Computational Toxicology最新文献

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

In this editorial we reflect on the past decade of developments in predictive toxicology, and in particular on the evolution of the Adverse Outcome Pathway (AOP) paradigm. Starting out as a concept, AOPs have become the focal point of a community of scientists, regulators and decision-makers. AOPs provide the mechanistic knowledge underpinning the development of Integrated Approaches to Testing and Assessment (IATA), including computational models now referred to as quantitative AOPs (qAOPs). With reference to recent and related works on qAOPs, we take a brief historical perspective and ask what is the next stage in modernising chemical toxicology beyond animal testing.

Computational methods for genomic dose–response integrate dose–response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene’s dose–response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose–response relationship, resulting in ‘co-regulated’ gene sets containing genes having different dose–response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose–response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose–response relationships, we better identify co-regulation clusters for genes that have co-expressed dose–response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.

Comedogenicity is a common adverse reaction to cosmetic ingredients that cause blackheads or pimples by blocking the pores, especially for acne-prone skin. Before animal testing was banned by European Commission in 2013, comedogenic potential of cosmetics were tested on rabbits. However, full replacement of animal tests by alternatives has not been possible yet. Therefore, there is a need for applying new approach methodologies. In this study, we aimed to develop a QSAR model to predict comedogenic potential of cosmetic ingredients by using different machine learning algorithms and types of molecular descriptors.

The dataset consists of 121 cosmetic ingredients including such as fatty acids, fatty alcohols and their derivatives and pigments tested on rabbit ears was obtained from the literature. 4837 molecular descriptors were calculated via various software. Different machine learning classification algorithms were used in the modelling studies with WEKA software. The model performance was evaluated by using 10-fold cross validation. All models were compared by the means of classification accuracy, area under the ROC curve, area under the precision-recall curve, MCC, F score, kappa statistic, sensitivity, specificity and the best model was chosen accordingly. The QSAR modelling results for two models are promising for comedogenicity prediction. The random forest models by the means of Mold2 and alvaDesc descriptors gave the successful results with 85.87% and 84.87% accuracy for the cross-validated models and 75.86% and 79.31% accuracy for the test sets. In conclusion, this study is the first step in terms of comedogenicity prediction. In the near future, advances in in silico modelling studies will provide us non-animal based alternative models by regarding animal rights and ethical issues for the safety evaluation of cosmetics.

New thiazole derivatives (2a-l) were synthesized via the reaction of 2-(3-cyano-4-isobutoxyphenyl)-4-methylthiazole-5-carboxylic acid with substituted phenyl amines. The anticancer activity of the synthesized thiazole derivatives was examined against MCF-7 (human breast), MDA-MB-231 (mammary carcinomas), HeLa (Cervical cancer), HT-29, HCT 116 (Colon cancer), and normal chang liver cancer cell lines, whereas cisplatin was employed as a positive control. The anticancer mechanisms were studied via apoptosis assessments, as well as molecular docking. The molecular docking study of potent compounds was carried out against the human epidermal growth factor receptor (HER2, PDB ID: 3RCD) as a possible target for anticancer activity using Auto Dock vina. ADMET results indicated that tested compounds have significant results within the close agreement of Lipinski’s rule of five. In addition, computational work employing density functional theory (DFT) was also carried out at the B3LYP/6-31G (d,p) level to investigate the electronic properties of the potent compounds. The frontier molecular orbital energy and atomic net charges were discussed.

In silico tools and resources are now used commonly in toxicology and to support the “Next Generation Risk Assessment” (NGRA) of cosmetics ingredients or materials. This review provides an overview of the approaches that are applied to assess the exposure and hazard of a cosmetic ingredient. For both hazard and exposure, databases of existing information are used routinely. In addition, for exposure, in silico approaches include the use of rules of thumb for systemic bioavailability as well as physiologically-based kinetics (PBK) and multi-scale models for estimating internal exposure at the organ or tissue level. (Internal) Thresholds of Toxicological Concern are applicable for the safety assessment of ingredients at low concentrations. The use of structural rules, (Quantitative) Structure-Activity Relationships ((Q)SARs) and read-across are the most typically applied modelling approaches to predict hazard. Data from exposure and hazard assessment are increasingly being brought together in NGRA to provide an overall assessment of the safety of a cosmetic ingredient. All in silico approaches are reviewed in terms of their maturity and robustness for use.

The increasing diversity of environmental chemicals in the environment, some of which may be developmental toxicants, is a public health concern. The aim of this work was to contribute to the development of rapid and effective methods to assess prenatal exposure. Quantitative structure–activity relationships (QSAR) modeling has emerged as a promising method in the development of a predictive model for the placental transfer of contaminants. Cord to maternal plasma or serum concentration ratios for 105 chemicals were extracted from the literature, and 214 molecular descriptors were generated for each of these chemicals. Ten predictive models were built using Molecular Operating Environment (MOE) software, and the Python and R programming languages. Training and test datasets were used, respectively, to build and validate the models. The Applicability Domain Tool v1.0 was used to determine the applicability domain. Models developed with the partial least squares regression method in MOE and SuperLearner in R showed the best precision and predictivity, with internal coefficients of determination (R²) of 0.88 and 0.82, cross-validated R²s of 0.72 and 0.57, and external R²s of 0.73 and 0.74, respectively. All test chemicals were within the domain of applicability. The results obtained in this study suggest that QSAR modeling can help estimate the placental transfer of environmental chemicals.

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).

Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.