Read-Across for Rat Oral Gavage Repeated-Dose Toxicity for Short-Chain Mono-Alkylphenols: A Case Study

Short-chain mono-alkylphenols provide an example of where a category-approach to read-across may be used to estimate the repeated-dose endpoint for a number of derivatives. Specifically, the NOAELs of 50 mg/kg bw/d for mono-methylphenols based on a LOAEL of very low systemic toxicity can be read across with confidence to untested mono-alkylphenols in the category. These simple alkylphenols are non-reactive and exhibit an unspecific, reversible polar narcosis mode of toxic action. Briefly, polar narcotics act via unspecific, reversible interactions with biological membranes in a manner similar to cataleptic anaesthetics. The read-across premise includes rapid and complete absorption via the gastrointestinal tract, distribution in the circulatory system, first-pass Phase 2 metabolism in the liver, and elimination of sulphates and glucuronides in the urine. Thus, toxicokinetic parameters are considered to be similar and have the same toxicological significance. Five analogues have high quality experimental oral repeated-dose toxicity data (i.e., OECD TG 408 or OECD TG 422). These repeated-dose toxicity test results exhibit qualitative consistency in symptoms. Typical findings include decreased body weight and slightly increased liver and kidney weights which are generally without concurrent histopathological effects. The sub-chronic findings are quantitatively consistent with the No Observed Adverse Effect Level (NOAEL) of ≥ 50 mg/kg bw/d. Chemical similarity between the analogues is readily defined, and data uncertainty associated with the similarities in toxicokinetic properties, as well as toxicodynamic properties, are low. Uncertainty associated with mechanistic relevance and completeness of the read-across is low-to-moderate, largely because there is no adverse outcome pathway or intermediate event data. Uncertainty associated with mechanistic relevance and completeness of the read-across is reduced by the concordance of in vivo, in vitro, USEPA toxicity forecaster (ToxCast) results, as well as the in silico data. The rat oral repeated-dose NOAEL values for the source substances can be read across to fill the data gaps of the untested analogues in this category with uncertainty deemed equivalent to results from a TG 408 assessment

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

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 study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were 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

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015

The Native Copper- and Zinc- Binding Protein Metallothionein Blocks Copper-Mediated Aβ Aggregation and Toxicity in Rat Cortical Neurons

Background: A major pathological hallmark of AD is the deposition of insoluble extracellular b-amyloid (Ab) plaques. There are compelling data suggesting that Ab aggregation is catalysed by reaction with the metals zinc and copper. Methodology/Principal Findings: We now report that the major human-expressed metallothionein (MT) subtype, MT-2A, is capable of preventing the in vitro copper-mediated aggregation of Ab1–40 and Ab1–42. This action of MT-2A appears to involve a metal-swap between Zn 7MT-2A and Cu(II)-Ab, since neither Cu 10MT-2A or carboxymethylated MT-2A blocked Cu(II)-Ab aggregation. Furthermore, Zn7MT-2A blocked Cu(II)-Ab induced changes in ionic homeostasis and subsequent neurotoxicity of cultured cortical neurons. Conclusions/Significance: These results indicate that MTs of the type represented by MT-2A are capable of protecting against Ab aggregation and toxicity. Given the recent interest in metal-chelation therapies for AD that remove metal from Ab leaving a metal-free Ab that can readily bind metals again, we believe that MT-2A might represent a different therapeuti

Genetic toxicology in silico protocol

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches