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

Saliency-based identification and recognition of pointed-at objects

Abstract — When persons interact, non-verbal cues are used to direct the attention of persons towards objects of interest. Achieving joint attention this way is an important aspect of natural communication. Most importantly, it allows to couple verbal descriptions with the visual appearance of objects, if the referred-to object is non-verbally indicated. In this contri-bution, we present a system that utilizes bottom-up saliency and pointing gestures to efficiently identify pointed-at objects. Furthermore, the system focuses the visual attention by steering a pan-tilt-zoom camera towards the object of interest and thus provides a suitable model-view for SIFT-based recognition and learning. We demonstrate the practical applicability of the proposed system through experimental evaluation in different environments with multiple pointers and objects

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively

Moir\'e Fringes in Conductive Atomic Force Microscopy

Moir\'e physics plays an important role for the characterization of functional materials and the engineering of physical properties in general, ranging from strain-driven transport phenomena to superconductivity. Here, we report the observation of moir\'e fringes in conductive atomic force microscopy (cAFM) scans gained on the model ferroelectric Er(Mn,Ti)O$_3$. By performing a systematic study of the impact of key experimental parameters on the emergent moir\'e fringes, such as scan angle and pixel density, we demonstrate that the observed fringes arise due to a superposition of the applied raster scanning and sample-intrinsic properties, classifying the measured modulation in conductance as a scanning moir\'e effect. Our findings are important for the investigation of local transport phenomena in moir\'e engineered materials by cAFM, providing a general guideline for distinguishing extrinsic from intrinsic moir\'e effects. Furthermore, the experiments provide a possible pathway for enhancing the sensitivity, pushing the resolution limit of local transport measurements by probing conductance variations at the spatial resolution limit via more long-ranged moir\'e patterns

Assessing Uncertainty in Read-Across: Questions to Evaluate Toxicity Predictions Based on Knowledge Gained from Case Studies

Read-across as an alternative assessment method for chemical toxicity has growing interest in both the regulatory and industrial communities. The pivotal means of acquiring acceptance of a read-across prediction is identifying and assessing uncertainties associated with it. This study has identified and summarised in a structured way the variety of uncertainties that potentially impact acceptance of a readacross argument. The main sources of uncertainty were established and divided into four main categories: i) the regulatory use of the prediction, ii) the data for the apical endpoint being assessed, iii) the readacross argumentation, and iv) the similarity justification. Specifically, the context of, and relevance to, the regulatory use of a read-across will dictate the acceptable level of uncertainties. The apical endpoint (or other) data must be of sufficient quality and relevance for data gap filling. Read-Across argumentation uncertainties include: 1) mechanistic plausibility (i.e., the knowledge of the chemical and biological mechanisms leading to toxicity), 2) completeness of the supporting evidence, 3) robustness of the supporting data, and 4) Weight-of-Evidence. In addition, similarity arguments for chemistry, physicochemical properties, toxicokinetics and toxicodynamics are linked to these read-across argumentation issues. To further progress in this area, a series of questions are proposed with the goal of addressing each type of uncertainty

Development of computational models for the prediction of the toxicity of nanomaterials

Extended abstrac

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Grouping of nanomaterials to read-across hazard endpoints: a review

The use of non-testing strategies like read-across in the hazard assessment of chemicals and nanomaterials (NMs) is deemed essential to perform the safety assessment of all NMs in due time and at lower costs. The identification of physicochemical (PC) properties affecting the hazard potential of NMs is crucial, as it could enable to predict impacts from similar NMs and outcomes of similar assays, reducing the need for experimental (and in particular animal) testing. This manuscript presents a review of approaches and available case studies on the grouping of NMs to read-across hazard endpoints. We include in this review grouping frameworks aimed at identifying hazard classes depending on PC properties, hazard classification modules in control banding (CB) approaches, and computational methods that can be used for grouping for read-across. The existing frameworks and case studies are systematically reported. Relevant nanospecific PC properties taken into account in the reviewed frameworks to support grouping are shape and surface properties (surface chemistry or reactivity) and hazard classes are identified on the basis of biopersistence, morphology, reactivity, and solubility.JRC.F.3-Chemicals Safety and Alternative Method

In vitro and in silico studies of the membrane permeability of natural flavonoids from Silybum marianum (L.) Gaertn. and their derivatives

Background: In recent years the number of natural products used as pharmaceuticals, components of dietary supplements and cosmetics has increased tremendously requiring more extensive evaluation of their pharmacokinetic properties. Purpose: This study aims at combining in vitro and in silico methods to evaluate the gastrointestinal absorption (GIA) of natural flavonolignans from milk thistle (Silybum marianum (L.) Gaertn.) and their derivatives. Methods: A parallel artificial membrane permeability assay (PAMPA) was used to evaluate the transcellular permeability of the plant main components. A dataset of 269 compounds with measured PAMPA values and specialized software tools for calculating molecular descriptors were utilized to develop a quantitative structure-activity relationship (QSAR) model to predict PAMPA permeability. Results: The PAMPA permeabilities of 7 compounds constituting the main components of the milk thistle were measured and their GIA was evaluated. A freely-available and easy to use QSAR model predicting PAMPA permeability from calculated physico-chemical molecular descriptors was derived and validated on an external dataset of 783 compounds with known GIA. The predicted permeability values correlated well with obtained in vitro results. The QSAR model was further applied to predict the GIA of 31 experimentally untested flavonolignans. Conclusions: According to both in vitro and in silico results most flavonolignans are highly permeable in the gastrointestinal tract, which is a prerequisite for sufficient bioavailability and use as lead structures in drug development. The combined in vitro/in silico approach can be used for the preliminary evaluation of GIA and to guide further laboratory experiments on pharmacokinetic characterization of bioactive compounds, including natural products

A mode-of-action ontology model for safety evaluation of chemicals: outcome of a series of workshops on repeated dose toxicity

Repeated dose toxicity evaluation aims at assessing the occurrence of adverse effects following chronic or repeated exposure to chemicals. Non-animal approaches have gained importance in the last decades because of ethical considerations as well as due to scientific reasons calling for more human-based strategies. A critical aspect of this challenge is linked to the capacity to cover a comprehensive set of interdependent mechanisms of action, link them to adverse effects and interpret their probability to be triggered in the light of the exposure at the (sub)cellular level. Inherent to its structured nature, an ontology addressing repeated dose toxicity could be a scientific and transparent way to achieve this goal. Additionally, repeated dose toxicity evaluation through the use of a harmonized ontology should be performed in a reproducible and consistent manner, while mimicking as accurately as possible human physiology and adaptivity. In this paper, the outcome of a series of workshops organized by Cosmetics Europe on this topic is reported. As such, this manuscript shows how experts set critical elements and ways of establishing a mode-of-action ontology model as a support to risk assessors aiming to perform animal-free safety evaluation of chemicals based on repeated dose toxicity data