A framework for categorizing sources of uncertainty in in silico toxicology methods: Considerations for chemical toxicity predictions

Abstract

Improving regulatory confidence and acceptance of in silico toxicology methods for chemical risk assessment requires assessment of associated uncertainties. Therefore, there is a need to identify and systematically categorize sources of uncertainty relevant to the methods and their predictions. In the present study, we analyzed studies that have characterized sources of uncertainty across commonly applied in silico toxicology methods. Our study reveals variations in the kind and number of uncertainty sources these studies cover. Additionally, the studies use different terminologies to describe similar sources of uncertainty; consequently, a majority of the sources considerably overlap. Building on an existing framework, we developed a new uncertainty categorization framework that systematically consolidates and categorizes the different uncertainty sources described in the analyzed studies. We then illustrate the importance of the developed framework through a case study involving QSAR prediction of the toxicity of five compounds, as well as compare it with the QSAR Assessment Framework (QAF). The framework can provide a structured (and potentially more transparent) understanding of where the uncertainties reside within in silico toxicology models and model predictions, thus promoting critical reflection on appropriate strategies to address the uncertainties. • Uncertainty communication is necessary for evaluating the robustness and validity of in silico toxicology methods. • A framework was developed to categorize uncertainty sources relevant to in silico toxicology methods. • Application of the framework was illustrated through chemical toxicity prediction using a QSAR model. • The relevance of the framework was discussed within the context of the OECD's (Q)SAR Assessment Framework.

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