Quantitative Adverse Outcome Pathway Research Articles

A Quantitative Adverse Outcome Pathway for Embryonic Activation of the Aryl Hydrocarbon Receptor of Fishes by Polycyclic Aromatic Hydrocarbons Leading to Decreased Fecundity at Adulthood.

Quantitative adverse outcome pathways (qAOPs) describe the response-response relationships that link the magnitude and/or duration of chemical interaction with a specific molecular target to the probability and/or severity of the resulting apical-level toxicity of regulatory relevance. The present study developed the first qAOP for latent toxicities showing that early life exposure adversely affects health at adulthood. Specifically, a qAOP for embryonic activation of the aryl hydrocarbon receptor 2 (AHR2) of fishes by polycyclic aromatic hydrocarbons (PAHs) leading to decreased fecundity of females at adulthood was developed by building on existing qAOPs for (1) activation of the AHR leading to early life mortality in birds and fishes, and (2) inhibition of cytochrome P450 aromatase activity leading to decreased fecundity in fishes. Using zebrafish (Danio rerio) as a model species and benzo[a]pyrene as a model PAH, three linked quantitative relationships were developed: (1) plasma estrogen in adult females as a function of embryonic exposure, (2) plasma vitellogenin in adult females as a function of plasma estrogen, and (3) fecundity of adult females as a function of plasma vitellogenin. A fourth quantitative relationship was developed for early life mortality as a function of sensitivity to activation of the AHR2 in a standardized in vitro AHR transactivation assay to integrate toxic equivalence calculations that would allow prediction of effects of exposure to untested PAHs. The accuracy of the predictions from the resulting qAOP were evaluated using experimental data from zebrafish exposed as embryos to another PAH, benzo[k]fluoranthene. The qAOP developed in the present study demonstrates the potential of the AOP framework in enabling consideration of latent toxicities in quantitative ecological risk assessments and regulatory decision-making. Environ Toxicol Chem 2024;00:1-12. © 2024 SETAC.

Exploring a mechanism-based approach for the identification of endocrine disruptors using Adverse Outcome Pathways (AOPs) and New Approach Methodologies (NAMs) : 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.

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.

Assessing the Toxicity of Benzotriazole Ultraviolet Stabilizers to Fishes: Insights into Aryl Hydrocarbon Receptor-Mediated Effects.

Benzotriazole ultraviolet stabilizers (BUVSs) are chemicals used to mitigate UV-induced damage to manufactured goods. Their presence in aquatic environments and biota raises concerns, as certain BUVSs activate the aryl hydrocarbon receptor (AhR), which is linked to adverse effects in fish. However, potencies of BUVSs as AhR agonists and species sensitivities to AhR activation are poorly understood. This study evaluated the toxicity of three BUVSs using embryotoxicity assays. Zebrafish (Danio rerio) embryos exposed to BUVSs by microinjection suffered dose-dependent increases in mortality, with LD50 values of 4772, 11 608, and 56 292 ng/g-egg for UV-P, UV-9, and UV-090, respectively. The potencies and species sensitivities to AhR2 activation by BUVSs were assessed using a luciferase reporter gene assay with COS-7 cells transfected with the AhR2 of zebrafish and eight other fishes. The rank order of potency for activation of the AhR2 from all nine species was UV-P > UV-9 > UV-090. However, AhR2s among species differed in sensitivities to activation by up to 100-fold. An approximate reversed rank order of species sensitivity was observed compared to the rank order of sensitivity to 2,3,7,8-tetrachlorodibenzo[p]dioxin, the prototypical AhR agonist. Despite this, a pre-existing quantitative adverse outcome pathway linking AhR activation to embryo lethality could predict embryotoxicities of BUVSs in zebrafish.

P01-05: Oxidative stress-based Systems Biology (SB) Model for building Quantitative Adverse Outcome Pathways (qAOPs)

P01-05: Oxidative stress-based Systems Biology (SB) Model for building Quantitative Adverse Outcome Pathways (qAOPs)

P01-01: A quantitative adverse outcome pathway for predicting medium-term lung function evolution after switching from combustible cigarettes to heated tobacco products

P01-01: A quantitative adverse outcome pathway for predicting medium-term lung function evolution after switching from combustible cigarettes to heated tobacco products

Potential of in vivo stress reporter models to reduce animal use and provide mechanistic insights in toxicity studies

Chemical risk assessment ensures protection from the toxic effects of drugs and manmade chemicals. To comply with regulatory guidance, studies in complex organisms are required, as well as mechanistic studies to establish the relevance of any toxicities observed to man. Although in vitro toxicity models are improving, in vivo studies remain central to this process. Such studies are invariably time-consuming and often involve large numbers of animals. New regulatory frameworks recommend the implementation of “smart” in vivo approaches to toxicity testing that can effectively assess safety for humans and comply with societal expectations for reduction in animal use. A major obstacle in reducing the animals required is the time-consuming and complexity of the pathological endpoints used as markers of toxicity. Such endpoints are prone to inter-animal variability, subjectivity and require harmonisation between testing sites. As a consequence, large numbers of animals per experimental group are required. To address this issue, we propose the implementation of sophisticated stress response reporter mice that we have developed. These reporter models provide early biomarkers of toxic potential in a highly reproducible manner at single-cell resolution, which can also be measured non-invasively and have been extensively validated in academic research as early biomarkers of stress responses for a wide range of chemicals at human-relevant exposures. In this report, we describe a new and previously generated models in our lab, provide the methodology required for their use and discuss how they have been used to inform on toxic risk (likelihood of chemical causing an adverse health effect). We propose our in vivo approach is more informative (refinement) and reduces the animal use (reduction) compared to traditional toxicity testing. These models could be incorporated into tiered toxicity testing and used in combination with in vitro assays to generate quantitative adverse outcome pathways and inform on toxic potential.

Salmonid Pituitary Cells as a Test System for Identifying Endocrine-Disrupting Compounds.

The pituitary gland is a central regulator of reproduction, producing two gonadotropins, follicle-stimulating hormone (Fsh) and luteinizing hormone (Lh), which regulate gonadal development, sex steroid synthesis, and gamete maturation. The present study sought to optimize an in vitro test system using pituitary cells isolated from previtellogenic female coho salmon and rainbow trout, focusing on fshb and lhb subunit gene expression. Initially, we optimized culture conditions for duration and benefits of culturing with and without addition of endogenous sex steroids (17β-estradiol [E2] or 11-ketotestosterone) or gonadotropin-releasing hormone (GnRH). The results suggest that culturing with and without E2 was valuable because it could mimic the (+) feedback effects on Lh that are observed from in vivo studies. After optimizing assay conditions, a suite of 12 contaminants and other hormones was evaluated for their effects on fshb and lhb gene expression. Each chemical was tested at four to five different concentrations up to solubility limitations in cell culture media. The results indicate that more chemicals alter lhb synthesis than fshb. The more potent chemicals were estrogens (E2 and 17α-ethynylestradiol) and the aromatizable androgen testosterone, which induced lhb. The estrogen antagonists 4-OH-tamoxifen and prochloraz decreased the E2-stimulated expression of lhb. Among several selective serotonin reuptake inhibitors tested, the sertraline metabolite norsertraline was notable for both increasing fshb synthesis and decreasing the E2 stimulation of lhb. These results indicate that diverse types of chemicals can alter gonadotropin production in fish. Furthermore, we have shown that pituitary cell culture is useful for screening chemicals with potential endocrine-disrupting activity and can support the development of quantitative adverse outcome pathways in fish. Environ Toxicol Chem 2023;42:1730-1742. © 2023 SETAC.

Reducing uncertainties in quantitative adverse outcome pathways by analysis of thyroid hormone in the neonatal rat brain.

A number of xenobiotics interfere with thyroid hormone (TH) signaling. Although adequate supplies of TH are necessary for normal brain development, regulatory reliance on serum TH as proxies for brain TH insufficiency is fraught with significant uncertainties. A more direct causal linkage to neurodevelopmental toxicity induced by TH-system disrupting chemicals is to measure TH in the target organ of most concern, the brain. However, the phospholipid-rich matrix of brain tissue presents challenges for TH extraction and measurement. We report optimized analytical procedures to extract TH in brain tissue of rats with recoveries >80% and low detection limits for T3, rT3, and T4 (0.013, 0.033, and 0.028 ng/g, respectively). Recovery of TH is augmented by enhancing phospholipid separation from TH using an anion exchange column coupled with a stringent column wash. Quality control measures incorporating a matrix-matched calibration procedure revealed excellent recovery and consistency across a large number of samples. Application of optimized procedures revealed age-dependent increases in neonatal brain T4, T3, and rT3 on the day of birth (postnatal day, PN0), PN2, PN6, and PN14. No sex-dependent differences in brain TH were observed at these ages, and similar TH levels were evident in perfused versus non-perfused brains. Implementation of a robust and reliable method to quantify TH in the fetal and neonatal rat brain will aid in the characterization of the thyroid-dependent chemical interference on neurodevelopment. A brain- in addition to a serum-based metric will reduce uncertainties in assessment of hazard and risk on the developing brain posed by thyroid system-disrupting chemicals.

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.

Establishing relationships between particle-induced in vitro and in vivo inflammation endpoints to better extrapolate between in vitro markers and in vivo fibrosis

BackgroundToxicity assessment for regulatory purposes is starting to move away from traditional in vivo methods and towards new approach methodologies (NAM) such as high-throughput in vitro models and computational tools. For materials with limited hazard information, utilising quantitative Adverse Outcome Pathways (AOPs) in a testing strategy involving NAM can produce information relevant for risk assessment. The aim of this work was to determine the feasibility of linking in vitro endpoints to in vivo events, and moreover to key events associated with the onset of a chosen adverse outcome to aid in the development of NAM testing strategies. To do this, we focussed on the adverse outcome pathway (AOP) relating to the onset of pulmonary fibrosis.ResultsWe extracted in vivo and in vitro dose–response information for particles known to induce this pulmonary fibrosis (crystalline silica, specifically α-quartz). To test the in vivo–in vitro extrapolation (IVIVE) determined for crystalline silica, cerium dioxide nanoparticles (nano-CeO2) were used as a case study allowing us to evaluate our findings with a less studied substance. The IVIVE methodology outlined in this paper is formed of five steps, which can be more generally summarised into two categories (i) aligning the in vivo and in vitro dosimetry, (ii) comparing the dose–response curves and derivation of conversion factors.ConclusionOur analysis shows promising results with regards to correlation of in vitro cytokine secretion to in vivo acute pulmonary inflammation assessed by polymorphonuclear leukocyte influx, most notable is the potential of using IL-6 and IL-1β cytokine secretion from simple in vitro submerged models as a screening tool to assess the likelihood of lung inflammation at an early stage in product development, hence allowing a more targeted investigation using either a smaller, more targeted in vivo study or in the future a more complex in vitro protocol. This paper also highlights the strengths and limitations as well as the current difficulties in performing IVIVE assessment and suggestions for overcoming these issues.

Potential of in vivo stress reporter models to reduce animal use and provide mechanistic insights in toxicity studies.

Chemical risk assessment ensures protection from the toxic effects of drugs and manmade chemicals. To comply with regulatory guidance, studies in complex organisms are required, as well as mechanistic studies to establish the relevance of any toxicities observed to man. Although in vitro toxicity models are improving, in vivo studies remain central to this process. Such studies are invariably time-consuming and often involve large numbers of animals. New regulatory frameworks recommend the implementation of "smart" in vivo approaches to toxicity testing that can effectively assess safety for humans and comply with societal expectations for reduction in animal use. A major obstacle in reducing the animals required is the time-consuming and complexity of the pathological endpoints used as markers of toxicity. Such endpoints are prone to inter-animal variability, subjectivity and require harmonisation between testing sites. As a consequence, large numbers of animals per experimental group are required. To address this issue, we propose the implementation of sophisticated stress response reporter mice that we have developed. These reporter models provide early biomarkers of toxic potential in a highly reproducible manner at single-cell resolution, which can also be measured non-invasively and have been extensively validated in academic research as early biomarkers of stress responses for a wide range of chemicals at human-relevant exposures. In this report, we describe a new and previously generated models in our lab, provide the methodology required for their use and discuss how they have been used to inform on toxic risk (likelihood of chemical causing an adverse health effect). We propose our in vivo approach is more informative (refinement) and reduces the animal use (reduction) compared to traditional toxicity testing. These models could be incorporated into tiered toxicity testing and used in combination with in vitro assays to generate quantitative adverse outcome pathways and inform on toxic potential.

Elucidating the Differences in Metal Toxicity by Quantitative Adverse Outcome Pathways.

Numerous studies have reported that the toxicity differences among metals are widespread; however, little is known about the mechanism of differences in metal toxicity to aquatic organisms due to the lack of quantitative understanding of their adverse outcome pathway. Here, we investigated the effects of Cd and Cu on bioaccumulation, gene expression, physiological responses, and apical effects in zebrafish larvae. RNA sequencing was conducted to provide supplementary mechanistic information for the effects of Cd and Cu exposure. On this basis, we proposed a quantitative adverse outcome pathway (qAOP) suitable for metal risk assessment of aquatic organisms. Our work provides a mechanistic explanation for the differences in metal toxicity where the strong bioaccumulation of Cu enables the newly accumulated Cu to reach the threshold that causes different adverse effects faster than Cd in zebrafish larvae, resulting in a higher toxicity of Cu than that of Cd. Furthermore, we proposed a parameter CIT/BCF (the ratio of internal threshold concentration and bioaccumulation factor) that helps to understand the toxicity differences by combining the information of bioaccumulation and internal threshold of adverse effects. This work demonstrated that qAOP is an effective quantitative tool for understanding the toxicity mechanism and highlight the importance of toxicokinetics and toxicodynamics at different biological levels in determining the metal toxicity.

Toxicokinetic Modeling of Per- and Polyfluoroalkyl Substance Concentrations within Developing Zebrafish (Danio rerio) Populations.

Per- and polyfluoroalkyl substances (PFAS) are pervasive environmental contaminants, and their relative stability and high bioaccumulation potential create a challenging risk assessment problem. Zebrafish (Danio rerio) data, in principle, can be synthesized within a quantitative adverse outcome pathway (qAOP) framework to link molecular activity with individual or population level hazards. However, even as qAOP models are still in their infancy, there is a need to link internal dose and toxicity endpoints in a more rigorous way to further not only qAOP models but adverse outcome pathway frameworks in general. We address this problem by suggesting refinements to the current state of toxicokinetic modeling for the early development zebrafish exposed to PFAS up to 120 h post-fertilization. Our approach describes two key physiological transformation phenomena of the developing zebrafish: dynamic volume of an individual and dynamic hatching of a population. We then explore two different modeling strategies to describe the mass transfer, with one strategy relying on classical kinetic rates and the other incorporating mechanisms of membrane transport and adsorption/binding potential. Moving forward, we discuss the challenges of extending this model in both timeframe and chemical class, in conjunction with providing a conceptual framework for its integration with ongoing qAOP modeling efforts.

SOC-V-09 Development of quantitative adverse outcome pathways to address the effects of PFAS on cholesterol metabolism. Benchmarking with human epidemiological data and comparison with threshold values

SOC-V-09 Development of quantitative adverse outcome pathways to address the effects of PFAS on cholesterol metabolism. Benchmarking with human epidemiological data and comparison with threshold values

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research

Purpose Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, its application in the analysis of transcriptional data for quantitative adverse outcome pathway (AOP) development is being explored. As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, the number of replicates and dose range on BMD outputs for radiation-induced genes and pathways. Materials and methods Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to 137Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose range over multiple dose points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs. Results Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of the number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset. Conclusion Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

Quantitative adverse outcome pathway (qAOP) using bayesian network model on comparative toxicity of multi-walled carbon nanotubes (MWCNTs): safe-by-design approach

While the various physicochemical properties of engineered nanomaterials influence their toxicities, their understanding is still incomplete. A predictive framework is required to develop safe nanomaterials, and a Bayesian network (BN) model based on adverse outcome pathway (AOP) can be utilized for this purpose. In this study, to explore the applicability of the AOP-based BN model in the development of safe nanomaterials, a comparative study was conducted on the change in the probability of toxicity pathways in response to changes in the dimensions and surface functionalization of multi-walled carbon nanotubes (MWCNTs). Based on the results of our previous study, we developed an AOP leading to cell death, and the experimental results were collected in human liver cells (HepG2) and bronchial epithelium cells (Beas-2B). The BN model was trained on these data to identify probabilistic causal relationships between key events. The results indicated that dimensions were the main influencing factor for lung cells, whereas -OH or -COOH surface functionalization and aspect ratio were the main influencing factors for liver cells. Endoplasmic reticulum stress was found to be a more sensitive pathway for dimensional changes, and oxidative stress was a more sensitive pathway for surface functionalization. Overall, our results suggest that the AOP-based BN model can be used to provide a scientific basis for the development of safe nanomaterials.

From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration.

Adverse outcome pathways (AOPs) include a sequence of events that connect a molecular-level initiating event with an adverse outcome at the cellular level for human health endpoints, or at the population level for ecological endpoints. When there is enough quantitative understanding of the relationships between key events in an AOP, a mathematical model may be developed to connect key events in a quantitative AOP (qAOP). Ideally, a qAOP will reduce the time and resources spent for chemical toxicity testing and risk assessment and enable the extrapolation of data collected at the molecular-level by in vitro assays, for example, to predict whether an adverse outcome may occur. Here, we review AOPs in the AOPWiki, an AOP repository, to determine best practices that would facilitate conversion from AOP to qAOP. Then, focusing on a particular case study, acetylcholinesterase inhibition leading to neurodegeneration, we describe specific methods and challenges. Examples of challenges include the availability and collection of quantitative data amenable to model development, the lack of studies that measure multiple key events, and model accessibility or transferability across platforms. We conclude with recommendations for improving key event and key event relationship descriptions in the AOPWiki that facilitate the transition of qualitative AOPs to qAOPs.

A quantitative AOP of mitochondrial toxicity based on data from three cell lines

Adverse Outcome Pathways (AOPs) are increasingly used to support the integration of in vitro data in hazard assessment for chemicals. Quantitative AOPs (qAOPs) use mathematical models to describe the relationship between key events (KEs). In this paper, data obtained in three cell lines, LHUMES, HepG2 and RPTEC/TERT1, using similar experimental protocols, was used to calibrate a qAOP of mitochondrial toxicity for two chemicals, rotenone and deguelin. The objectives were to determine whether the same qAOP could be used for the three cell types, and to test chemical-independence by cross-validation with a dataset obtained on eight other chemicals in LHUMES cells. Repeating the calibration approach for both chemicals in three cell lines highlighted various practical difficulties. Even when the same readouts of KEs are measured, the mathematical functions used to describe the key event relationships may not be the same. Cross-validation in LHUMES cells was attempted by estimating chemical-specific potency at the molecular initiating events and using the rest of the calibrated qAOP to predict downstream KEs: toxicity of azoxystrobin, carboxine, mepronil and thifluzamide was underestimated.Selection of most relevant readouts and accurate characterization of the molecular initiating event for cross-validation are critical when designing in vitro experiments targeted at calibrating qAOPs.

Comparison of piecewise structural equation modeling and Bayesian network for de novo construction of a quantitative adverse outcome pathway network.

Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to its predictive nature, alignment with quantitative risk assessment, and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and BN are suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches could identify the most influencing linear AOP in a complex network and evaluate the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The advantages and limitations of the two approaches for qAOPN construction are discussed in detail, and suggestions on optimal workflows of PSEM and BN are provided to guide future qAOPN development.