Adverse Outcome Pathway Framework Research Articles

Antioxidant Systematic Alteration Was Responsible for Injuries Inflicted on the Marine Blue Mussel Mytilus edulis Following Strontium Exposure

The ionic properties of strontium (Sr), a significant artificial radionuclide in the marine environment, were estimated using a stable nuclide-substituting experimental system under controlled laboratory conditions. The bio-accumulation of Sr and its impacts, as well as any possible hidden mechanisms, were evaluated based on the physiological alterations of the sentinel blue mussel Mytilus edulis. The mussels were exposed to a series of stress-inducing concentrations, with the highest solubility being 0.2 g/L. No acute lethality was observed during the experiment, but sublethal damage was evident. Sr accumulated in a tissue-specific way, and hemolymph was the target, with the highest accumulating concentration being 64.46 µg/g wet weight (ww). At the molecular level, increases in the levels of reactive oxygen species (ROS) and malondialdehyde (MDA) and changes in ROS components (H2O2, O2−, and -OH) and antioxidant system activity indicated that the redox equilibrium state in hemocytes was disturbed. Furthermore, the rise in the hemocyte micronucleus (MN) rate (4‰ in the high-concentration group) implied DNA damage. At the cellular level, the structures of hemocytes were damaged, especially with respect to lysosomes, which play a crucial role in phagocytosis. Lysosomal membrane stability (LMS) was also affected, and both acid phosphatase (ACP) and alkaline phosphatase (AKP) activities were reduced, resulting in a significant decline in phagocytosis. The hemolymph population structure at the organ level was disturbed, with large changes in hemocyte number and mortality rate, along with changes in component ratios. These toxic effects were evaluated by employing the adverse outcome pathway (AOP) framework. The results suggested that the disruption of intracellular redox homeostasis is a possible explanation for Sr-induced toxicity in M. edulis.

Exploring a mechanism-based approach for the identification of endocrine disruptors using Adverse Outcome Pathways and New Approach Methodologies: A perfluorooctane sulfonic acid case study

Endocrine disruptors (EDs) pose a serious threat to human health and the environment and require a comprehensive evaluation to be identified. The identification of EDs require a substantial amount of data, both in vitro and in vivo, due to the current scientific criteria in the EU. At the same time, the EU strives to reduce animal testing due to concerns regarding animal welfare and sensitivity of animal studies to adequately detect adverse effects relevant for human health. Perfluorooctane sulfonic acid (PFOS) is a persistent organic pollutant that is suspected to be an ED based on academic research, however it is not identified as such from a regulatory perspective. It has previously been shown that PFOS has the potential to cause neurotoxicity as well as affect the thyroid system, and it is known that specific thyroid hormone levels are critical in the development of the brain during. In this work, the aim was to evaluate a mechanism-based approach to identify ED properties of PFOS based on the Adverse Outcome Pathway (AOP) framework and using New Approach Methods (NAMs), by comparing this approach to an ED assessment based on the currently available guidance document. An AOP network (AOPN) was generated for the thyroid modality, and AOPs leading to developmental neurotoxicity (DNT) were identified. A literature search and screening process based on the AOPN, and systematic review methodology, was performed, followed by a rigorous Weight-of-Evidence (WoE) assessment. Evidence was mapped back onto the AOPN used for the literature search, to identify possible endocrine Modes-of-Action (MoAs) for PFOS and data gaps in the two assessments. It could be concluded that PFOS fulfils the criteria for ED classification in the standard ED assessment, but not in the mechanism-based assessment. The need for quantitative information, such as quantitative AOPs, for the mechanism-based approach is discussed. The possibility of a directly neurotoxic alternative MoA was also highlighted based on available in vitro data. Opportunities and challenges with implementing AOPs and NAMs into the regulatory assessment of EDs, and assessing hazard in the Next Generation Risk Assessment, is discussed. This case study exploring the mechanism-based approach to ED identification represents an important step toward more accurate and predictive assessment of EDs based on AOPs and NAMs, and to the Next Generation Risk Assessment (NGRA) concept.

Ketamine induces insomnia-like symptom of zebrafish at environmentally relevant concentrations by mediating GABAergic synapse.

Although the stimulative effects on the normal behaviors of fish posed by ketamine (KET) were well-studied, the adverse effects on the behavioral functions induced by KET at nighttime were unknown. Here, we used zebrafish larvae as a model exposed to KET (10, 50, 100, and 250 ng/L) at environmental levels for 21 days. The behavioral functions at nighttime, morphological changes during exposure stage, and alterations on the associated genes transcriptional levels of fish were determined. The difficultly initiating sleep was found in the fish exposed to KET, while the sleep duration of the animals was at the normal levels in exposure groups. The significant suppressions of the developmentally relevant genes, including bmp2, bmp4, and pth2ra were consistent with the developmental abnormalities of fish found in exposure groups. Moreover, the expression of γ-aminobutyric acid (GABA) receptor increased and melatonin (MTN) receptor decreased while the levels of GABA and MTN remained unchanged after exposure, by gene expression analysis and molecular docking. In addition, the transcriptional expression of apoptotic genes, including tp53, aifm1, and casp6, was significantly upregulated by KET. After a 7-day recovery, the insomnia-like behaviors (shorter sleep duration) were observed in zebrafish from the 250 ng/L-KET group. Accordingly, the adverse outcome pathway framework of KET was constructed by prognostic assessment of zebrafish larvae. This study suggested that the adverse outcomes of KET on the sleep health of organisms at environmentally relevant concentrations should be concerned.

Building an adverse outcome pathway network for estrogen-, androgen- and steroidogenesis-mediated reproductive toxicity.

Introduction: Adverse Outcome Pathways (AOPs) can support both testing and assessment of endocrine disruptors (EDs). There is, however, a need for further development of the AOP framework to improve its applicability in a regulatory context. Here we have inventoried the AOP-wiki to identify all existing AOPs related to mammalian reproductive toxicity arising from disruption to the estrogen, androgen, and steroidogenesis modalities. Core key events (KEs) shared between relevant AOPs were also identified to aid in further AOP network (AOPN) development. Methods: A systematic approach using two different methods was applied to screen and search the entire AOP-wiki library. An AOPN was visualized using Cytoscape. Manual refinement was performed to remove AOPS devoid of any KEs and/or KERs. Results: Fifty-eight AOPs relevant for mammalian reproductive toxicity were originally identified, with 42 AOPs included in the final AOPN. Several of the KEs and KE relationships (KERs) described similar events and were thus merged to optimize AOPN construction. Sixteen sub-networks related to effects on hormone levels or hormone activity, cancer outcomes, male and female reproductive systems, and overall effects on fertility and reproduction were identified within the AOPN. Twenty-six KEs and 11 KERs were identified as core blocks of knowledge in the AOPN, of which 19 core KEs are already included as parameters in current OECD and US EPA test guidelines. Discussion: The AOPN highlights knowledge gaps that can be targeted for further development of a more complete AOPN that can support the identification and assessment of EDs.

Single-cell and multi-omics analyses highlight cancer-associated fibroblasts-induced immune evasion and epithelial mesenchymal transition for smoking bladder cancer

Tobacco carcinogens are recognized as critical hazard factors for bladder tumorigenesis, affecting the prognosis of patients through aromatic amines components. However, the specific function of tobacco carcinogens and systematic assessment models in the prognosis of bladder cancer remains poorly elucidated. We retrieved bladder cancer specific tobacco carcinogens-related genes from Comparative Toxicogenomic Database, our Nanjing Bladder Cancer cohort and TCGA database. Gene×gene interaction method was utilized to establish a prognostic signature. Integrative assessment of immunogenomics, tumor microenvironments and single-cell RNA-seq were performed to illustrate the internal relations of key events from different levels. Finally, we comprehensively identified 33 essential tobacco carcinogens-related genes to construct a novel prognostic signature, and found that high-risk patients were characterized by significantly worse overall survival (HR=2.25; Plog-rank < 0.01). Single-cell RNA-seq and multi-omics analysis demonstrated that cancer-associated fibroblasts mediated the crosstalk between epithelial-mesenchymal transition progression and immune evasion. Moreover, adverse outcome pathway framework was established to facilitate our understanding to the tobacco carcinogens-triggered bladder tumorigenesis. Our study systematically provided immune microenvironmental alternations for smoking-induced adverse survival outcomes in bladder cancer. These findings facilitated the integrative multi-omics insights into risk assessment and toxic mechanisms of tobacco carcinogens.

Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure

Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults.

Ferroptosis participated in inhaled polystyrene nanoplastics-induced liver injury and fibrosis

The emerging contaminant nanoplastics (NPs) have received considerable attention. Due to their tiny size and unique colloidal properties, NPs could more easily enter the body and cross biological barriers with inhalation exposure. While NPs-induced hepatotoxicity has been reported, the hepatic impact of inhaled NPs was still unknown. To close this gap, a 40 nm polystyrene NPs (PS-NPs) inhalation exposure mice model was developed to explore the hepatotoxicity during acute (1 week), subacute (4 weeks), and subchronic period (12 weeks), with four exposure doses (0, 16, 40, and 100 μg/day). Results showed that inhaled PS-NPs caused a remarkable increase of ALT, AST, and ALP with a decrease of CHE, indicating liver dysfunction. Various histological abnormalities and significantly higher levels of inflammation in a dose- and time-dependent manner were observed. Moreover, after 4 weeks and 12 weeks of exposure, Masson staining and upregulated expression of TGF-β, α-SMA, and Col1a1 identified that inhaled PS-NPs exposure triggered the progression of liver fibrosis with the exposure time prolonged. From the mechanistic perspective, transcriptome analysis revealed that ferroptosis was involved in PS-NPs-induced liver hepatotoxicity, and key features of ferroptosis were detected, including persistent oxidative stress, iron overload, increased LPO, mitochondria damage, and the expression changes of GPX4, TFRC, and Ferritin. And in vitro and in vivo recovery tests showed that ferroptosis inhibitor Fer-1 treatment alleviated liver injury and fibrosis. The above results confirmed the critical role of ferroptosis in PS-NPs-induced hepatotoxicity. Furthermore, to better conclude our findings and understand the mechanistic causality within it, an adverse outcome pathway (AOP) framework was established. In total, this present study conducted the first experimental assessment of inhalation exposure to PS-NPs on the liver, identified that continuous inhaled PS-NPs could cause liver injury and fibrosis, and PS-NPs- ferroptosis provided a novel mechanistic explanation.

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.

Construction of an adverse outcome pathway framework based on integrated data to evaluate arsenic-induced non-alcoholic fatty liver disease

Arsenic is a recognized environmental pollutant naturally occurring in aquifers through geological processes. Toxicological studies have revealed that liver is the main target organ harmed by arsenic exposure. However, systematic studies of non-alcoholic fatty liver disease (NAFLD) are not comprehensive, and information regarding threats and risk assessment remains insufficient. This research aimed to examine the association between arsenic exposure and NAFLD and uncover the role of molecular initiating events and key events in disease development using the Adverse Outcome Pathway (AOP). Data from 8,104 adults in the National Health and Nutrition Examination Survey were used to explore the relationship between urinary arsenic and NAFLD. In a logistic regression model, urinary inorganic arsenic levels positively correlated with NAFLD (odds ratio = 1.12, 95 % confidence interval = 1.07–1.16). Subsequently, to gain a deeper understanding of arsenic-induced NAFLD, an AOP framework was constructed, revealing that arsenic exposure led to elevate levels of TNF-α, which regulated the NF-κB pathway and led to hepatic lipid deposition, causing NAFLD. This AOP was assessed as “high” according to the Organization for Economic Co-operation and Development users’ handbook, and in vitro and in vivo models validated the AOP framework. In summary, this study highlights the potential mechanisms of arsenic-induced NAFLD. We combined the AOP with classical toxicological approaches with a view of establishing, rapidly and accurately, the lowest level at which environmental arsenic exposure can have adverse effects on the body, thereby contributing to risk assessment strategies for arsenic exposure through iterative and animal modeling at the population level.

Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways – Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

Toxicity assessment of organophosphate flame retardant triphenyl phosphate (TPHP) on intestines in mice

Triphenyl phosphate (TPHP), one widely used organophosphate flame retardant, has attracted accumulating attention due to its high detection rate in human biological samples. Up to date, the effects of TPHP exposure on intestinal health remain unexplored. In this study, BALB/c mice were used as a model and exposed to TPHP at dose of 2, 10, or 50 mg/kg body weight for 28 days. We observed Crohn’s disease-like features in ileum and ulcerative colitis disease-like features in colon, such as shorter colon length, ileum/colon structure impairment, intestinal epithelial cell apoptosis, enrichment of proinflammatory cytokines and immune cells, and disruption of tight junction. Furthermore, we found that TPHP induced production of reactive oxygen species and apoptosis in intestinal epithelial Caco-2 cells, accompanied by disruption of tight junction between cells. To understand the molecular mechanism underlying TPHP-induced changes in intestines, we build the adverse outcome pathway (AOP) framework based on Comparative Toxicogenomics and GeneCards database. The AOP framework revealed that PI3K/AKT and FoxO signaling pathway might be associated with cellular apoptosis, an increase in ROS production, and increased inflammation response in mouse ileum and colon tissues challenged with TPHP. These results identified that TPHP induced IBD-like features and provided new perspectives for toxicity evaluation of TPHP.

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction.

Assessing chemical safety is essential to evaluate the potential risks of chemical exposure to human health and the environment. Traditional methods relying on animal testing are being replaced by 3R (reduction, refinement, and replacement) principle-based alternatives, mainly depending on in vitro test methods and the Adverse Outcome Pathway framework. However, these approaches often focus on the properties of the compound, missing the broader chemical-biological interaction perspective. Currently, the lack of comprehensive molecular characterization of the in vitro test system results in limited real-world representation and contextualization of the toxicological effect under study. Leveraging omics data strengthens the understanding of the responses of different biological systems, emphasizing holistic chemical-biological interactions when developing in vitro methods. Here, we discuss the relevance of meticulous test system characterization on two safety assessment relevant scenarios and how omics-based, data-driven approaches can improve the future generation of alternative methods.

An adverse outcome pathway for chemical-induced Parkinson’s disease: Calcium is key

Exposure to pesticides is associated with an increased risk of developing Parkinson’s disease (PD). Currently, rodent-based risk assessment studies cannot adequately capture neurodegenerative effects of pesticides due to a lack of human-relevant endpoints targeted at neurodegeneration. Thus, there is a need for improvement of the risk assessment guidelines. Specifically, a mechanistic assessment strategy, based on human physiology and (patho)biology is needed, which can be applied in next generation risk assessment. The Adverse Outcome Pathway (AOP) framework is particularly well-suited to provide the mechanistic basis for such a strategy. Here, we conducted a semi-systematic review in Embase and MEDLINE, focused on neurodegeneration and pesticides, to develop an AOP network for parkinsonian motor symptoms. Articles were labelled and included/excluded using the online platform Sysrev. Only primary articles, written in English, focused on effects of pesticides or PD model compounds in models for the brain were included. A total of 66 articles, out of the 1700 screened, was included. PD symptoms are caused by loss of function and ultimately death of dopaminergic neurons in the substantia nigra (SN). Our literature review highlights that a unique feature of these cells that increases their vulnerability is their reliance on continuous low-level influx of calcium. As such, excess intracellular calcium was identified as a central early Key Event (KE). This KE can lead to death of dopaminergic neurons of the SN, and eventually parkinsonian motor symptoms, via four distinct pathways: 1) activation of calpains, 2) endoplasmic reticulum stress, 3) impairment of protein degradation, and 4) oxidative damage. Several receptors have been identified that may serve as molecular initiating events (MIEs) to trigger one or more of these pathways. The proposed AOP network provides the biological basis that can be used to develop a mechanistic testing strategy that captures neurodegenerative effects of pesticides.

The relationship of organophosphate flame retardants with hyperuricemia and gout via the inflammatory response: An integrated approach

BackgroundEvidence regarding the relationships between organophosphate flame retardants (OPFRs) and hyperuricemia and gout as well as the underlying mechanisms remains scarce, but some evidence indicates that inflammation might play a key role. ObjectivesUsing an integrated approach, we aim to elucidate the associations of urinary metabolite OPFRs (m-OPFRs) with hyperuricemia and gout. MethodsCross-sectional analyses using data from the National Health and Nutrition Examination Survey were performed to reveal the associations. Adults with complete data on five m-OPFRs with high detection frequencies and outcomes were enrolled. We used multivariate logistic regression, restricted cubic spline (RCS), and Bayesian kernel machine regression (BKMR) methods to account for single, nonlinear, and joint effects. The mediating effect of the inflammatory response was also estimated. Moreover, adverse outcome pathways (AOPs) based on network analysis were further constructed to reveal the underlying mechanism. ResultsMultivariate logistic models revealed that bis(2-chloroethyl) phosphate (BCEP) significantly increased risk of hyperuricemia (OR [95 % CI]: 1.165 [1.047, 1.296]) in the fully adjusted model. Elevated levels of bis(1-chloro-2-propyl) phosphate were associated with gout (OR [95 % CI]: 1.293 [1.015, 1.647]). No nonlinear relationship was observed in RCS. There was a positive association between mixed m-OPFRs and hyperuricemia risk in BMKR, with bis(1,3-dichloro-2-propyl) phosphate and BCEP being the main contributors (PIP > 0.5). We found that the inflammatory response significantly mediated the association between BCEP and hyperuricemia (P < 0.05). Network topology analysis identified seven genes and six phenotypes related to OPFR exposure and hyperuricemia. The AOP framework suggested that the inflammatory response, especially the activation of the TNF pathway, played a core role in the above relationships. ConclusionOur results first revealed that individual and mixed OPFRs were associated with hyperuricemia, in which the inflammatory response plays an important role. Further longitudinal studies are warranted to consolidate or refute our main findings.

Per- and polyfluoroalkyl substances and cardiometabolic diseases: A review.

Per- and polyfluoroalkyl substances (PFAS) are a group of persistent and widespread environmental pollutants that represent a high concern for human health. They have been shown to be associated with several important physiological processes such as lipid metabolism and the immune system. Consequently, PFAS are suspected to play a role in cardiometabolic disease development. However, the evidence regarding associations between PFAS and overt cardiovascular disease and type 2 diabetes remains limited and inconsistent. To address this, we conducted a review of the epidemiological evidence. A deeper understanding of potential underlying molecular mechanisms may help to explain inconsistencies in epidemiological findings. Thus, to gain more mechanistic insight, we also summarized evidence from omics and laboratory studies into an adverse outcome pathway framework. Our observations indicate the potential for associations of PFAS with multiple molecular pathways that could have opposite associations with disease risk, which could be further modified by mixture composition, lifestyle factors or genetic polymorphisms. This identifies the need for exposome studies considering mixture effects, the use of multi-omics data to gain insight in relevant pathways and the integration of epidemiological and laboratory studies to enhance mechanistic understanding and causal inference. Improved comprehension is essential for environmental health risk assessments.

Sub-lethal toxicity of five disinfection by-products on microalgae determined by flow cytometry – Lines of evidence for adverse outcome pathways

Standardised tests are often used to determine the ecotoxicity of chemicals and focus mainly on one or a few generic endpoints (e.g. mortality, growth), but information on the sub-cellular processes leading to these effects remain usually partial or missing. Flow cytometry (FCM) can be a practical tool to study the physiological responses of individual cells (such as microalgae) exposed to a stress via the use of fluorochromes and their morphology and natural autofluorescence. This work aimed to assess the effects of five chlorine-based disinfection by-products (DBPs) taken individually on growth and sub-cellular endpoints of the green microalgae Raphidocelis subcapitata. These five DBPs, characteristic of a chlorinated effluent, are the following monochloroacetic acid (MCAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), bromochloroacetic acid (BCAA) and 1,1-dichloropropan-2-one (1,1-DCP). Results showed that 1,1-DCP had the strongest effect on growth inhibition (EC50 = 1.8 mg.L-1), followed by MCAA, TCAA, BCAA and DCAA (EC50 of 10.1, 15.7, 27.3 and 64.5 mg.L-1 respectively). Neutral lipid content, reactive oxygen species (ROS) formation, red autofluorescence, green autofluorescence, size and intracellular complexity were significantly affected by the exposure to the five DBPs. Only mitochondrial membrane potential did not show any variation. Important cellular damages (>10%) were observed for only two of the chemicals (BCAA and 1,1-DCP) and were probably due to ROS formation. The most sensitive and informative sub-lethal parameter studied was metabolic activity (esterase activity), for which three types of response were observed. Combining all this information, an adverse outcome pathways framework was proposed to explain the effect of the targeted chemicals on R. subcapitata. Based on these results, both FCM sub-cellular analysis and conventional endpoint of algal toxicity were found to be complementary approaches.

Deciphering the toxicity-effect relationship and action patterns of traditional Chinese medicines from a smart data perspective: a comprehensive review.

In Chinese medicine, the primary considerations revolve around toxicity and effect. The clinical goal is to achieve maximize effect while minimizing toxicity. Nevertheless, both clinical and experimental research has revealed a distinct relationship between these two patterns of action in toxic Traditional Chinese Medicines (TCM). These TCM often exhibit characteristic "double-sided" or "multi-faceted" features under varying pathological conditions, transitioning between effective and toxic roles. This complexity adds a layer of challenge to unraveling the ultimate objectives of Traditional Chinese medicine. To address this complexity, various hypotheses have been proposed to explain the toxicity and effect of Traditional Chinese Medicines. These hypotheses encompass the magic shrapnel theory for effect, the adverse outcome pathway framework, and the indirect toxic theory for toxicity. This review primarily focuses on high-, medium-, and low-toxicity Traditional Chinese Medicines as listed in Chinese Pharmacopoeia. It aims to elucidate the essential intrinsic mechanisms and elements contributing to their toxicity and effectiveness. The critical factors influencing the mechanisms of toxicity and effect are the optimal dosage and duration of TCM administration. However, unraveling the toxic-effect relationships in TCM presents a formidable challenge due to its multi-target and multi-pathway mechanisms of action. We propose the integration of multi-omics technology to comprehensively analyze the fundamental metabolites, mechanisms of action, and toxic effects of TCM. This comprehensive approach can provide valuable insights into the intricate relationship between the effect and toxicity of these TCM.

Construction, evaluation, and AOP framework-based application of the EpPRS as a genetic surrogate for assessing environmental pollutants

BackgroundEnvironmental pollutant measurement is essential for accurate health risk assessment. However, the detection of humans’ internal exposure to pollutants is cost-intensive and consumes time and energy. Polygenic risk scores (PRSs) have been widely applied in genetic studies of complex trait diseases. It is important to construct a genetically relevant environmental surrogate for pollutant exposure and to explore its utility for disease prediction and risk assessment. ObjectivesThis study enrolled 714 individuals with complete genomic data and exposomic data on 22 plasma-persistent organic pollutants (POPs). MethodsWe first conducted 22 POP genome-wide association studies (GWAS) and constructed the corresponding environmental pollutant-based PRS (EpPRS) by clumping and P value thresholding (C + T), lassosum, and PRS-CS methods. The best-fit EpPRS was chosen by its regression R2. An adverse outcome pathway (AOP) framework was developed to assess the effects of contaminants on candidate diseases. Furthermore, Mendelian randomization (MR) analysis was performed to explore the causal association between POPs and cancer risk. ResultsThe C + T method produced the best-performing EpPRSs for 7 PCBs and 4 PBDEs. EpPRSs replicated the correlations of environmental exposure measurements based on consistent patterns. The diagnostic performance of type 2 diabetes mellitus (T2DM) PRS was improved by the combined model of T2DM-EpPRS of PCB126/BDE153. Finally, the AKT1-mediated AOP framework illustrated that PCB126 and BDE153 may increase the risk of T2DM by decreasing AKT1 phosphorylation through the cGMP-PKG pathway and promoting abnormal glucose homeostasis. MR analysis showed that digestive system tumors, such as colorectal cancer and biliary tract cancer, are more sensitive to POP exposure. ConclusionsEpPRSs can serve as a proxy for assessing pollutant internal exposure. The application of the EpPRS to disease risk assessment can reveal the toxic pathway and mode of action linking exposure and disease in detail, providing a basis for the development of environmental pollutant control strategies.

P01-11: Hematotoxicity of lead, cadmium, mercury and arsenic low dosed mixtures relevant to environmental exposure: the Adverse Outcome Pathway framework

P01-11: Hematotoxicity of lead, cadmium, mercury and arsenic low dosed mixtures relevant to environmental exposure: the Adverse Outcome Pathway framework

Expanding adverse outcome pathways towards one health models for nanosafety.

The ever-growing production of nano-enabled products has generated the need for dedicated risk assessment strategies that ensure safety for humans and the environment. Transdisciplinary approaches are needed to support the development of new technologies while respecting environmental limits, as also highlighted by the EU Green Deal Chemicals Strategy for Sustainability and its safe and sustainable by design (SSbD) framework. The One Health concept offers a holistic multiscale approach for the assessment of nanosafety. However, toxicology is not yet capable of explaining the interaction between chemicals and biological systems at the multiscale level and in the context of the One Health framework. Furthermore, there is a disconnect between chemical safety assessment, epidemiology, and other fields of biology that, if unified, would enable the adoption of the One Health model. The development of mechanistic toxicology and the generation of omics data has provided important biological knowledge of the response of individual biological systems to nanomaterials (NMs). On the other hand, epigenetic data have the potential to inform on interspecies mechanisms of adaptation. These data types, however, need to be linked to concepts that support their intuitive interpretation. Adverse Outcome Pathways (AOPs) represent an evolving framework to anchor existing knowledge to chemical risk assessment. In this perspective, we discuss the possibility of integrating multi-level toxicogenomics data, including toxicoepigenetic insights, into the AOP framework. We anticipate that this new direction of toxicogenomics can support the development of One Health models applicable to groups of chemicals and to multiple species in the tree of life.