Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.
Keywords: FAIR data; adverse outcome pathways (AOPs); machine-actionability; trust; visibility.
New approach methodologies (NAMs) can detect biological phenomena that occur before they add up to serious problems like cancer, infertility, death, and others. NAMs detect key events (KE) along well-proven and agreed adverse outcome pathways (AOP). If a substance tests positive in a NAM for an upstream KE, this signals an early warning that actual adversity might follow. However, what if the knowledge about these AOPs is a well-kept secret? And what if decision-makers find AOPs too exotic to apply in risk assessment? This is where FAIR comes in! FAIR stands for making information findable, accessible, interoperable and re-useable. It aims to increase availability, usefulness, and trustworthiness of data. Here, we show that by interpreting the FAIR principles beyond a purely technical level, AOPs can ring in a new era of 3Rs applicability ‒ by increasing their visibility and making their creation process more transparent and reproducible.