The Chronic Toxicity of Endocrine-Disrupting Chemical to Daphnia magna: A Transcriptome and Network Analysis of TNT Exposure.

Endocrine-disrupting chemicals (EDCs) impair growth and development. While EDCs can occur naturally in aquatic ecosystems, they are continuously introduced through anthropogenic activities such as industrial effluents, pharmaceutical production, wastewater, and mining. To elucidate the chronic toxicological effects of endocrine-disrupting chemicals (EDCs) on aquatic organisms, we collected experimental data from a standardized chronic exposure test using Daphnia magna (D. magna), individuals of which were exposed to a potential EDC, trinitrotoluene (TNT). The chronic toxicity effects of this compound were explored through differential gene expression, gene ontology, network construction, and putative adverse outcome pathway (AOP) proposition. Our findings suggest that TNT has detrimental effects on the upstream signaling of Tcf/Lef, potentially adversely impacting oocyte maturation and early development. This study employs diverse bioinformatics approaches to elucidate the gene-level toxicological effects of chronic TNT exposure on aquatic ecosystems. The results provide valuable insights into the molecular mechanisms of the adverse impacts of TNT through network construction and putative AOP proposition.

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish.

Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-mediated adverse outcomes on female zebrafish fertility. This model started with chemical fingerprints as the input layer and was implemented through a five-layer virtual AR-induced adverse outcome pathway (AOP). Results indicated that the AR-DNN effectively and accurately screens new reproductive toxicants (AUC = 0.94, accuracy = 0.85), providing potential toxicity pathways. Furthermore, 1477 and 2448 chemicals that could lead to infertility were identified in the plastic additives list (PLASTICMAP, n = 7112) and the Inventory of Existing Chemical Substances in China (IECSC, n = 17741), respectively. Colourants containing steroid-like structures are the major active plastic additives that might lower female zebrafish fertility through AR binding, DNA binding, and transcriptional activation. While active IECSC chemicals primarily have the same fragments, such as benzonitrile, nitrobenzene, and quinolone. The predicted toxicity pathways were consistent with existing fish evidence, demonstrating the model's applicability. This knowledge-based approach offers a promising computational toxicology strategy for predicting and characterising the endocrine-disrupting effects and toxic mechanisms of organic chemicals, potentially leading to more efficient and cost-effective screening of EDCs.

Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives.

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/ . Finally, we showed the utility of the constructed plastic additives-AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives-AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

A systematic review and BMD modeling approach to develop an AOP for humidifier disinfectant-induced pulmonary fibrosis and cell death.

Dosimetry modeling and point of departure (POD) estimation using in vitro data are essential for mechanism-based hazard identification and risk assessment. This study aimed to develop a putative adverse outcome pathway (AOP) for humidifier disinfectant (HD) substances used in South Korea through a systematic review and benchmark dose (BMD) modeling. We collected in vitro toxicological studies on HD substances, including polyhexamethylene guanidine hydrochloride (PHMG-HCl), PHMG phosphate (PHMG-p), a mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT), CMIT, and MIT from scientific databases. A total of 193 sets of dose-response data were extracted from 34 articles reporting in vitro experimental results of HD toxicity. The risk of bias (RoB) in each study was assessed following the office of health assessment and translation (OHAT) guideline. The BMD of each HD substance at different toxicity endpoints was estimated using the US Environmental Protection Agency (EPA) BMD software (BMDS). Interspecies- or interorgan differences or most critical effects in the toxicity of the HD substances were analyzed using a 95% lower confidence limit of the BMD (BMDL). We found a critical molecular event and cells susceptible to each HD substance and constructed an AOP of PHMG-p- or CMIT/MIT-induced damage. Notably, PHMG-p induced ATP depletion at the lowest in vitro concentration, endoplasmic reticulum (ER) stress, epithelial-to-mesenchymal transition (EMT), inflammation, leading to fibrosis. CMIT/MIT enhanced mitochondrial reactive oxygen species (ROS) production, oxidative stress, mitochondrial dysfunction, resulting in cell death. Our approach will increase the current understanding of the effects of HD substances on human health and contribute to evidence-based risk assessment of these compounds.

Chemical respiratory sensitization-Current status of mechanistic understanding, knowledge gaps and possible identification methods of sensitizers.

Respiratory sensitization is a complex immunological process eventually leading to hypersensitivity following re-exposure to the chemical. A frequent consequence is occupational asthma, which may occur after long latency periods. Although chemical-induced respiratory hypersensitivity has been known for decades, there are currently no comprehensive and validated approaches available for the prospective identification of chemicals that induce respiratory sensitization, while the expectations of new approach methodologies (NAMs) are high. A great hope is that due to a better understanding of the molecular key events, new methods can be developed now. However, this is a big challenge due to the different chemical classes to which respiratory sensitizers belong, as well as because of the complexity of the response and the late manifestation of symptoms. In this review article, the current information on respiratory sensitization related processes is summarized by introducing it in the available adverse outcome pathway (AOP) concept. Potentially useful models for prediction are discussed. Knowledge gaps and gaps of regulatory concern are identified.

Corrigendum: Applying new approach methodologies to assess next-generation tobacco and nicotine products.

[This corrects the article DOI: 10.3389/ftox.2024.1376118.].

Applying new approach methodologies to assess next-generation tobacco and nicotine products.

In vitro toxicology research has accelerated with the use of in silico, computational approaches and human in vitro tissue systems, facilitating major improvements evaluating the safety and health risks of novel consumer products. Innovation in molecular and cellular biology has shifted testing paradigms, with less reliance on low-throughput animal data and greater use of medium- and high-throughput in vitro cellular screening approaches. These new approach methodologies (NAMs) are being implemented in other industry sectors for chemical testing, screening candidate drugs and prototype consumer products, driven by the need for reliable, human-relevant approaches. Routine toxicological methods are largely unchanged since development over 50 years ago, using high-doses and often employing in vivo testing. Several disadvantages are encountered conducting or extrapolating data from animal studies due to differences in metabolism or exposure. The last decade saw considerable advancement in the development of in vitro tools and capabilities, and the challenges of the next decade will be integrating these platforms into applied product testing and acceptance by regulatory bodies. Governmental and validation agencies have launched and applied frameworks and "roadmaps" to support agile validation and acceptance of NAMs. Next-generation tobacco and nicotine products (NGPs) have the potential to offer reduced risks to smokers compared to cigarettes. These include heated tobacco products (HTPs) that heat but do not burn tobacco; vapor products also termed electronic nicotine delivery systems (ENDS), that heat an e-liquid to produce an inhalable aerosol; oral smokeless tobacco products (e.g., Swedish-style snus) and tobacco-free oral nicotine pouches. With the increased availability of NGPs and the requirement of scientific studies to support regulatory approval, NAMs approaches can supplement the assessment of NGPs. This review explores how NAMs can be applied to assess NGPs, highlighting key considerations, including the use of appropriate in vitro model systems, deploying screening approaches for hazard identification, and the importance of test article characterization. The importance and opportunity for fit-for-purpose testing and method standardization are discussed, highlighting the value of industry and cross-industry collaborations. Supporting the development of methods that are accepted by regulatory bodies could lead to the implementation of NAMs for tobacco and nicotine NGP testing.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics.

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1 µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity.

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

Tris(2-chloroethyl) phosphate (TCEP) exposure inhibits the epithelial-mesenchymal transition (EMT), mesoderm differentiation, and cardiovascular development in early chicken embryos.

Tris(2-chloroethyl) phosphate (TCEP) is an organophosphorus flame retardant used worldwide and has been detected in the tissues and eggs of wild birds. Our previous study reported that exposure to TCEP induced developmental delay and cardiovascular dysfunction with attenuated heart rate and vasculogenesis in early chicken embryos. This study aimed to investigate the molecular mechanisms underlying the cardiovascular effects of TCEP on chicken embryos using cardiac transcriptome analysis and to examine whether TCEP exposure affects epithelial-mesenchymal transition (EMT) and mesoderm differentiation during gastrulation. Transcriptome analysis revealed that TCEP exposure decreased the expression of cardiac conduction-related genes and transcription factors on day 5 of incubation. In extraembryonic blood vessels, the expression levels of genes related to fibroblast growth factor (FGF) and vascular endothelial growth factor (VEGF) were significantly reduced by TCEP exposure and vasculogenesis was suppressed. TCEP exposure also attenuated Snail family transcriptional repressor 2 (SNAI2) and T-box transcription factor T (TBXT) signaling in the chicken primitive streak, indicating that TCEP inhibits EMT and mesoderm differentiation during gastrulation at the early developmental stage. These effects on EMT and mesoderm differentiation may be related to subsequent phenotypic defects, including suppression of heart development and blood vessel formation.

Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks.

Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint. We then increased the complexity of encoders, incorporating graph-based methods, auto-encoders, and character embeddings employed in neural language processing. We also developed a multimodal neural network architecture, capable of considering the complementary nature of different chemical representations simultaneously. The potential of this approach, compared to more conventional architectures designed to handle a single encoder, becomes apparent when the amount of data increases.

Zinc oxide nanoparticles disrupt development and function of the olfactory sensory system impairing olfaction-mediated behaviour in zebrafish.

Zinc (Zn) is an essential metal present in numerous enzymes throughout the body, playing a vital role in animal and human health. However, the increasing use of zinc oxide nanomaterials (ZnONPs) in a diverse range of products has raised concerns regarding their potential impacts on health and the environment. Despite these concerns, the toxicity of ZnONP exposure on animal health remain poorly understood. To help address this knowledge gap, we have developed a highly sensitive oxidative stress (OS) biosensor zebrafish capable of detecting cell/tissue-specific OS responses to low doses of various oxidative stressors, including Zn, in a live fish embryo. Using live-imaging analysis with this biosensor zebrafish embryo, we discovered that the olfactory sensory neurons in the brain are especially sensitive to ZnOP exposure. Furthermore, through studies monitoring neutrophil migration and neuronal activation in the embryonic brain and via behaviour analysis, we have found that sub-lethal doses of ZnONPs (ranging from 0.033 to 1 mg/L nominal concentrations), which had no visible effect on embryo growth or morphology, cause significant localised inflammation, disrupting the neurophysiology of olfactory brain tissues and ultimately impaired olfaction-mediated behaviour. Collectively, these findings establish a potent and important effect mechanism for ZnONP toxicity, indicating the olfactory sensory system as the primary target for ZnONPs as an environmental toxicant in aquatic environments. Our result also highlights that even low doses of ZnONPs can have detrimental effects on the olfactory sensory system, surpassing previous expectations. The importance of olfaction in environment sensing, sex behaviours and overall fitness across species raises concerns about the potential impact of ZnONPs on olfaction-mediated brain function and behaviour in animals and humans. Our study emphasises the need for greater consideration of the potential risks associated with these nanomaterials.

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis.

Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.

Identification of an adverse outcome pathway (AOP) for chemical-induced craniofacial anomalies using the transgenic zebrafish model.

Craniofacial anomalies are one of the most frequent birth defects worldwide and are often caused by genetic and environmental factors such as pharmaceuticals and chemical agents. Although identifying adverse outcome pathways (AOPs) is a central issue for evaluating the teratogenicity, the AOP causing craniofacial anomalies has not been identified. Recently, zebrafish has gained interest as an emerging model for predicting teratogenicity because of high throughput, cost-effectiveness and availability of various tools for examining teratogenic mechanisms. Here, we established zebrafish sox10-EGFP reporter lines to visualize cranial neural crest cells (CNCCs) and have identified the AOPs for craniofacial anomalies. When we exposed the transgenic embryos to teratogens that were reported to cause craniofacial anomalies in mammals, CNCC migration and subsequent morphogenesis of the first pharyngeal arch were impaired at 24 hours post-fertilization. We also found that cell proliferation and apoptosis of the migratory CNCCs were disturbed, which would be key events of the AOP. From these results, we propose that our sox10-EGFP reporter lines serve as a valuable model for detecting craniofacial skeletal abnormalities, from early to late developmental stages. Given that the developmental process of CNCCs around this stage is highly conserved between zebrafish and mammals, our findings can be extrapolated to mammalian craniofacial development and thus help in predicting craniofacial anomalies in human.

Towards a science-based testing strategy to identify maternal thyroid hormone imbalance and neurodevelopmental effects in the progeny - Part IV: the ECETOC and CLE Proposal for a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS).

Following the European Commission Endocrine Disruptor Criteria, substances shall be considered as having endocrine disrupting properties if they (a) elicit adverse effects, (b) have endocrine activity, and (c) the two are linked by an endocrine mode-of-action (MoA) unless the MoA is not relevant for humans. A comprehensive, structured approach to assess whether substances meet the Endocrine Disruptor Criteria for the thyroid modality (EDC-T) is currently unavailable. Here, the European Centre for Ecotoxicology and Toxicology of Chemicals Thyroxine Task Force and CropLife Europe propose a Thyroid Function-Related Neurodevelopmental Toxicity Testing and Assessment Scheme (Thyroid-NDT-TAS). In Tier 0, before entering the Thyroid-NDT-TAS, all available in vivo, in vitro and in silico data are submitted to weight-of-evidence (WoE) evaluations to determine whether the substance of interest poses a concern for thyroid disruption. If so, Tier 1 of the Thyroid-NDT-TAS includes an initial MoA and human relevance assessment (structured by the key events of possibly relevant adverse outcome pathways) and the generation of supportive in vitro/in silico data, if relevant. Only if Tier 1 is inconclusive, Tier 2 involves higher-tier testing to generate further thyroid- and/or neurodevelopment-related data. Tier 3 includes the final MoA and human relevance assessment and an overarching WoE evaluation to draw a conclusion on whether, or not, the substance meets the EDC-T. The Thyroid-NDT-TAS is based on the state-of-the-science, and it has been developed to minimise animal testing. To make human safety assessments more accurate, it is recommended to apply the Thyroid-NDT-TAS during future regulatory assessments.

Transcriptomics-based analysis of sex-differentiated mechanisms of hepatotoxicity in zebrafish after long-term exposure to bisphenol AF.

Bisphenol AF (BPAF) is an emerging endocrine-disrupting chemical (EDC) prevalent in the environment as one of the main substitutes for bisphenol A. Sex-specific effects of EDCs have been commonly reported and closely linked to sexually dimorphic patterns of hormone metabolism and related gene expression during different exposure windows, but our understanding of these mechanisms is still limited. Here, following 28-day exposure of adult zebrafish to an environmentally relevant concentration of BPAF at 10 µg/L, the global transcriptional networks applying RNA sequencing (RNA-seq) and Ingenuity Pathway Analysis (IPA) were respectively investigated in the male and female fish liver, connecting the sex-dependent toxicity of the long-term exposure of BPAF to molecular responses. As a result, more differentially expressed genes (DEGs) were detected in males (811) than in females (195), and spermatogenesis was the most enriched Gene Ontology (GO) functional classification in males, while circadian regulation of gene expression was the most enriched GO term in females. The expression levels of selected DEGs were routinely verified using qRT-PCR, which showed consistent alterations with the transcriptional changes in RNA-seq data. The causal network analysis by IPA suggested that the adverse outcomes of BPAF in males including liver damage, apoptosis, inflammation of organ, and liver carcinoma, associated with the regulation of several key DEGs detected in RNA-seq, could be linked to the activation of upstream regulatory molecules ifnα, yap1, and ptger2; while, the inhibition of upstream regulators hif1α, ifng, and igf1, leading to the down-regulated expression of several key DEGs, might be involved in BPAF's effects in females. Furthermore, BPAF exposure altered hepatic histological structure and inhibited antioxidant capability in both male and female livers. Overall, this study revealed different regulation networks involved in the sex-dependent effects of BPAF on the fish liver, and these detected DEGs upon BPAF exposure might be used as potential biomarkers for further assessing sex-specific hepatotoxicity following environmental EDC exposure.

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review.

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment.

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation.

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.