Hi, I'm Catherine Willett, Senior Director of Science and Federal Affairs at the Humane Society of the United States and Humane Society International. Today, I'm going to tell you about the adverse outcome pathway framework, the foundation for predictive toxicology. As an overview in today's presentation, we're going to hear about the need and the opportunity for a shift from empirically measured to predictive toxicology. I'm going to tell you about the concept of toxicological pathways as adverse outcome pathways, I'll introduce you to the OECD AOP framework. We'll talk about building, evaluating, and using AOPs in safety decisions. Then I'll briefly go over the online tools available to everyone, including an online AOP course. The need and opportunity for a new approach to toxicology, including predictive tools. To better understand the context of why AOPs or any biological pathway information is so important, it's good to have a look at what's causing the need for change, and to see how AOPs can help be a part of the solution. There are several drivers coming from virtually all sectors of the chemical safety universe. For example, in pharmaceuticals 92 percent of drug candidates fail in clinical studies, and the average drug developed by a major pharmaceutical company costs at least $4 billion and it can be as much as $11 billion. In addition, there's a need to assess novel types of chemistries for example, nanomaterials. With respect to industrial chemicals, there's a growing concern over the lack of data. There are several tens of thousands of chemicals in the environment already. There are also large-scale regulatory programs that have recently become law in different regions of the world for example REACH in the European Union, and K-REACH in South Korea and also new chemical regulations in China. With respect to pesticides, registration of each pesticide ingredient and formulation requires the use of approximately 8,000 animals, millions of US dollars, and takes over a decade. In addition, there's a need to identify greener chemistries. With respect to cosmetics, there are legislative bans on cosmetic animal testing and or trade in 37 countries and counting. At the same time, there's an increasing consumer concern over cosmetic safety, all of this is leading to the need for safety assessment tools that do not involve animal testing, are faster and cheaper and more effective. A closer look at the drug development pipeline shows that the number of successful drugs coming to market remain stagnant, in spite of increasing investment in preclinical research and testing, resulting in drug development costs as I mentioned in excess of a billion dollars per successfully marketed drug. At the same time, there's really an urgent need to better leverage our existing knowledge. We have so much data, biologists have been generating an increasing amount of data for decades. In fact, a 2014 nature blog estimated that global scientific output doubles every nine years, too much for us to really digest and comprehend. Where is this data? It's largely in journal articles and reports, it's in laboratory notebooks and agency archives, it's in institutional and governmental databases. What we desperately need is better access and better organization of data, which will lead to better understanding. This better understanding will allow us to design better research and testing approaches. But how do we get from here, where the data is largely in PDF format, fragmented, siloed, and with a good portion of it considered proprietary, to here with data accessible by searching, and machine-readable, linked together in a comprehensive way? All of which will facilitate collaboration, model building, and avoids duplication of research in testing. This is a question that AOPs are designed to answer. I know you've seen this before in other courses. But as a result of the inadequacies of the current system of Chemical Safety Assessment, the EPA commissioned the National Academy of Sciences to convene an expert panel to come up with a solution. They tasked the panel with designing what they believe would be the ideal way of assessing chemical safety, given what we currently know and our current state of technological tools. The resulting report published in 2007 and entitled "Toxicity Testing in the 21st Century: A Vision and a Strategy", described the transformation of toxicity testing from a system based on whole animal testing to one founded primarily on in vitro methods, that evaluate changes in biological processes using cells, cell lines, or cellular components preferably of human origin. Including tests that assess critical mechanistic endpoints involved in the induction of overt toxic effects, rather than the effects themselves. In other words, they're describing a move away from empirically measured toxicity, toward an approach where toxicity is predicted based on upstream biological events. Because of our vast accumulating knowledge about biology, as well as rapid advances in technology and computing capacity, many things are possible that were not possible when our current testing scheme was devised. We can now capitalize on advances in chemistry, biology, and engineering and more fully utilize our existing knowledge. We can increase assessment capacity or throughput, by using automation and robots to assess tens of thousands of chemical samples, and hundreds of assays in days or weeks, which results in an increase in efficiency. We can now increase relevance to humans or other species of concern by using more sophisticated cell and tissue culturing systems, for example organs-on-a-chip. All of these advances in technology and vast accumulating data are terrific, but they need to systematically understand the information and apply the technology in efficient ways. Here's where AOPs come in.
