Discover Toxicology was inaugurated in May 2024 as a peer-reviewed, open access platform intended to publish research across all aspects of toxicology and its applications in research, development, and society. Its founding premise, the editors explain, is to give researchers, practitioners, policymakers, and stakeholders a venue to exchange knowledge, share best practices, and collaborate on solutions to pressing toxicological challenges. Like other journals in the Discover series, it welcomes all valid research, including null results, regardless of perceived impact, provided the work meets the standards of rigor and quality associated with Springer Nature. That policy is a deliberate counterweight to publication cultures that reward only headline-grabbing findings, a bias the editors argue has left critical dynamics of toxicological mechanisms in low-resourced countries understudied.

The breadth of the journal’s ambition is reflected in its Editorial Board, whose listed expertise spans toxins and venoms, clinical and preclinical pharmacology and toxicology, bioinformatics and cheminformatics, computational chemistry, ecotoxicity, regulatory toxicology, emerging contaminants, food safety, genetic toxicology, analytical chemistry, risk assessment, mechanisms of toxicity, omics, immunotoxicology, forensic pathology, and occupational exposure assessment. In the editorial, each Section Editor introduces the domain he or she represents, offering an unusually candid view of the technical questions the journal considers most pressing.

Professor Adekunle A. Bakare of the University of Ibadan, Nigeria, anchors the genotoxicology section. His laboratory studies the genotoxicity and mutagenicity of xenobiotics, the foreign chemicals that urbanization and industrialization have made almost impossible to avoid. Using in vitro and in vivo bioassays, his group examines the cytotoxic, genotoxic, and mutagenic effects of municipal solid waste leachates, industrial effluents, pesticides, analgesics, medicinal plant extracts, antiretroviral and antituberculosis drugs, metal and metal oxide nanoparticles, and electronic waste elutriates. The stakes, he argues, are generational: DNA damage from environmental xenobiotics is implicated not only in cancer and birth defects but also in heart disease, cellular aging, immune dysfunction, altered metabolism, neurodegenerative disease, and cataracts, and germline damage may affect future as well as current generations. He invites submissions on genotoxicity testing approaches, predictive toxicology, toxicogenomics, reproductive toxicology, epigenetics, gene expression analysis of DNA toxicity, artificial intelligence applied to DNA damage, and the links between genotoxicity and carcinogenesis.

Ajay Vikram Singh, a senior scientist at the German Federal Institute for Risk Assessment (BfR) in Berlin, represents the computational and nanotoxicology frontier. Working within an institute of more than 750 scientists that advises the German government on food and product safety, chemical risks, contaminants, animal protection, and consumer health, Singh combines advanced computational models, artificial intelligence, and nanoscale characterization to decipher how chemicals, nanomaterials, and biological systems interact. The goal is proactive safety assessment: predicting toxicity before products reach the market and enabling the design of inherently safer, so-called safer-by-design materials. He highlights the integration of multi-omics data with computational approaches, the nanobiophysics of mechanistic toxicology, and the regulatory challenges posed by complex novel materials, and he welcomes manuscripts using in silico methods, AI and machine learning-driven predictive toxicology, high-throughput screening data analysis, and mechanistic studies of engineered nanomaterials.

Edmond Sanganyado, associate professor at the University of Saskatchewan, works at the intersection of analytical chemistry and systems biology, developing tools that link exposure to toxicological effect through advanced omics technologies. He frames three questions that he believes will define the field: how to detect and quantify known and unknown toxicants and their metabolites quickly, cheaply, and reliably in real samples; how complex mixtures of pollutants affect organisms, ecosystems, and humans over a lifetime; and how to identify toxic substances in ways that stand up in court, keep pace with drug trends, and support public health. Big data, artificial intelligence, high-resolution mass spectrometry, and new approach methodologies, or NAMs, are driving all three disciplines, analytical, environmental, and forensic toxicology, toward mixture-based paradigms and toward reducing and replacing animal testing. But he cautions that publication norms emphasizing narrow novelty risk leaving the toxicology of low-resourced countries chronically understudied.

Neuropharmacologist João Paulo Capela of Portugal’s Fernando Pessoa University and the University of Porto brings the journal’s coverage to drugs of abuse and clinical toxicology. His research probes the brain actions of amphetamine-type stimulants and methylphenidate, both as illicit substances and as prescribed treatments for attention deficit hyperactivity disorder and other brain disorders. His central concern is translation: whether work is done in vitro or in animals, the purpose of mechanistic toxicology is to transfer findings to the human situation in order to prevent, mitigate, or treat adverse drug effects. He sees artificial intelligence-based tools as a promising means of elevating that mechanistic understanding, and he argues that new methodologies and models are essential for surveying how drugs and toxicants inflict damage at the cellular and molecular level.

Ecotoxicologist Maranda Esterhuizen, affiliated with the University of Helsinki and Häme University of Applied Sciences in Finland, specializes in pollution impact assessment and ecological restoration through nature-based solutions, with a deliberately transdisciplinary approach bridging environmental science and policy. She describes environmental toxicology as standing at a critical juncture, confronting complex chemical mixtures and climate-induced shifts in pollutant behavior, particularly in rapidly urbanizing regions. Her section invites research using adverse outcome pathways, omics technologies, and predictive modeling to understand toxicity across biological scales, and she singles out studies integrating climate change dynamics, urbanization, and chemical mixture interactions as especially welcome, because they mirror the compounded pressures ecosystems actually face.

Food and computational toxicologist Yu-Syuan Luo of National Taiwan University completes the editorial leadership roster. His focus is on human-relevant, mechanism-informed chemical safety evaluation at a time when data gaps for emerging contaminants, low-dose exposures, and complex mixtures impede timely regulatory decisions. Food toxicology, he notes, is pivotal for assessing ingredients, contaminants, and food-contact materials, especially for endpoints such as endocrine disruption and mixture toxicity. Computational toxicology complements it with scalable predictive tools, including in silico modeling, omics-based profiling, and data-driven hazard identification and prioritization, supporting the global shift away from traditional animal testing and toward more efficient, transparent, forward-looking risk assessment.

Taken together, the editorial sketches a discipline in methodological upheaval: from single-compound testing toward mixtures, from animal models toward new approach methodologies, from reactive hazard characterization toward AI-assisted prediction and safer-by-design chemistry. By welcoming null results and prioritizing rigor over novelty, Discover Toxicology is positioning itself as a home for precisely the unglamorous, reproducible, and globally inclusive work that this transition requires, and the editors close with an open invitation to researchers worldwide to submit work spanning fundamental questions and real-world applications alike.

Subject of Research: An editorial by the section editors of the open access journal Discover Toxicology outlining research priorities across genotoxicology, computational and nanotoxicology, ecotoxicology, neurotoxicology, and food toxicology.

Article Title: Discover Toxicology, the future journal for your toxicology research

Article References: Bakare, A. A., Singh, A. V., Sanganyado, E., Capela, J. P., Esterhuizen, M., Luo, Y.-S., & Guo, Y. (2026). Discover Toxicology, the future journal for your toxicology research. Discover Toxicology, 3(1), Article 12. https://doi.org/10.1007/s44339-026-00053-1

Image Credits: AI Generated

DOI: 10.1007/s44339-026-00053-1

Keywords: Discover Toxicology, toxicology, open access publishing, genotoxicity, xenobiotics, computational toxicology, nanotoxicology, ecotoxicology, new approach methodologies, food toxicology, chemical mixtures, risk assessment

Endocrine-disrupting chemicals, or EDCs, are synthetic compounds that can mimic, block or scramble hormonal signaling. They are woven into modern life almost invisibly: bisphenol A (BPA) leaches from polycarbonate plastics, epoxy-can linings and thermal receipt paper; perfluorooctanoic acid (PFOA) belongs to the sprawling family of per- and polyfluoroalkyl substances, or PFAS, long used in nonstick cookware, water-repellent textiles and firefighting foams; di(2-ethylhexyl) phthalate (DEHP) softens vinyl products and medical tubing; the flame retardant BDE-209 sheds from consumer goods into household dust; the pesticide DDT, banned in most countries decades ago, persists in soils and fatty tissue; and the dioxin TCDD, an industrial byproduct, ranks among the most potent synthetic toxins ever characterized. Because the thyroid gland depends on precisely tuned hormonal feedback to regulate metabolism, growth and development, it is considered acutely vulnerable to such exposures. Global burden analyses have documented a steep, decades-long rise in thyroid cancer incidence, and while improved detection explains part of the trend, environmental contributors remain a live scientific concern. Epidemiological studies have linked several of these chemicals to thyroid dysfunction, nodules and cancer risk, yet the molecular steps that turn exposure into tumor progression have remained stubbornly opaque—precisely the gap the new study set out to close.

The Wuhan-led team began not at the laboratory bench but at the computer. They first ran all six chemicals—BPA, PFOA, DDT, BDE-209, TCDD and DEHP—through ADMETlab 3.0, a machine-learning web platform that predicts a compound’s absorption, distribution, metabolism, excretion and toxicity directly from its molecular structure, providing a standardized read on how hazardous each molecule is likely to be. They then mined the Comparative Toxicogenomics Database, a curated public repository that logs which genes have been experimentally shown to respond to which chemicals. In parallel, the researchers compiled lists of genes implicated in thyroid cancer from multiple disease databases. Overlaying the chemical-response gene sets with the cancer gene sets produced a shared space of 1,113 EDC–thyroid cancer targets: genes that both respond to endocrine-disrupting chemicals and participate in malignant thyroid disease.

To give that list biological meaning, the team performed pathway enrichment analysis, a statistical method that tests whether a set of genes clusters within particular signaling networks more densely than chance would predict. The 1,113 shared targets concentrated strikingly in three circuits. The PI3K–Akt pathway, a core growth-control cascade promoting cell survival, proliferation and metabolism, is among the most frequently dysregulated networks in human cancer. The FoxO transcription factor family acts downstream of Akt and governs cell-cycle arrest, DNA repair, apoptosis and oxidative-stress resistance—functions of special relevance in the thyroid, where hormone synthesis inherently generates reactive oxygen species. The AGE–RAGE axis, which couples advanced glycation end products to their cell-surface receptor RAGE, sustains chronic inflammatory signaling and has previously been implicated in the migratory behavior of thyroid cancer cells. Convergence of EDC-responsive genes on these pro-growth, pro-survival, pro-inflammatory circuits handed the researchers their first mechanistic hypothesis: chemical exposure may be rewiring the very pathways that decide whether a thyroid tumor grows, spreads or dies.

The next question was which of those 1,113 genes actually separate tumor tissue from healthy thyroid. Differential expression analysis of transcriptomic datasets filtered the candidates down to genes consistently dysregulated in cancer, and the shortlist then went to machine learning. The team applied least absolute shrinkage and selection operator (Lasso) regression, an algorithm that penalizes model complexity and drives the coefficients of uninformative genes to exactly zero, compressing hundreds of features into a minimal signature. The surviving genes were classified with linear discriminant analysis (LDA), a supervised method that separates patient groups along an optimal linear boundary. The resulting six-gene diagnostic model—FN1, which encodes the extracellular-matrix protein fibronectin; BCL2, a canonical anti-apoptotic gene; CD44; CDKN1A, which encodes the cell-cycle brake p21; CTNNB1, the gene for β-catenin at the core of WNT signaling; and JUN, a pillar of the AP-1 transcription factor—achieved an average area under the receiver operating characteristic curve (AUC) of 0.976 across a training cohort and three independent validation cohorts. An AUC of 1.0 signifies perfect discrimination and 0.5 mere coin-flipping, so a value nearing 0.98 represents diagnostic performance close to clinical grade.

Within that six-gene panel, one name kept rising to the top. Evaluated alone, CD44 achieved AUC values of 0.950 in the training set, 0.801 in the external dataset GSE27155, 0.878 in GSE29265 and 0.938 in GSE153659—performance that persisted across cohorts generated by different laboratories on different platforms, a robustness that matters because datasets built independently are far less likely to share hidden technical biases. To understand why the algorithm leaned so heavily on this gene, the researchers deployed SHAP analysis, short for SHapley Additive exPlanations, a game-theoretic framework borrowed from economics that distributes the credit for every prediction among the features that produced it. In the SHAP ranking, CD44 made the largest single contribution to the model’s output, evidence that the machine had not latched onto a statistical artifact but onto the gene that best captured the boundary between tumor and healthy tissue.

CD44 is no obscure molecule. It encodes a transmembrane glycoprotein that serves as the principal cell-surface receptor for hyaluronic acid, the gel-like polymer filling the space between cells, and through that interaction it governs adhesion, migration and invasion. It is a defining marker of cancer stem cells—the self-renewing subpopulation thought to seed relapse and shrug off therapy—and has been tied to progression and metastasis across many tumor types, including papillary thyroid carcinoma. The new study layered further dimensions onto that profile. Immune infiltration analysis indicated that CD44 expression covaries with the makeup of the tumor immune microenvironment, the mix of macrophages, T cells and other immune players surrounding a growing tumor. Survival analysis of data from The Cancer Genome Atlas linked CD44 levels to patient prognosis, and single-cell RNA sequencing positioned the gene as a marker of shifting cellular states within malignant cells. Taken together, these analyses cast CD44 as sitting at the junction where environmental stress, tumor identity and immune context meet.

The most provocative question was whether the chemicals themselves can physically engage CD44. To probe it, the team used molecular docking, a computational technique that fits flexible small molecules into the three-dimensional structure of a protein’s binding region and scores how well each one lodges there. Docking produced plausible binding poses for BPA, DEHP and PFOA on CD44, with PFOA—the eight-carbon fluorinated compound infamous for its nearly unbreakable carbon–fluorine backbone—returning the most favorable predicted docking score. The researchers then stress-tested each protein–ligand complex with 200-nanosecond molecular dynamics simulations, which track every atom of the pair in a simulated water environment and reveal whether an interaction holds together or falls apart over realistic molecular timescales. The complexes remained plausible across the simulated run, supporting—though not yet proving—direct physical contact between these environmental chemicals and the CD44 protein. The authors are careful with language here: docking scores and simulated stability are computational hypotheses that will need confirmation by direct biophysical measurements such as surface plasmon resonance or calorimetry.

Computational hypotheses were not the endpoint. In the laboratory, the team confirmed that CD44 is expressed at higher levels in thyroid cancer tissues and thyroid cancer cell lines than in normal counterparts. When the cells were exposed to endocrine-disrupting chemicals, CD44 expression climbed further. The decisive experiment followed: using molecular tools to knock down CD44—silencing the gene so its protein is no longer made—the researchers showed that the gains in proliferation, colony formation and migration that ordinarily follow EDC exposure were substantially attenuated. Colony-forming assays, which measure how many single cells can found entire colonies, and migration assays, which track how quickly cells close a wound-like gap or invade through a membrane, are standard readouts of malignant potential. In effect, the experiment closed a loop: chemical exposure raises CD44, elevated CD44 licenses aggressive behavior, and removing CD44 strips much of that behavior away.

The authors frame CD44 as a candidate target associated with EDC-responsive malignant phenotypes in thyroid cancer—a deliberately measured formulation that reflects both the strength and the limits of the evidence. The study does not claim that endocrine-disrupting chemicals initiate thyroid cancer, and a docking score is not a demonstrated drug-like interaction. What it does establish is a coherent, multi-layered chain of evidence: computational toxicity profiling, toxicogenomic mining, pathway convergence, a near-clinically accurate six-gene diagnostic model, single-gene robustness across independent cohorts, microenvironmental and single-cell corroboration, structural simulation, and finally laboratory intervention. If future work confirms direct CD44 binding by these chemicals in living systems and validates the diagnostic panel in prospective patient cohorts, the implications are considerable, because CD44 is already pursued as an oncology target through antibodies and hyaluronan-based drug delivery strategies, offering a plausible road from biomarker to intervention. In the meantime, the study adds molecular weight to a public-health argument that has been building for years: curbing exposure to endocrine-disrupting chemicals is not merely an endocrine issue—it may be an oncological one.