NAM journal最新文献

This study investigated the inhibitory potential of selected phytochemicals and conventional drugs against gastric H⁺/K⁺-ATPase, an important therapeutic target associated with acid-related gastrointestinal disorders such as peptic ulcers and gastroesophageal reflux disease. Computational approaches, including molecular docking, molecular dynamics simulations, MMGBSA free energy calculations, and ADMET profiling, were employed to evaluate the pharmacological relevance of the selected compounds. Molecular docking analysis revealed that several compounds exhibited favorable interactions with the target enzyme, with ciprofloxacin demonstrating stronger binding affinity than 3-hydroxypropyl oleate in both active-site and blind docking studies. Interaction analyses further showed that ciprofloxacin formed stable hydrogen bonds, hydrophobic interactions, and attractive charge interactions within the enzyme binding pocket. Molecular dynamics simulations confirmed the superior structural stability of the ciprofloxacin-protein complex through lower RMSD, RMSF, RoG, and SASA values compared with 3-hydroxypropyl oleate. Although MMGBSA calculations indicated slightly stronger total binding free energy for 3-hydroxypropyl oleate, ciprofloxacin displayed better overall compactness and interaction stability throughout the simulation period. ADMET and pharmoglyph analyses further established ciprofloxacin as the more promising candidate due to its favorable drug-likeness, high gastrointestinal absorption, acceptable solubility, lower metabolic risks, and excellent PharmoScore. The integrated computational findings suggest that ciprofloxacin possesses significant inhibitory potential against gastric H⁺/K⁺-ATPase and may serve as a promising scaffold for the development of safer and more effective anti-ulcer therapies.

The Enhanced Ames Test (EAT) is currently recommended by regulatory authorities for evaluating the mutagenicity of N-nitrosamine drug impurities, including nitrosamine drug substance-related impurities (NDSRIs). In previous studies, we developed protocols to evaluate the genotoxicity and mutagenicity of NDSRIs in both two-dimensional (2D) and three-dimensional (3D) HepaRG cell cultures as a follow-up to verify EAT findings. In this study, we investigated the genotoxicity and mutagenicity of six EAT-positive NDSRIs, N-nitroso-desmethyl-diphenhydramine, N-nitroso-duloxetine, N-nitroso-lorcaserin, N-nitroso-nortriptyline, N-nitroso-propranolol, and N-nitroso-sertraline, in 3D HepaRG spheroids using a 14-day treatment protocol. Genotoxicity was evaluated using the CometChip DNA damage assay and the micronucleus (MN) assay, while mutagenesis was measured with an error-corrected sequencing (ECS) method, High-Fidelity sequencing (HiFi-seq). The six EAT-positive NDSRIs induced time- and concentration-dependent cytotoxicity in HepaRG spheroids. All except N-nitroso-lorcaserin caused DNA damage, while only N-nitroso-propranolol induced MN formation at acceptable levels of cytotoxicity. Notably, all six EAT-positive NDSRIs significantly increased mutation frequency in HepaRG spheroids, each exhibiting distinct mutational spectra. Quantitative analysis revealed varying levels of DNA damage and mutagenic potency among the six NDSRIs and two previously reported NDSRIs (N-nitroso-varenicline and N-nitroso-fluoxetine). These findings represent an important first step towards supporting the use of 3D HepaRG spheroids as a human-relevant in vitro new approach methodology (NAM) for mutagenicity assessment as a follow-up to the EAT. The results also provide evidence for genotoxic potency ranking of NDSRIs and offer mechanistic insights through mutational spectrum analysis.

The Skin Allergy Risk Assessment - Integrated Chemical Environment (SARA-ICE) is a Bayesian framework for quantitative skin sensitization hazard and potency assessment and estimates an ED₀₁, the dermal dose associated with a 1% sensitization probability in humans. SARA-ICE has two implementations: (i) SARA-ICE DA, a fixed defined approach (DA) for point-of-departure derivation (PoD), accepted by the Organisation for Economic Co-operation and Development and usable under Mutual Acceptance of Data; and (ii) SARA-ICE Extended, which returns ED₀₁ distributions, a prediction uncertainty ratio, and probabilities for assignments to Globally Harmonized System of Classification and Labeling of Chemicals (GHS) categories. Using in chemico and in vitro skin sensitization data on 180 substances of interest to U.S. federal agencies, we evaluated the performance of both SARA-ICE versions against human and murine data and OECD Guideline 497 DAs. SARA-ICE Extended showed high sensitivity for identifying sensitizers (up to 100% vs. human data), with balanced accuracy spanning 64-97%; its specificity for non-sensitizers was lower but consistent with comparator methods. For GHS subcategorization, accuracy ranged from 33-77% with low underprediction (8-21%), with concordance highest with the Guideline 497 DAs. We demonstrate application of the SARA-ICE DA using PoDs defined from the ED₀₁ in a quantitative risk assessment across a range of exposures. PoD -based risk calls from SARA-ICE were broadly comparable to HPPT-derived metrics and generally more protective than LLNA-derived PoDs. Collectively, these results support SARA-ICE as an approach for hazard identification and exposure-led safety assessment within next-generation risk assessment frameworks.

New approach methodologies (NAMs) for immunotoxicity are critically needed for rapidly identifying immunotoxicants, filling gaps for data-poor chemicals, and reducing the number of vertebrate animals used in toxicity testing. Current immunotoxicity testing guidelines rely on low-throughput rodent models that do not specifically assess key functions of the immune system, including chemotaxis. Alteration of chemotaxis is considered a key characteristic by which immunotoxicants may detrimentally impact the immune system. Current methods for measuring chemotaxis in a high-throughput manner are lacking. To address this, we have developed an in vitro assay to measure chemotaxis in two different immune cell types using transwell culture plates and an automated high-content imaging platform in a multi-concentration format. Our results corroborated other studies showing that Jurkat T cells and neutrophil-like HL60 (nHL60) cells respond to chemoattractants CXCL12 and fMLP, respectively, in a biphasic manner. Cytochalasin D, a specific inhibitor of actin polymerization, inhibited chemotaxis of both Jurkat cells and nHL60 cells in a concentration-dependent manner. An inhibitor of CXCR4 also decreased Jurkat chemotaxis towards CXCL12, as did cyclosporin A, a known T cell immunotoxicant. Rapamycin and cyclosporin A did not inhibit nHL60 chemotaxis. Assay performance metrics indicate that Jurkat cells and nHL60 cells may both be suitable for screening a wide variety of immunotoxicants, but more studies are needed to assess sensitivity and specificity. This work represents a NAM for identifying potential immunotoxicants that inhibit chemotaxis for future large-scale screening applications.

Despite rapid advances in human-relevant assay technologies and other innovative in vitro and in silico approaches, the safety assessment of chemicals and pharmaceutical drugs continues to heavily rely on animal testing, in part due to slow regulatory adaptation and a lack of transitional strategies that clear the way for the phasing out of animal testing while demonstrating the preservation of current safety standards. To overcome this hurdle, several international efforts are currently focused on systematically integrating new approach methodologies (NAMs) into regulatory frameworks with the goal of phasing out animal testing in the safety assessments. The present paper highlights the need for pragmatic transitional initiatives that reduce animal use while preserving scientific rigor and regulatory confidence by focusing on the IHI VICT3R project, a collaborative effort aimed at reducing animal use in safety assessments of drugs and chemicals by replacing concurrent control groups with virtual control groups (VCGs). The VICT3R project focuses on refining and implementing the VCG methodology, particularly in the standardization, curation and adequate matching of historical control data (HCD), as well as regulatory qualification. Beyond regulatory applications, the VCG concept constitutes a scalable paradigm for reducing the experimental animal use in academic and industrial research. By demonstrating how data-driven approaches can reduce animal use without compromising scientific quality, VICT3R offers an expandable model for advancing the global transition toward non-animal testing frameworks. Moreover, implementing the VCG methodology in a regulatory context could pave the way to the successful incorporation of other NAMs. The paper stresses the required paradigm shift, which involves adequate qualification procedures, cross-sector collaboration, and a cultural shift away from animal testing.

Background and purpose: New approach methodologies (NAMs) including microphysiological systems in combination with human induced pluripotent stem cell (hiPSC)-derived neural cells and multielectrode array (MEA) have demonstrated utility for evaluating electrophysiological effects of CNS active compounds including those with potential seizurogenic liability. Here, we extend a neural NAM assay to assessment of an opioid agonist and reversal agent.

Methods: An in vitro human NAM composed of hiPSC-neurons and hiPSC-astrocytes coupled with MEA platform was used to assess spontaneous neural electrophysiology. Neural cells were cultured on MEA plates then treated with varying concentrations of the μ-opioid receptor agonist DAMGO (D-Ala(2)-mephe(4)-gly-ol(5))enkephalin). Following DAMGO treatment, the opioid antagonist naloxone [10 μM] was added to each well to evaluate phenotypic reversal.

Results: The opioid agonist DAMGO modulated neural electrophysiological activity in a concentration-dependent manner relative to vehicle control. These changes were observed across neural parameters relating to overall neural activity, single electrode burst, network burst, and synchronicity. These effects were reversed by the opioid antagonist naloxone. Moreover, DAMGO treatment disrupted higher order baseline neural patterns. Finally, subpopulation analysis revealed divergent opioid-induced higher order network response influenced by baseline network activity.

Conclusions: These findings demonstrate the hiPSC neural NAM can characterize human network electrical activity at baseline and following exposure to an opioid agonist and antagonist pair. Future studies will investigate additional opioids and reversal agents, as well as other MEA metrics and activity patterns that may best describe concentration- and time-dependent changes in neural activity.

Skin sensitisation safety decisions for consumer goods predominantly use approaches such as Quantitative Risk Assessment (QRA) to judge whether a dermal exposure is low risk by comparing a point of departure (PoD) against an expected consumer exposure. Traditionally, the no-expected-sensitisation-induction level (NESIL), derived from human or murine studies, has provided this PoD. Safety Assessment Factors (SAFs) are used to adjust the NESIL to derive an Acceptable Exposure Level (AEL). Exposures are considered low risk when the AEL exceeds the Consumer Exposure Level (AEL:CEL > 1). An equivalent approach evaluates the Margin of Exposure (MoE), defined as the PoD/CEL, and deems exposure low risk when the MoE exceeds a predetermined threshold. As new approach methodologies (NAMs) increasingly supply PoDs, a key question is whether MoE and SAFs, established for human and murine NESILs, are appropriate for NAM-based PoDs, or whether recalibration is required to maintain protectiveness. We propose a statistical framework that converts precedent MoE and SAFs used with traditional NESILs into statistically equivalent thresholds for NAM-derived PoDs. The framework uses benchmark consumer exposure scenarios for skin-sensitising ingredients and proposes a risk-matching approach that enables conversion of traditional acceptable MoEs/SAFs to NAM PoD-appropriate equivalents. Assuming a risk assessor can specify the desired MoE or SAF for a traditional NESIL, the output is a simple conversion table for NAM PoD-appropriate equivalent values. We provide a conversion table for PoDs from SARA-ICE, the GARDskin dose-response assay, and the multiple-regression approach and illustrate application with a simple, illustrative case study.

[This corrects the article DOI: 10.1016/j.namjnl.2025.100035.].

[This corrects the article DOI: 10.1016/j.namjnl.2025.100034.].

The global demand for perfluorinated and polyfluorinated compounds (PFCs), including the subclass of per- and polyfluoroalkyl substances (PFASs), has grown due to their extreme stability and resistance to heat, enabling diverse industrial applications. However, their environmental persistence and potential health risks have earned some the title 'forever chemicals'. Regulations have been imposed to limit the use and release of PFCs into the environment. Computational studies offer a great alternative to animal studies, enabling broader, faster, and effective risk assessment while also being cost-effective. This study reports the development and validation of Quantitative Read-Across Structure-Activity Relationship (q-RASAR) models for the rodent toxicity of PFCs. This approach predicts toxicity for new compounds based on their structural resemblance to chemicals with known toxicity profiles. We employed a Partial Least Squares (PLS) regression algorithm to predict acute toxicity using oral pLD₅₀ and inhalation pLC₅₀ endpoints in both rat and mouse models. Our models demonstrated substantially improved predictive performance compared to previous research, with notably higher Q² _F1 (an external validation metric) values across all four datasets: 0.969 for pLD₅₀ rats, 0.867 for pLD₅₀ mice, 0.917 for pLC₅₀ rats, and 0.938 for pLC₅₀ mice. This work addresses important gaps in the toxicity assessment of PFCs by providing models with more reliable predictions while adhering to the 3Rs principle (Replacement, Reduction, Refinement) of animal testing. The developed models were utilized to predict the toxicity of a true external set of commercially relevant PFCs, classifying previously uncharacterized compounds as potentially toxic or non-toxic, aiding future risk prioritization efforts.