The ability of a compound to cause adverse effects to the liver is one of the most common reasons for drug development failures and the withdrawal of drugs from the market. Such adverse effects can vary tremendously in severity, leading to an array of possible drug-induced liver injuries (DILIs). As a result, it is not surprising that drug development has evolved into a complex and multifaceted process including methods aiming to identify potential liver toxicities. Unfortunately, hepatotoxicity remains one of the most complex and poorly understood areas of human toxicity; thus it is a significant challenge to identify potential hepatotoxins. The performance of existing methods to identify hepatotoxicity requires improvement. The current study details a scheme for generating chemical categories and the development of structural alerts able to identify potential hepatotoxins. The study utilized a diverse 951-compound dataset and used structural similarity methods to produce a number of structurally restricted categories. From these categories, 16 structural alerts associated with observed human hepatotoxicity were developed. Furthermore, the mechanism(s) by which these compounds cause hepatotoxicity were investigated and a mechanistic rationale was proposed, where possible, to yield mechanistically supported structural alerts. Alerts of this nature have the potential to be used in the screening of compounds to highlight potential hepatotoxicity, whilst the chemical categories themselves are important in applying read-across approaches. The scheme presented in this study also has the potential to act as a knowledge generator serving as an excellent starting platform from which to conduct additional toxicological studies.

Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.

There are no in vivo repeated-dose data for the vast majority of β-olefinic alcohols.However, there are robust and consistent ex vivo data suggesting many of these chemicals are metabolically transformed, especially in the liver, to reactive electrophilic toxicants which react in a mechanistically similar manner to acrolein, the reactive metabolite of 2-propen-1-ol. Hence, an evaluation was conducted to determine suitability of 2-propen-1-ol as a read-across analogue for other β-olefinic alcohols. The pivotal issue to applying read-across to the proposed category is the confirmation of the biotransformation to metabolites having the same mechanism of electrophilic reactivity, via the same metabolic pathway, with a rate of transformation sufficient to induce the same in vivo outcome. The applicability domain for this case study was limited to small (C3 to C6) primary and secondary -olefinic alcohols. Mechanistically, these -unsaturated alcohols are considered to be readily metabolised by alcohol dehydrogenase to polarised α, -unsaturated aldehydes and ketones. These metabolites are able to react via the Michael addition reaction mechanism with thiol groups in proteins resulting in cellular apoptosis and/or necrosis. The addition of the non-animal in chemico reactivity data (50% depletion of free glutathione) reduced the uncertainty so the read-across prediction for the straight-chain olefinic -unsaturated alcohols is deemed equivalent to a standard test. Specifically, the rat oral 90-day repeated-dose No Observed Adverse Effect Level (NOAEL) for 2-propen-1-ol of 6 mg/kg body weight bw/d in males based on increase in relative weight of liver and 25 mg/kg bw/d in females based on bile duct hyperplasia and periportal hepatocyte hypertrophy in the liver, is read across to fill data gaps for the straight-chained analogues.