Computational Toxicology最新文献

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.

Changes in the regulatory landscape of chemical safety assessment call for the use of New Approach Methodologies (NAMs) including read-across to fill data gaps. One critical aspect of analogue evaluation is the extent to which target and source analogues are metabolically similar. In this study, a set of 37 structurally diverse chemicals were compiled from the EPA ToxCast inventory to compare and contrast a selection of metabolism in silico tools, in terms of their coverage and performance relative to metabolism information reported in the literature. The aim was to build understanding of the scope and capabilities of these tools and how they could be utilised in a read-across assessment. The tools were Systematic Generation of Metabolites (SyGMa), Meteor Nexus, BioTransformer, Tissue Metabolism Simulator (TIMES), OECD Toolbox, and Chemical Transformation Simulator (CTS). Performance was characterised by sensitivity and precision determined by comparing predictions against literature reported metabolites (from 44 publications). A coverage score was derived to provide a relative quantitative comparison between the tools. Meteor, TIMES, Toolbox, and CTS predictions were run in batch mode, using default settings. SyGMa and BioTransformer were run with user-defined settings, (two passes of phase I and one pass of phase II). Hierarchical clustering revealed high similarity between TIMES and Toolbox. SyGMa had the highest coverage, matching an average of 38.63% of predictions generated by the other tools though was prone to significant overprediction. It generated 5125 metabolites, which represented 54.67% of all predictions. Precision and sensitivity values ranged from 1.1 to 29% and 14.7–28.3% respectively. The Toolbox had the highest performance overall. A case study was presented for 3,4-Toluenediamine (3,4-TDA), assessed for the derivation of screening-level Provisional Peer Reviewed Toxicity Values (PPRTVs), was used to demonstrate the practical role in silico metabolism information can play in analogue evaluation as part of a read-across approach.

In silico toxicology protocols are currently needed to support the acceptance and deployment of computational toxicology methods as alternative methods for health hazard identification. Such protocols combine relevant in silico results with available experimental data to derive an assessment of major toxicological endpoints supported by a confidence score reflecting the uncertainty in the assessment. The protocols also identify relevant effects and/or mechanisms which can be used to guide the assessment of a toxicological endpoint. In addition, sufficient documentation of procedures and methods used to support an assessment is essential for both internal and external decision-making. The combination of relevant data, confidence scoring, and reporting provides a hazard assessment framework intended to increase the acceptance of in silico results in a toxicologic assessment. This article describes key principles and components of such protocols, including the hazard assessment framework and recommendations demonstrating how evaluating relevance, completeness, and confidence can be performed and documented. Also discussed are criteria used to develop an in silico protocol based on the state of the science and the importance of developing position papers to outline roadmaps for future in silico protocols used to guide assessments of more complex toxicological endpoints, such as cancer or neurotoxicity. The current status of providing such protocols is summarized for specific in silico protocols that are already published, in development, or planned.

Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are “safe”, to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be “constructively disruptive” to the current toxicological paradigm, using the “Causal Revolution” to bring about a “Toxicological Revolution” more rapidly.