The ability to accurately and rapidly sequence the human genome at a relatively low cost is likely to bring about widespread changes in the way medicine is practiced, including the introduction of novel genomics-based therapies for patients (Pasche and Absher, 2011). However, the process of discovering and developing a new drug or therapy is extremely costly and time consuming. Recently, it has been estimated that the creation of a new medicine costs on average more than $2 billion and takes 10 years to reach patients (DiMasi et al., 2016). The challenges associated with bringing new medicines to market have led many pharmaceutical companies to seek out innovative methods for streamlining their drug discovery research.
One way to increase the odds of success for compounds in the drug development pipeline is to adopt genetically guided strategies for drug discovery. A recent analysis of approved medicines indicated that using human genetic data to support the selection of drug targets and indications can roughly double the chances that a given drug will be clinically successful compared with drugs lacking genetic support (Nelson et al., 2015). The use of genetic information for drug discovery and development has led to a variety of innovations and new approaches in both the private and public sectors.
Recognizing the potential drug discovery benefits of collecting genetic and phenotypic information across specific populations, pharma-
1 The planning committee’s role was limited to planning the workshop. The Proceedings of a Workshop has been prepared by the rapporteurs as a factual account of what occurred at the workshop. Statements, recommendations, and opinions expressed are those of individual presenters and participants and have not been endorsed or verified by the National Academies of Sciences, Engineering, and Medicine. They should not be construed as reflecting any group consensus.
ceutical companies have started collaborating with healthcare systems and private companies that have curated genetic bioresources, or large databases of genomic information. Large-scale cohort studies offer an effective way to collect and store information that can be used to assess gene–environment interactions, identify new potential drug targets, understand the role of certain genetic variants in the drug response, and further elucidate the underlying mechanisms of disease onset and progression. The goal of many of these institutional partnerships is to identify and validate potential therapeutic targets, develop biomarker assays, and produce new and better targeted medicines. One example of this type of arrangement is the partnership between Regeneron Pharmaceuticals and the Geisinger Health System. Patient samples collected by Geisinger undergo DNA sequencing, providing researchers with information they can use to better understand the relationship between genes and human diseases.
At the same time, government agencies and academic researchers have new opportunities to work together to create new genetic bioresources and more sophisticated analytical tools. In these workshop proceedings, genetic bioresources are referred to as studies or initiatives in which biological specimens are collected from individuals with the intent to sequence DNA for research and/or discovery purposes. For example, the Precision Medicine Initiative (PMI), a program announced by President Obama in 2015, will assemble a cohort of 1 million U.S. volunteers for longitudinal research, including genetic studies (Collins and Varmus, 2015). Programs such as the PMI have the potential to spur new and innovative business models for integrating scientific and technological expertise across sectors and allowing for greater access to genomic data of clinical research participants. However, many questions remain about the design of large cohort studies, the types of data that should be collected, and which business models could engage stakeholders most effectively. To examine how genetic bioresources could be used to improve drug discovery and target validation, the Roundtable on Genomics and Precision Health (previously called the Roundtable on Translating Genomic-Based Research for Health) worked with the Forum on Drug Discovery, Development, and Translation to host a workshop on March 22, 2016, in Washington, DC, titled Deriving Drug Discovery Value from Large-Scale Genetic Bioresources.2
Participants at the workshop explored the current landscape of genomics-enabled drug discovery activities in industry, academia, and government; examined enabling partnerships and business models; and considered gaps and best practices for collecting population data for the purpose of improving the drug discovery process. (Box 1-1 lists the objectives of the workshop.) A wide variety of stakeholders presented their perspectives and participated in workshop discussions, including representatives of pharmaceutical companies, academic research institutes, health care systems, direct-to-consumer genetics companies, and patient advocacy groups.
Over the course of the workshop, several participants made suggestions for steps that could be taken within the public and private sectors to use large-scale genetic bioresources for the purpose of accelerating drug discovery. These suggestions are synthesized in Box 5-1 in the final chapter of these proceedings.
Recently, the Forum on Drug Discovery, Development, and Translation has been mapping out the landscape of drug discovery and development and developing a tool to identify points in the ecosystem where bottlenecks and challenges arise, said Russ Altman, co-chair of the Forum on Drug Discovery, Development, and Translation and the Kenneth Fong Professor and a professor of bioengineering, genetics, medicine, and (by courtesy) computer science at Stanford University. Several of the activities of the Roundtable on Genomics and Precision Health in the past 2 years have been focused on clinical genomics and translation, so
the opportunity to jointly present this workshop comes at an excellent time, said Geoffrey Ginsburg, co-chair of the Roundtable on Genomics and Precision Health and the director of the Center for Applied Genomics and Precision Medicine at Duke University Medical Center. New large-scale genetic bioresources, activities, tools, and institutional arrangements are creating tipping points in science, culture, and business, said Nadeem Sarwar, president of Andover Product Creation Innovation Systems at Eisai Inc., and chair of the workshop. These tipping points are providing unprecedented opportunities to accelerate the delivery of innovative, targeted new medicines, Sarwar said.
Workshop speakers focused on new research and ideas in three primary areas: large cohort studies, genome-enabled discovery activities, and novel business models that support the development and use of data from bioresources. Throughout the workshop there was robust discussion of short- and long-term options for collaboration, translational research, and accelerated progress in the area of genomics-enabled drug discovery.
Designing Large Cohorts to Maximize Discovery Capabilities
Genetic and phenotypic data gathered from large cohorts make it possible to detect common genetic variants that increase or decrease the risk of disease, rare variants that can be used to identify drug targets, and genetic subpopulations in which particular treatments may be most effective, said Kári Stefánsson, the chairman and chief executive officer of deCODE genetics.
Several large studies that involve bioresources are already under way, including the Electronic Medical Records and Genomics (eMERGE) Network in the United States, and the Oxford Biobank in the United Kingdom, among others. Cohort studies are especially valuable when they include data collected over long periods of time, have extensive population participation, and allow for genotype- or phenotype-based recall studies, according to Mark Daly, the co-director of the Program in Medical and Population Genetics at the Broad Institute of the Massachusetts Institute of Technology and Harvard University. In addition, integrated approaches that include general population studies, family-based studies, research on founder populations, and studies of phenotypic-specific cohorts could potentially maximize opportunities for drug discovery, said Aris Baras, the vice president and co-head of the Regeneron Genetics Center. However, important issues can arise during cohort design, including
challenges with data sharing, informed consent, privacy, and analytical and computational issues, all of which are discussed in Chapter 2.
Genomics-Enabled Discovery Activities Related to Bioresources
Large genetic bioresources can facilitate the use of sophisticated analytical tools, new approaches to target validation and biomarker development, and studies of allelic variation in drug response (The Michael J. Fox Foundation, 2015; Whirl-Carrillo et al., 2012). Individual workshop speakers discussed discovery activities, many of which are in the precompetitive realm, that are enabling a greater understanding of human disease biology and of drug–target interactions across a wide variety of diseases (see Chapter 3). One example of this type of discovery activity is the Parkinson’s Progression Markers Initiative (PPMI) of The Michael J. Fox Foundation. The PPMI is collecting and sharing data from subjects with Parkinson’s disease in order to carry out research on—and develop biomarkers of—the mechanism and progression of Parkinson’s disease.
Although genome-wide association studies (GWASs) have identified several thousand genetic variants that confer genetic risk for common diseases, there are several reasons that these results have not yet been translated into the predicted plethora of new medicines, including possible population stratification in GWASs, the lack of functional links between risk variants that lie in non-coding regions of the genome and the disorders they are connected to, and high rates of attrition for drug candidates that enter phase 2, “proof-of-concept” studies (Bunnage, 2011; McClellan and King, 2010). Target validation is an important step in the drug discovery process because it can help bridge the gap between basic science research and the development of new medicines (Smith, 2003). As a way to collaborate on target validation and accelerate translation to new medicines, GlaxoSmithKline (GSK), the European Bioinformatics Institute, and the Wellcome Trust Sanger Institute established the Centre for Therapeutic Target Validation (CTTV). The CTTV brings together expertise in drug discovery, chemistry, functional genomics, and electronic health records to study potential drug targets in the precompetitive realm, said Lon Cardon, the senior vice president of alternative discovery and development and the head of target sciences at GSK.
In addition to target validation, genomics-enabled drug discovery
requires a thorough understanding of the molecular, cellular, and organismal consequences of diseases and their treatments, said workshop speaker Tim Rolph, the vice president of program value enhancement at Pfizer Inc. Drug discovery programs will be successful if they can modulate and assay the activity of drug targets within cell and model systems and combine that with a better understanding of the biology of the potential target, according to Sally John, the vice president of computational biology and genomics at Biogen.
Business Models That Support Drug Discovery Across Stakeholder Groups
Progress of drug discovery and development efforts that begin within a single stakeholder organization can be greatly accelerated when such efforts are taken advantage of by larger collaborations involving multiple stakeholder groups. For example, patient and disease advocacy groups can gather genotypic and phenotypic data for investigators at academic institutions or pharmaceutical companies, help recruit individuals into clinical trials, provide treatment recommendations, and educate patient populations (see Chapter 4).
Multi-stakeholder collaborations supporting target identification, clinical development, patient stratification, and pharmacogenetics can similarly accelerate drug discovery and development (Hodes and Buckholtz, 2016). One such activity is the Accelerating Medicines Partnership (AMP), an effort that combines resources and expertise from different sectors such as government, industry, and the nonprofit sector, while increasing the scope, coordination, and efficiency of ongoing and future studies.
Collaborative partnerships also can result in the development of drug discovery tools and resources that can further increase participation in and the effectiveness of the collaboration. For example, knowledge portals such as AMP can allow integrated interrogation across multiple datasets while maintaining individual-level data privacy, said David Wholley, the director of research partnerships for the Foundation for the National Institutes of Health (FNIH). A major consideration in such collaborations is making sure that the initiatives are sustainable from scientific, financial, and administrative perspectives, so that promising opportunities can be realized.
Another example of a collaborative model discussed by workshop participants was the risk-sharing approach provided by the Structural
Genomics Consortium (SGC). The SGC brings together government agencies, academic scientists, philanthropic organizations, pharmaceutical companies, and others to provide resources for discovering and validating drug targets. As an open access research model, the SGC focuses on advancing the science, and therefore projects are less affected by commercial, institutional, or other financial interests.
Looking Toward the Future
The workshop participants discussed short-term and long-term options that could support genomics-based drug discovery. Individual speakers supported achieving a broader understanding of the underlying biology of human disease as a possible way to accelerate the drug discovery process. For example, there is much more to be learned about the role that genetic variants play in disease onset and progression. New technologies and computational tools, such as the development of “tissues on a chip” to understand biological mechanisms and test hypotheses, may constitute a disruptive change which hastens progress.
Another issue emphasized by some workshop participants was the importance of incentives for sharing data and resources among investigators in both the private and public sectors. Greater collaboration across disciplines could increase the sharing of data and expertise and the subsequent understanding of disease mechanisms. Collaborative efforts could reduce duplication, enhance progress, and break down siloes that create barriers within and among sectors. Other workshop speakers noted that many collaborative partnerships in genomics have already been formed, and that the sharing of research data is commonplace. However, it was also pointed out by individual speakers that the use of data from cohort studies around the world may be limited by restrictions on who can access the data and how that access is provided. One way to expand data sharing from these cohorts is to share summary-level data, said Stefánsson. Summary data are available for download through AMP, said David Wholley, but the ability to interrogate individual level data and develop reliable tools is a key aspect of AMP.
Following this introductory chapter, Chapter 2 reviews the features of and challenges associated with large cohorts set up for genetics re-
search and drug discovery. Examples of the useful features of large cohort studies suggested by some participants include consistent data collection methods; a large, stable, and engaged pool of participants; and the ability to perform genotype- or phenotype-based recall studies. Potential challenges associated with drug discovery using data collected from cohort studies include incomplete annotation of intergenic sequences and a lack of knowledge about the effects of rare variants. These benefits and challenges are discussed within the context of specific genomic cohort efforts in Finland, Iceland, an integrated translational medicine institute, a pharmaceutical company, and a direct-to-consumer genetics company. Perspectives on data quality, data sharing, and privacy are also shared in this chapter.
Chapter 3 focuses on drug discovery activities that can maximize the usefulness of genetic bioresources. One such activity is an informatics knowledge portal that curates and details information about the relationship between drugs and genes. This tool allows researchers to test their hypotheses in silico, saving precious time and funds. Another activity is public–private collaboration, where the goal is to bridge the gap from basic research discoveries to new medicines by expanding the precompetitive space and accelerating target validation. One program developed by a patient advocacy organization features open sharing of all data and is addressing the critical lack of biomarkers for Parkinson’s disease. Understanding the biology of potential drug targets and pathways is an important step in maximizing findings from genetic cohort studies.
Chapter 4 features a discussion of multiple business models across government, academia, patient advocacy groups, and industry that are designed to hasten genomics-enabled drug discovery. The goals, scope, design, and available results from each model are presented for consideration and comparison.
The proceedings culminate in Chapter 5, which describes potential actionable ideas proposed by individual speakers for ways to improve genomics-based drug discovery. The chapter begins with an example of a new technology, an in vitro “tissue chip” platform for growing human tissues that has the potential to cause disruptive change in drug discovery and development. Individual speakers proposed the idea that cross-sector collaboration and increased data sharing could create a new ecosystem that could help to fulfill the potential for drug discovery that genomic resources offer.