Dataset Information

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

ABSTRACT: Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

SUBMITTER: Burgoon LD

PROVIDER: S-EPMC7397752 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Burgoon Lyle D LD Angrish Michelle M Garcia-Reyero Natalia N Pollesch Nathan N Zupanic Anze A Perkins Edward E

Risk analysis : an official publication of the Society for Risk Analysis 20191113 3

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data ...[more]

PMID: 31721239

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Project description:The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Project description:BackgroundIn light of the vulnerability of the developing brain, mixture risk assessment (MRA) for the evaluation of developmental neurotoxicity (DNT) should be implemented, since infants and children are co-exposed to more than one chemical at a time. One possible approach to tackle MRA could be to cluster DNT chemicals in a mixture on the basis of their mode of action (MoA) into 'similar' and 'dissimilar', but still contributing to the same adverse outcome, and anchor DNT assays to common key events (CKEs) identified in DNT-specific adverse outcome pathways (AOPs). Moreover, the use of human in vitro models, such as induced pluripotent stem cell (hiPSC)-derived neuronal and glial cultures would enable mechanistic understanding of chemically-induced adverse effects, avoiding species extrapolation.MethodsHiPSC-derived neural progenitors differentiated into mixed cultures of neurons and astrocytes were used to assess the effects of acute (3?days) and repeated dose (14?days) treatments with single chemicals and in mixtures belonging to different classes (i.e., lead(II) chloride and methylmercury chloride (heavy metals), chlorpyrifos (pesticide), bisphenol A (organic compound and endocrine disrupter), valproic acid (drug), and PCB138 (persistent organic pollutant and endocrine disrupter), which are associated with cognitive deficits, including learning and memory impairment in children. Selected chemicals were grouped based on their mode of action (MoA) into 'similar' and 'dissimilar' MoA compounds and their effects on synaptogenesis, neurite outgrowth, and brain derived neurotrophic factor (BDNF) protein levels, identified as CKEs in currently available AOPs relevant to DNT, were evaluated by immunocytochemistry and high content imaging analysis.ResultsChemicals working through similar MoA (i.e., alterations of BDNF levels), at non-cytotoxic (IC20/100), very low toxic (IC5), or moderately toxic (IC20) concentrations, induce DNT effects in mixtures, as shown by increased number of neurons, impairment of neurite outgrowth and synaptogenesis (the most sensitive endpoint as confirmed by mathematical modelling) and increase of BDNF levels, to a certain extent reproducing autism-like cellular changes observed in the brain of autistic children.ConclusionsOur findings suggest that the use of human iPSC-derived mixed neuronal/glial cultures applied to a battery of assays anchored to key events of an AOP network represents a valuable approach to identify mixtures of chemicals with potential to cause learning and memory impairment in children.

Project description:Adverse outcome pathways (AOP) contain summarizing sequences of key events (KE) relating toxicant exposures to specific adverse outcomes (AO) observed in exposed populations. Quantitative AOPs (qAOP) provide frameworks to assess AO risks based on KE quantification and the modeling of their relationships. This work aimed to develop the quantitative component of AOP 411 <Oxidative stress leading to decreased lung function>. To examine the pertinence of the resulting qAOP-based risk assessment approach, we applied it to predict the medium-term lung function evolution after switching from combustible cigarettes (CC) to heated tobacco products (HTP). We used publicly available data for oxidative stress (in vitro), ciliary beating frequency (in vitro), mucociliary clearance (human), and lung function (human). Following the course of smoking-related lung function decline (Fletcher and Peto, 1977, PMID 871704), we assumed a non-monotonic time evolution where the risk of the AO <decreased lung function> begins to decline after quitting smoking CC or switching to HTP use. Consequently, we designed our qAOP to evaluate the relative reduction (RR) of AO risk for HTP switching compared to the two scenarios of continuing or quitting CC consumption. We developed both data- and physiology-based KE relationship (KER) models. Despite sparse data with respect to dose- and time-response relationships, we successfully connected the measured KEs through in silico KER models. We then used the resulting qAOP to evaluate the exposure effects of switching from CC smoking to HTP use along AOP 411. This approach enabled in silico prediction of the AO RR for which in vivo data were not yet available. In summary, our qAOP provides a multiscale mechanistic approach to evaluate medium-term lung function evolution for the use case of switching from CC smoking to HTP use. The presented approach demonstrates how 21st Century Toxicology principles such as in vitro testing and in silico predictions can be integrated to achieve a meaningful risk assessment approach. It also represents a starting point for integrating additional AOP into an extended AOP network that includes other mechanisms leading to decreased lung function.

Dataset Information

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Publications

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

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