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.

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In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

Going back to the 19th century, there is a long history of developing an understanding of how the physicochemical properties and chemical structure of a molecule affect its biological activity. Such approaches, commonly now referred to as being in silico , are formalised into (quantitative) structure–activity relationships ((Q)SARs) and read-across to predict toxicity and Physiologically-Based Pharmacokinetic models, to predict the distribution of chemicals in vivo and to allow for the extrapolation from in vitro effects. There have been many drivers for the development of in silico approaches to predict toxicity, fate and distribution. Notable among these have been the needs of various industrial sectors to assess the hazard of chemicals rapidly and efficiently, in terms of cost and animal use. These needs have been amplified globally by legislation aiming to improve animal welfare, and to respond to ethical concerns, as well as to regulate new and existing chemicals. In addition, key advances in chemoinformatics, computational power, and the connectivity of the internet have all played a role in the advancement of in silico approaches.

Computational Methods to Predict Toxicity

Emerging Big Data technologies and the growing amount of data in predictive toxicology (and in chemistry in general) require new solutions and methods for large-scale data and model storage, as well as for model representation and analysis. Knowledge extraction from big and diverse toxicology and chemistry data results in mathematical models that are used to organise and systematise data and structure patterns. Consequently, next to the developments in data organisation and analysis, the systematic representation and organisation of descriptive and predictive qualitative and quantitative structure–activity relationships, (Q)SARs, is equally important. Therefore, full attention from model developers is required to make the new knowledge derived from the data and models easily accessible and usable. This chapter considers issues related to the organisation of (Q)SAR models and gives an overview of the file and data formats used to organise predictive models as well as their storage solutions in the era of data expansion.

CHAPTER 6:Storing and Using Qualitative and Quantitative Structure–Activity Relationships in the Era of Toxicological and Chemical Data Expansion

There are a wide variety of established toxicity databases being routinely used to support hazard and risk assessment. The increasing number and size of toxicology databases and methods for automatically recording toxicology studies has led to a diverse series of formats for organisation of the information. Currently, such approaches span from simple one-to-one data exchange formats to complex formats incorporating semantic representations and data models covering additional information (such as experimental design, results and findings). Such developments support the volume, variety and value of (big) data and the transition from insular, isolated and simple structures to big data technologies for big toxicology data resources. This chapter reviews current solutions for the most known resources and identifies opportunities and gaps that such resources show in the era of big data. It covers a variety of approaches to data exchange and database design for organising toxicity and related data, highlights a number of databases with toxicity data as well as information on alternative approaches, and discusses project management and regulatory compliance databases. The chapter concludes with reflections on how these systems can integrate diverse toxicology-related data in preparation for the adoption of new big data technologies.

CHAPTER 4:Organisation of Toxicological Data in Databases

Introduction: The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven ...

/pdf/a-critical-review-of-adverse-effects-to-the-kidney-4xyysdk0e8.pdf

A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction

COSMOS DB as an international share point for exchanging regulatory and toxicity data of cosmetics ingredients and related substances

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COSMOS DB as an international share point for exchanging regulatory and toxicity data of cosmetics ingredients and related substances