebrafish (Danio rerio)

-Fathead minnow (Pimephales promelas)

-Three-spined stickleback (Gasterosteus aculeatus)

The models also include new mechanistic approaches for including the effects of growth and temperature in generic model. Physiological parameters and their inter-individual variability estimated based on the results of extensive literature searches and specific experimental data are provided in Excel under (Grech et al., 2018; DOI: 10.5281/zenodo.1414332). For each species, the PBTK model has been implemented for nine regulated compounds and environmental contaminants (with log k_ow from -0.9 to 6.8) to predict whole-body concentrations, assess the overall bioconcentration as well as the concentration in fish’s organs. Each generic PBK model includes:

1. A deterministic modelling method which represents only a single animal.

2. A probabilistic modelling approach to simulate the individual differences of the physiology within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data and were accurate for most chemicals. Our model can be used to assess the influence of physiological and environmental factors on the TK of chemicals and provide guidance for assessing such critical factors in environmental risk assessment. For PB-TK modelling purposes, species and chemical specific kinetics (e.g clearance,Cmax etc…) should be provided by the user. 

The full data collection and implementation of the models using case studies are described in Grech et al., 2018  (https://doi.org/10.1016/j.scitotenv.2018.09.163)