4.2.13. Adverse Outcome Pathways

Author: Dick Roelofs

Reviewers: Nico van Straalen, Dries Knapen

 

Learning objectives:

You should be able to

 

Keywords: Molecular initiation event, key event, in vitro assay, high throughput assay, pathway

 

 

Introduction

Over the past two decades the availability of molecular, biochemical and genomics data has exponentially increased. Data are now available for a phylogenetically broad range of living organisms, from prokaryotes to humans. This has tremendously advanced our knowledge and mechanistic understanding of biological systems, which is highly beneficial for different fields of biological research such as genetics, evolutionary biology and agricultural sciences. Being an applied biological science, toxicology has not yet tapped this wealth of data, because it is difficult to incorporate mechanistic data when assessing chemical safety in relation to human health and the environment. However, society is increasingly concerned about the release of industrial chemicals with little or no hazard- or risk information. Consequently, a much larger number of chemicals need to be considered for potential adverse effects on human health and ecosystem functioning. To meet this challenge it is necessary to deploy fast, cost-effective and high throughput approaches that can predict potential toxicity of substances and replace traditional tests based on survival and reproduction that run for weeks or months and often are quite labour-intensive. A major challenge is however, to link these fast in vitro and in vivo assays to endpoints used in current risk assessment. This challenge was picked up by defining the adverse outcome pathway (AOP) framework, for the first time proposed by the Gerald Ankley and co-workers from the United States Environmental Protection Agency, US-EPA (Ankley et al., 2010).

 

The framework

The AOP framework is defined as an evolution of prior pathway-based concepts, most notably mechanisms and modes of action, for assembling and depicting toxicological data across biological levels of organization (Ankley and Edwards, 2018). An AOP is a graphical representation of a series of measurable key events (KEs). A key event is a measurable directional change in the state of a biological process. KEs can be linked to one another through key event relationships (KERs; see Figure 1). The first KE is depicted as the “molecular initiating event” (MIE), and represents the interaction of the chemical with a biological receptor that activates subsequent key events. The key event relationships should ideally be based on causal evidence. A cascade of key events can eventually result in an adverse outcome (AO) at the individual or population level. The MIE and AO are specialized KEs, but treated like any other KE in the AOP framework.

 

Figure 1. The Adverse Outcome Pathway framework. Various measurable data streams are linked in a causal manner and eventually are related to outcomes essential for risk assessment. MIE, molecular initiating event; KE, key event; KER, key event relationship; AO, adverse outcome. Adapted from Ankley and Edwards (2018) by Kees van Gestel.

 

The aim of an AOP is to represent and describe, in a simplified way, how responses at the molecular- and cellular level are translated to impacts on development, reproduction and survival, which are relevant endpoints in risk assessment (Villeneuve et al., 2014). Five core concepts have been defined in the development of AOPs:

  1. AOPs are not chemical specific, they are biological pathways;
  2. AOPs are modular, they refer to a designated and defined metabolic cascade, even if that cascade interacts with other biological processes;
  3. individual AOPs are developed as pragmatic units;
  4. networks of multiple AOPs sharing KEs and KERs are functional units of prediction for real-world scenarios; and
  5. AOPs are living documents that may change over time based on new scientific insights.

Generally, AOPs are simplified linear pathways but different AOPs can be organized in networks with shared nodes. The AOP networks are actually the functional units of prediction, because they represent the complex biological interactions that occur in response to exposure to a toxicant or a mixture of toxicants. Analysis of the intersections (shared key events) of different AOPs making up a network can reveal unexpected biological connections (Villeneuve et al., 2014).

 

Molecular initiating events and key events

Typically, an AOP consists of only one MIE, and one AO, connected to each other by a potentially unlimited number of KEs and KERs. The MIE is considered to be the first anchor of an AOP at the molecular level, where stressors directly interact with the biological receptor. Identification of the MIE mostly relies on chemical analysis, in silico analysis or in chemico and in vitro data. For instance, the MIE for AOPs related to estrogen receptor activation involves the binding of chemicals to the estrogen receptor, thereby triggering a cascade of effects in hormone-related metabolism (see the section on Endocrine disruption). The MIE for AOPs related to skin sensitization (see below) involves the covalent interaction of chemicals to skin proteins in skin cells, an event called haptenization (Vinken, 2013).

A wide range of biological data can support the understanding of KEs. Usually, early KEs (directly linked to MIEs) are assessed using in vitro assays, but may include in vivo data at the cellular level, while intermediate and late KEs rely on tissue-, organ- or whole organism measurements (Figure 1). Key-event measurements are also related to data from high-throughput screening and/or data generated by different -omics technologies. This is actually where the true value of the AOP framework comes in, since it is currently the only framework able to reach such a high level of data integration in the context of risk assessment. It is even possible to integrate comparative data from phylogenetically divergent organisms into key event measurements, valid across species, which could facilitate the evaluation of species sensitivity (Lalone et al., 2018). The final AO output is usually represented by apical responses, already described as standard guidelines accepted and instrumental in regulatory decision-making, which include endpoints such as development, growth, reproduction and survival.

Development of the AOP framework is currently supported by US-Environmental Protection Agency, EU Joint Research Centers (ERC) and the Organization for Economic Cooperation and Development (OECD). Moreover, OECD has sponsored the development of an open access searchable database AOPWiki (https://aopwiki.org/), comprising over 250 AOPs with associated MIEs, KEs and KERs, and more than 400 stressors. New AOPs are added regularly. The database also has a system for specifying the confidence to be placed in an AOP. Where KEs and KERs are supported by direct, specifically designed, experimental evidence, high confidence is placed in them. In other cases confidence is considered moderate or low, e.g. when there is a lack of supporting data or conflicting evidence.

 

Case example: covalent protein binding leading to skin sensitization (AOP40)

Skin sensitization is characterized by a two-step process, a sensitization phase and an elicitation phase. The first contact of electrophile compounds with the skin covalently modifies skin proteins and generates an immunological memory due to generated antigen/allergen specific T-cells. During the elicitation phase, repeated contact with the compound elicits the allergic reaction defined as allergic contact dermatitis, which usually develops into a lifelong effect. This is an important endpoint for safety assessment of personal care products, traditionally evaluated by in vivo assays. Based on changed public opinion the European Chemical Agency (ECHA) decided to move away from whole animal skin tests, and developed alternative assessment strategies. During sensitization, the MIE takes place when the chemical enters the skin, where it forms a stable complex with skin-specific carrier proteins (hapten complexes), which are immunogenic. A subsequent KE comprises inflammation and oxidative defense via a signaling cascade called the Keap1/Nrf2 signalling pathway (Kelch-like ECH-associated protein 1 / nuclear factor erythroid 2 related factor 2). At the same time, a second KE is defined as dendritic cell activation and maturation. This results into movement of dendritic cells to lymph nodes, where the hapten complex is presented to naive T-cells. The third KE describes the proliferation of hapten-specific T-cells and subsequent movement of antigen-specific memory cells that circulate in the body. Upon a second contact with the compound, these memory T-cells secrete cytokines that cause an inflammation reaction leading to the AO including red rash, blisters, and burning skin (Vinken et al., 2017). This AOP is designated AOP40 in the database of adverse outcome pathways.

Figure 2. Adverse Outcome Pathway for covalent protein binding leading to skin sensitization (AOP40). Adapted from Vinken et al. (2017) by Kees van Gestel.

 

A suite of high throughput in vitro assays have now been developed to quantify the intermediate KEs in AOP40. These data formed the basis for the development of a Bayesian network analysis that can predict the potential for skin sensitization. This example highlights the use of pathway-derived data organized in an AOP, ultimately leading to an alternative fast screening method that may replace a conventional method using animal experiments.

 

References

Ankley, G.T., Bennett, R.S., Erickson, R.J., Hoff, D.J., Hornung, M.W., Johnson, R.D., Mount, D.R., Nichols, J.W., Russom, C.L., Schmieder, P.K., Serrrano, J.A., Tietge, J.E., Villeneuve, D.L. (2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry 29, 730–741.

Ankley, G.T., Edwards, S.W. (2018). The adverse outcome pathway: A multifaceted framework supporting 21st century toxicology. Current Opinion in Toxicology 9, 1–7.

LaLone, C.A., Villeneuve, D.L., Doering, J.A., Blackwell, B.R., Transue, T.R., Simmons, C.W., Swintek, J., Degitz, S.J., Williams, A.J., Ankley, G.T. (2018). Evidence for cross species extrapolation of mammalian-based high-throughput screening assay results. Environmental Science and Technology 18, 13960-13971.

Villeneuve, D.L., Crump, D., Garcia-Reyero, N., Hecker, M., Hutchinson, T.H., LaLone, C.A., Landesmann, B, Lettieri, T., Munn, S., Nepelska, M., Ottinger, M.A., Vergauwen, L., Whelan, M. (2014). Adverse Utcome Pathway development I: Strategies and principles. Toxicological Sciences 142, 312-320.

Vinken, M. (2013). The adverse outcome pathway concept: A pragmatic tool in toxicology. Toxicology 312, 158–165.

Vinken, M., Knapen, D., Vergauwen, L., Hengstler, J.G., Angrish, M., Whelan, M. (2017). Adverse outcome pathways: a concise introduction for toxicologists. Archives of Toxicology 91, 3697–3707.