Approaching Chemical Safety Assessment Through Application of Integrated Approaches to Testing and Assessment: Combining Mechanistic Information Derived from Adverse Outcome Pathways and Alternative Methods

2017 ◽  
Vol 3 (3) ◽  
pp. 227-233 ◽  
Author(s):  
Magdalini Sachana ◽  
Eeva Leinala
Keyword(s):  
Safety Assessment ◽  
Adverse Outcome ◽  
Alternative Methods ◽  
Chemical Safety ◽  
Integrated Approaches ◽  
Adverse Outcome Pathways

scholarly journals Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA)

2014 ◽  
Vol 70 (3) ◽  
pp. 629-640 ◽  
Author(s):  
Knut Erik Tollefsen ◽  
Stefan Scholz ◽  
Mark T. Cronin ◽  
Stephen W. Edwards ◽  
Joop de Knecht ◽  
...  
Keyword(s):  
Adverse Outcome ◽  
Integrated Approaches ◽  
Adverse Outcome Pathways

scholarly journals Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

2021 ◽  
Vol 9 ◽  
Author(s):  
Qier Wu ◽  
Youcef Bagdad ◽  
Olivier Taboureau ◽  
Karine Audouze
Keyword(s):  
Health Effects ◽  
Drug Safety ◽  
Safety Assessment ◽  
Drug Exposure ◽  
Adverse Outcome ◽  
Information Loss ◽  
Weight Of Evidence ◽  
Bipartite Network ◽  
Biological Organization ◽  
Adverse Outcome Pathways

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

scholarly journals Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 708 ◽  
Author(s):  
Angela Serra ◽  
Michele Fratello ◽  
Luca Cattelani ◽  
Irene Liampa ◽  
Georgia Melagraki ◽  
...  
Keyword(s):  
Regression Models ◽  
Safety Assessment ◽  
State Of The Art ◽  
Data Modelling ◽  
Predictive Toxicology ◽  
Chemical Safety ◽  
Biomarkers Of Exposure ◽  
Transcriptomics Data ◽  
Modelling Techniques ◽  
Adverse Outcome Pathways

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

scholarly journals Adverse outcome pathways – development and potential regulatory application

2021 ◽  
Vol 9 (3) ◽  
pp. 2-13
Author(s):  
Thania Rios Rossi Lima ◽  
◽  
Nathália Pereira de Souza ◽  
Lílian Cristina Pereira ◽  
João Lauro Viana de Camargo ◽  
...  
Keyword(s):  
Decision Making ◽  
Scientific Community ◽  
Adverse Outcome ◽  
Adverse Outcomes ◽  
Empirical Science ◽  
Chemical Safety ◽  
Mechanisms Of Toxicity ◽  
Testing Procedures ◽  
Regulatory Decision ◽  
Adverse Outcome Pathways

Introduction: Over the last two decades, chemical safety assessment and regulatory toxicology have progressed from empirical science based on direct observation of apical adverse outcomes in whole organisms to a predictive practice that infers outcomes and risks on the basis of accumulated understanding of toxicological mechanisms and modes of action. Objective: To provide general concepts on how Adverse Outcome Pathways (AOPs) are developed and examples related to skin sensitization, endocrine, disruption, and mitochondrial dysfunction. Method: Narrative review based on data of the scientific literature relevant to the theme addressed and on the experience of the authors. Results: An AOP framework provides a systematic approach to organize knowledge about mechanisms of toxicity that may inform analytical domains in regulatory decision-making. AOPs are open structures that may indicate not only data gaps in the understanding of a toxicity process, but also testing procedures that will generate the necessary knowledge to fill those gaps. Every AOP should be continuously refined through the collaborative efforts of the scientific community. Depending on the amount and detail of information that is successively inserted, AOP may progress from the stage of a putative AOP to the stages of qualitative and quantitative AOPs, which are more fit-for-purpose to support regulatory decision-making. Conclusions: Continuous collaboration between AOP developers within the scientific community and the regulatory corps toward the development of this mechanistic structure will support the advancement of toxicological sciences, regardless of its immediate application for regulatory purposes.

scholarly journals A Pragmatic Approach to Adverse Outcome Pathway Development and Evaluation

2021 ◽  
Author(s):  
Terje Svingen ◽  
Daniel L Villeneuve ◽  
Dries Knapen ◽  
Eleftheria Maria Panagiotou ◽  
Monica Kam Draskau ◽  
...  
Keyword(s):  
Systematic Review ◽  
Safety Assessment ◽  
Adverse Outcome ◽  
Adverse Outcome Pathway ◽  
Pragmatic Approach ◽  
Chemical Safety ◽  
Regulatory Toxicology ◽  
Adverse Health Outcomes ◽  
Wide Acceptance ◽  
Modular Units

Abstract The adverse outcome pathway (AOP) framework provides a practical means for organizing scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. It has reached wide acceptance as a tool to aid chemical safety assessment and regulatory toxicology by supporting a systematic way of predicting adverse health outcomes based on accumulated mechanistic knowledge. A major challenge for broader application of the AOP concept in regulatory toxicology, however, has been developing robust AOPs to a level where they are peer reviewed and accepted. This is because the amount of work required to substantiate the modular units of a complete AOP is considerable, to the point where it can take years from start to finish. To help alleviate this bottleneck, we propose a more pragmatic approach to AOP development whereby the focus becomes on smaller blocks. First, we argue that the key event relationship (KER) should be formally recognized as the core building block of knowledge assembly within the AOP knowledge base (AOP-KB), albeit framing them within full AOPs to ensure regulatory utility. Second, we argue that KERs should be developed using systematic review approaches, but only in cases where the underlying concept does not build on what is considered canonical knowledge. In cases where knowledge is considered canonical, rigorous systematic review approaches should not be required. It is our hope that these approaches will contribute to increasing the pace at which the AOP-KB is populated with AOPs with utility for chemical safety assessors and regulators.

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