Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.
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ABSTRACT:
SUBMITTER: Spinu N
PROVIDER: S-EPMC7261727 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.
Spinu Nicoleta N Cronin Mark T D MTD Enoch Steven J SJ Madden Judith C JC Worth Andrew P AP
Archives of toxicology 20200518 5
The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP mode ...[more]
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Project description:An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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Project description:Toxicity prediction using quantitative structure-activity relationship has achieved significant progress in recent years. However, most existing machine learning methods in toxicity prediction utilize only one type of feature representation and one type of neural network, which essentially restricts their performance. Moreover, methods that use more than one type of feature representation struggle with the aggregation of information captured within the features since they use predetermined aggregation formulas. In this paper, we propose a deep learning framework for quantitative toxicity prediction using five individual base deep learning models and their own base feature representations. We then propose to adopt a meta ensemble approach using another separate deep learning model to perform aggregation of the outputs of the individual base deep learning models. We train our deep learning models in a weighted multitask fashion combining four quantitative toxicity data sets of LD50, IGC50, LC50, and LC50-DM and minimizing the root-mean-square errors. Compared to the current state-of-the-art toxicity prediction method TopTox on LD50, IGC50, and LC50-DM, that is, three out of four data sets, our method, respectively, obtains 5.46, 16.67, and 6.34% better root-mean-square errors, 6.41, 11.80, and 12.16% better mean absolute errors, and 5.21, 7.36, and 2.54% better coefficients of determination. We named our method QuantitativeTox, and our implementation is available from the GitHub repository https://github.com/Abdulk084/QuantitativeTox.
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Project description: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.
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Project description:The U.S. Environmental Protection Agency's ToxCast program has screened thousands of chemicals for biological activity, primarily using high-throughput in vitro bioassays. Adverse outcome pathways (AOPs) offer a means to link pathway-specific biological activities with potential apical effects relevant to risk assessors. Thus, efforts are underway to develop AOPs relevant to pathway-specific perturbations detected in ToxCast assays. Previous work identified a "cytotoxic burst" (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared with the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). The prioritization approaches did not appear to be influenced by inter-assay differences in chemical bioavailability. Furthermore, the outcomes were robust based on a variety of different parameters used to define the CTB.
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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.
2024-02-19 | E-MTAB-13344 | biostudies-arrayexpress
Project description:Organization of existing and emerging toxicological knowledge into adverse outcome pathway (AOP) descriptions can facilitate greater application of mechanistic data, including those derived through high-throughput in vitro, high content omics and imaging, and biomarker approaches, in risk-based decision making. The previously ad hoc process of AOP development is being formalized through development of internationally harmonized guidance and principles. The goal of this article was to outline the information content desired for formal AOP description and some rules of thumb and best practices intended to facilitate reuse and connectivity of elements of an AOP description in a knowledgebase and network context. For example, key events (KEs) are measurements of change in biological state that are indicative of progression of a perturbation toward a specified adverse outcome. Best practices for KE description suggest that each KE should be defined as an independent measurement made at a particular level of biological organization. The concept of "functional equivalence" can help guide both decisions about how many KEs to include in an AOP and the specificity with which they are defined. Likewise, in describing both KEs and evidence that supports a causal linkage or statistical association between them (ie, a key event relationship; KER), best practice is to build from and contribute to existing KE or KER descriptions in the AOP knowledgebase rather than creating redundant descriptions. The best practices proposed address many of the challenges and uncertainties related to AOP development and help promote a consistent and reliable, yet flexible approach.
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Project description:Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses (or previously undefined emergent patterns of response) are introduced. Along with a companion article (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
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