Abstract
The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.
Highlights
Data requirements for environmental risk assessments of chemicals are rapidly increasing, for example in regulatory processes at the European and global level, and for the development of new chemical products with more benign profiles
To assess the effectiveness and validity of our MultiGeneration evaluation approach, we summarize the results from the procedures detailed in the Experiments section in the following
Due to the nature of the Multi-Generation evaluation approach, where pathways potentially have an infinite number of true negatives, the false positive rate cannot be computed
Summary
Data requirements for environmental risk assessments of chemicals are rapidly increasing, for example in regulatory processes at the European (cf. REACH [1]) and global level, and for the development of new chemical products with more benign profiles. This includes increasing knowledge about transformation products of these chemicals in the environment and increases the need for prediction methods of metabolism and Conceptually, biotransformation pathways represent the chemical changes a given starting compound (referred to as root compound in the remainder of the text) undergoes upon biotransformation. The iteration terminates when a threshold is reached, or when no new compounds are created
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