Results of a round-robin exercise on read-across

The VERA software: Implementation of the acute fish toxicity endpoint and its application to pharmaceutical compounds

The Virtual Extensive Read-Across software (VERA) is a new tool for read-across using a global similarity score, molecular groups, and structural alerts to find clusters of similar substances; these clusters are then used to identify suitable similar substances and make an assessment for the target substance. A beta version of VERA GUI is free and available at vegahub.eu; the source code of the VERA algorithm is available on GitHub. In the past we described its use to assess carcinogenicity, a classification endpoint. The aim here is to extend the automated read-across approach to assess continuous endpoints as well. We addressed acute fish toxicity. VERA evaluation on the acute fish toxicity endpoint was done on a dataset containing general substances (pesticides, industrial products, biocides, etc.), obtaining an overall R2 of 0.68. We employed the VERA algorithm also on active pharmaceutical ingredients (APIs). We included a portion of the APIs in the training dataset to predict APIs, successfully achieving an overall R2 of 0.63. VERA evaluates the assessment's reliability, and we reached an R2 of 0.78 and Root Mean Square Error (RMSE) of 0.44 for predictions with high reliability.

QSAR Methods

This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties, and toxicology in particular, modeling. Since about three decades, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Animal and wet experiments, aimed at providing a standardized result about a biological property, can be mimicked by modeling methods, globally called in silico methods, all characterized by deducing properties starting from the chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (quantitative structure-activity relationships), and models that check relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. Virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data

Read-across approaches continue to evolve as does their utility in the field of risk assessment. Previously we presented our generalised read-across (GenRA) approach (Shah et al., 2016), which utilises chemical descriptor and/or in vitro bioactivity data to make read-across predictions on the basis of the similarity weighted average of nearest neighbours. The current public version of GenRA predicts 574 apical outcomes as a binary call from repeat dose toxicity studies available in ToxRefDB (Helman et al., 2019). Here we investigated the application of GenRA to quantitative values, specifically using a large dataset of rat oral acute LD50 toxicity data (LD50 values for 7011 discrete chemicals) that had been collected under the auspices of the ICCVAM acute toxicity workgroup (ATWG). GenRA LD50 predictions were made based on the following criteria - chemicals were characterised by Morgan chemical fingerprints with a minimum similarity threshold of 0.5 and a maximum of 10 nearest neighbours over the entire dataset. An R² value of 0.61 and RMSE of 0.58 was achieved based on these parameters. Monte Carlo cross validation was then used to estimate confidence in the R². Cross validated R² values were found to fall in the range of 0.47 to 0.62. However, when evaluating GenRA locally to clusters of mechanistically or structurally-similar chemicals, average R² values improved up to 0.91. GenRA can be extended to make reasonable quantitative predictions of acute oral rodent toxicity with improved performance exhibited for specific local domains.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r² = 0.8129, CCC = 0.8979 and Q² = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.

Exploring current read-across applications and needs among selected U.S. Federal Agencies

Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.

Navigating through the minefield of read-across tools: A review of in silico tools for grouping

Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. Tools have also been developed to facilitate read-across development and application. Here, we describe a number of publicly available read-across tools in the context of the category/analogue workflow and review their respective capabilities, strengths and weaknesses. No single tool addresses all aspects of the workflow. We highlight how the different tools complement each other and some of the opportunities for their further development to address the continued evolution of read-across.

What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps

Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.