Predictive Toxicology Research Articles

Computational Drug Design Approaches for the Identification of Novel Antidiabetic Compounds from Natural Resources through Molecular Docking, ADMET, and Toxicological Studies.

Type 2 diabetes mellitus (T2DM) is usually depicted by relative insulin deficiency, raised blood glucose levels, and the predominant risk factor, insulin resistance. Hence, the development of insulin sensitizer drugs targeting PPAR-γ receptors has expanded enormous interest as an attractive choice for T2DM treatment. Thiazolidinediones (TZD) enhance insulin sensitivity either by directly functioning on gene transcription of the PPARγ receptor related to glucose homeostasis or by systemic sensitization of insulin and, therefore, improved hyperglycemia in a wide range of patients. However, severe complications and adverse effects of TZDs necessitate the development of an efficacious and reliable insulin sensitizer from alternative resources. On the contrary, Nature is a rich source of anticipated effective and safer medicine; more than fifty percent of drugs on the market are developed from natural products. Hence, searching for a new PPAR-γ agonist from bioactive secondary compounds of medicinal plants along with greater efficacy and safety is a recognized and consistent tactic for developing novel antidiabetic agents. Pulicaria jaubertii is a fragrant perennial aromatic plant with anti-inflammatory, antidiabetic, antimicrobial, antimalarial, and insecticidal properties. The current study was designed to use a computer-aided drug design to explore the best antidiabetic compounds from P. jaubertii. Herein, the molecular docking study of 80 investigated ligands against the PPAR-γ receptor identifies the highest docking score for five ligands ranging from -8.9 kcal/mol to 8.0 kcal/mol, which is also more significant than the standard drug pioglitazone (-7.7 kcal/mol) determined by the PyRx 8.0 virtual screening software. GLN286, CYS285, SER289, TYR473, MET364, ARG288, ILE341, and LEU333 residues are found to be significant contributors to the non-bonded interaction between ligands and receptors. Molecular electrostatic potential (MEP), DFT, molecular orbital (MO), ADMET, and toxicological analyses were performed on the selected five high-scored ligands of P. jaubertii. Results documented that all investigated ligands, especially L4, show considerably excellent profiles in molecular docking, MEP, DFT, MO, ADMET, and toxicological predictions, suggesting our drug-designing approaches may contribute to the development of a novel antidiabetic drug for the treatment of T2DM from natural resources.

Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications

Physiologically relevant in vitro models are a priority in predictive toxicology to replace and/or reduce animal experiments. The compromised toxicant metabolism of many immortalized human liver cell lines grown as monolayers as compared to in vivo metabolism limits their physiological relevance. However, recent efforts to culture liver cells in a 3D environment, such as spheroids, to better mimic the in vivo conditions, may enhance the toxicant metabolism of human liver cell lines. In this study, we characterized the dynamic changes in the transcriptome of HepG2/C3A hepatocarcinoma cell spheroids maintained in a clinostat system (CelVivo) to gain insight into the metabolic capacity of this model as a function of spheroid size and culture time. We assessed morphological changes (size, necrotic core), cell health, and proliferation rate from initial spheroid seeding to 35 days of continuous culture in conjunction with a time-course (0, 3, 7, 10, 14, 21, 28 days) of the transcriptome (TempO-Seq, BioSpyder). The phenotypic characteristics of HepG2/C3A growing in spheroids were comparable to monolayer growth until ∼Day 12 (Day 10–14) when a significant decrease in cell doubling rate was noted which was concurrent with down-regulation of cell proliferation and cell cycle pathways over this time period. Principal component analysis of the transcriptome data suggests that the Day 3, 7, and 10 spheroids are pronouncedly different from the Day 14, 21, and 28 spheroids in support of a biological transition time point during the long-term 3D spheroid cultures. The expression of genes encoding cellular components involved in toxicant metabolism and transport rapidly increased during the early time points of spheroids to peak at Day 7 or Day 10 as compared to monolayer cultures with a gradual decrease in expression with further culture, suggesting the most metabolically responsive time window for exposure studies. Overall, we provide baseline information on the cellular and molecular characterization, with a particular focus on toxicant metabolic capacity dynamics and cell growth, of HepG2/C3A 3D spheroid cultures over time.

The Potential of Molecular Docking for Predictive Toxicology.

Molecular modeling techniques are widely used in medicinal chemistry for the study of biological targets, the rational design of new drugs, or the investigation of their mechanism of action.They are also applied in toxicology to identify chemical potential harmful effects.Molecular docking is a computational technique to predict the ligand binding mode and evaluate the interaction energy with a biological target.This chapter describes a computational workflow to predict possible endocrine disruptors on peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor involved in glucose and lipid metabolism. The analyzed compounds are food contact chemicals, natural or synthetic substances intentionally added to food or released from the package or during production or technological processes.

Molecular toxicological prediction of the ethanolic extract of the species Siparuna guianensis

The species Siparuna guianensis has anti-inflammatory, analgesic and anti-infectious potential, but there is a lack of safety and toxicity parameters for the medicinal use of the plant. The aim of this study was to identify the phytochemical compounds and carry out the molecular toxicological prediction of the ethanolic extract of the species Siparuna guianensis. The ethanolic extract of the leaves of S. guianensis was obtained and GC-MS gas chromatography was carried out to identify the secondary metabolites, for subsequent quantification of the in silico toxicological properties in terms of carcinogenicity in rodents, Ames mutagenicity, eye irritation, skin irritation and sensitization. The molecules 2-undecanone, decanoic acid, ethyl ester of decanoic acid and 2-tetradecanone were the most satisfactory, with no risk of carcinogenicity in female mice, mutagenicity, skin sensitization or eye irritation, which suggests that the plant could be a promising and safe source for the development of future drugs with low toxicological potential.

Degradation and mineralization of atrazine by ozonation: A toxicological prediction by QSAR toolbox

This work aims to investigate the atrazine (ATZ) mitigation by an advanced oxidative process. Atrazine is one an effective herbicide which has been detected in water sources, causing contamination problems. To address the persistent issue of contamination, ATZ degradation and mineralization were studied by ozonation. In addition, the eco-toxicity of the possible degradation byproducts was also evaluated by the Quantitative Structure-Activity Relationship (QSAR) OECD toolbox. To evaluate the influence and predict the optimum conditions of the ozonation process and the reaction time on the degradation of ATZ, as well as, the percentage of mineralization, an experimental design was performed based on factorial design 23 methodology with center-point analysis. Total organic carbon (TOC) analyses and High-Performance Liquid Chromatography (HPLC) were employed to evaluate the efficiency of ATZ mitigation. The optimal conditions were achieved at an ozone flow rate of 0.4 mL/min, oxidation time = 30 min, and pH=8 where 100 % of ATZ was degraded and the highest percentage of mineralization was obtained (25.61 %). The potential toxicity of the residual concentration of ATZ was obtained by comparing with the values predicted by the QSAR tool, by comparing the outcomes. It was possible to come to the conclusion that the approach had positive implications for environmental safety. The values obtained are below the values considered toxic in aquatic environments, in almost all experiments. Low-concentration byproduct formation suggests that the degradation routes lead to low-hazardous concentrations of compounds for the environment. This implies the ozone treatment strategy might offer a long-term remedy for the ATZ.

Chiral Azepines: In silico Potential in Cancer and Neurodegenerative Diseases, a Chemical Analysis

Background: Chiral azepines are synthesized with remarkable diastereoselectivity, but their biological activity has not been investigated. However, benzazepines have demonstrated notable effects, particularly on the central nervous system (CNS) and infections. This characteristic attracts the interest of bioinformatic investigations in this new family, as their structural similarity can confirm their potential based on their kinship or discovering new options for biological potential. Objective: Possible interaction targets of previously synthesized chiral azepines are investigated. This study involves examining the interaction between these targets, conducting molecular docking analysis, ADME (administration, distribution, metabolism, excretion), and toxicology prediction to assess biological potential. Methods: Modeling 3D-optimized structural, virtual screening, molecular docking, ADME, and toxicological studies were performed. Results: Structural analysis demonstrated potential against neurodegenerative diseases and cancer. In Molecular docking against cancer, pathways dependent on MAP2K1 and COX-2 exhibited energetically superior inhibitors than reference drugs, namely azepines 1, 3, and 6. Additionally, azepines 1 and 8 exhibited selective impacts against GSK3 and HMG-CoA-Reductase, respectively. Azepine 6 demonstrated an effect on CNS vs. GSK3 and HMG-CoA-Reductase, as well as potential against Alzheimer's disease; however, with a lower energy level with subunit 33 GABA-receptor. ADMETx investigations indicated satisfactory results for azepines. However, the opening of the cycle results in adverse effects and increased bioaccumulation, indicating the importance of preserving the integrity of azepine to propose its biological effect. Conclusion: Chiral azepines exhibit significant biological potential, particularly azepine 6 with a methyl substituent, which demonstrates multitarget potential. In addition, p-nitro phenyl substituent makes it highly selective towards CNS diseases. These findings indicate a strong relationship between biological activity and the stability of chiral azepines.

Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches.

Agranulocytosis, induced by non-chemotherapy drugs, is a serious medical condition that presents a formidable challenge in predictive toxicology due to its idiosyncratic nature and complex mechanisms. In this study, we assembled a dataset of 759 compounds and applied a rigorous feature selection process prior to employing ensemble machine learning classifiers to forecast non-chemotherapy drug-induced agranulocytosis (NCDIA) toxicity. The balanced bagging classifier combined with a gradient boosting decision tree (BBC + GBDT), utilizing the combined descriptor set of DS and RDKit comprising 237 features, emerged as the top-performing model, with an external validation AUC of 0.9164, ACC of 83.55%, and MCC of 0.6095. The model's predictive reliability was further substantiated by an applicability domain analysis. Feature importance, assessed through permutation importance within the BBC + GBDT model, highlighted key molecular properties that significantly influence NCDIA toxicity. Additionally, 16 structural alerts identified by SARpy software further revealed potential molecular signatures associated with toxicity, enriching our understanding of the underlying mechanisms. We also applied the constructed models to assess the NCDIA toxicity of novel drugs approved by FDA. This study advances predictive toxicology by providing a framework to assess and mitigate agranulocytosis risks, ensuring the safety of pharmaceutical development and facilitating post-market surveillance of new drugs.

Aryl hydrocarbon receptor-dependent toxicity by retene requires metabolic competence.

Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds frequently detected in the environment with widely varying toxicities. Many PAHs activate the aryl hydrocarbon receptor (AHR), inducing the expression of a battery of genes, including xenobiotic metabolizing enzymes like cytochrome P450s (CYPs); however, not all PAHs act via this mechanism. We screened several parent and substituted PAHs in in vitro AHR activation assays to classify their unique activity. Retene (1-methyl-7-isopropylphenanthrene) displays Ahr2-dependent teratogenicity in zebrafish, but did not activate human AHR or zebrafish Ahr2, suggesting a retene metabolite activates Ahr2 in zebrafish to induce developmental toxicity. To investigate the role of metabolism in retene toxicity, studies were performed to determine the functional role of cyp1a, cyp1b1, and the microbiome in retene toxicity, identify the zebrafish window of susceptibility, and measure retene uptake, loss, and metabolite formation in vivo. Cyp1a-null fish were generated using CRISPR-Cas9. Cyp1a-null fish showed increased sensitivity to retene toxicity, whereas Cyp1b1-null fish were less susceptible, and microbiome elimination had no significant effect. Zebrafish required exposure to retene between 24 and 48 hours post fertilization (hpf) to exhibit toxicity. After static exposure, retene concentrations in zebrafish embryos increased until 24 hpf, peaked between 24 and 36 hpf, and decreased rapidly thereafter. We detected retene metabolites at 36 and 48 hpf, indicating metabolic onset preceding toxicity. This study highlights the value of combining molecular and systems biology approaches with mechanistic and predictive toxicology to interrogate the role of biotransformation in AHR-dependent toxicity.

Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment

The EU-ToxRisk project (2016–2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape.Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021–2026)).

Artificial intelligence for ophthalmic drug discovery and development: Capabilities, applications, and challenges

The integration of artificial intelligence (AI) into ophthalmic drug discovery and development presents transformative opportunities to address the inherent complexities and challenges of creating targeted therapies for eye diseases. The ability of AI to process vast datasets can facilitate the discovery of novel drug candidates, improve predictions of drug efficacy and safety, and streamline the drug development pipeline. Applications can range from enhancing target identification and compound screening to refining predictive toxicology. However, challenges such as data limitations, computational demands, model interpretability, and ethical considerations remain. Despite these hurdles, the integration of AI with emerging technologies and its potential to optimize clinical trials signifies a new era of innovation in ophthalmology, emphasizing its critical role in addressing current challenges and advancing therapeutic development. In this paper, we explore the role of AI in ophthalmic drug discovery, highlighting its potential to address critical challenges in the field and delineating its impact across various stages of drug development.

Molecular Docking and Dynamics Simulation study for Anticancer Phytoconstituents inhibiting breast cancer

The main challenge in the fight against breast cancer (BC) is to create new medications that are more selective for cancer cells and have fewer adverse effects. SPDEF (Sam pointed domain containing ETS transcription factor) is a prostate-derived ETS factor that maintains homeostasis, differentiation of epithelial tissues and heritable alterations in cancer. Previously, SPDEF has been shown to be associated with BC subtypes and is found to be down-regulated in invasive breast cancer cell lines which is supportive of its role in tumor suppression. In the current study, SPDEF protein was used as a potential breast cancer therapeutic target. A total of fifteen phytoconstituents were evaluated as potential drug ligands against SPDEF using various in silico approaches. Genistein, allicin, 2-hydroxychalcone and ajoene were free from any of the predicted toxicological endpoints. As per toxicological endpoints prediction study, the median lethal dosage (LD50) values for these phytoconstituents varied from 159 to 3919 mg/Kg. Our results indicate that out of fifteen, three phytoconstituents silibinin (-7.7 kcal/mol), codonolactone (-6.1 kcal/mol) and genistein (-6.1 kcal/mol), showing the lowest binding energies, had more significant inhibitory effects against SPDEF protein. We selected these three phytoconstituents on the basis of binding scores for molecular dynamic simulations at 200 ns to study their protein-ligand complex stability. Out of the three, silibinin-receptor complex had more stability. The present analysis shows that silibinin could be a potential therapeutic compound against breast cancer as assessed by its binding interaction and stability analyses against SPDEF.

In-silico green toxicology approach toward discovering safer ligands for development of safe-by-design metal–organic frameworks

A vast variety of chemical compounds have been fabricated and commercialized, they not only result in industrial exposure during manufacturing and usage, but also have environmental impacts throughout their whole life cycle. Consequently, attempts to assess the risk of chemicals in terms of toxicology have never ceased. In-silico toxicology, also known as predictive toxicology, has advanced significantly over the last decade as a result of the drawbacks of experimental investigations. In this study, ProTox-III was applied to predict the toxicity of the ligands used for metal–organic framework (MOF) design and synthesis. Initially, 35 ligands, that have been frequently utilized for MOF synthesis and fabrication, were selected. Subsequently, canonical simplified molecular-input line-entry system (SMILES) of ligands were extracted from the PUBCHEM database and inserted into the ProTox-III online server. Ultimately, webserver outputs including LD50 and the probability of toxicological endpoints (cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and ecotoxicity) were obtained and organized. According to retrieved LD50 data, the safest ligand was 5-hydroxyisophthalic. In contrast, the most hazardous ligand was 5-chlorobenzimidazole, with an LD50 of 8 mg/kg. Among evaluated endpoints, ecotoxicity was the most active and was detected in several imidazolate ligands. This data can open new horizons in design and development of green MOFs.

Theoretical Exploration of the Antioxidant Activity, Chemopreventive, and Antineoplastic Potentials of Molecules Present in Morinda lucida, Momordica charantia, and Vernonanthura polyanthes

Cancer is one of the leading causes of death worldwide and one of the top four causes of premature death in most countries. Several studies have shown that secondary metabolites found in medicinal plants have anticancer, antimutagenic, anti-genotoxic, and chemopreventive effects. Ethnobotany knowledge points to Morinda lucida, Momordica charantia, and Vernonanthura polyanthes as anticancer plants, turning them into promising candidates for cancer treatment. This study analyzed M. lucida, M. charantia, and V. polyanthes compounds to identify potential antioxidant, chemopreventive, and anticancer agents through in silico analysis. We performed pharmacokinetics, biological activities, and toxicological predictions besides molecular docking. Theoretical calculations on thermodynamic grounds of the antioxidant properties were determined using density functional theory. The molecules damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside identified in M. lucida, M. charantia, and V. polyanthes, respectively, presented good anticancer potential and were chosen for the molecular docking study and DFT calculations. According to Lipinski’s criteria, damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside were classified as potential drugs, especially for the pharmacokinetics criteria. Damnacanthal and momordicin I had high absorption in the gastrointestinal treatment, while quercetin-3-[Formula: see text]-rutinoside had low absorption. These compounds also demonstrated antineoplastic, antimutagenic, and cancer-preventive activity, according to the in silico analysis. In this sense, damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside were chosen to test their potential interactions with the beta estrogen receptor, vascular endothelial growth factor receptor 2, and xanthine dehydrogenase, respectively, and intermolecular interactions were determined. Regarding the antioxidant potential of the structures, two different mechanisms were scrutinized with the help of their respective molecular descriptors, such as hydrogen atom transfer (HAT) and one-electron transfer (ET). In conclusion, this computational study revealed that damnacanthal, momordicin I, and quercetin-3-[Formula: see text]rutinoside are promising chemoprotective and anticancer molecules with favorable pharmacokinetics and toxicological properties.

Comparison of three toxicological profile prediction tools and experimental data, applied on a class of compounds – Vitamin A derivatives

Comparison of three toxicological profile prediction tools and experimental data, applied on a class of compounds – Vitamin A derivatives

Deep Learning for Predictive Toxicology Assessment Early Detection of Adverse Drug Reactions.

Predicting adverse drug reactions (ADRs) early in the drug development process is crucial for ensuring drug safety and reducing costly late-stage failures. Traditional methods for toxicity assessment rely heavily on animal testing and empirical observations, which are often time-consuming, expensive, and ethically questionable. In recent years, deep learning techniques have emerged as powerful tools for predictive toxicology, offering the potential to accelerate the identification of potential ADRs while reducing reliance on animal models.This paper reviews the current state-of-the-art deep learning approaches for predictive toxicology assessment, focusing on their applications in the early detection of ADRs. [1],[2] We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), and their utilization in analyzing diverse data types such as chemical structures, omics data, and adverse event reports.Furthermore, we examine the challenges and limitations associated with deep learning-based predictive toxicology, including data availability, model interpretability, and regulatory acceptance. We also explore ongoing efforts to address these challenges, such as the development of standardized datasets, explainable AI techniques, and collaborations between academia, industry, and regulatory agencies.Overall, this paper highlights the potential of deep learning for early detection of ADRs in drug development and underscores the need for continued research and collaboration to realize the full benefits of these techniques in ensuring drug safety and improving public health.

Evaluation of toxicity of thymoquinone, as a bioactive compound of nigella sativa using isolated mitochondria from different organs

The purpose of this study is to examine thymoquinone (TQ), as a bioactive compound of Nigella sativa, from a safety point of view and to evaluate the possible toxic effects on isolated mitochondria as a screening tool for drug safety and predictive toxicology. Mitochondria were isolated form different organs (brain, heart, kidney and liver) and treated with various concentrations of TQ (0–2000 µM). Mitochondrial toxicity parameters such as succinate dehydrogenases (SDH) activity, mitochondrial swelling, lipid peroxidation, reactive oxygen species (ROS) formation and mitochondrial membrane potential (MMP) collapse were analyzed. Results showed that various concentrations of TQ did not induce any toxicity or abnormalities in isolated mitochondria obtained from brain, heart, kidney and liver. We demonstrated for the first time that TQ does not cause any detrimental effect on isolated mitochondria from different organs of rat. Our data highlight that TQ does not directly interact with mitochondria and disrupt the mitochondrial functions.

MACHINE LEARNING IN DRUG DISCOVERY: A CRITICAL REVIEW OF APPLICATIONS AND CHALLENGES

This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges. By addressing these hurdles and leveraging the capabilities of ML, the drug discovery process can be significantly accelerated, paving the way for the development of new therapeutics. This review calls for continued research, collaboration, and dialogue among stakeholders to realize the transformative potential of ML in drug discovery fully.
 Keywords: Machine Learning, Drug Discovery, Predictive Toxicology, Data Quality, Interdisciplinary Collaboration.

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

In silico and pharmacokinetic studies of glucomoringin from Moringa oleifera root for Alzheimer's disease like pathology.

Aim: The aim of this research is to investigate the potential of glucomoringin, derived from Moringa oleifera, as a therapeutic agent for Alzheimer's disease through in silico analysis. Materials & methods: This study employs in silico or computational methodologies, including pkCSM, Swiss ADME, OSIRIS® property explorer, PASS online web resource and MOLINSPIRATION® software, to predict the pharmacokinetic characteristics and biological activity of glucomoringin. Results & conclusion: Molecular docking indicates strong binding to I-1β and the pharmacokinetic profile shows cytochrome P450 enzyme inhibition, prompting further research for dosing strategies. Toxicological predictions affirm safety, while bioactivity assessments demonstrate versatility in modulating essential pathways. glucomoringin's potential for Alzheimer's treatment, emphasizing the need for additional empirical research.

Reproductive toxicity of DDT in the Japanese medaka fish model: Revisiting the impacts of DDT+ on female reproductive health

The organochlorine pesticide dichlorodiphenyltrichloroethane (DDT) is an endocrine-disrupting compound (EDC) that has been banned by most countries for decades. However, it continues to be detected in nearly all humans and wildlife due to its biological and environmental persistence. The ovarian dysgenesis syndrome hypothesis speculates that exposure to EDCs during sensitive developmental windows such as early gonadal differentiation lead to reproductive disorders later in life. Yet, mechanisms by which DDT affects developing gonads remain unclear due to the inherent challenge of getting developmental exposure data from adults presenting with reproductive disease. The Japanese medaka (Oryzias latipes) is a valuable fish model for sex-specific toxicological studies due to its chromosomal sex determination, external embryonic development, short generation time, and extensively mapped genome. It is well documented that medaka exposed to DDT and its metabolites and byproducts (herein referred to as DDT+) at different developmental time points experience permanent alterations in gonadal morphology, reproductive success, and molecular and hormonal signaling. However, the overwhelming majority of studies focus primarily on functional and morphological outcomes in males and females and have rarely investigated long-term transcriptional or molecular effects. This review summarizes previous experimental findings and the state of our knowledge concerning toxic effects DDT + on reproductive development, fertility, and health in the valuable medaka model. It also identifies gaps in knowledge, emphasizing a need for more focus on molecular mechanisms of ovarian endocrine disruption using enhanced molecular tools that have become increasingly available over the past few decades. Furthermore, DDT forms a myriad of over 45 metabolites and transformation products in biota and the environment, very few of which have been evaluated for environmental abundance or health effects. This reinforces the demand for high throughput and economical in vivo models for predictive toxicology screening, and the Japanese medaka is uniquely positioned to meet this need.