In silico assessment of aquatic bioaccumulation: advances from chemometrics and QSAR modelling

This doctoral dissertation addresses the current open problems in the prediction of aquatic bioaccumulation through computational methods, which is a priority according to many national and international regulations. QSAR (Quantitative Structure-Activity Relationship) techniques are amongst the most-widely used in silico methods, as they (1) allow performing a quick analysis of hundreds of thousands of compounds, and (2) exploit advanced statistical and mathematical techniques for model development. QSAR is an effective tool for data-gap filling, testing prioritization and reduction of cost, time and number of animals used for experimental studies. The project addressed two issues (1) the non-dietary bioaccumulation of chemicals (expressed through the Bioconcentration Factor, BCF), which has been extensively modelled but still has some critical points, (2) the dietary bioaccumulation of chemicals, which was never modelled in a QSAR setting. Fish were used as the target, as they the benchmark organisms for bioaccumulation assessment. In the first phase, the performance of 9 BCF models of different complexity was analyzed and critically compared on a manually-curated dataset of fish BCFww for 1056 compounds. Results revealed that the descriptor-based models, despite their complexity, take only partially into account the processes that influence bioconcentration and show the same biases of KOW-based predictions (KOW = octanol-water partition coefficient). The KOW-driven over- and underestimation of BCF by are due to additional processes, such as metabolism or storage within non-lipid tissues. For this reason, a scheme was developed to predict whether a compound (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g., proteins), or (3) is metabolized/eliminated. The developed approach is based on two validated QSAR classification trees, characterized by descriptor interpretability and simplicity. They allowed to: (1) detect compounds that are potentially under- or overestimated on the basis of KOW, and (2) gather mechanistic insights about the structural features connected to the mechanisms of bioconcentration. The proposed classification scheme was then applied to the prediction of BCF for regulatory purposes, resulting in an expert system with better performance than the existing models. The second target of the study was the dietary bioaccumulation, quantified through the Biomagnification Factor (BMF). After collecting a BMF dataset of 214 compounds, the experimental values were compared with BCF and KOW. This highlighted that, in some cases, the diet can be the prevalent bioaccumulation route. For this reason, two QSAR models were developed and special attention was given to model validation and robustness, applicability domain assessment and descriptor understanding. The mechanistic interpretation highlighted that part of the mechanisms that control BCF also control BMF, while some other mechanisms are different. This model can be used as additional modelling tool for the assessment of the bioaccumulation, as it can give additional information than BCF. In conclusion, this dissertation provided a set of efficient tools to estimate the chemical’s propensity to bioaccumulate within the food chain, also adding new mechanistic knowledge to the field.