NAM journal最新文献

We recently gave advice for bad (traditional) toxicologists (Rabbit, 2025)but we also have advice for the new breed of computational toxicologists. So many ways to do a lousy job….

Pharmacophore modeling, QSAR, molecular docking, in silico ADME, and toxicity prediction have been conducted on 2-aziridinyl- and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate-based malaria parasite inhibitors. In this study, we aim to clarify the binding mode and drug-likeness of these derivatives. Ultimately, 63 compounds were utilized to develop a model. The model was statistically significant, exhibiting a high correlation coefficient (R² = 0.8066), a cross-validation coefficient (Q²_loo = 0.7559), and an F value of 43.3149. The results from external validation also demonstrate strong predictive power (R²_ext = 0.7325). In addition to passing Tropsha's test for predictive ability, the resulting model also successfully passed the Y-randomization test. The model's applicability domain (AD) was clearly defined, indicating that in the case of a new prediction, it is evident whether that prediction can be safely made. To assess the impact of the docking protocol, a co-crystallized ligand from the protein's ligand-binding domain was selected and re-docked. The results after redocking and the co-crystallized ligand were aligned, with a mean root-mean-square deviation along their lines of 1.703 Å. Using the DTM protein co-crystal ligand (PDB ID: 1MVT) as a template, the pharmacophore hypotheses were produced. A screening of PubChem databases resulted in 40 hits that passed the screening requirements for docking against the DTM receptor through the Pharmit server. The top two docked compounds having the highest binding affinities from -11.5 to -11.1 kcal/mol were selected. A further ADMET analysis showed that the selected compounds have values within the acceptable range. Molecular dynamics simulation analysis of inhibitor potentials against the malaria enzyme, compound 18 has strong inhibitory potential in treating malaria diseases. Findings from this study inform on possible ways of designing new inhibitors for malaria parasites.

Despite the use of strict acceptance criteria in validated methods for in vitro toxicology safety non-animal methods, the evaluation of statistical uncertainties in the results and their impact on the reproducibility and reliability of these methods is limited. Technical and operational variations in intra- and inter-laboratory settings can interfere with the results, and measurement uncertainty estimations are crucial for a better understanding of the potential sources of errors and ensuring that the assays are under control. The measurement uncertainty characterizes the dispersion of values, which can be attributed to the measurement results. This study applied the Monte Carlo statistical model to calculate the measurement uncertainty for the half-maximal inhibitory concentration (IC50) of sodium dodecyl sulfate (SDS) using OECD Guidance Document 129. We focused on acute oral cytotoxicity in vitro and developed a specific tool for this purpose. The Monte Carlo method enabled the identification of the contributions to the measurement uncertainty of IC50 and their confidence intervals for the in vitro cytotoxicity assay recommended by OECD GD 129. Furthermore, the statistical tool developed in this study, although specifically applied to OECD GD 129, has the potential to be adapted and extended to other in vitro toxicological assays based on dose-response curves, thus supporting broader applications in New Approach Methodologies (NAMs) by enhancing reproducibility and confidence in alternative methods validation processes. In conclusion, this study demonstrates the effectiveness of the Monte Carlo statistical model in quantifying the measurement uncertainty of sodium dodecyl sulfate (SDS) IC50 values, with a comprehensive analysis yielding a combined IC50 of 34.6 μg/mL with a combined uncertainty of 1.21 μg/mL. These findings highlight the importance of incorporating metrological evaluations to increase the reliability of in vitro methods. By enabling more informed decision-making, this study can significantly contribute to the advancement of toxicological research and NAMs.

Public expectations and regulatory demands have driven a paradigm shift in cosmetic ingredient safety assessment. Next-generation risk assessment (NGRA) and new approach methodologies (NAMs) provide an opportunity to implement alternative procedures which are more ethical and biologically-relevant, have the potential to become more cost-effective and time-efficient, and are at least as protective of human health as traditional, animal-based approaches. However, the sheer number and diversity of the rapidly-evolving NAMs currently available can be confusing and overwhelming to safety assessors. To successfully transition from principles to routine application, NAMs must gain acceptance within safety assessment communities. Harmonizing NAMs and establishing standardized risk assessment workflows are paramount to gaining regulatory acceptance. Consequently, the International Cooperation on Cosmetics Regulation (ICCR) has tasked a team of scientists from regulatory authorities and the cosmetic industry to define best practices which align with the ICCR principles of NGRA for cosmetic ingredients, to help advance the acceptance and use of NAMs in cosmetic safety assessment and, where applicable, regulatory decision-making.

Recognizing the need of implementing new approach methodologies (NAMs) in the pharmaceutical industry to establish health-based limits, this study employs an innovative strategy for deriving the permitted daily exposure (PDE) value, using vancomycin as a study case. We integrated in vitro toxicity data with physiologically based pharmacokinetic modeling-facilitated reverse dosimetry (PBPK-RD) to support setting health-based exposure limits, ensuring safety in the context of vancomycin-induced nephrotoxicity. A PBPK model for vancomycin was developed using PK-Sim v.11.3 in both rodents and humans, and its predictive performance was verified by comparing observed and simulated pharmacokinetic profiles. The PBPK-RD approach was then applied to translate in vitro concentration-response data into in vivo dose-response estimates in humans, resulting in a predicted Benchmark Dose Lower Confidence Limit (BMDL₅) value of 0.01 mg/kg/day. This value served as the Point of Departure (PoD) for Permitted Daily Exposure (PDE) calculation, corrected for body weight of 50 kg and accounting for uncertainty factors (F). F1 and F2 were set to 1 due to using human data and incorporating interindividual variability within the model, respectively. F3 was set to 10 to address potential chronic exposure effects, while F4 and F5 were each set to 1, reflecting confidence in the dataset and the conservative nature of the PoD. This approach derived a PDE of 0.05 mg/day, lower than the traditionally calculated PDE (0.2 mg/day) based on the lowest clinical dose as the PoD. This study demonstrates the potential of PBPK-RD as a NAM-based strategy for refining toxicological assessments within the pharmaceutical industry.

Objective: The aim of this study is to investigate the potential molecular mechanisms underlying the induction of erectile dysfunction toxicity by Acetyl Tributyl Citrate through the application of network toxicology and molecular docking techniques.

Methods: Potential targets associated with ATBC were identified using the ChEMBL and SwissTargetPrediction databases, while potential targets related to ED were explored through Genecards, OMIM, and TTD databases. A cross-gene target network diagram for ATBC-ED was constructed using Cytoscape 3.7.2 software, and relevant targets were imported into the STRING database to establish a protein-protein interaction network. Comprehensive analyses of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways for these intersecting genes were performed utilizing DAVID database and microbioinformatics software. Among these pathways, we selected five core genes-EP300, EZH2, HDAC1, HDAC2, and SMARCA4-for molecular docking studies with Autodock.

Results: Ultimately, we identified 59 potential targets associated with ATBC exposure and ED. Subsequent optimization using STRING and Cytoscape software highlighted the top 20 core targets including EP300, EZH2, HDAC1, HDAC2, and SMARCA4. The core targets induced by ATBC in relation to ED are primarily enriched in processes involving chromatin remodeling; negative regulation of RNA polymerase II transcription; nuclear-cytoplasmic transport; chromatin binding; as well as thyroid hormone signaling pathway interactions. Molecular docking confirmed a high binding affinity between ATBC and these core targets.

Conclusion: In summary, these findings suggest that ATBC may induce erectile dysfunction by modulating chromatin remodeling processes along with negative regulation of RNA polymerase II transcription as well as thyroid hormone signaling pathways.

While valuable for hazard identification, in vitro genotoxicity tests that are conducted in 2D monolayer cell cultures possess limitations to accurately envisage in vivo outcomes and may result in unnecessary follow-up in vivo studies. One limitation is their relevance to actual human exposure and the assessment of genotoxicity in tissues representative of "sites-of-first-contact" with genotoxic agents. The revised OECD testing guidelines had emphasized the need for considering site-of-first-contact effect when carrying out genotoxic hazard investigations. To date, only in vivo studies can adequately address the oral/gastrointestinal route of exposure and there exists no validated in vitro assay for ingestible substances. Previously, we described the development of a new human Reconstructed Intestine Micronuclei Cytome (RICyt) assay using EpiIntestinal™ microtissues for site-of-contact genotoxic hazard identification of orally ingested materials. The human small intestine is where approximately 90 % of the digestion and adsorption of food occurs and a major exposure site of ingested genotoxicants, thus a key site for evaluation. Here, we have investigated on the qualification of the RICyt assay in genotoxic hazard identification. The assay platform was challenged with a training set of 16 test materials which include standard reference genotoxins, non-genotoxins and food relevant substances with various mode of actions. The overall RICyt accuracy was 93.3 % with sensitivity of 83.3 % and specificity of 100 %. Majority of the test materials were correctly identified though yielded one false negative (KBrO₃) and one equivocal outcome (EUG). MCT, a known genotoxin, with no known effect on the GI tract, did not induce significant MN, showcasing the ability of the assay to detect tissue specific genotoxicity. These results emphasize that the RICyt assay is promising in vitro tool for genotoxic hazard identification of orally ingested substances.

Data unavailability in health reference values poses a significant challenge in chemical risk assessment. Hazard banding (HB) offers a practical solution by categorizing chemicals into bands that represent increasing levels of health hazard. However, the strength and validity of these bands are often based on health-based guidance values from small sample sizes due to data scarcity. This study aims to develop quantitative hazard bandings using an expanded dataset of probabilistic reference dose values (pRfD) and endocrine-related quantitative high-throughput screening (qHTS) data. We hypothesize that quantitative hazard bandings can accurately reflect chemical hazards' nature and severity. pRfD values (n = 10,145) and oral equivalent doses (OEDs) from qHTS data (n = 3,520) were categorized into quintiles to derive five hazard bands (HB_pRfD and HB_{qHTS_endo}). We evaluated the relationships between the assigned hazard bands and GHS health statements using heatmap and correlation analyses. Our findings suggest that HB_pRfD effectively captures the inherent severity of chemical hazards, while HB_{qHTS_endo} shows lower confidence in hazard characterization. Despite challenges with the discordance between animal toxicity data and qHTS data, integrating in vitro mechanistic evidence with animal data may improve predictive accuracy for higher-tier toxicity endpoints.

Despite some considerable advancement in developing different forms of new approach methodologies (NAMs) to assess the risk of chemicals, there is still limited progress in their application for regulatory purposes in the European Union (EU). Distinct divisions exist between non-governmental organisations (NGOs) with an animal welfare agenda, and those focusing on environmental protection. The conflicting views on NAMs held by these NGOs are a point of concern for policy makers in particular. This report gathers and reviews the perspectives of selected animal welfare and environmental NGO representatives who responded to our small-scale, anonymous online survey. The responses were analysed to identify areas where there is some space to accommodate the views of both groups. There exists strong consensus on the question of whether EU chemical legislation on risk assessment is sufficiently well adapted to incorporate new approach methodologies. Both NGO groups do acknowledge the merit of scientific advances to aid decision making, albeit with the cautious need to ensure that the information to truly determine the safe use of chemicals is available. Broad consensus was identified: i) for the need of a more precise definition of NAMs by making reference to using NAMs in specific contexts; ii) acknowledging that existing processes are not adequate to validate new approach methodologies; and iii) that there remain clear barriers to the regulatory deployment of NAMs. Finally, moderate consensus was identified for the question of whether animals are an effective and appropriate surrogate to assess the impact chemicals have on environmental health, with both groups acknowledging, to an extent, that animal tests in this context can be useful indicators and NAMs could assist in identifying further effects in complex systems. Understanding these areas of common ground will benefit future policy decision-making related to the acceptance of NAMs in chemical safety assessments.

The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Simultaneously, wet lab experimentation is associated with particularly high costs, does not adhere to principles of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalysed reaction to determine Michaelis-Menten maximal velocities (V _max). The molecular fingerprints employed include RCDK standard fingerprints (1024 bits), MACCS keys (166 bits), PubChem fingerprints (881 bits), and E-States fingerprints (79 bits). These were integrated to produce reaction fingerprints. The data entries were sourced from SABIO RK, providing a concrete framework to support training procedures. After the data preprocessing stage, the dataset was randomly split into the training set (70 %), validation set (10 %), and test set (20 %) ensuring unique amino acid sequences for each subset. The data points with structures similar to the ones used to train the model as well as uncommon reactions were employed to further test the model. The developed models were optimized during the training procedure to predict V _max values efficiently and reliably. Utilizing a fully connected neural network, these models can be applied to all organisms. Amino acid proportions of enzymes were also tested resulting in an unreliable predictor for the V _max value. During testing, the model demonstrated better performance on known structures compared to unseen data. In the given use case, the model trained solely on enzyme representations achieved an R-squared of 0.45 on unseen data and 0.70 on known structures. When enzyme representations were integrated with RCDK fingerprints, the model achieved an R-squared of 0.46 on unseen data and 0.62 on known structures.