Adverse Outcome Pathways and the Paradox of Complex Simplicity
Dries Knapen · Environmental Toxicology and Chemistry · 2021
An adverse outcome pathway (AOP) is a description of the sequence of causally linked events, spanning multiple levels of biological organization, required to produce a toxic effect when an organism is exposed to a stressor such as a chemical. In essence, an AOP is a depiction of a complex toxicological process in a simplified, stepwise, sequential format. An AOP tries to describe the responses of complex biological systems whose behavior is intrinsically difficult to model because of the many relationships and interactions between the various components of the system and because of the emergent properties that often arise as a result of such interactions. An important question, therefore, is whether the AOP framework itself should, by design, also be a complex system to be able to capture the toxicological reality or whether a simplified representation of the perturbed biology is indeed sufficient. This question has been raised ever since the introduction of the AOP framework in 2010 (Ankley et al., 2010). Some argue that the "linear" nature of AOPs is too simple in terms of conceptual model design and data type availability to adequately capture the complexity of any realistic toxicological scenario, even when considering AOP networks that are formed by connecting different linear AOPs. Others feel that the AOP framework is too complex and too overwhelming to be useful. Development of an AOP can be perceived as a daunting task, requiring the knowledge and evidence to connect all the dots between the different events, including establishing causality and essentiality, documenting dose and time concordance, and so on. Thus, AOPs are often perceived as being too simple and too complex at the same time, raising concerns that they may in fact slow down hazard and risk assessment instead of being a catalyst for supporting 21st-century toxicology. Of course, this paradox is not new, and it is certainly not unique to the AOP framework. Humanity has been studying complex systems for centuries. Intuitively one tries to reduce complexity to be able to understand reality and deal with it in a practical manner. At the same time there is a natural tendency to add ideas and functionality, and therefore complexity, to almost anything we design to increase its relevance and utility. Researchers in complex systems view the main task of modeling to capture, rather than reduce, the complexity of the systems of interest; but the question remains what the level of complexity of the model should be to achieve this. To further explore the paradox of "complex simplicity," it is helpful to consider the relationship between complexity and simplicity and to distinguish between different types of simplicity. While simplicity and complexity are usually perceived as extreme ends of the same spectrum, they can also be seen as interchangeable. True simplicity can indeed be utterly complex. Steve Jobs, the founder and late chief executive officer of Apple, said on this matter, When you start looking at a problem and it seems really simple, you don't really understand the complexity of the problem. Then you get into the problem, and you see that it's really complicated, and you come up with all these convoluted solutions. That's sort of the middle, and that's where most people stop. But the really great person will keep on going and find the key, the underlying principle of the problem—and come up with an elegant, really beautiful solution that works (Levy, 2006). In other words, the development of a new idea or paradigm evolves through different phases, starting from a pragmatically simple idea, leading to an intermediate phase of necessary added complexity, and ending in a final state of what is sometimes referred to as "informed simplicity," gradually increasing its utility throughout the process (Figure 1). Of course, Jobs made his statement in the context of Apple products, the development of the iPhone—a highly complex but very simple-to-use product—out of the analog telephone via "smart" intermediate technology such as BlackBerry-type devices being a good example. The idea of aiming for evolved, informed simplicity applies equally well to the history and future of the AOP framework. At their inception AOPs were intended to be pragmatically simple. Individual linear AOPs, in particular, were conceived as a deliberate simplification of complex biology to serve as a functional unit of development intended to support regulatory decision-making. For example, it was explicitly prescribed that AOPs should contain only a limited number of essential, measurable events leading to the relevant toxicity endpoint and should not necessarily provide a comprehensive description of every aspect of the biology involved. And even though AOP networks were envisioned to be able to better capture the complexity of biological systems, they were initially seen as relatively simple constructs as well, only made up of the information contained within the constituent AOPs with no additional layers of network-specific properties, data, or metadata. It soon became apparent that there was a need to expand the AOP development toolbox. Over the last decade and through the present, the AOP framework has been gradually refined to suit the evolving needs of AOP developers and users, and a variety of features and properties have been added to accommodate a number of perceived challenges and shortcomings. For example, it was considered how feedback loops and modulating factors could be integrated into the framework. A lot of thought and consideration went into the best way to define and describe an AOP's domain of applicability, for example, in terms of taxonomic scope (LaLone et al., 2013), life stage specificity, or sex specificity. A system of layers was proposed to add information to AOPs and AOP networks without directly affecting the underlying pragmatic simplicity of the construct. Filters were then proposed as a way to reduce the ever-growing complexity of large AOP networks (Knapen et al., 2018). The use of ontology terms was introduced to standardize the description of different pathway components (Ives et al., 2017), systematic approaches were explored for collecting the available scientific evidence supporting an AOP, and Bradford Hill–based criteria were established for evaluating that evidence (Becker et al., 2015). Methods were developed to analyze, extract, and benchmark the information that is contained within complex AOP networks (Pollesch et al., 2019; Villeneuve et al., 2018). While many of these framework improvements are now well established, not all are yet fully developed or understood. Developing the AOP framework does remain an active area of science, and the framework continues to evolve. The progress that has been made in the past few years is significant however, and it is likely that we are almost in the middle of the simplicity continuum (see Figure 1). What we need now is a way to bring all the available information, technology, and concepts together. Most importantly, there is a need for a new, advanced data model to link and integrate all the data and associated metadata that make up AOPs and AOP networks and a new conceptual way for a user to interact with these data components to explore and interrogate the AOP ecosystem. Fortunately, the necessary steps in this direction are currently being taken: a new data model is indeed under development, and the AOP-wiki is evolving into a mature and sophisticated interface for both AOP developers and users. This brings us back to our question of whether AOPs themselves, or the AOP framework itself, should be a complex system by design. The answer is that the AOP framework needs to achieve a state of informed simplicity where it still looks, feels, and handles as simply and elegantly as originally designed but at the same time incorporates more of the great complexities that make up life. Many of the ingredients are in place, much like most of the individual pieces of technology required to build the iPhone existed just before it was invented. The only thing that is left to do is to search for the key, the underlying, principle of the problem—and come up with an elegant, really beautiful solution that works.