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Toxicology Research: Understanding and Predicting Compound-Induced Toxicity

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Toxicology".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 4524

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Special Issue Editor

Prof. Dr. Miriam Beatríz Virgolini

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Guest Editor

Departamento de Farmacología Otto Orsingher, Instituto de Farmacología Experimental de Córdoba-Consejo Nacional de Investigaciones Técnicas (IFEC-CONICET), Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba 5000, Argentina
Interests: neurotoxicology; caenorhabditis elegans; metals; pesticides; drugs of abuse

Special Issue Information

Dear Colleagues,

Mechanistic toxicology has evolved into a more integrative vision, moving beyond isolated molecular or cellular events to a broader understanding of toxicity across biological levels in accordance with a systems perspective. The framework involving adverse-outcome pathways plays a central role in this shift, offering a sequentially organized level of biological associations, with a molecular initiating event triggering a series of key events and an observable adverse outcome at the individual or population level.

This framework supports the advancement of next-generation risk assessment and new approach methodologies, which include in silico, in vitro, and non-mammalian in vivo tools. Together, these approaches aim to improve toxicity testing, enabling the identification of early and key effect biomarkers for the better protection of human health.

In this Special Issue, we aim to highlight recent progress in the application of adverse-outcome pathway to predictive toxicology, emphasizing mechanistic insight and innovation across emerging and traditional toxicological platforms.

Suitable topics include, but are not limited to, the following areas:

Toxicology;
Pharmacology;
Toxicological risk assessment;
Mechanisms of toxicity

Prof. Dr. Miriam Beatríz Virgolini
Guest Editor

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Keywords

toxicity
adverse-outcome pathway
metals
pesticides
drugs of abuse
pharmaceuticals

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Published Papers (4 papers)

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Research

16 pages, 2089 KB

Open AccessArticle

The Activation of Aldehyde Dehydrogenase 2 (ALDH2) by Alda-1 and Flurbiprofen as a Common Mechanism to Reduce Alcohol Intake in Rats

by Juan Manuel Torres, Carolina Ponce, Vicente Pérez, Ignacio Gutiérrez-Vega, María Elena Quintanilla, David Vásquez and Mario Rivera-Meza

Int. J. Mol. Sci. 2026, 27(7), 3248; https://doi.org/10.3390/ijms27073248 - 3 Apr 2026

Viewed by 722

Abstract

Excessive alcohol consumption causes millions of deaths annually, yet current pharmacological treatments for alcohol use disorders show limited efficacy and poor adherence, creating an urgent need for new therapeutic alternatives. Aldehyde dehydrogenase 2 (ALDH2) metabolizes acetaldehyde, a key mediator of the rewarding effects of alcohol in the brain, making ALDH2 activation a promising therapeutic target. This study investigated whether flurbiprofen, an FDA-approved nonsteroidal anti-inflammatory drug that activates ALDH2, reduces alcohol intake compared to the experimental ALDH2 activator Alda-1 and the structurally similar NSAID ibuprofen. Male alcohol-preferring UChB rats received oral flurbiprofen (2.5–10 mg/kg), Alda-1 (5 mg/kg), or ibuprofen (5 mg/kg) during acquisition and chronic phases of voluntary alcohol consumption under a two-bottle free-choice paradigm. Both flurbiprofen and Alda-1 reduced alcohol intake by approximately 60% and similarly increased ALDH2 activity 3–4-fold in brain and liver tissues. Ibuprofen showed modest effects (25% alcohol intake reduction). In vitro assays confirmed that flurbiprofen and Alda-1, but not ibuprofen, activated ALDH2 in PC-12 cells. Enzymatic assays and molecular docking revealed that Alda-1 lacks cyclooxygenase-inhibitory activity, unlike flurbiprofen, suggesting that ALDH2 activation is the primary mechanism underlying reduced alcohol consumption. These findings identify flurbiprofen as a clinically available ALDH2 activator with significant translational potential for treating alcohol use disorders. Full article

(This article belongs to the Special Issue Toxicology Research: Understanding and Predicting Compound-Induced Toxicity)

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35 pages, 20381 KB

Open AccessArticle

Ochratoxin A and Clear Cell Renal Cell Carcinoma: Exploring Potential Molecular Links Through Network Toxicology and Machine Learning

by Chenjie Huang, Lulu Wei, Wenqi Yuan, Yaohong Lu, Ziyou Yan and Gedi Zhang

Int. J. Mol. Sci. 2026, 27(7), 2971; https://doi.org/10.3390/ijms27072971 - 25 Mar 2026

Viewed by 661

Abstract

Ochratoxin A (OTA), a prevalent food contaminant, is closely linked to the development of various cancers, including clear cell renal cell carcinoma (ccRCC). However, the potential mechanisms remain to be explored. In this study, we employed network toxicology, machine learning, and molecular docking techniques to systematically investigate the potential molecular mechanisms underlying OTA-associated ccRCC. We normalized transcriptional data from two Gene Expression Omnibus (GEO) datasets and analyzed it using differential expression analysis and weighted gene co-expression network analysis (WGCNA), identifying 3224 ccRCC-associated target genes. These were intersected with 232 predicted OTA target genes, yielding a total of 56 overlapping targets. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these targets were primarily enriched in critical biological processes, including extracellular matrix remodeling, immune microenvironment regulation, signaling pathway transduction, cellular metabolism, and protein homeostasis. Machine learning analysis identified “glmBoost + RF” (a sequential combination of feature selection and classifier) as the optimal model, from which nine key genes were extracted. SHapley Additive exPlanations (SHAP) analysis revealed five core genes (IGFBP3, ITGA5, PYGL, SLC22A8, LTB4R), with IGFBP3 and ITGA5 serving as the principal driver genes of the model. Validation of the model’s diagnostic efficacy and single-cell transcriptome analysis indicated that the core genes exhibited significant differential expression patterns, cell-type-specific expression characteristics, and high independent diagnostic efficacy. Molecular docking analyses predicted stable interactions between OTA and the core target proteins. These findings suggest potential molecular links between OTA exposure and ccRCC, providing a foundation for hypothesis generation and future experimental validation. Full article

(This article belongs to the Special Issue Toxicology Research: Understanding and Predicting Compound-Induced Toxicity)

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19 pages, 464 KB

Open AccessArticle

QSAR Modeling for Predicting IC₅₀ and GI₅₀ Values for Human Cell Lines Used in Toxicological Studies

by Alexey A. Lagunin, Elena Y. Lisitsa, Anastasia V. Rudik, Sergey M. Ivanov, Alexander V. Dmitriev, Elena S. Muraviova, Dmitry A. Filimonov and Vladimir V. Poroikov

Int. J. Mol. Sci. 2025, 26(24), 12063; https://doi.org/10.3390/ijms262412063 - 15 Dec 2025

Viewed by 1388

Abstract

Assessing cytotoxicity towards human cells is a critical step in preclinical drug development. In preclinical toxicology, human cell lines allow for the analysis of both general and organ-specific toxicity, thus, helping reduce development time and costs. Predicting cytotoxic IC₅₀ and GI₅₀ values facilitates the early evaluation of new pharmaceutical agents by assessing the possible therapeutic window. Ten non-tumor and 10 tumor cell lines commonly used in toxicology were selected to develop QSAR models using GUSAR software and ChEMBL data. GUSAR employs atom-centric electrotopological QNA and substructural MNA descriptors to encode molecular structure and utilizes the RBF–SCR algorithm to train QSAR models. The best-performing models (R² > 0.5, RMSE < 0.8; mean R² = 0.691, mean RMSE = 0.584) were selected using 5-fold cross-validation. These models were implemented in the freely available web application CLC-Pred 2.0 (Cell Line Cytotoxicity Predictor), initially developed for qualitative prediction of cytotoxicity in human cell lines. Full article

(This article belongs to the Special Issue Toxicology Research: Understanding and Predicting Compound-Induced Toxicity)

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20 pages, 3470 KB

Open AccessArticle

ALH Inhibition as a Molecular Initiating Event in the Adverse Outcome Pathway of Benomyl Toxicity in Caenorhabditis elegans: Relevance for Parkinsonism

by Lucía Eugenia Fernandez-Hubeid, Romina Deza-Ponzio, Paula Alejandra Albrecht, Verónica Leonor Romero, Candelaria Gonzales-Moreno, Melisa Rut Ferreyra, Yanina Soledad Moran and Miriam Beatriz Virgolini

Int. J. Mol. Sci. 2025, 26(18), 9163; https://doi.org/10.3390/ijms26189163 - 19 Sep 2025

Viewed by 1229

Abstract

Dithiocarbamate fungicides, including benomyl (methyl 1-butylcarbamoyl-2-benzimidazolecarbamate), share a common mechanism of toxicity by inhibiting aldehyde dehydrogenases (ALDHs), enzymes essential for detoxifying reactive aldehydes. One such aldehyde, 3,4-dihydroxyphenylacetaldehyde (DOPAL), a dopamine metabolite, is implicated in the catecholaldehyde hypothesis of Parkinson’s disease. This study examines ALDH inhibition as the molecular initiating event (MIE) within an adverse outcome pathway (AOP) leading to neurotoxicity. Caenorhabditis elegans at the L4 stage were exposed for 24 h to 10 or 100 μM benomyl. While 10 μM had no significant effect on lethality, growth, or reproduction, 100 μM induced adverse effects, albeit with low lethality. Both doses inhibited ALH activity, an effect mitigated by Alda-1, a selective ALDH activator. Alda-1 alone increased ALH-1 protein levels but did not alter benomyl-induced protein localization and relative abundance. Benomyl exposure also elevated oxidative stress markers—superoxide dismutase, catalase, and lipid peroxidation—which Alda-1 reduced. Neurotoxicity was evidenced by dopaminergic dysfunction, including impaired basal slowing response, neuronal morphological abnormalities, and reduced locomotion upon optogenetic activation. Fluorescent reporter assays confirmed ALH-1 presence in dopaminergic neurons. These results identify ALH inhibition as the MIE in benomyl-induced neurotoxicity, linking dopaminergic degeneration and redox imbalance to the catecholaldehyde hypothesis, and providing mechanistic insights into an AOP relevant to neurodegenerative disorders. Full article

(This article belongs to the Special Issue Toxicology Research: Understanding and Predicting Compound-Induced Toxicity)

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