Quantitative consensus of bioaccumulation models for integrated testing strategies

A quantitative consensus model based on bioconcentration factor (BCF) predictions obtained from five quantitative structure–activity relationship models was developed for bioaccumulation assessment as an integrated testing approach for waiving. Three categories were considered: non-bioaccumulative, bioaccumulative and very bioaccumulative. Five in silico BCF models were selected and included into a quantitative consensus model by means of the continuous formulation of Bayes' theorem. The discrete likelihoods commonly used in the qualitative Bayesian model were substituted by probability density functions to reduce the loss of information that occurred when continuous BCF values were distributed across the three bioaccumulation categories. Results showed that the continuous Bayesian model yielded the best classification predictions compared not only to the discrete Bayesian model, but also to the individual BCF models. The proposed quantitative consensus model proved to be a suitable approach for integrated testing strategies for continuous endpoints of environmental interest.

► Quantitative consensus can harmonize predictions from several non-testing methods.
► Bayesian consensus outperforms individual QSAR models for bioaccumulation assessment.
► Bayes-based consensus can boost endpoint predictions within an ITS workflow.
► Probability thresholds allow safety margins in regulatory decision making.
► Bayesian consensus can be applied to integrate an arbitrary number of models.