Categorical Modeling of the Flow Pattern of Liquid Organic Compounds Between Blade Electrodes Using Semiempirical and ab initio Quantum Chemical Descriptors

For a data set of 30 organic fluids, categorical modeling has been employed to predict the flow pattern under an external electric field. To this end, a previously generated data set was augmented by 10 compounds with new experimental results, and quantum chemical methods have been used to characterize the geometric and electronic structure of the molecules on both the semiempirical and ab initio levels of theory. Both linear discriminant analysis (LDA) and binary logistic regression (BLR) have been employed to model the flow rate (high vs. low) and flow direction (left vs. right). For the flow rate, good LDA and BLR calibration statistics using the dipole moment, hydrophobicity and some charged partial surface area (CPSA) descriptors is accompanied with moderate prediction statistics, as evaluated through simulated external validation, and activity scrambling shows that chance correlation is not relevant. Additional neural network analyses yielded no stable models due to constraints imposed by the data set size. For the flow direction, LDA and BLR calibration and prediction statistics show more variation among the different models generated, with an overall performance inferior to the one for the flow rate. Here, besides CPSA descriptors, two parameters characterizing the softness of the electronic structure are involved. In general, BLR is slightly superior to LDA for both properties. The results are discussed in terms of contingency table statistics and with respect to the mechanistic meaning of molecular descriptors

Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

Abstract not availableJRC.I-Institute for Health and Consumer Protection (Ispra