In SilicoPrediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

Abstract

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

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

“…There is a rich history of such "structural alerts" and their conversion into usable computational models. 20 There are many good reasons for this, e.g. they are easy to define and comprehend -hence aiding in their transparency.…”

“…The ability to use the pathway-derived data to extract further information and knowledge is one of their advantages, especially when they can be formalised into computational models. [20][21] When associated with chemical structure, these models, also called in silico approaches, can provide a direct linkage between chemistry and adverse effect leveraging the content of the AOP to support the meaning and interpretation of the model. [22][23] In silico models for toxicity prediction vary from structural alerts derived from structureactivity relationships (SARs) through to quantitative structure-activity relationships (QSARs) which are suitable for the prediction of potency.…”

Unlocking the potential of in silico chemical safety assessment – A report on a cross-sector symposium on current opportunities and future challenges

“…This is seen as a powerful tool for data gap filling, with successes in areas such as the completion of dossiers for REACH, although with many questions still left unanswered, such as those relating to acceptance and the translatability to humans and, for environmental risk assessment, a broader spectrum of species. There is increasing coverage of structural alerts for many endpoints that have previously proven difficult to model [16] , [17] with further work on-going.…”

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

“…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].…”