Computational Toxicology最新文献

Accurate in-silico models of human skin are required to obtain the uptake/release of molecules across the skin layers to supplement the in-vivo/in-vitro experiments for faster development/testing of cosmetics and drugs. We aim to develop an in-silico skin permeation model by extending the multiscale modeling framework developed earlier for skin’s top layer to deeper layer and compared the outcomes with in-vitro experimental permeation data of 43 cosmetic-relevant molecules across human skin.

In this study, we have extended a multiscale modeling framework, with realistic heterogeneous stratum corneum (SC) comprising of network of permeable lipids and corneocytes, followed by homogeneous viable epidermis and dermis. The diffusion coefficients of molecules in lipid layer were determined using molecular dynamics simulations, whereas the diffusion coefficients in other layers and all the partition coefficients were calculated from correlations reported in literature. These parameters were then used in the macroscopic models to predict the release profiles of drugs through the deeper skin layers. The obtained release profiles were in good agreement with available experimental data for most of the molecules. The reported model could provide insight into cosmetics/drugs skin permeation and act as a time-saving and efficient guiding tool for performing targeted experiments.

Macitentan is a dual endothelin receptor antagonist indicated for the treatment of pulmonary arterial hypertension, a chronic and complex disease. Under different stress conditions, such as changes in pH and temperature, the drug can generate a large number of degradation products, while many process-related impurities can occur during the four main synthetic routes. The assessment of the potential toxicity of these impurities is an essential regulatory requirement for the quality and safety of drugs. The goal of this study was to identify all metabolites and potential impurities for macitentan and evaluate their in silico toxicity. Thirty-five compounds related to macitentan were found reported in the literature, two of which were described simultaneously as metabolites, degradation products, and process-related impurities. In the present study, the main degradation products and the conditions under which they could be formed, and the major impurities according to the synthetic route, are discussed. The types and amounts of process-related impurities were dependent on the synthesis route and process controls, while macitentan was found to be more susceptible to degradation in acidic media resulting in the most different types of degradation products. The structure of each compound was generated and the potential risk for mutagenicity and carcinogenicity were determined using three different in silico platforms, in addition the metabolic substrate/inhibition profile for each compound was assessed. Overall, five compounds were considered critical as they had a possible toxicity risk in terms of mutagenicity, tumorigenicity, irritation, and reproductive effects. These data support the current legislation for raw materials and pharmaceutical products containing macitentan as to prevent any adverse effects from this drug.

Cancer is the second leading cause of death worldwide. Among various anticancer drug targets, mTOR is noteworthy. Numerous first-generation mTOR inhibitors are already approved and few second-generation mTOR inhibitors targeting the kinase domain are in the clinical trials, but yet to reach the market, and many lead to serious toxicities. Here we are focused to discover some novel kinase inhibitors from the ZINC database which may effectively inhibit mTOR kinase. For this, computational chemistry and pharmacophore-based ZINC database search has been adopted. Series of virtual screening analysis lead to the discovery of 5 active hits. Among these 5, compound 4 (ZINC79476038) having binding energy of −8.9 Kcal/mol shows maximum interactions within the binding pocket. Study proved that all these compounds can potentially inhibit mTOR kinase and can be successfully developed as anticancer agents. We further proved that these compounds are not only active for general cancers like lung, breast, colon, and other peripheral cancers but also equally active in CNS, targeting numerous brain cancers.

The Analog Identification Methodology (AIM) was developed over 20 years ago to identify analogues to support read-across at the US Environmental Protection Agency. However, the current public version of the standalone tool, released in 2012, is no longer usable on Windows operating systems supported by Microsoft. Additionally, the structural logic for analogue selection is based on older, customised Simplified molecular-input-line-entry system (SMILES)-type features that are incompatible with modern cheminformatics tools. Given these limitations, a case study was undertaken to explore a more transparent, extensible method of implementing the AIM fragments using Chemical Subgraphs and Reactions Mark-up Language (CSRML). A CSRML file was developed to codify the original AIM fragments, and the extent to which AIM fragments were faithfully replicated was assessed using the AIM Database. The overall mean performance of the CSRML-AIM across all fragments in terms of sensitivity, specificity, and Jaccard similarity was 89.5%, 99.9%, and 82.2%, respectively. Comparing the AIM fragments with public ToxPrints using a large set of ∼25,000 substances of regulatory interest to EPA found them to be dissimilar, with an average maximum Jaccard score of 0.24 for AIM and 0.29 for ToxPrint fingerprints. Both fragment sets were then used as inputs in the automated read-across approach, Generalised Read-Across (GenRA), to evaluate the quality of fit in predicting rat acute oral toxicity LD₅₀ values with the coefficient of determination (R²) and root mean squared error (RMSE). The performance of AIM fragments was R²=0.434 and RMSE=0.663 whereas that of ToxPrints was R²=0.477 and RMSE=0.638. A bootstrap resampling using 100 iterations found the mean and the 95th confidence interval of R² to be 0.349 [0.319, 0.379] for AIM fragments and 0.377 [0.338, 0.412] for ToxPrints. Although AIM and ToxPrints performed similarly in predicting LD_50, they differed in their performance at a local level, revealing that their features can offer complementary insights.

Exposure to chemicals generally occurs in the form of mixtures. However, the great majority of the toxicity data, upon which chemical safety decisions are based, relate only to single compounds. It is currently unfeasible to test a fully representative proportion of mixtures for potential harmful effects and, as such, in silico modelling provides a practical solution to inform safety assessment. Traditional methodologies for deriving estimations of mixture effects, exemplified by principles such as concentration addition (CA) and independent action (IA), are limited as regards the scope of chemical combinations to which they can reliably be applied. Development of appropriate quantitative structure-activity relationships (QSARs) has been put forward as a solution to the shortcomings present within these techniques – allowing for the potential formulation of versatile predictive tools capable of capturing the activities of a full contingent of possible mixtures. This review addresses the current state-of-the-art as regards application of QSAR towards mixture toxicity, discussing the challenges inherent in the task, whilst considering the strengths and limitations of existing approaches. Forty studies are examined within – through reference to several characteristic elements including the nature of the chemicals and endpoints modelled, the form of descriptors adopted, and the principles behind the statistical techniques employed. Recommendations are in turn provided for practices which may assist in further advancing the field, most notably with regards to ensuring confidence in the acquired predictions.

Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency’s Adverse Outcome Pathway Database (AOP-DB; aopdb.epa.gov) and the UK Biobank’s genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.

The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose–response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA’s Benchmark Dose software and NTP’s BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.

Acetylcholinesterase and butyrylcholinesterase enzymes are therapeutic target for Alzheimer disease and their inhibitors play a vital role for the treatment of this disease. A new series of benzoxazole based 1,3-thiazole hybrid scaffolds (1–20) were synthesized and assessed for acetylcholinesterase and butyrylcholinesterase inhibition profile and then characterized by using different spectroscopic tools such as ¹H NMR, ¹³C NMR and HREI-MS spectroscopy. Four scaffolds such as 1, 4, 12 and 19 showed AChE potency almost comparable to standard drug having IC₅₀ values 0.692 ± 0.087, 0.947 ± 0.089, 0.38 ± 0.016 and 0.742 ± 0.042 µM, while nine scaffolds such as 1, 4, 6, 8, 9, 12, 13, 14 and 19 showed superior BuChE potency than standard drug having IC₅₀ values 2.54 ± 0.10, 1.79 ± 0.20, 3.25 ± 0.18, 2.48 ± 0.05, 1.33 ± 0.05, 2.19 ± 0.08, 2.81 ± 0.20, 2.23 ± 0.10 and 2.10 ± 0.05 µM respectively. Nonetheless, remaining analogs were found to have moderate activity. Among the synthesized series, analogs 12 (IC₅₀ = 0.38 ± 0.016 µM) and 9 (IC₅₀ = 1.33 ± 0.05 µM) were identified as the most potent inhibitors of acetylcholinesterase and butyrylcholinesterase enzymes. In addition, the molecular docking studies were carried out to find out the possible binding mode of interactions of most active analogs with enzymes active site and results supported the experimental data.

A safety assessment of recycled high-density polyethylene (rHDPE) in cosmetic packaging was performed based on guidelines published by the European Food Safety Authority (EFSA) on the use of recycled plastics for food packaging. EFSA guidelines require demonstration that the concentration of selected representative chemical contaminants in recycled plastic resulting from exposure from food is lower than the threshold of toxicological concern (TTC) for genotoxic substances of 0.0025 µg/kg bw/day. To investigate the highest concentration (Cmod) of representative chemical contaminants, that would not exceed the genotoxic TTC, when migrating from rHDPE packaging to foodstuffs, used as cosmetic formulation surrogates, we used mathematical modeling software (MIGRATEST®EXP). The Cmod values of representative chemical contaminants were then compared with the EFSA-reported residual concentration (Cres) of each contaminant in the rHDPE. For each of the cosmetic product/packaging combinations evaluated, we found that the modeled values were clearly lower for Cmod than Cres, i.e., the recycling process could effectively reduce potential contaminants of rHDPE to levels that would not result in daily consumer exposure from cosmetic use exceeding the genotoxic TTC. For skin sensitization, we modeled a worst-case scenario and assumed 100 % of each representative chemical contaminant migrates into the cosmetic formulation from rHDPE. We then calculated the consumer exposure level for each contaminant based on the dose per unit area and compared it with the dermal sensitization threshold (DST) for reactive materials, which is 64 µg/cm². In each case, we demonstrated that the migration of each representative chemical contaminant from rHDPE into each cosmetic formulation was far below the DST, confirming that there is no appreciable risk of sensitization for protein-reactive chemicals. In conclusion, these data support the safe use of rHDPE in the packaging of cosmetic products for leave-on and rinse-off applications.

Computational modelling (in silico) methods based on quantitative structure-activity relationship ((Q)SAR) models, are powerful tools for the assessment of the potential “persistency, mobility, and toxicity” (PMT) of pharmaceuticals compounds. Moreover, the use of (Q)SAR models, is recommended by European Union’s REACH Regulation. In this context, the aims of this research were estimating, for the first time and based by REACH guidelines, the PMT potentials of 115 most sold pharmaceuticals in São Paulo Metropolitan Region (a megacity with 21 million of Brazilian), through five (Q)SAR updated models, namely: the OPERA QSAR; the VEGA QSAR (Version 1.1.5); the EPI Suite (Version 4.11); the ECOSAR (Version, 2.0); and the QSAR Toolbox (Version 4.5). This study prioritized the in-silico predictions from the OPERA and the VEGA, because both QSARs can generate reliable predictions, i.e., they have detailed information about the applicability domains. In silico predictions were performed considering ten endpoints: (i) Molecular weight (MW); (ii) “STP total removal”: Sewage Treatment Plant; (iii) Octanol-water partition coefficient (KOW); (iv) Predicted ready biodegradability; (v) Soil organic adsorption coefficient (KOC); (vi) “Short-term and long-term ecological assessments”; (vii) “Carcinogenicity”; (viii) “Mutagenicity”; (ix) “Estrogen receptor binding”; (x) “Cramer decision tree”. The main results showed that: (a) These 115 pharmaceuticals cover a wide range of so-called small molecules (range from 100 to 600 MW); (b) In STP, a predicted removal lower than 10 % was found for 76 pharmaceuticals; (c) Additionally, 101 chemicals has low (Log KOW <2.5), or medium sorption potential (2.5< log KOW <4.0); (d) Ultimately, 36 PPCPs were considered “persistent” after a weight-of-evidence assessment. In addiction, 17 among these 36 persistent chemicals, were classified as “very mobile” in water (log KOC <3). Finally, only three among 17 PPCPs, namely ciprofibrate, fluconazole and metoclopramide, exhibited one or more toxic characteristics (described in items vi – x). These results it will be possible to alert about the potential risks arising from the indiscriminate disposal of these PPCPs along the water sources of this Brazilian mega metropolis.