Computational Toxicology最新文献

The present study offers the work on the hydrazone derivatives of dehydroacetic acid to be considered for computational and synthetic studies. The hydrazone derivatives of dehydroacetic acid were designed with different electron-withdrawing and electron-releasing substituents. The hydrazones and the parent compound (dehydroacetic acid) were subjected to computational studies to evaluate their pharmacological properties. The compounds were assessed by applying Lipinski’s rule followed by ADMET predictions. Among all the derivatives under studies, 4-hydroxy-6-methyl-3-(1-(2-(2-nitrophenyl) hydrazineylidene) ethyl)-2H-pyran-2-one was found to be the most effective derivative which was further evaluated against BSA, trypsin, amylase, lipase and cathepsins (B and H) by using docking studies. The computational results were also verified experimentally by synthesizing the derivative as well as performing enzyme inhibition studies on the synthesized hydrazone and the parent compound.

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for > 2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure–activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85–98 %) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71 % with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81 % using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD₅₀) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD₅₀-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD₅₀, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

The definition, characterisation and assessment of the similarity between target and source molecules are cornerstones of the acceptance of a read-across prediction to fill a data gap for a toxicological endpoint. There is much guidance and many frameworks which are applicable in a regulatory context, but as yet no formalised process exists by which to determine whether or not the properties of an analogue (or chemicals within a category) fall within an appropriate domain from which a reliable read-across prediction can be made. This investigation has synthesised much of the existing knowledge in this area into a practical strategy to enable the domain of a read-across prediction to be defined, in terms of chemistry (structure and properties), toxicodynamics and toxicokinetics. The strategy is robust, comprehensive, flexible, and can be implemented readily. It enables the relative similarity and dissimilarity, between target and source molecules, for both the analogue and category approaches, to be analysed and provides a basis for alternative scenarios such as read-across based on formation of a common metabolite or biological profile to be defiend. Herein, the read-across domains for the repeated dose toxicity of a group of triazoles and imidazoles have been evaluated. The most challenging aspect to this approach will continue to be determining what is an “acceptable” degree of similarity when performing read-across for a specific purpose.

Establishing the reliability of simulated metabolism continues to be pivotal in accepting predictions of both fate and toxicological endpoints, especially when metabolic activation of a parent chemical is deemed crucial. A quintessential way of estimating the reliability of simulated metabolism is by comparing a simulated metabolic map with an appropriate documented metabolic map. The approach is constructed on two core parts - experimental and theoretical corroboration. Specifically, the three-layer algorithm is used to support experimentally the adequacy of the simulated maps. The first layer defines similarity boundaries between the parent chemical or metabolite starting the sequence, the root of the simulated series of biotransformations, and the corresponding initial structure of the analogue from the database with documented maps. Different criteria (e.g., the commonality between organic functional groups) are used for this rationale. The second layer delineates the metabolic transformation sequences applied to the target chemical or the initial metabolite of the transformation sequence. The last layer establishes the similarity between the final transformation product in the simulated and documented sequences. To support the adequacy of the simulated molecular transformations, a library of theoretical knowledge is used, providing mechanistic justification on applied transformations. The results of applications of the above procedure are shown using two examples.

Phospholipidosis (PL) describes the accumulation of phospholipids in lysosomes of cells of various tissues after prolonged exposure with drug like compounds. These cellular findings can result in a delay of drug development, cause increased costs in affected projects and potentially may halt a drug development program. The early detection of compounds which potentially cause phospholipidosis therefore is desirable for risk mitigation. Here we describe an in silico consensus model for the detection of phospholipigenic potential of small molecules. The model was trained on in house in vitro data yielding an accuracy of 94%. By employing model agnostic explainability methods, we could show that the model learns reasonable molecular properties. The consensus model showed good performance on underrepresented PL-active compounds in clusters of similar molecules of the test dataset and on external in vitro and in vivo validation data of highly structural dissimilarity to the training data. Using the external in vitro data, an applicability domain of the model was deduced.

Chronic exposure to disinfection by-products (DBPs) via swimming in chlorinated pools can damage the genetic material and even cause cancers in humans. To assess the intermittent chronic internal exposure to DBPs in swimming pool water, a physiologically based kinetic (PBK) modeling framework was introduced to simulate daily average internal exposure doses of DBPs that can be linked to the corresponding daily average external doses. Biotransfer factor (BTF), i.e., the steady-state concentration ratio between human bodies and swimming pool water, was applied to measure the bioaccumulation potential of chemicals in organs and tissues. The results simulated for the four selected trihalomethanes (THMs) (i.e., chloroform, bromoform, dibromochloromethane, and bromodichloromethane) showed that lungs had the highest simulated BTF among human organs and tissues, with the inhalation route showing the maximum contribution to the overall external dose. In addition, route-specific analysis indicated that chronic internal exposure doses of THMs via oral and dermal routes were negligible compared to the inhalation route. Theoretical simulation using the dissipation coefficient of THMs in the air can help optimize the design and operation of swimming pools to substantially reduce chronic internal exposure doses of THMs. The simulated results for time-dependent chloroform concentration in human blood agreed with the reported data and can be further improved once more information about the THM concentrations in breathing zones of swimmers is obtained, indicating that the proposed model can be used as a practical tool to assess intermittent chronic internal exposure of THMs in swimming pool water. In future studies, human exposure to THMs via other pathways (e.g., drinking water, showering, and bathing) can be incorporated into the proposed model to comprehensively evaluate the internal exposure doses of THMs in humans.

Traditional QSAR models predict mutagenicity solely based on structural alerts for the interaction of parent chemicals or their metabolites with target macromolecules. In the present work, it is demonstrated that the presence of an alert is necessary to identify damage but it is not always sufficient to assess mutagenic potential. This is addressed by accounting for the kinetics of simulating metabolism and formation of adducts with macromolecules. The mutagenic potential of chemicals is related to the degree to which selected macromolecules are altered. This extent is estimated by the amount of formed DNA/protein adducts. Here the effect of modelling kinetic factors is investigated for chemicals having documented in vitro negative and in vivo positive data in mutagenicity and clastogenicity tests of similar capacity - in vitro Ames vs in vivo TGR and in vitro CA vs in vivo MN tests. Two factors justify the conflict in mutagenicity data: the differences in enzyme expression in the in vitro vs in vivo metabolism and the difference in exposure time for in vitro and in vivo tests. Addressing these factors required simulating the formation of DNA/protein adducts and introducing empirically-defined thresholds for the amounts of the adducts leading to mutagenic potential.

The platform of OECD Toolbox version 4.5 was used for building an automated decision tree for filling data gaps for rat acute oral toxicity (AOT) by read-across (RA). Our previous publications have described the workflow of the AOT tree and conducted verification and validation studies on it. The overall uncertainty in the AOT workflow is low as the similarity in mechanistic probability, metabolism and 2D structure are maximized in the RA analogue selection process. The endpoint, rat oral LD50, is well-defined and has universal regulatory acceptance. Since OECD test guidelines are followed in generating the database, the data are widely recognized to be of the highest quality. The credibility of the workflow is high as it meets the critical factors of being based on confirmed assumptions, having demonstrated concordance and consistency, permitting the ability to explain AOT-related mechanisms and modes of action, and being simple in design. Additionally, the Z-score and probability distribution methods of assessing the uncertainty of a particular RA are discussed. Two examples of numerical and classification uncertainty are presented. These cases represent the extremes observed in a series of target chemical-based predictions that the authors observed when testing the workflow. The reliability and relevance associated with the workflow are high. However, the completeness and weights-of-evidence varied markedly among possible RA scenarios and particular target substances.

Context

An apparent deviation from nonlinearity in cancer dose-response was reported for various carcinogens. In particular, some studies hypothesized that in mesothelioma, the exposure-response relationship can be modelled as a power function with exponent from 0.6 to 1. However, various factors can affect the shape of the dose-response, producing the apparent supralinear trend.

Objective

To develop a mathematical model that would demonstrate a relationship of mesothelioma lifetime risk and exposure duration, with various assumptions about a hazard rate function.

Methods

Two different hazard rate functions – the Peto model and the two-stage clonal expansion (TSCE) model – were considered. The analytical formulas for lifetime risk were developed, with and without a lifetable correction. Standard calculus methods were applied to test the shape of the lifetime risk curve.

Results

For both Peto and TSCE models, it was shown that mesothelioma lifetime risk changes supralinearly with duration; the exponent of the power function was ranging from 0.68 to 0.89. However, the dose-response curve by cumulative exposure is close to linearity and is linear if the exposure duration would be constant. The model has been tested for chrysotile asbestos cohorts, with a good agreement demonstrated with published mesothelioma excess mortality (R=0.88, p<0.0041).

Conclusion

For mesothelioma, the observed deviation from linearity in the dose-response relationship can be potentially explained by the impact of a change in the duration of exposure. In a meta-analysis, this deviation can be eliminated by standardizing the mortality data for various cohorts by duration of exposure.

Short Abstract

An apparent deviation from nonlinearity in cancer dose-response was reported for various carcinogens. We applied two different hazard rate equations – the Peto model and the two-stage clonal expansion (TSCE) model – to pleural mesothelioma mortality. The analytical formulas for lifetime risk were developed. For both the Peto and TSCE models, it was shown that mesothelioma lifetime risk changes supralinearly with duration. However, the dose-response curve for cumulative exposure is close to linearity.