Computational Toxicology最新文献

Human CYP2D6 contributes extensively to the biotransformation of important therapeutic drugs. CYP2D6 substrate and inhibitor specificity may be affected by genetic polymorphism. This study aimed to characterize interactions of three typical ligands, 3-cyano-7-ethoxycoumarin, fluoxetine and terbinafine with six CYP2D6 variants using molecular docking simulations. The compounds were docked individually to the CYP2D6 models based on published crystal structure (PDB code: 3TBG). All ligands bound within the active site pocket near the heme. Binding involved residues found in critical secondary structures that formed the active site boundary: B-C loop, F helix, F-G loop, β-1 strands and I helix. Twenty-five amino acids were involved in the binding, and all were located in the known substrate recognition sites. Hydrophobic bonds involving phenylalanine (Phe120, Phe384) dominated CEC binding whereas electrostatic bonds between the protonated nitrogen with acidic residues (Glu216, Glu222, Asp301) dominated in binding of fluoxetine and terbinafine. Collectively, the subtle structural changes in the active site and substrate access channels induced by the mutations in the variants contributed to differential ligand docking poses. This study has provided insights into important molecular properties for CYP2D6 catalysis and inhibition, and formed basis for further exploration of structural determinants for potency and specificity of CYP2D6 ligands.

Over-activation of extra-synaptic NMDARs by excessive glutamate is known to cause excitotoxicity. The molecular mechanism of how this excitotoxicity occurs was revealed recently. This paper presents the results of in silico studies aimed at finding potential small-molecule inhibitors that can block this mechanism, namely the extra-synaptic NMDAR/TRPM4 interaction. We screened for small molecules according to 2D (at least Tanimoto threshold was 90%) and/or 3D similarity, molecular weight, lipophilicity using control compounds (C8 and C19) targeting this interaction. We then pre-filtered these molecules according to their drug-likeness and toxicity profiles. After pre-filtering, we performed a docking study against the extra-synaptic NMDAR/TRPM4 interaction with the remaining 26 compounds. In addition, we determined that selected compounds exhibit low affinity for classical NMDAR ligand binding sites. Ultimately, we identified four novel compounds (C8-12, C8-15, C19-3, C19-4) that could block the extra-synaptic NMDAR/TRPM4 interaction without inhibiting the normal function of synaptic NMDARs.

The change in age-related mortality patterns is an important characteristic of the population that can be used as a metric of risk by comparing exposed and non-exposed populations.

In this paper, the mortality parameters were predicted for populations exposed to crystalline silica, a proven lung carcinogen.

Seven hazard functions were tested for a dose–response relationship between lung cancer and characteristics of exposure. Life tables were calculated, along with parameters of the Gompertz-Makeham model for the force of mortality.

It was demonstrated, in particular, that exposure to crystalline silica in the range from 0.03 to 0.3 mg/m³ for 40 years starting at age 20 causes a predicted drop in average life expectancy in the range of from 0.15 to 1.38 years.

It was demonstrated that the lost life expectancy linearly correlates with relative risk (R = 0.995, R² = 0.989, p< 0.00001). The probability of the life expectancy increasing while relative risk decreases was as low as 0.01.

It was found that exponential parameter α of the Gompertz-Makeham equation increases with crystalline silica exposure, while the two linear parameters A and R (which are negatively correlated between each other) increase or decrease with exposure depending on the duration and onset age. Modal age of death in the cohort decreases with cumulative exposure with R = -0.977, R² = 0.954, p < 0.0001.

Based on several different approaches, it was suggested that the threshold of cumulative crystalline silica exposure concentration causing statistically significant change in the cohort life tables can be found in the range from 1.81 to 2.50 mg/m³-years. The change of average age of death in exposed male population does not exceed 1% below cumulative exposure of 3.5 mg/m³-years, and does not exceed 5% at cumulative exposure less than 9.8 mg/m³-years. It shows that no significant acceleration of death rate with age is happening even at the high levels of exposure to crystalline silica.

The study demonstrated the value and advantages of the use of life expectancy and other lifetable characteristics as a tool for quantitative risk assessment.

Botulinum neurotoxin (BoNT) is a highly toxic protein and a Tier 1 Biodefense Select Agent and Toxin. BoNT is also a widely used therapeutic and cosmetic. Despite the toxicological and pharmacological interest, little is known about its biodistribution in the body. The objective herein was to develop a dose-dependent, species-specific physiologically-based toxicokinetic (PBTK) model of BoNT biodistribution in rodents following a single intravenous dose. The PBTK model was based on published physiologically-based pharmacokinetic (PBPK) models of therapeutic monoclonal antibody (mAb) biodistribution because the size and charge of BoNT is nearly identical to a typical IgG₄ mAb and size/charge are main factors governing protein biodistribution. Physiological compartments included the circulation, lymphatics and tissues grouped by capillary pore characteristics. Host species-specific parameters included weight, plasma volume, lymph volume/flow, and tissue interstitial fluid parameters. BoNT parameters included extravasation from blood to tissues, charge, binding to internal lamella or cholinergic neuron receptors. Parameter values were obtained from the literature or estimated using an Approximate Bayesian Computation-Sequential Monte Carlo algorithm, to fit the model to published mouse BoNT low-dose, time-course plasma concentration data. Fits captured the low-dose mouse data well and parameter estimates appeared biologically plausible. The fully-parameterized model was then used to simulate mouse high-dose IV data. Model results compared well with published data. Finally, the model was re-parameterized to reflect rat physiology. Model toxicokinetics agreed well with published rat BoNT intravenous data for two different sized rats with different intravenous doses (an a priori cross-species extrapolation). These results suggested the BoNT model predicted dose-dependent biodistribution in rodents, and for rats, without any BoNT-specific data from rats. To our knowledge, this represented a first-in-kind physiologically-based model for a large protein toxin. Results are discussed in general and in the context of human simulations to support BoNT risk assessment and therapeutic research objectives.

Physiologically based pharmacokinetic (PBPK) modeling is used to predict the pharmacokinetic behavior of xenobiotics in humans. During pregnancy, anatomical and physiological parameters are modified leading to toxicokinetics’ changes of substances in the body. Considering these physiological parameters change in the building processes of pregnancy PBPK (p-PBPK) model is essential to have accurate estimates of tissue/organ concentrations for the pregnant women but also for the fetus.

The review aims to summarize which specific processes are considered in the building of p-PBPK models and may be useful at the early stages of p-PBPK modeling.

To achieve this objective, a literature search focusing on anatomical, physiological, and biochemical parameters impacted by pregnancy was conducted. Most of the time, p-PBPK models do not include all the specific processes identified but only the most impacting ones on the global kinetics, depending mainly on the substance of interest. Allometric relations were identified to be classically included in the pregnancy models to describe the modifications induced by pregnancy to overcome the lack of data usually observed for the gestation. However, more and more data are gathered for pregnancy leading to the introduction of more data-based equations in PBPK modeling.

The most common strategy for p-PBPK development is based on the development of adult PBPK models that are then adapted to specific populations such as pregnant women. The adult PBPK model structure is modified to account for the pregnancy by adding specific compartments of fetal development and also specific compartments that are impacted during the pregnancy such as fat or mammary glands. Extrapolation of pregnant rat model is the other common strategy option used more specifically for environmental substances.

Overall, further data on maternal and fetal pharmacokinetics are needed to validate the xenobiotic exposure predictions during pregnancy, using for example in vitro, in vivo or ex vivo experiments.

This paper presents the development of toxicity of some saturated and phthalate carboxylic diesters (CDE) quantified by experimentally measured (Mes) and calculated (C) values using the Hydractinia echinata (invertebrate) Toxicity Screening Test System (HeTSTS) and the Köln Model (KM) algorithm. The validity of the investigation model is confirmed by the results for three other aquatic organisms: the ciliate protozoan Tetrahymena pyriformis, the freshwater fish Pimephales promelas and the freshwater crustacean Daphnia magna test systems have shown that the evolution of effectiveness is similar, although the absolute values are different. CDE undergoes rapid, irreversible, selective and abiotic –OH^– nucleophilic catalyzed monohydrolysis with the formation of the substrate amphiphilic carboxylate monoester (CME), saturated or phthalate and alcohol (AL) as a xenobiotic (SbX) binary mixture in stoichiometric proportion. The Mes represents the inverse of the logarithm of the diester concentration (molL^-1), which determines the 50% reduction in metamorphosis of H. echinata from larva to polyp and is influenced by the saturated carbon atom (Cs) of the molecular substructure involved in monohydrolysis. According to the KM algorithm, Cs is the Elementary Specific Interaction Parameter (ESIP) with a specific and constant toxicity value – identical in different substances – depending on the nature of the organism that allows the calculation of toxicity predictions in C. AL is the fingerprint of the mixture (FP) because it influences the diffusion of CMEs through the cell membrane to cellular receptors (CRs). Generally, the Mes and C, are the predicted ECOSAR and calculated C* values form the Class Regulated Increased Toxicity (CRIT) and Class Regulated Decreased Toxicities (CRDT) series. The use of H. echinata in toxicity determinations is an alternative for the study of the relevant ecological impact of chemical oxidative stress on aquatic organisms and, consequently, on human health.

Artemisinin and its semi-synthetic derivatives are not only indicated for malaria but also cancer, inflammatory and autoimmune diseases. Its inflammatory and immunosuppressive target is PI3K/AKT pathways. The structural and kinetic aspect of the PI3K inhibition was investigated in the current study using computational approaches. Binding energies of dihydroartemisinin (DHA) to p¹¹⁰-PI3K-β was computed using the MMPBSA method in comparison with the standard inhibitor (GD9). Kinetic parameter (K_on/K_off) was also evaluated for the complexes using adaptive sampling protocols and Markov state model analysis. p¹¹⁰-PI3K- β dynamics and community network analysis were also performed following conventional Molecular dynamics simulation. The results showed −63.99 ± 1.53 and −74.14 ± 3.47 (Kj/mol) binding energies for DHA and GD9 respectively. K_on/K_off estimates for DHA and GD9 are 12.4, and 2.13 (M⁻¹) respectively. Analysis of the trajectories showed that DHA selectively partitions into p¹¹⁰-PI3K- β affinity pocket, forces open conformation, and kept catalytic pocket-M783 in a flat conformation whilst forcing large displacement around the C2-domain. In conclusion, DHA is a high affinity (slow-binding, slow-dissociating), flat-conformation p¹¹⁰-PI3K- β inhibitor.

The development and application of predictive models towards toxicity of engineered nanomaterials is still far from being satisfactory. One promising contribution to confront this challenge is to effectively augment the performance of machine learning classifiers by progressing the approach towards balancing experimental toxicity data. We propose an improved balancing methodology by fitting the in-vitro toxicological dose-response datasets of engineered nanomaterials to three, four, and five, free parameter dose-response models. The four-free parameter model displays the best fit (in terms of adjusted R²) for most of the examined data. The fitted curve yields, in each case, a continuous sequence of data points, which extends the restricted experimental data and generates additional fitted data points for the minority class, leading to the formation of balanced data for predicting the nanoparticle’s toxicology by decision tree classifiers. The ability to best predict the experimental toxicity data, by applying the decision tree model, was tested by forming three versions of the same experimental data: the imbalanced raw experimental data, the balanced data by applying the common Synthetic Minority Oversampling Technique, and by using the approach of Balanced Fitted Dose-Response method, introduced in the present study. We demonstrate that our approach provides improved performance of decision trees in predicting nanoparticles’ toxicity, a method that pertains also to chemical toxicity, central in health and environmental research.

As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether in silico toxicity models could be used to predict the sensitisation hazard and potency potential of E&Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&Ls administered by any route of administration.