Lay groundwork for building the infrastructure necessary for the move to a human pathway-based approach for understanding disease and for drug discovery
June 26 – 27, 2017
5635 Fishers Lane
National Institutes of Health (off campus)
Chris Austin (NCATS/NIH), Brian Berridge (GSK), Warren Casey (NIEHS/NIH), Suzy Fitzpatrick (FDA), Robert Kavlock (EPA), Troy Seidle (HSI), Anton Simeonov (NCATS/NIH), Dan Tagle (NCATS/NIH), Catherine Willett (HSUS/HSI)
The objective of this workshop is to explore existing systems biology projects and approaches and how these projects might be better coordinated to optimally improve disease understanding and interventions.
Despite investment of billions of dollars over the past few decades, development of new drugs and other potential disease interventions remain elusive and immensely expensive. The average pre-approval cost of research and development for a successful drug is estimated to be US$2.6 billion  and the number of new drugs approved per billion US dollars spent has halved roughly every 9 years since 1950, decreasing around 80-fold in inflation-adjusted terms . More than 90% of compounds entering clinical trials fail to gain regulatory approval, mainly as a result of insufficient efficacy and/or unacceptable toxicity, because of the limited predictive value of preclinical studies . There is a growing recognition that, to increase the success rate, a stronger focus on human-relevant data is needed [4, 5]. In fact, the realization of similar failures in existing methods for evaluating chemical safety has led the National Institutes of Health, the US Environmental Protection Agency, the US Department of Defense (Defense Advanced Research Projects Agency), as well as the European Commission and European industry, to invest hundreds of millions of dollars to develop more relevant, efficient methods to understand chemical toxicity – and to leverage that understanding to improve human and environmental health [6-8].
Fundamental to these new approaches is the concept of using knowledge of biological pathways or networks (systems biology) to improve our understanding of toxicity and disease. This concept is being developed and implemented in toxicology as “Adverse Outcome Pathways” (AOPs). A collaboration between the European Commission, the US Environmental Protection Agency and the Organization for
Economic Coordination and Development (OECD) is developing the necessary software and databases to generate an “AOP Knowledgebase” . The AOP KB is central to the OECD work on chemical safety as well as both the SEURAT and EU-ToxRisk projects. There are currently more than 100 projects in the AOP Wiki originating from 14 different countries and organizations . The goal of the AOP Wiki project is to create a highly-curated knowledgebase of interlinking network of biological information relating to toxicological outcomes – a systems biology knowledgebase for predicting adverse effects caused by chemical exposure. These are the same biological networks that are involved in disease and are affected by drugs. Significant investment is also being made in systems biology approaches to medicine and disease [11-14]. These approaches are focused on mining existing literature, building associative biological networks from ‘omics data (e.g. genomics, proteomics, metabolomics) and pre-clinical and clinical data. These projects are making great strides in developing data mining and bioinformatics capability to collect and organize data.
This workshop is intended to bring representatives from several of these projects to a single venue to identify barriers and opportunities and make recommendations regarding what is needed to achieve the goal of fully implementing a human systems-biology platform for understanding disease and improving interventions.
Keynote: A call to Action
Session 1: Setting the stage: what is needed and why
a. Current issues with disease models/drug pipeline
b. How might a “pathway-based” approach help (e.g. AOPs)?
c. Case Study(s) in 21st Century disease models
d. From a clinical point of view
e. Discussion questions
Session 2: Big data projects: turning information into knowledge and knowledge into action
a. NIH Big Data to Knowledge overview
c. Human data projects
d. Discussion questions
Session 3: Current tools to support pathway/based approaches: what have we learned so far, and what existing projects/tools/information can we build from? (overviews/successes/challenges):
a. Tox21 and beyond
b. Organs on a chip: applications for testing and research
c. Perspective from NCI
d. Systems pharmacology
e. Discussion questions
Session 4: Coordination and support: how to make this work
a. Role of funding agencies
b. Role of the pharmaceutical industry
c. Role of regulatory agencies
d. Discussion questions
Session 5: Wrap-up: Summary of discussions and recommendations
1. DiMasi, J.A., H.G. Grabowski, and R.W. Hansen, Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ, 2016. 47: p. 20-33.
2. Jack W. Scannell, A.B., Helen Boldon & Brian Warrington, Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery 2012. 11: p. 191-200.
3. Plenge, R.M., E.M. Scolnick, and D. Altshuler, Validating therapeutic targets through human genetics. Nat Rev Drug Discov, 2013. 12(8): p. 581-94.
4. Zerhouni, E.A., Turning the Titanic. Sci. Transl. Med, 2014. 6: p. 221ed2.
5. Committee on Toxicity Testing and Assessment of Environmental Agents, N. and R. Council, Toxicity Testing in the 21st Century: A Vision and a Strategy. 2007, Washington DC: The National Academies Press. 113.
6. SEURAT-1, SEURAT-1 Annual Report, in The Proof of Concept Case Studies, T. Gocht, and Schwarz. M., Editor. 2014: France.
7. http://www.lincsproject.org/. The LINCS Consortium.
8. http://www.eu-toxrisk.eu/. EU ToxRisk.
9. http://aopkb.org/. AOP Knowledge base.
10. https://aopwiki.org/. AOP wiki.
11. http://www.imi.europa.eu/. Innovative Medicines Initiative.
12. http://www.vph-institute.org/what-is-vph-institute.html. The Virtual Physiological Human.
13. https://ncats.nih.gov/. National Center for Advancing Transnational Sciences (NCATS).
14. https://datascience.nih.gov/bd2k. Big Data to Knowledge (BD2K).
16. GSK, GSK to create independent research institute with goal of radically changing and improving medicines development. 2015.