Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Abstract

The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs).

Development and analysis of an adverse outcome pathway network for human neurotoxicity

“…Based on an understanding of the nature of MIEs, in silico models can be derived and, as a result, inform IATA and read-across. For example, several types of MIEs with associated AOPs have been distinguished and described by Cronin and Richarz (2017), including covalent reactivity, changes in receptor or enzyme activity. The different types of MIEs are identified in the AOP network for neurotoxicity including chronic receptor inhibition (binding of antagonist to NMDA receptors) and activation (binding of agonist to ionotropic glutamate receptors, binding of inhibitor to NADH-ubiquinone oxidoreductase (complex I), binding to SH/SeH proteins involved in the protection against oxidative stress, inhibition of thyroperoxidase, inhibition of Na +/I− symporter (NIS)), covalent reactivity (protein adduct formation) and enzyme activation (CYP2E1 activation).…”

A critical review of adverse effects to the kidney: mechanisms, data sources, andin silicotools to assist prediction

“…The AOP framework facilitates the organisation of mechanistic knowledge and grants validity and robustness to data included in the OECD-sponsored AOP Knowledge Base (AOP-KB), [26, http://aopkb.org]. Mechanistic data gathered and organised in the form of AOPs serve as a robust basis for the development of computational toxicology models [10,27]. If an MIE and/or Key Events (KEs) have been defined and respective data are available, a prediction approach to estimate a substance's potential to elicit one of more of these may be achieved using the knowledge in the AOP-KB and the public literature.…”

“…The individual endpoints and apical effects are described in more detail in the remainder of Section 2. Additionally, AOPs may aid the grouping of chemicals for read-across [10]. Only a handful of kidney and bladder related AOPs have 6 been developed and proposed so far which implies that only a small amount of MIEs and KEs have been defined.…”

“…for hepatic toxicity [8]. In recent years, much emphasis has been placed on understanding the underlying mechanisms of liver toxicity which have led to the development of several Adverse Outcome Pathways (AOPs), many SAs and QSARs [8][9][10]. The relative successes with the development of alternatives for identifying liver toxicants has demonstrated that success can be achieved and it is possible to address other organ level toxicity in a similar manner.…”

“…Considering that renal toxicity is a major drug safety issue, standard testing which often does not investigate underlying mechanisms has proven not to be an adequate assessment approach. As such, this is an opportunity for the application of computational approaches that utilise the AOP paradigm coupled with an understanding of the chemistry underpinning the MIE [10,15] to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. In addition in silico approaches using multi-scale data have been demonstrated to provide valuable insight into hepatotoxicity pathways and the assessment of inter-individual variability [16,17].…”