MOSAICbioacc




The MOSAICbioacc application is a turn-key web tool providing bioaccumulation metrics (BCF/BMF/BSAF) from a toxicokinetic (TK) model fitted to accumulation-depuration data. It is designed to fulfil the requirements of regulators when examining applications for market authorization of active substances.
Learn more

Toxicokinetic/toxicodynamic (TKTD) models are used to describe and predict the toxicity and the effects of chemical substances on individual traits based on experimental data. The toxicokinetic (TK) part describes the relationship between the exposure medium and the organism, considering various processes such as ADME (accumulation, depuration, metabolization and excretion)1. Regulation No 283/2013 (EU)2 defines the data requirements for active substances of plant protection products in marketing authorization applications. In particular, a bioaccumulation study on fish is required following OECD guideline 3053. Achieved in agreement with EFSA's scientific opinion on good modeling practices4,5, this ready-to-use on-line service allows to easily estimate bioaccumulation metrics (namely, BCF/BMF/BSAF) as required in a regulatory framework, accounting for bioaccumulation of parent compounds and their metabolites through biotransformation. MOSAICbioacc does not expect any input besides the accumulation-depuration datasets according to exposure concentrations. The service automatically fits the TK model, suitably selected for the user by the application, and optimizes its parameters. Then, the service provides the corresponding bioaccumulation metrics, as well as all goodness-of-fit criteria required to carefully check the reliability of the results6. All calculations are based on JAGS software and companion R packages rjags and jagsUI 7,8,9. More details about the underlying modeling framework can be found in the user guide and companion research papers10.





Contact: sandrine.charles@univ-lyon1.fr

Delta version (updated on 2021-08-26)


This work is supported by the EUR H2O’Lyon (ANR-17-EURE-0018) of Université de Lyon (UdL), within the program “Investissements d’Avenir” operated by the French National Research Agency (ANR).

Data upload


Data Visualisation

Exposure:

Model and parameters


Accumulation:

(Select one or several exposure media)

Depuration:

(Select involved processes)


Click here for more information about parameters meaning.

This is the deterministic part of the model with:



Click here for more information about the model (deterministic and stochastic parts) and here for solving equations.
Click here for more information about the type of fitting algorithm used, the number of iterations required...


Bioaccumulation metric calculation


Click here for more information about the BCF calculation.

Median and 95% uncertainty limits of bioaccumulation metrics:





Probability density of the bioaccumulation metric. The middle dotted line represents the median value, and the dotted lines at the right and left are the 2.5 and 97.5% quantiles.


From the fit below, if your data are very close to the asymptotic steady-state at the end of the accumulation phase, you can ask for the BCFss.



Click here for more information about the BCFpw calculation.
Median and 95% uncertainty limits of bioaccumulation metrics:



Probability density of the bioaccumulation metric. The middle dotted line represents the median value, and the dotted lines at the right and left are the 2.5 and 97.5% quantiles.


From the fit below, if your data are very close to the asymptotic steady-state at the end of the accumulation phase, you can ask for the BCFss :



Click here for more information about the BSAF calculation.
Median and 95% uncertainty limits of bioaccumulation metrics:



Probability density of the bioaccumulation metric. The middle dotted line represents the median value, and the dotted lines at the right and left are the 2.5 and 97.5% quantiles.


From the fit below, if your data are very close to the asymptotic steady-state at the end of the accumulation phase, you can ask for the BSAFss :



Click here for more information about the BMF calculation.
Median and 95% uncertainty limits of bioaccumulation metrics:



Probability density of the bioaccumulation metric. The middle dotted line represents the median value, and the dotted lines at the right and left are the 2.5 and 97.5% quantiles.


From the fit below, if your data are very close to the asymptotic steady-state at the end of the accumulation phase, you can ask for the BMFss :



Fitting results



Quantiles of estimated parameters:


Measured (black dots) and predicted contaminant concentrations in the organism (μg.g-1). Median predictions are symbolized by the orange plain line and the uncertainty bands by the gray zone which is delimited by the 2.5% and 97.5% quantiles in orange dotted lines. The black dotted vertical line indicates the separation between the accumulation phase and the depuration phase.

Goodness-of-fit criteria

Goodness-of-fit criteria are given below in our prioritized order; the PPC and the prior-posterior comparison are the most important to check; if they do not correspond to the expectation, you must consider your results with an even more particular attention. More details

If goodness-of-fit criteria do not fully match the expectations, go to the appendix of the user guide to learn more about the interpretation.

Posterior Predictive Check

The PPC shows the observed values against their corresponding estimated predictions (black dots), along with their 95% credible interval (vertical segments). If the fit is correct, we expect to see 95% of the data within the intervals. Ideally observations and predictions should coincide, so we would expect to see black dots along the first bisector y = x (plain black line). The 95% credible intervals are colored in green if they overlap this line, in red otherwise. User guide

Priors and Posteriors

The prior distribution is represented by the gray area and the posterior distribution by the orange area. The accuracy of the model parameter estimation can be visualized by comparing prior and posterior distributions: the overall expectation is to get a narrower posterior distribution compared to the prior one, what reflects that data contributed enough to precisely estimate parameters. The deterministic part represents the mean trend, while the stochastic part describes the variability around this mean trend. User guide

Correlations between parameters

By default, a colored matrix allows to see at a glance the most correlated (green, Pearson coefficient closed to 1), anti-correlated (orange, Pearson coefficient closed to -1) or not correlated (Pearson coefficient closed to 0) parameters.
Correlations between parameters are also visualized by projecting the joint posterior distribution in a plot matrix with planes of parameter pairs (lower triangular elements), marginal posterior distribution of each model parameter (diagonal), and Pearson correlation coefficients (upper triangular elements). Correlations are expected to be low (reflected by “potatoid” shapes of density lines in orange); a leaning elliptical shape translates high correlation (positive if leaning to the right, negative if leaning to the left). The deterministic part represents the mean trend, while the stochastic part describes the variability around this mean trend. User guide

Potential Scale Reduction Factors



Convergence of the MCMC chains can be check with the Gelman-Rubin diagnostic expressed with the potential scale reduction factor (PSRF). Approximate convergence is better diagnosed when the PSRF is below to 1.0111, 12. User guide

Watanabe–Akaike information criterion



Information criteria offer a computationally appealing way of estimating the generalization performance of the model. A fully Bayesian criterion is the widely applicable information criterion (WAIC) by Watanabe a penalized deviance statistics accounting for the uncertainty in the parameters and can be used also for singular models. WAIC is widely used in model comparison for a same dataset (e.g., with or without \(k_{ee}\)). Sub-models with lower WAIC values will be preferred13. User guide

Traces of MCMC iterations

A traceplot is an essential plot for assessing convergence and diagnosing of MCMC chains. It shows the time series of the sampling process leading to the posterior distribution. Different colors are used for each of the chains (here 3) to assess within-chain convergence. The three chains are expected to converge towards the same values. The deterministic part represents the mean trend, while the stochastic part describes the variability around this mean trend. User guide

Downloads


The MOSAICbioacc application also provides a prediction tool.
  • This tool allows interactive simulations of a toxicokinetic (TK) model for a constant exposure duration in order to predict what could be observed when measuring internal concentration over time. Such a tool can be helpful in designing future experiments for a given species/compound combination (see vignette for more details).
  • This tool also allows users to propagate parameter uncertainty from a previous TK analysis (such as those provided by MOSAICbioacc) in order to perform prediction together with an uncertainty band around the median curve.

                    

                    

                    Try with the posteriors table of our integrated examples. You can also use parameter estimates obtained from a previous fit analysis
                                      with MOSAICbioacc.
 To import another joint posterior distribution or manually fill in point parameters values, see
                                      the 'Distributed parameters' section below.

                                      Note: with distributed parameters and complex TK models, calculations may be long. Please be patient!
                  

                  

                    

                      

                        Exposure routes 
                        

                        
(Select one or several exposure media)

                      

                      

                        

                          

                            
                          

                        

                        

                      

                      

                        

                          

                            
                          

                        

                        

                      

                      

                        

                          

                            
                          

                        

                        

                      

                      

                        

                          

                            
                          

                        

                        

                      

                      

                    

                    

                      

                        Elimination processes 
                        

                        
(Select involved processes)

                      

                      

                        

                          

                            
                          

                        

                      

                      

                        

                          

                            
                          

                        

                        

                          

                            
                            

                              
                              
                            

                          

                        

                      

                      

                        

                          

                            
                          

                        

                        

                          

                            
                            
                          

                        

                      

                    

                    Click 
           here for more information about parameters meaning.
                  

                  

                    

                      

                        
Exposure concentration(s)
 (expressed in µg/mL) 

                        

                        

                          

                            

                              

                                
                                
                              

                            

                            

                              

                                
                                
                              

                            

                          

                          

                            

                              

                                
                                
                              

                            

                            

                              

                                
                                
                              

                            

                          

                        

                      

                      

                        

                          

                            

                              
                              
                            

                          

                          

                            

                              
                              
                            

                          

                        

                        

                          
                          

                            
                            
                          

                        

                      

                    

                    

                      

                        

                          
                          

                            
                            
                          

                        

                        

                        

                          Give a single value for each parameter.
                          

                            
Uptake parameters

                          

                          

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              
                              
                              
                              
                              
                              
                              
                              
                            

                          

                          

                          

                            
Elimination parameters

                          

                          

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              
                            

                            

                              

                                

                                  
                                  
                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  

                                    
                                    
                                  

                                

                              

                              
                              
                              
                              
                              
                              
                              
                              
                            

                          

                        

                        

                          Choose a joint posterior distribution for parameters (csv or txt file).
                          

                            
                            

                              
                              
                            

                            

                              

                            

                          

                          

                            

                              

                                
                                

                                  

                                    
                                  

                                  

                                    
                                  

                                  

                                    
                                  

                                

                              

                            

                            

                              
                            

                          

                        

                      

                    

                  

                  

                  

                  

                    

                      
                    

                  

                

                

                  

                    

                      
                    

                    

                      

                        

                          

                            Prediction results
                          

                        
                        

                        

                          

                            
                          

                        

                        
This plot displays the prediction of the internal concentrations over time. Median prediction 
                     is symbolized by the orange plain line. If parameters are distributed, the uncertainty band is 
                     symbolized by the gray zone which is delimited by the 2.5% and 97.5% quantiles in orange dotted lines.

                      

                    

                  

                

                

                  

                    

                      
                    

                    

                      

                        

                          

                            Validation tool
                          

                        
                        

                        

                          

                            
                          

                        

                        
This plot displays the prediction of the internal concentrations over time. Median prediction 
                      is symbolized by the orange plain line. If parameters are distributed, the uncertainty band is 
                      symbolized by the gray zone which is delimited by the 2.5% and 97.5% quantiles in orange dotted lines.

                      

                    

                  

                

              

            

          

        

      

    

    

      

        
        

          

            

            [1] Mackay, D. and Fraser, A. 2000. Bioaccumulation of persistent organic chemicals: Mechanisms and
models. Environmental Pollution. 110:375-391. https://doi.org/10.1016/S0269-7491(00)00162-7

[2] Commission Regulation (EU) No 283/2013 of 1 March 2013 setting out the data requirements for
active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament
and of the Council concerning the placing of plant protection products on the market Text with EEA
relevance. Pages 1–84. Official Journal of the European Union, 93, 3.4.2013. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32013R0283

[3] OECD (2012) Test No. 305: Bioaccumulation in Fish: Aqueous and Dietary Exposure, OECD Guidelines for the Testing of Chemicals,
Section 3, OECD Edition, Paris, https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32013R0283

[4] Charles, S., P. Veber, and M. L. Delignette-Muller. 2018. MOSAIC: a web-interface for statistical
analyses in ecotoxicology. Environmental Science and Pollution Research, 25:11295-11302. https://doi.org/10.1007/s11356-017-9809-4

[5] EFSA. 2014. Scientific Opinion on good modelling practice in the context of mechanistic effect
models for risk assessment of plant protection products. EFSA Journal 12. https://www.efsa.europa.eu/en/efsajournal/pub/3589

[6] Fernández-i-Marín, X. (2016). ggmcmc: Analysis of MCMC samples and Bayesian inference.Journal of Statistical Software, 70(9), 1-20. https://doi.org/10.18637/jss.v070.i09 

[7] Plummer, M. (2019). rjags: Bayesian Graphical Models using MCMC. R package version 4-10. https://CRAN.R-project.org/package=rjags

[8] R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

[9] Kellner, K. (2019). jagsUI: A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses. Version 1.5.1. https://cran.r-project.org/web/packages/jagsUI/index.html

[10] Ratier, A. et al. (2021).  A new on-line tool for the calculation of bioaccumulation metrics of active substances within living organisms: MOSAICBioacc. bioRxiv. https://www.biorxiv.org/content/10.1101/2020.07.07.185835v1.

[11] Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7:457-511. https://www.jstor.org/stable/2246093

[12] Vehtari, A. et al. (2019). Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC.  arXiv:1903.08008v3. https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.1214%2F20-BA1221&v=8607c76e

[13] Spiegelhalter, D., Best, N., Carlin, B. and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society Series B, 64: 583-639. https://doi.org/10.1111/1467-9868.00353.

[14] Ciccia, T. (2019). Accumulation et devenir du mercure chez l'espèce sentinelle Gammarus fossarum : de l'expérimentation au développement d'un modèle toxicocinétique multi-compartiments. Rapport de stage de Master 2, INRAE.

[15] Crookes, M. and Brooke D. (2011). Estimation of fish bioconcentration factor (BCF) from depuration data. Report. Environmental Agency, Bristol, UK. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/291527/scho0811buce-e-e.pdf.

[16] Larisch, W., Brown, T.N. and Goss, K.‐U. (2017). A toxicokinetic model for fish including multiphase sorption features. Environmental Toxicology and Chemistry, 3: 1538-1546. https://doi.org/10.1002/etc.3677.

[17] Ashauer, R.  et al. (2012). Significance of xenobiotic metabolism for bioaccumulation kinetics of organic chemicals in Gammarus pulex. Environmental Science Technologie, 46: 3498-3508. https://doi.org/10.1021/es204611h.

[18] Schuler, L.J.  et al. (2003). Toxicokinetics of sediment-sorbed benzo[a]pyrene and hexachlorobiphenyl using the freshwater invertebrates Hyalella azteca, Chironomus tentans, and Lumbriculus variegatus. Environmental Toxicology and Chemistry, 22: 439-449. https://doi.org/10.1002/etc.5620220227.

          

          

            

            
            Sandrine Charles, Professor at University Lyon 1

            Stéphane Delmotte, Software Engineer at LBBE

            Miléna Kaag, Engineer at LBBE

            Yacout Lahlou, Engineer student at INSA Lyon

            Christelle Lopes, Associate Professor at University Lyon 1

            Gauthier Multari, bachelor student at University Lyon 1

            Nino Molin, Engineer student at INSA Lyon

            Aude Ratier, post-doc at LBBE

            Aurélie Silberchicot, Software Engineer at LBBE