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Well, good morning everybody. Welcome to the members of PCAST, to the OSTP PCAST secretariat, and to the members of the science and technology community who have joined us for this PCAST meeting. Thank you all for being here, and thanks to those who are watching on the web.

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As usual, we have a full agenda this morning. We will be hearing about the National Science Foundation, Science and Engineering Indicators, the latest report, the 2016 report. We will be having a panel on One Health, and we will be having a panel of cancer researchers

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talking about frontiers in cancer research, and then we will have public comment. So without any further ado, let me ask my co-chair Eric Lander if he has any words of welcome before we launch into the program. Well, let me add my welcome to John's, to everyone here in the room, to everyone watching

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on the web now, and in the archived version someday to come, and especially to PCAST itself who remains tremendously active. Many studies ongoing, more studies under discussion. It's just fantastic to see the energy and commitment of the group, and I want to express

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my thanks. Good. So without further ado, I want to welcome Bathika Khan from the National Science Foundation. If you would take your place at the front, she is going to tell us about the science

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and engineering indicators heavily used by many of us to keep track of progress of the science and engineering enterprise in the United States and around the world. So, Bathika, thank you very much for being with us, and we look forward to your remarks. And her title is up on the screen, Director of the Science and Engineering Indicators

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Program at the NSF. Great. Thank you, Dr. Holdren. Good morning, everyone.

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It's a pleasure to give you a briefing on NSF's Science and Engineering Indicators Report, which we just released two months ago in January of this year. Here is an outline of what I hope to cover today. I'll give you some brief background information on the indicators report.

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I will then give you an in-depth look at some of the major data and trends from the 2016 report. Focus on a number of indicators, math and science performance of U.S. 15-year-olds, S&E degree production, research and development, patents, S&E publications, and knowledge-intensive

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production, all within an international context. And I will wrap up by highlighting some coming attractions in our indicators-related product lines. Science and Engineering Indicators, or indicators in short, is a congressionally mandated biennial

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report on the U.S. and international science and engineering enterprise. The report provides comprehensive data drawn from a high-quality variety of, wide variety of federal, non-federal, and international data sources. The report provides the data in context in a policy-neutral and policy-relevant way.

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And the report does not offer any policy options or policy recommendations. Indicators is prepared by NSF's National Center for Science and Engineering Statistics, or NCSS, under the guidance of the National Science Board. NCSS is one of the 13 principal statistical agencies in the U.S. and housed in NSF's social,

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behavioral, and economic sciences directorate. And the report is subject to extensive review by subject matter experts, federal science agencies including NSF internal reviewers, and national science board members for accuracy, coverage, and balance of the information presented.

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As I just mentioned, in January of this year, we released the 22nd edition of the Indicators Report. And beginning with this edition, indicators will be published as a web-based digital report rather than a printed volume.

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We are very excited about a new digital format. We believe that the report's new digital format will improve access to and understanding of this rich statistical resource. Our new website is responsive on multiple platforms, allows for a greater degree of interactivity

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and easier navigation across chapters and topics. The complete content of the report is downloadable as a PDF. I wouldn't recommend it. The main report just chapters 1 through 7 without any of the appendix tables is 900 plus pages.

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So you can customize and print part of the report as necessary. Because of the central nature of the topic, which is U.S. and global science, engineering, and technology, the report has a wide and diverse audience, including but not limited to policymakers, researchers, journalists, and academics.

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This is why we produce a suite of products, which we sometimes refer to as the indicator's ecosystem, which utilizes a variety of presentation styles to make the content accessible to a wide and diverse audience. So the main report is organized around seven chapters, the topics of which are K through

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12, science and math education, S&E higher education and workforce, research and development, knowledge intensive industries, innovation and global landscape, and public attitudes and understanding in science and technology. In addition, there is an overview chapter, as well as science and engineering indicators

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digest, both of which bring together the major themes and stories from across the volume, and thus draw connections among the different topics. The overview, for example, provides a policy-neutral synthesis on the major themes, whereas the digest uses colorful graphics and brief narrative text to provide a broad brush summary.

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In addition, we have a state indicator's data tool, which provides detailed state-level data on many of these indicators. And what's interesting about the state data tool is that in addition to providing the data in tabular format, the tool utilizes visualization, like maps and histograms, to

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easily and readily demonstrate geographic trends. And the board, you know, on top of the main report and the tool, the National Science Board authors, one or more companion reports, which draw on the data in indicators, but unlike indicators, offer policy recommendations on various issues related to science and engineering

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education and R&D policy in keeping with the board's statutory responsibility to shed light on these issues. So with that background, let's dive into the data to explore the world of science and engineering. As I mentioned, I'll discuss trends in a number of areas, education, degree production, research

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and development, patents, S&E publication, and knowledge-intensive industries. So let me start by giving you a brief context for the data we will discuss. This is sort of the big picture tying all the indicators together. The international and domestic science and engineering trends that we discussed in the

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report can be understood in light of the worldwide trend toward more knowledge-intensive economies and increasing collaboration and competition in science and engineering. In knowledge-intensive economies, science and engineering research, its commercial utilization and other intellectual work are of growing importance.

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Such economies rely increasingly on a skilled workforce as well as sustained development in R&D to produce discoveries and knowledge streams that form the core of knowledge-intensive production, whether in manufacturing industries that produce aerospace, computers, pharmaceuticals, or in services industries that provide financial business, R&D, health and education services.

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So knowledge-intensive production is growing worldwide, and it's increasingly a key feature of both developing and developed economies. So at a high level, education, R&D, and knowledge-intensive industries are intimately linked, and they allow countries to integrate as well as compete in the global marketplace.

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As well as address challenges in the areas of energy, health, climate, and other important domains. And we are very much in a globally connected world, so international trade, supplier chains, international immobiles, students and workers, and their exchange of ideas, global collaboration,

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and global infrastructure tie and connect this very global landscape together. This graph shows average math scores for 15-year-old students in 32 developed countries. The data are collected by OECD. And the main story here is that the average math scores of U.S. students are toward the

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bottom of this list. And students from 23 developed nations statistically scored higher than students in the United States. And this graph shows the average science scores, tells a similar story.

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Students from about 16 OECD countries statistically outperformed our students. So that's math and science assessment scores. This graph shows S&E bachelor's degree production in selected countries. The main story here is that developing countries, primarily China and India, China is represented

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by the black line, and India is actually not on this graphic. We don't have as complete a time series for India. But China and India have seen a very steep increase in their bachelor's level degree production.

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Actually, this shows S&E, but China has seen a steep increase in both S&E and non-S&E areas in bachelor's degree production since the turn of the century. So as of 2012, China and India together produced nearly half of the global total of S&E first university degrees, which are essentially bachelor's level degrees.

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The European Union produced another 12 percent, and the U.S. produced another 9 percent. While there are some challenges associated with comparability of international data, particularly those between U.S. and China, it's clear that China is putting great emphasis on building their S&E capabilities.

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Some brief context on rapid growth rates. Rapid growth rates frequently accompany the early stages of economic and technical development, and this low, as society is mature. So as developing countries devote a lot of resources in R&D, education, and knowledge-intensive

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production, their initially rapid growth rates can exceed those seen in developed countries and allow some of them to approach the capabilities of the developed world. Having said that, though, the long and sustained increases that we have seen in China in a number of areas are quite remarkable.

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This graph shows S&E doctoral degree production, and just like bachelor's level degrees, China represented by the solid black line, and India as well, again India is not on this graphic, have seen a steep increase in their S&E doctoral degree production since the turn of the century. The U.S. total is represented by the solid red line.

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The U.S. however produces more S&E doctoral level degrees than China or India. Now understanding the relationship between degree production in a country and capabilities of its workforce is complicated by immigration, particularly for countries like the United States, which is a popular destination for international students.

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In the U.S., a considerable proportion of S&E doctoral degrees, nearly 40 percent, go to temporary visa holders, and this proportion is higher, about half or more, in computer science, engineering, and economics. However, if past trends continue, nearly two-thirds of these doctoral degree recipients with temporary

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visas will remain in the U.S. for subsequent employment. We call that stay rate. We will now look at data on R&D spending. This graph shows the concentration of global R&D in selected regions.

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East, South, East, and South Asia, that's the Asia Pacific region, North America and Europe together account for nearly 90 percent of global R&D. This graph shows concentration in 2000 and 2013. A notable trend, so global R&D, similar to degree production, global R&D is highly concentrated

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geographically. A notable trend over the past decade has been the rapid increase in R&D spending in the Asia Pacific region, primarily led by China, relative to other R&D performing areas, and as a result of these differential growth rates, the proportion of global R&D taking place

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in the Asia Pacific region has increased from about 25 percent in 2000 to nearly 40 percent in 2013. Can I ask one question? Are those dollar figures corrected for inflation, or are those current dollars?

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They're current dollars. So the proportion between 2000 and 2013 in the Asia Pacific region has gone up from about 25 to 40 percent, and you can see that the corresponding shares in North America and Europe have both declined.

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Now with that global snapshot in mind, let's look at R&D spending in selected countries and the European Union. Again, these are in current dollars. The U.S. and both public and private R&D are included in this graphic.

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The U.S. spends the most on R&D of any single country, accounted for about 27 percent of the estimated global total of $1.7 trillion, and China was second. So the U.S. was followed by China, who spent about 20 percent of the global total in 2013, and China's R&D total, the upward sloping black line, is approaching that of the European

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Union. And you can see the slope of that black line. South Korea has seen a very steep growth in R&D spending over time at around 20 percent average annual rate between 2003 and 2013, compared to about 5 percent average annual

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rate in the United States and EU. Another country that has seen quite an impressive growth is South Korea. It's hard to see the slope because of the scale, but South Korea has seen about an 11 percent average annual growth in the 10-year period between 2003 and 2013.

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Now it accounts for about 4 percent of global R&D. Now let me just focus on the U.S. data for just a moment. Looking at the red line, you can see the flattening in the U.S. growth rate, beginning in 2008 during the Great Recession.

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And then, you know, after 2010, 2011, the uptick in growth returned. The story there is during the Great Recession, U.S. R&D was held up by Federal R&D, which got a boost from ARRA, the American Recovery and Reinvestment Act of 2009. After about 2010, 2011, though, Federal R&D has been on a declining trend by then business

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R&D recovered and has been, you know, got on an upward trend. So the uptick in growth that we have seen in the recent past was driven by the business sector. And if you look at the last five-year period between 2008 and 2013, overall we have seen

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a growth in U.S. R&D, but R&D growth did not outpace GDP growth, okay, in the most recent five-year period. And that's a departure from a long-term historical trend. So just some context there.

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This graph shows R&D intensity, which is R&D as a proportion of GDP. South Korea and Israel, Israel is not on this graphic, have the highest R&D intensity at about 4%. R&D intensity of the big R&D spending countries, U.S., China, and the European Union ranged

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between 2% and 3%. And you know, China's R&D intensity represented by the black line has exceeded that of the European Union. Now, some more context, R&D intensity is a convenient indicator for understanding countries'

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priority to advancing science and technology. It is normalized for size. However, keep in mind that governments have only limited direct control over the size of their economy, as well as total R&D spending, because businesses tend to be the leading

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source of R&D funding in many countries, right? This is why achieving a specific R&D to GDP ratio may be a matter of some luck. Additionally, countries have a tendency to specialize in somewhat different types of activities.

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For example, China does a lot less basic research than the U.S. China spends about 5% of its total R&D spending on basic research compared to 17% in the U.S. Having said that, though, 5% of a very large number means that China does more basic research than France.

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These are some important context for understanding who has comparative advantage in what and how the pieces of the global puzzle fit together. Some context behind the numbers. Really quickly, speaking of relative focus on research versus development, we see a similar

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phenomenon in global efforts to generate clean energy. Commercial investment worldwide in clean energy is focused primarily on development of later stages of technology in relatively mature areas like wind and solar. If you look at total commercial investment globally, China attracts the most, about nearly

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a third, followed by the European Union and the U.S. By contrast, the U.S. leads in a very small segment of total commercial energy that focuses on venture capital and private equity investment, and that part focuses on emerging and future trends in clean energy technologies, again, sort of similar phenomenon that we see across

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the board. In addition to R&D spending, we also look at peer-reviewed S&E publication output of countries and region, and consistent with the trends seen in R&D spending and doctoral degree production, China has seen a rapid increase in its S&E publication output, and

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the share of worldwide S&E publications that China accounts for now is comparable to the U.S. in terms of the number. In terms of the number, China's share of the global total is comparable to the U.S. Now in terms of the number, that's an important caveat because U.S. publications are more

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influential. U.S. publications receive the most citations after we adjust for the size of the research pool of each country, and the U.S. shares this distinction with Canada, Switzerland, the Nordic countries, and the United Kingdom.

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This graph shows data on patents. The share of USPTO patents awarded to inventors in the United States, Japan, E.U., and other developed and developing countries. Patents are a broad but imperfect indicator of invention.

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The propensity to patent vary across technology areas, and many patents don't lead to commercial products or useful invention. But nonetheless, this is one area where we have international data. Unlike R&D spending data, though, unlike R&D spending and education data, the patent data

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show a dominance of the developed world. USPTO awarded about 300,000 patents in 2014, and the largest share of which went to the United States, followed by Japan and the European Union. China and India, which are included in the developing country total in that orange part

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of the vertical bar, the shares going to inventors in China and India are quite small. Now, for any national patent office, the data on the number of patents don't tell us anything about the quality of those patents, which is why we look at triadic patents. Triadic patents are patents where inventors simultaneously seek patent protection in three

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of the world's largest markets, the US, Europe, and Japan, and they indicate patents that are likely to have high commercial value. Just like USPTO patents, US, Japan, and E.U., so the developed part of the world, account for the vast majority of triadic patents.

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The shares going to China and South Korea are increasing, so they accounted for about 10% of triadic patents in 2012, but they still receive far fewer triadic patents than the global leaders. I'll wrap up my presentation with showing you some data on knowledge and technology-intensive

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industries, or KTI industries. KTI industries include high-tech manufacturing and knowledge-intensive services. This graph shows high-tech manufacturing, which includes aerospace, computer and communications equipment, semiconductors, pharmaceuticals, and scientific instruments.

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High-tech manufacturing is an order of magnitude smaller than knowledge-intensive services, but they are a big lever of R&D, and they account for almost half of all business R&D in the US. In high-tech manufacturing, the US retains a slim lead over China.

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The US accounts for about 29%, and China accounts for about 27% of global output, and again, by the steep slope of that black line, you can see that China has seen a dramatic increase over time, so output has risen by a factor of 10 between 2001 and 2014. In some context, we see the curves converging, but some context which is helpful is, US and

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China concentrate in somewhat different types of activities, so the US is particularly strong in aerospace and scientific instruments, the supply chain for which is largely domestic. China is the largest global producer of ICT, information and communications technology goods, and pharmaceuticals.

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And ICT production is the most globalized among high-tech industries, and usually the supply chain span multiple countries. So the part of production in China is heavily reliant on final assembly of inputs and components that are either designed or produced in other countries.

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So the activity in China is particularly reliant on this final phase of the supply chain. And very quickly, you can see how countries have recovered from the Great Recession. If you look at the US line, you sort of see the flattening around the Great Recession, and the uptick in growth seems to be back.

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China just kept on going, was barely affected during the Great Recession. If you look at the green line for EU, output has been largely stagnant due to weakness of member country economies. In Japan, the light blue line output has actually declined since the Great Recession.

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So it's quite interesting to see how countries recovered from the Great Recession. And the last slide shows commercial knowledge intensive services, includes business, financial and information services. Business services include computer programming, R&D services, those are some examples.

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Financial services include banking, insurance, securities. Information includes traditional phone, wireless communication, TV distribution, etc. Unlike high-tech manufacturing, though, in commercial knowledge intensive services, global output is concentrated in the developed world.

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The US and EU together account for more than half of the global output. China remains weak, accounts for 10% of the global output, but again is making rapid progress. Other countries actually have seen quite an impressive growth, particularly Brazil, India and Russia.

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And the growth in Brazil was led by financial and information services, and India was led by business services, and particularly computer programming, and Russia's growth was led by business and financial services. And very similar trends here in terms of recovery of the recession.

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And you know, when we talked about SNE publications, I wanted to add, yes, China has seen a lot of China's deep growth, nearing US in terms of number of publications, but other developing countries, India and Brazil, have seen dramatic growth also. So in summary, how are we doing in the US?

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The global SNE landscape has experienced quite dramatic shifts over time. Over time, we have seen a catching up in particular indicators of SNE activity in parts of the developing world. We have seen specialized concentrations for developed nations that have historically

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led global efforts in SNE, right? So a multi-polar world for SNE is emerging after many decades of leadership by the developed world. These developments have taken place in the context of an increasingly interconnected

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world, capacity building around the world in R&D and human capital, improvements in communications technology and greater international collaboration in SNE. So the US still leads in a number of areas, SNE doctoral degree production, high impact SNE publications, intellectual property, knowledge and technology intensive production.

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But you know, parts of the developing world, particularly the Asia Pacific region led by China, is working on improving their capabilities in these areas. So we see these hubs of growing importance globally. And we will continue to watch this fast-changing global landscape.

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Very quickly, I want to highlight some coming attractions in our indicators-related product lines. I mentioned that the National Science Board authors multiple companion briefs. So they plan to release multiple companion briefs in 2016 and 2017.

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Those have been redesigned to be short, timely rigorous and born digital. So please stay tuned for those. The board is also in the final stages of producing an infographic, which is a visual tool for understanding some of the key global trends that I just showed you.

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The board is also organizing a user workshop to rethink the future of indicators. So how can we make our data more relevant? Do we have the right mix of products? How do we balance the high quality of our data and analysis in a world that expects

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shorter cycles for information turnaround? The workshop will be a first step in addressing some of these questions. And speaking of data quality, I wanted to quickly highlight that we work with our international counterparts to develop internationally comparable data that are useful and relevant for policymakers

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and a wide variety of data users. Already 10 years, the OECD engages the policy community, data users and data providers into an open dialogue to review and develop its long-term agenda on science, technology and innovation or STI data and indicators.

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This event is known as the OECD Blue Sky Forum. The last forum was in 2006, where Dr. John Marburger provided the keynote speech. The upcoming 2016 forum will be in Belgium in September, and we will carefully review the outcome of the forum to identify STI indicators for inclusion in future volumes of indicators.

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And finally, WMPD, Women, Minorities and Persons with Disabilities in Science and Engineering is another Congressional-Emandated publication that we produce, which has detailed data on employment and occupation in S&E. Thank you, and I'm happy to take any questions you may have.

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Okay, thank you very much. This is our usual format of PCAST members raising their flags when they would like to comment. Craig Mundie was the first one up. Thank you, Tika.

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That was a good presentation. I was looking through the digest version that you handed us. And one thing that strikes me both in your presentation and most of the graphs that are there, with the single exception of one, the B-Graph on page seven, U.S. R&D performance

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by type of R&D and performance in that performing sector. Historically, all of this has said R&D, R&D, R&D like that was one thing. In that one graph, you break it down into basic, applied, and development. I think the separation is absolutely crucial, particularly if you're trying to make policy

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decisions and predict the future. And I think the fact, I mean, many of the trends are troubling enough, whether in education, et cetera. You look at China and then recognize that they're still sort of developing.

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I mean, they have four times our population. They have a dilution of apparent GDP per capita GDP as a result, and yet we measure even comparing against that and look not so good. So I'm curious whether, as you go forward, you'd entertain the idea of very systematically

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reporting on basic science, applied research, and development, and really, in a disciplined way, keep them separate. So that's a great question. Yes, basic applied development.

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Although we get some criticism because R&D is not as linear as the basic, as the trio would suggest, basic applied development, but it is a very important distinction. And again, the digest presents a very small subset of indicators. If you look at chapter four of the indicators report, we have a lot more analysis by what

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we call type of work, which is basic applied and development. And you can see how the funding and the performance looks like by sectors and some international trends also. Your remarks demonstrated you have the data.

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What troubles me is when the digest version, which is largely what policymakers are going to consume, is presented, and it tends to re-consolidate everything. I think it really masks how bad our situation is on a projected basis. And so I'm sure the data is there.

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It's all in how it comes out. I just offer that thought. Thank you for the comment. I am next on my list.

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And let me start by saying thank you not only for the presentation, but for the amazing work that you and your team do on this document. I have often commented that I may be one of the few people around the country who has used the great, fat, now 900-page annual or every other year report as a textbook in a

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course on introduction to science and technology policy. It's the best such introduction, I think, out there. The question I have, though, has to do with timeliness and updating. The principle shortcoming of the document is, if you look at this one, for example,

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in most graphs the latest data are for 2013. A few you have 2014, particularly for the United States. But mostly it's 2013. And since it only comes out every other year, in 2017 we'll still be looking at a version

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of this that has 2013 data. The question is, in the electronic age, when so many more of the relevant basic statistics are becoming available online, including in other countries, what would it take for you to sort of lift your game in terms of the timeliness of the data, including the possibility

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of issuing sort of an interim report online with some of the most important data updated in between the every two-year publication of the giant volume? Yes, thanks for that comment. Point well taken.

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Having worked at a policy shop before I joined NSF, I know how timeliness is at the heart of being relevant. And the board is, this is very central to the board. The board frequently engages in this conversation.

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And our hope is, we are taking baby steps, right? This is, the production process for indicators is about 18 months, and it is a biennial publication. A lot of coordination happens across federal agencies, contractors, data providers, data users.

00:36:36.640 - 00:37:01.800

Our hope is, the digital format is a first step, it's a baby step, but it's a first step in the right direction. And we are very much interested in looking into what kind of efficiency gains can be had in the production process from this digital version, and how can we build on that, perhaps

00:37:01.800 - 00:37:22.800

to provide interim data updates? Because again, we are in this world where policy makers and data users, they expect timely relevant data. So one avenue that we are exploring is, perhaps some intermediate data, publishing those

00:37:22.800 - 00:37:54.240

intermediate data via either science board companion report or NCSS publication vehicles. So I mentioned WMPD, some of the workforce and education data will be updated in WMPD. Some of the KTI production data, some of the R&D data, we are looking into publishing some updated data at some point before 2018, either via NCSS or our board publication vehicles.

00:37:54.240 - 00:38:12.080

But that's a great comment, and we are also looking forward to this workshop, which is again a first step in exploring some of these options of how do we become more relevant? So what are the options and opportunities, how our data gets used, and how can we focus on the cycle?

00:38:12.080 - 00:38:28.880

And the cycle is central to, again, sort of the board talks, whenever the board talks about rethinking indicators, I see two main themes, which is data updates, interim data updates, and outreach. So we will be working on this.

00:38:28.880 - 00:38:34.820

It's very much central to what the board is thinking about. Good. Thank you very much. Maxine Savitz is next.

00:38:34.820 - 00:39:00.520

I also thank you and find them very, it's very useful over the time. It's somewhat related to Craig's basic and applied, but it's, and it's the U.S. expenditures, you know, the amount is, in the 80s we crossed over where the federal government stopped being the major funder, and industry, a business more, and business tends to be more applied.

00:39:00.520 - 00:39:17.000

But do you break down in the other countries, where you give the total R&D, do we know the breakout like in China, how much is business versus... For some of the major countries, we have that data. So for the U.S., we have very detailed data by, you know, the type of work, and you are

00:39:17.000 - 00:39:38.160

absolutely right. You see that somewhere in the 1980s, businesses sort of crossed the 50% line, and became, you know, the major funder of R&D in the U.S., actually my PowerPoint presentation had a slide in the back where it shows how the R&D ecosystem in the U.S. has changed over time.

00:39:38.160 - 00:39:55.520

So sort of federal share has been going down, and business share has been going up, and it's, yes, exactly. So the black line is the business, so somewhere in the 1980s, the two crossed, and businesses became the majority funder.

00:39:55.520 - 00:40:14.200

And I believe... Funding source for U.S. basic research, again, sort of, shows the proportion of basic research funded by federal government, business, universities, and colleges. And the same data, I have the same data for development.

00:40:14.200 - 00:40:27.120

In development, you see that businesses do the majority, whereas this is basic research, federal government does it. And yeah, for some of the countries, we do have that breakdown available, not for all countries.

00:40:27.120 - 00:40:33.120

So for China, we have that breakdown available. And so that will be in... I can go online and get that data. Yes.

00:40:33.120 - 00:40:40.840

That data is in Chapter 4, which is national and international trends in research and development. Good. Thank you.

00:40:40.840 - 00:40:50.840

The last question will be from Michael McQuade. Thank you very much. I would continue to echo my colleagues. I think both at the detail level and at the macro level, it continues to be a very useful

00:40:50.840 - 00:41:03.400

report. The timeliness, something we all hope gets better. So two technical questions. On the graphs that show the geographic spend, the sort of center section, is that where

00:41:03.400 - 00:41:22.320

the dollars are actually spent or where the ownership of the dollars comes from? So as U.S. expenditures include what U.S. companies spend overseas? U.S. expenditure includes... You know, that's a good question.

00:41:22.320 - 00:41:34.400

We do break the data down for multinational enterprises. No, I believe it's expenditure in the U.S., actually. So it's where the money is actually spent? Yes.

00:41:34.400 - 00:41:48.560

Yes. And I kept referring to Chapter 4. Chapter 4 has a section on multinational corporations. And there you can look at U.S. M&E's and how much they are spending in the U.S. versus

00:41:48.560 - 00:42:01.000

how much their foreign affiliates are spending abroad. And you can look at the opposite also. So foreign multinational enterprises, how much they are spending in the U.S. Yeah.

00:42:01.000 - 00:42:27.120

Javad Tapir, have you recall any comments or trends in that data? Off the top of my head, I know that if you look at U.S. M&E's and their foreign affiliates and the R&D that they do in the... I think it's about in the 40 billion range that U.S. M&E's, foreign affiliates, the R&D

00:42:27.120 - 00:42:47.960

that they do abroad. And an interesting trend there is this. In terms of the countries that host that R&D, so foreign affiliates of U.S. M&E's and where they are spending their R&D, the host countries are very much in the developed world.

00:42:47.960 - 00:43:10.240

So I believe Europe and Japan, that's where most of this R&D is hosted. One exception is professional, scientific, and technical services. And there you see that in addition to these traditional centers, Europe and Japan, China and India are playing an increasingly important role.

00:43:10.240 - 00:43:19.560

So yes, I mean, if you go to chapter four, you can find much more details than what I've just given you. Okay. Thank you.

00:43:19.560 - 00:43:25.280

Thank you. Okay. Well, that takes us to the end of our allotted time for this topic, Batika. Thank you very much for that presentation.

00:43:25.280 - 00:43:45.800

Thank you. Thank you for the work that you and your team do on this most valuable compendium. We look forward to continuing to draw on it as we contemplate the state of the U.S. federal R&D enterprise and the larger research, development, and innovation enterprise, including in relation

00:43:45.800 - 00:44:03.920

to the rest of the world. We are now going to make a transition to the next topic. The next topic is One Health. And the moderator for that discussion will be my PCAST co-chair, Dr. Eric Lander.

00:44:03.920 - 00:44:29.520

Great. And if I can invite the panel to come up to the front, if we've got them here. So One Health is a succinct description of the fact that we can't just think about human health and animal health as separate matters.

00:44:29.520 - 00:44:49.600

That from the point of view of, for example, infectious organisms, the development of antibiotic resistance, many, many things, pathogens don't really have such sharp lines between humans and animals. They move back and forth between reservoirs, sometimes within the life cycle of individual

00:44:49.600 - 00:45:07.360

pathogens, sometimes over the course of evolutionary time, which for pathogens can be rather short, sometimes decades. So for all of those reasons, a number of agencies within the United States government have been thinking about a much more integrative approach to health.

00:45:07.360 - 00:45:33.200

And PCAST encountered this point of view very squarely in its report on combating antibiotic resistance, but it's a broader concept than that to be thinking about One Health. And today we're joined by three speakers who can help us think broadly about this. We have Marguerite Papuano from, I guess, the CDC liaison to the FDA, so maybe from

00:45:33.200 - 00:45:51.920

both CDC and FDA. And of course, that's already a really important sign of the oneness of the One Health is that it stretches across CDC and FDA and USDA. We have Ellen Silbergeld from Johns Hopkins University, Professor of Epidemiology, Environmental

00:45:51.920 - 00:46:08.720

Health Sciences, Health Policy, and Management. And we're also joined by Keith Hamilton at Kansas State University, where he's the Executive Director of International Programs in the College of Veterinary Medicine. We haven't actually discussed the order in which you guys are going to go, but I'm guessing

00:46:08.720 - 00:46:24.560

that maybe Dr. Papuano is going to start, and we're going to move down the panel. So welcome. Thank you so much for coming, and we're very eager to hear from you. Is that on?

00:46:24.560 - 00:46:42.280

Great. Well, good morning, and thank you very much for the invitation to share my thoughts and views on One Health with you today. How I view it, the opportunities and benefits it affords, current gaps and challenges, what's

00:46:42.280 - 00:47:03.760

required to implement it most fully, and some positive examples of One Health to date. A definition used for One Health very frequently is that which was developed by a task force of the American Veterinary Medical Association and the American Medical Association. And it's on my title slide.

00:47:03.760 - 00:47:35.280

The collaborative effort of multiple disciplines working locally, nationally, globally to attain optimal health for people, animals, and our environment. My introduction to One Health concepts began in parasitology class in veterinary school in the early 1970s, followed by the study of epidemiology and graduate school where

00:47:35.280 - 00:47:53.280

I learned about webs of causation and the interconnectedness between human animal and environmental health under the mentorship of Dr. Calvin Schwabbe, who literally wrote the book Veterinary Medicine and Human Health. I went on to work as an epidemiologist at the Centers for Disease Control and Prevention

00:47:53.280 - 00:48:11.920

for 24 and a half years. I also have worked as a professor of infectious diseases in the School of Public Health at the University of Minnesota for three years and as executive director for the Association of American Veterinary Medical Colleges for four years.

00:48:11.920 - 00:48:37.720

At CDC, I have worked in a variety of positions and programs, none which directly involved animal clinical care, but in which I applied my comparative medical education and training to bring a different perspective to the table in solving global public health problems, such as malaria, HIV-AIDS, evidence-based public health, and surveillance of infectious

00:48:37.720 - 00:48:56.000

diseases. Over the years, I came to understand that a One Health approach was important to most effectively address the full spectrum of challenges confronting the health of people, animals, and our environment.

00:48:56.000 - 00:49:28.160

For many years, I used this very graphic to convey the broad spectrum of One Health going beyond infectious diseases, which many do connote most closely with the topic. Largely following increasing urbanization of our human population, people began to lose awareness and understanding of our fundamental connections and reliance on nature, biodiversity,

00:49:29.080 - 00:49:51.680

and ecosystem health, until a continual series of highly visible emerging infectious disease outbreaks began to occur globally. West Nile, bovine spongiform and sepulophathy, highly pathogenic avian influenza, H1N1 influenza, SARS, Ebola, MERS, and it goes on.

00:49:51.680 - 00:50:18.040

The transmission of pathogens from animals to people led an Institute of Medicine National Resource Council Committee in 2009 on sustaining global surveillance and response to emerging zoonotic diseases, of which I was a co-chair, to include this diagram in our report, emphasizing the pathogen and disease pathways in animals before transmission into the human population,

00:50:22.160 - 00:50:48.240

and then major, highlighting major drivers of human population and environmental drivers creating the environment for disease emergence, transmission and spread. The small squares located on the bottom of the X-axis in the report actually refer to a series of interventions, which can be applied to intervene and prevent and control these

00:50:49.880 - 00:51:12.360

disease transmission pathways. The drivers include human population factors, food and energy, production and consumption factors, climate change, globalization, and others. So another way I have, in my own involvement of One Health, have come to look at the full

00:51:12.360 - 00:51:42.120

spectrum of One Health is shown on this slide, where individual clinical and population disease challenges are represented in the top and the middle tiers, but with this graphic there is a much better reflection that these two top tiers are a consequence of the bottom tier, which is an unbalanced, what we're seeing now, comes from an unbalanced ecosystem that

00:51:46.880 - 00:52:14.080

is being impacted on by food and energy, population and consumption for our rapidly growing human population, on our way to 8 billion people in growing. To date, the majority of efforts in One Health have been aimed at the middle and the top tiers, with the critical importance of One Health to ensuring sustainable, healthy ecosystems

00:52:14.520 - 00:52:36.280

upon which all species depend is, in my view, the most important gap and has been largely overlooked as an opportunity to apply the One Health approach. And environmental expertise, for the most part, has not come in fully as a full partner in our One Health try-on.

00:52:36.280 - 00:53:02.680

Addressing the bottom tier again, in my opinion, is the most important One Health challenge and gap and opportunity of our time. Related gaps and areas of concern include failure to adopt a comprehensive, whole systems framework to identifying and addressing our complex health problems of the day, with multiple

00:53:02.680 - 00:53:29.040

disciplines and sectors participating, moving away from single sectors and disciplines, attempting to solve single problems, single species issues, an approach which in very early stages inevitably results in unanticipated other problems, those problems unanticipated by a single discipline trying to solve a single problem.

00:53:29.040 - 00:53:54.800

Other important gaps include a lack of appropriate metrics and a lack of One Health competencies and disciplines among the professions. Improving One Health into action, then, requires policies and funding that enable and reward collaboration across sectors, teamwork in the field, working at the front lines, multidisciplinary

00:53:54.880 - 00:54:23.120

integrated research, cross-departmental integrated education and training, and continual evaluation and improvement. To implement One Health, therefore, follows, including leadership, failing to frame our complex health problems in a comprehensive framework, and then not bringing different disciplines

00:54:23.200 - 00:54:55.760

to the table to work together to solve those problems, siloed funding, and I use the term jurisdictional in the sense of government departments, and missions, which prevent introverts agency and departmental funds coming together to help fund the work, this collaborative work that's needed, and also the lack, therefore, of rewards and incentives. Confusion over

00:54:55.760 - 00:55:19.920

what One Health is, and I've listed a couple of different terms that have been used, so again it's confusing to a lot of people, and then there are some basic workforce issues which I'd be happy to comment in if there's further questions. At this stage, however, and all this said, I will close my remarks with several positive examples of One Health

00:55:22.560 - 00:55:45.560

applications. First, there's a growing awareness of the importance of the approach by public and private sectors to solve a variety of health problems, and the leadership by PCAS and application of this to antibiotic resistance is about one example. International recognition, upon which you will hear more shortly. The report on planetary health by the Lancet

00:55:45.560 - 00:56:11.720

Rockefeller Planetary Health Commission. New sources of funding for One Health approaches to complex problem solving, and I've listed a variety of examples there on some of the funds more are needed. Successful community programs have been initiated, re-establishing human connections to nature, ecosystems, and all other species that we share the earth with.

00:56:13.240 - 00:56:43.720

Workforce studies that highlight the need to support assurance of a One Health workforce. There's a growing number of One Health academic educational and research programs and centers in our universities, and most recently, and I'll close with this, is a recent legislation introduced by Senator Al Franken from Minnesota. It's just been introduced, the One Health

00:56:43.720 - 00:57:08.940

Act of 2016. Although it still is focused on the middle tier for the most part of most of the pyramid I've used, if passed, it would be an important step forward to building that enabling environment to which I referred to previously. Thank you very much. That's great. Thank you for that big framing of One Health. It's very good to see the whole

00:57:08.940 - 00:57:26.180

frame there. We'll turn next to Dr. Silbergeld, and we'll hear from each of our speakers, and then we'll turn to PCAST for general discussion. Dr. Silbergeld? Thank you very much, and thank you for the invitation to participate. I am going to focus much more narrowly, taking advantage of the fact that I knew that you would present the

00:57:26.180 - 00:57:52.560

overview, and talk about one issue in which the One Health perspective is absolutely critical, and it is a bridging point between ecosystems and non-human and human populations, and that has to do with agriculture, which takes place in the environment, involves on human species, and eventually involves ourselves as persons who interact with agriculture as well as consume

00:57:52.560 - 00:58:16.960

its products. I also want to pay a great deal of homage to the many DVMs who have been my students at Johns Hopkins, who have really educated me in how to cross-talk between our disciplines. Thank you very much. Okay. So my outline here is really to talk about how the One Health perspective is, from my experience of over 15 years doing this kind

00:58:16.960 - 00:58:44.960

of research, moving from a really deep, geeky, mechanistic involvement in toxicology, to understanding much more broader perspectives as best I can in the domain of intensive food animal production. Let's see. That's this. It's not working. Okay. I'm sorry. I guess this is not going to work. So I'll talk about some ecological impacts briefly. Health impacts

00:58:50.960 - 00:59:13.960

a great deal, and then how thinking ecologically, which is something I had some earlier training in, in terms of applying spatiotemporal analyses, and my engineering background in terms of understanding flow of materials quite literally when I talk about resistance genes, and then the interactions of what we look at as microbiomes and also macrobiomes by which we might talk

00:59:13.960 - 00:59:34.960

about populations of organisms, and leave finally some critical questions and research. There are extraordinary ecological impacts of the way in which we produce animals. This was an outbreak of harmful algal blooms in my state of Maryland in the heart of the chicken industry, which was a driving factor through eutrophication of surface waters and the over

00:59:34.960 - 00:59:56.960

production of algae, including harm species that could generate neurotoxins. This is the Pocomoke River, and what you see in these long white buildings are chicken growing houses. My state is actually a part of the fourth most dense, most productive region of the country in producing broiler chickens, a fact that was little known. The fact that it affected

00:59:56.960 - 01:00:22.960

non-human species is also evident in the insert picture in the upper left-hand corner, which was this characteristic kind of scarring of fish and fish kills that occurred putatively in association with fisteria, although we've never really been able to pin this down mechanistically. But ecologically, there's sort of a doomsday machine that we've created in the way we've

01:00:22.960 - 01:00:44.960

developed intensive agriculture. Much of it in many countries of the world takes place in highly sensitive coastal ecosystems. On the top line, you see North Carolina, which is the second or first, there's a war with Iowa over this, in hog production in the United States, and it's right in the Tidewater Zone, which was heavily impacted by Hurricane Floyd, as you may

01:00:44.960 - 01:01:09.960

recall, leading to the destruction of the industry, which was rapidly rebuilt with no change in practice. And the lower area is showing the Pearl River Aquaculture System, which also really takes place in the Pearl River Delta in Guangzhou and China. Therefore, I mean by saying agriculture is embedded in ecosystems. Now, animals, humans, and food are extensively

01:01:09.960 - 01:01:31.960

connected. As my colleague just said, most emerging infectious diseases that have been recorded over the past 30 years are zoonotic in origin. Now, there's kind of a circular logic there, because that's sort of where people look. But nonetheless, it is an important flow of pathogens, as we've seen most recently. But I want to make the point, because many of my

01:01:31.960 - 01:01:58.960

colleagues in one medicine like to go out and look at monkeys in the rainforest, and that's much more fun than going to chicken houses on the eastern shore. But I want to state very clearly that the most intensive and extensive animal-human contexts involve agriculture and human interactions with domesticated animals, not wildlife. So if we're really trying to make a big impact on this nexus in

01:01:58.960 - 01:02:22.960

order to prevent damage and disease in all species, I think we have to give considerably more attention than we have to agriculture. Our production methods disrupt shared ecosystems. As I've told you, and then, of course, a great focus of my work, coming admittedly from a human health background, has been the use of antimicrobials in food animal production, which drives antimicrobial

01:02:22.960 - 01:02:49.960

resistance emergence and dissemination through microbiomes of animals, humans, and ecosystems, which is what I mean by materials flow. So this is one of the most recent, although now quite old, analyses of emerging infectious diseases. And what these data show is that most of these diseases that have been charted over the past 60 years or so are actually drug-resistant, and that's what makes them

01:02:49.960 - 01:03:15.960

novel. In many cases, they're the same old pathogens that we knew quite well, like Staph aureus, but now they are requiring highly dangerous treats of single or even multi-drug-resistance phenotypes. They're also largely zoonotic in origin, as shown on the right-hand side of this slide. So I think we see here an opportunity to pose the question as to whether our interactions with animals,

01:03:15.960 - 01:03:44.960

which include the administration of subtherapeutic doses to antibiotics, are linking these two prevalences, as shown in these graphs. And I do want to say from the human health point of view that the burden of antibiotic resistance is enormous. This is from the UK review on antimicrobial resistance conducted by the Ministry of Health, and it just shows the burden of deaths attributable to antimicrobial

01:03:44.960 - 01:04:10.960

resistance every year. This is a projection to 2050, but many of you may know that with the discovery of salistin-resistance genes in pigs last December, the Lancet and others said, we may really have now crossed over the threshold to the end of the antibiotic era, not to be a doomsday stare. The drivers of antimicrobial resistance, which we've known forever, and Fleming predicted them and

01:04:10.960 - 01:04:38.960

Letterberg gave us some of the mechanisms, really indicate that all antimicrobial resistance, all antimicrobial use, inevitably contributes to selection for resistance. And now we know also, starting with Letterberg's work, that it drives the remarkable ability for pathogens and commensals to exchange resistance genes without cell division. Therefore, speaking as a simple engineer, the intensity of antibiotic use has

01:04:38.960 - 01:05:04.960

got to have something to do with the intensity of the emergence and perhaps the comprehensiveness of poly-resistant phenotypes. Inappropriate antibiotic use is particularly dangerous in this setting of kind of inevitable forces between us and the microbial world, selects and disseminates resistance particularly well. Now this is my definition of inappropriate use, I'm sorry to say this, and that is the

01:05:04.960 - 01:05:33.960

antimicrobial use in animal feeds. And despite various hand-waving by the Food and Drug Administration, nothing is really happening here. I just went and looked last night at the most recent report for FDA and actually uses of antimicrobial drugs in animal feeds has increased since the FDA began issuing its totally ineffective statements about what industry should or should not do. And it is a major component of total antibiotic

01:05:33.960 - 01:06:02.960

production in this country and in many other countries. Now we've adopted a kind of ecologic perspective on trying to figure out how this operates in ecosystems and among and within various populations in which we are now really not focusing so much on specific pathogens or even specific drugs, but the movement of genes and gene flow within microbiomes in animals, within soils and ecosystems, and finally

01:06:02.960 - 01:06:28.960

within the food supply and even within us. Some of these webs of transfer, you know, are quite literal. This is one of my students, one of my very good DVM students now at CDC who did her thesis looking at methicillin resistance in pig farms in Peru. And while she was there, this is what is known by the industry as a lagoon, which is where the waste is stored in what I would call an open cesspit, and it's drained largely

01:06:28.960 - 01:07:00.960

now. But what you see are migratory birds hanging out in this lagoon because there's a lot of spilled feeds and other nutrients for the picking here, and they are then going to go up and down the western Pacific flyway carrying these resistant genes to a variety of other wildlife species as well as human populations. This also contributes very directly in a molecular sense to building the capacity of microbiomes in terms of the

01:07:00.960 - 01:07:32.960

genetic tools at their call for resisting novel or continued antibiotic use. And this is a study by Nandi in which they looked at the resistance genes that move from poultry litter into soil, and we have a temporal study going now on the eastern shore of Maryland to look at the historical patterns of the enrichment of soils that have been receiving animal waste over the period of intensive food animal production and antibiotic use.

01:07:32.960 - 01:08:01.960

By the way, the use of antibiotics in animal feeds began two years after the production of Streptomycin, so it was very early, and it's continued unabated to this time. Gordon Wright and others have suggested that a way to look at this is the concept of the resistome, which exists within microbiomes, that's two ohms for one sentence, and it's all the antibiotic resistance genes and other mechanisms, so it can be transmissible genetic elements which provide microbial

01:08:01.960 - 01:08:31.960

communities with the resources to resist both natural and anthropogenically applied antibiotic stress. And this can be very complex. This is a very nice story showing that these communities are highly organized, and we need to understand this organization so we can figure out how these genes flow within and between microbial communities. And I've really thought of this as a kind of, excuse me, for this cloud computing aspect of the way bacteria behave in that they

01:08:31.960 - 01:09:04.960

really tap into this resistome, which can even be external to bacteria. It can be genes within soil, as Jose Martinez has pointed out, and through quorum sensing and signaling can actually call upon that resource and incorporation when a population or subpopulation is under threat. And of course the information that's encoded within the resistome is genetic. And this is just, Jose Martinez speaks kind of riff on this concept of antibiotics and antibiotic resistance genes in the

01:09:04.960 - 01:09:37.960

environment and his suggestion that we should think about these as environmental pollutants, and that in areas of intensive use and intensive disposal of waste that contain animal waste, even human waste, this could in fact be a domain in which we really need to enlarge our focus on what antibiotic of environmental pollutants mean. It's not just food, of course, because these operations take place within human communities as well as in ecosystems and therefore a variety of populations of animals and humans

01:09:37.960 - 01:10:04.960

can be exposed. We ultimately may all be exposed through the consumption of livestock and poultry products, even of vegetables that are grown in soils where animal waste have been used for fertilizer. And it's really in our feeling now a story of many microbiomes and our best way to try and understand it really does, as the previous speakers say, obliterate any boundaries between various host species. Thank you for your attention.

01:10:04.960 - 01:10:35.960

That's great. Thank you. And we particularly appreciate your very frank comments about some of the issues there, the issues that PCAS has indeed engaged with in the past. And then we'll turn to Dr. Hamilton. And after Dr. Hamilton will have an opportunity for a general discussion with the PCAS. So, yeah, thank you for the opportunity for being here. Before I started at K-State, I worked for an organization called the OIE, which is the

01:10:35.960 - 01:11:10.960

World Organization for Animal Health. And I worked on a number of One Health projects with WHO and FAO. So my perspective is really an international policy perspective. So that's what I'll talk about today. And I'll focus on emerging infectious diseases. And as our other speakers have pointed out, diseases like Ebola, MERS, Pandemic H1N1, SARS, bird flu and Nipah virus all emerged over the last 10 years and caused widespread international concern. Several of these events were declared by WHO to be public health

01:11:10.960 - 01:11:43.960

emergencies of international concern, which has a legal basis. And as I said, all of these diseases have an animal origin. They don't come from outer space. They come from animals. And it's an absolute certainty that in the future infectious diseases will continue to emerge and have an impact on humans. And they will continue to emerge from animals. And as Marguerite and Ellen pointed out, the drivers for disease emergence include climate change, land use and population growth. And as a result, we're going to see greater

01:11:43.960 - 01:12:19.960

opportunities for diseases to emerge and for them to spread rapidly around the world. Although it's not possible to predict when and where the next disease will emerge, it is possible to be prepared and to put systems in place to detect and respond to these diseases quickly and minimize their impact. And we need no reminding that in addition to the terrible human and societal costs of emerging infectious diseases, they also have a very significant economic cost. And it's well acknowledged, there's been many reports published by World Bank

01:12:19.960 - 01:12:59.960

and other organizations demonstrating that the costs of dealing with biological disasters are far greater than the relatively modest costs of investing in health systems globally and in preparedness. In very oversimplified terms, when it comes to infectious diseases, the animal and human health sectors collaborate in three broad areas. The first area is really where the experiences of the human health sector dealing with purely human infectious diseases can help the animal health sector fight animal diseases with comparable approaches and vice versa.

01:12:59.960 - 01:13:40.960

And a good example of this is two diseases, smallpox and rindepest, smallpox a human disease, and rindepest an animal disease. And the two sectors shared their experiences for the betterment of each eradication campaign, and they're going forward sharing experiences with polio eradication and looking ahead to measles. Other infectious diseases cause significant problems for both animals and humans, for example, bird flu H5N1, antimicrobial resistance, brucellosis. So for these diseases it's really in the mutual interest of the animal health sector

01:13:40.960 - 01:14:11.960

and the public health sector to do something because there'll be positive impacts for agriculture as well as human health. And just to point out that antimicrobial resistance is also a big issue for food and animal health as well as public health because if you run out of antibiotics it will have a big impact on animal health and welfare and food production. The third category is really infectious agents which are shared by human and animal populations which have a very serious impact on human health

01:14:11.960 - 01:14:47.960

but don't really have an impact on agriculture or the animal health sector. And examples of these types of diseases would be MERS which has a very mild clinical disease in camels, they get a runny nose, but it's caused very serious problems with human health in the Middle East and led to hundreds of deaths in people. Also rabies doesn't have a very significant effect on agriculture or animal health but it kills up to 60,000 people a year in the most horrible way, mainly children in Asia and Africa.

01:14:47.960 - 01:15:21.960

So for this third category the animal health sector is now playing an increasing role in taking action to protect human health when there are very little immediate benefits for animal productivity or agriculture. But it needs more resources to do this because traditionally it's been focused on controlling diseases that have an agricultural impact. And for the diseases and events like antimicrobial resistance that have less of an impact on agricultural productivity but which do have an impact on human health

01:15:21.960 - 01:15:53.960

there may often be no perceived immediate benefit when farmers and producers take action. But the intervention should still be applied because it's for the good of the public and for humanity. So in such cases financial, behavioral and social incentives are needed to get producers and industry to take action and economic investments are also required when there is no immediate return for the producer or the industry when they take action.

01:15:53.960 - 01:16:29.960

And a fairly simple message but the best way to reduce the impact of zoonotic diseases, these are the diseases spread from animals to humans is to tackle it in the animal source and to prevent spillover to the human population at an early stage. This is true of many endemic and exotic animal diseases such as avian flu, rabies, brucellosis. And detection and control in the animal source is much cheaper and more effective than trying to deal with the disease once it has spilled over into the human population.

01:16:29.960 - 01:17:06.960

And zoonotic diseases can be detected and controlled in animal populations before humans are infected when you have strong well-governed and well-resourced veterinary services. But unfortunately in many parts of the world due to underinvestment in veterinary surveillance many of these diseases such as rift valley fever, Ebola, avian flu are only detected in humans even when they have been circulating in animals for weeks or months before. But if action had been taken earlier it could have prevented a significant human health problem.

01:17:06.960 - 01:17:52.960

On a global level the inability and the weakness of one country's health system to detect and respond to a disease event is a threat to all other countries. If they don't detect disease and contain it quickly disease spread rapidly across borders showing little respect for national borders and controls. Likewise if one country doesn't take action on an issue such as antimicrobial resistance that is also detriment to the whole international community. But conversely investments in one country's health systems and investments in making that health system better also protect all other countries and a positive investment for the international community.

01:17:52.960 - 01:18:33.960

So when diseases do emerge as we've seen over the last ten years it's very important that research and development into that new disease starts immediately so that scientists can work out what they're dealing with and how best to control the disease and what its likely impact is. But this requires a rapid mobilization of resources and streamlined regulatory processes to get the products onto the market quickly. We also need to incentivize research and development because for many of these one health interventions there isn't necessarily a large market for the products and pharma and industry in the private sector can't see an immediate return on their investments

01:18:33.960 - 01:19:19.960

so there does need to be investments from philanthropists and government to stimulate this research. And WHO is currently working on a research and development blueprint action to prevent epidemics which is aiming to coordinate research globally to work towards agreed priorities. If research is coordinated there can be a much more efficient use of the limited resources to achieve useful and worthwhile goals that will have an impact. The blueprint also aims to develop mechanisms for incentivizing research which is for the public good but may not necessarily be commercially attractive.

01:19:19.960 - 01:20:08.960

And they're also looking at developing mechanisms for creating mechanisms so that research and development can be set into action as soon as a disease emerges. Now this is a sort of 10 to 12 month process and they're engaging policy makers from the human and animal sectors at the moment and engaging scientists as well to come up with a plan and hopefully that will be rolled out in one to two years. And WHO and OIA discussing the implications for one health on the 11th of April this next month. So because it's now widely accepted that the health of animals humans and the ecosystem is inextricably linked there's been considerable interest in promoting one health and this has started to reap rewards at the international level.

01:20:08.960 - 01:20:42.960

The WHO, the OIA and the FAO are now working much more closely together and have formalized their working relationship and their priorities in a tripartite agreement. They've also developed common strategies to tackle one health problems. And thanks to these new intergovernmental one health mechanisms data about influenza viruses circulating in animals are being shared rapidly with the human health sector to help with the preparation of human vaccines. Action is being taken to eliminate rabies from street dogs.

01:20:42.960 - 01:21:15.960

The human and animal health sectors are working jointly to promote the prudent use of antimicrobials and to promote models in other countries that have worked well such as in the Netherlands and the UK. And the health and security sectors are working more closely together to reduce threats from bioterrorism. However, things are working well at the international level but at the national level implementation of a one health approach is very patchy. Some countries embrace one health, other countries don't.

01:21:15.960 - 01:21:33.960

And a sort of barriers include politics and funding. In some countries the public health sector sees it has more to lose from one health and the veterinary sector has more to gain. Status and culture in some countries veterinarians have a very low status in other countries. They have a high status.

01:21:33.960 - 01:22:20.960

And also the historical context in some countries like my home country the UK we've had to deal with one health crisis like BSE. So the public health and animal health sectors have been forced together. But OIE and FAO and WHO have they're focusing their efforts now on helping their member countries strengthen their health systems and strengthen governance of their health systems to respond to all events rather than respond to react to one crisis after another. And WHO and OIE have developed a framework to assist member countries to strengthen their ability to meet the competencies shared by national human and animal health services which are required to tackle one health threats.

01:22:20.960 - 01:22:54.960

And they do this by helping their country to assess its ability to tackle one health threats based on a number of core competencies and then this reveals the gaps and then they work with the country to unfill those gaps. This is a harmonized approach which has been developed in consultation with the member countries themselves and policy experts. And the framework also helps countries to better comply with their legally binding international standards. That's the IHR set by WHO and OIE's international standards.

01:22:54.960 - 01:23:25.960

But they're finding on a global level the strength and governance of health services is still woefully inadequate. And as I mentioned earlier one country's weak health systems is a threat to all other countries including the USA. So in conclusion the key messages are that global public health security is not possible without animal health security. It's much more cost effective to invest in preparedness not just in the U.S. but other countries because a problem in another country is a threat to the U.S.

01:23:25.960 - 01:24:02.960

than react to disasters. Further investments needed to advance one health and that means building capacity of health services in other countries and also incentivizing action from producers and incentivizing research where there's not necessarily a commercial benefit. And another plea from my experience is really for people for countries to use the existing capacity building frameworks which have already been agreed by member countries such as the one set by WHO and OIE rather than countries developing their own frameworks.

01:24:02.960 - 01:24:44.960

A lot of countries are inundated with different agencies and countries approaching them to assess their health systems when it would be much more efficient to use the system that has been agreed internationally already. And this is just a reminder this has taken in there a trail at the time of the last U.S. election but just a reminder that all these countries are looking to the U.S. and what happens in the U.S. does have an impact on countries in all corners of the world as I'm sure you know. Thank you. Thank you. That's great. What a wonderful set of presentations that I think really have covered the landscape very beautifully.

01:24:44.960 - 01:25:42.960

I know this in an area of great interest. We are pattern is to put up our flags. I did not note the exact order so I'm just going to go around the room here with Rosina first and then Chris and Barbara and then Susan and then Dan I did note just came in last there so I will at least put Susan ahead of Dan. So Rosina thank you all that's incredibly sobering so I thought maybe I would try to give two points of light. I actually wanted to come back to the first speaker and the challenges to health at different levels in that pyramid that you showed us and how most of the attention is focused initially on species and populations and indeed I think most of the talks have focused on that very important middle tier but I wanted to come back to that large bottom which is about ecosystems and environment and how very important that understanding needs to be integrated back up into those

01:25:42.960 - 01:26:49.960

two tiers and to that end I just wanted to remind us all that PCAST actually did an early report on sustaining environmental capital five years ago but in I think what's exciting progress to executive orders are kind of moving that bottom pyramid up into the middle. Most recently President Obama issued an executive order that all of the federal agencies have to figure out how to value ecosystem services in their normal course of action and prior to that how all agencies through another executive order have to think about climate change and build that thinking into their contributing missions and mandates going forward and so I think that's very interesting pushing at that large bottom into the very important middle area that you focused on today and then a second point of good news as an educator at now two great public universities the systems approach that you all call for is just so much more natural to the students of today than it was to us

01:26:49.960 - 01:27:48.960

and I'm seeing an incredible profusion of dual degrees between students in natural resources and then public health or engineering or public policy or urban planning and business and I think those are really the great hope that we can get this entire pyramid built together much more quickly than the way we were trained historically so I just wanted to offer two points of good news. Thanks. I'd like to however cast a sobering note which you already raised and that is that unfortunately the funding agencies have not adopted this kind of approach and if I told you the various parts of agencies from which funding has come from our research you would be astounded. There is no conversation if may I be frank at the NIH between the Environmental Health Institute and the Institute that's concerned with infectious diseases.

01:27:48.960 - 01:28:31.960

If you send in a grant to one of them they say we don't do pathogens you send it into the other one they say we don't do environments. We got funded for some of our research from of all places from NIOSH because workers were involved now of course that's not all we were studying but what you're saying I think is true that the students are hungry the policymakers are hungry we're all hungry for this kind of thing but it is a devil to get funding for it. One of our funding has come from foundations and NSF bless its heart usually when they see the word Johns Hopkins School of Public Health say oh no go to NIH. And if you have a conversation with NIH leadership about that?

01:28:31.960 - 01:28:51.960

I've spoken to the director of NIHS. How about the director of NIH? Well since it doesn't seem to involve genes Dr. Lander with all due respect he doesn't seem to be interested. Well with due respect I would actually ask before concluding such things.

01:28:51.960 - 01:29:31.960

I think it's probably worth investigating that I think it's a good thing to ask about because you know the idea of inter-institute discussions at the NIH is a very important one. We have many different institutes and you raise a really valid point and I would press it. It's a good point. To the point I mean there's only there's a set of priorities that NIAID has and even so if it involves anything from outside of animal models of human disease there just is no interest right?

01:29:32.960 - 01:30:20.960

Nonetheless I just don't want to conclude it without the experimental evidence of trying to raise the conversation that's all. I would just like to say on that bottom tier too and I think it was a gap in one health coming out of the gate where the focus was on the top two tiers that environmental health scientists went elsewhere. And that was where I believe the planetary health effort and report then came from but as you read that report and their work that is one health. That goes back to that opening definition and but now you have the problem of disciplines talking and collaborating with each other and people have commented you can't get environmental health specialists and health specialists in the same room to talk to each other.

01:30:21.960 - 01:30:50.960

Well I'd like to suggest that your committee opened this conversation with the director of NIAID. I'm just a lowly worm so you have influence. Yeah I do think that there are opportunities to convene that could get people in the same room and I wouldn't be shocked if there was receptivity at the very least to convening to discuss these questions. The one health concept has certainly brought people's attention to this idea much more.

01:30:50.960 - 01:31:03.960

And who knows you might find receptivity at least for a meeting to talk about how those kinds of connections could be built more. Whatever I'm an optimist about such things. Oh well. Chris.

01:31:03.960 - 01:31:23.960

Thank you. I want to thank the panel for a set of interesting presentations. My question is really almost a kind of footnote as for Dr. Silvergalb. You commented that the use of antimicrobial drugs and animal feeds has increased since the FDA statements which I assume you mean the the voluntary guidance is recently released.

01:31:23.960 - 01:31:54.960

Could you break that down for us a little bit and in particular has the use of human medical use relevant antibiotics also increased since those guidances. Yes. There are reports issued episodically by FDA under a doofah which was legislation requiring them to report on uses and animals. And if you take the two most recent ones one was issued in 19 2011 and the other one was just issued in 2015.

01:31:54.960 - 01:32:18.960

There is an increase if I may and that they do break this down in between so called medically important and not. But if I may speak from the perspective of my friends in the microbial world they could care less. And we know that exposure to a medically unimportant drug can drive a multi drug resistance cassette on a plasmid among bacteria. So I think we have to get over this.

01:32:18.960 - 01:32:48.960

And therefore you know if we continue for example almost all methicillin resistant staph aureus which arises from animal populations and there are some molecular tags to discriminate. Contains a tetracycline gene. Now who cares about tetracycline. But if you expose those bacteria in a in vitro situation which we've done you can drive horizontal gene transfer of a multi drug cassette among staph aureus populations with tetracycline.

01:32:50.960 - 01:33:10.960

So I think we've talked in this group about just that issue before. But even even if one just focuses on the putatively human medical use relevant that is also increased. Thank you. And just I'll ask do we have experimental evidence of that in animals that use will drive cassettes.

01:33:10.960 - 01:33:27.960

Yes. It's really been very elegant studies done by USDA. Dr. Fedorka create particularly as well as at Iowa and other institutions which show that if you look at the gut microbiome of animals you can see these phenomena going on. Great.

01:33:27.960 - 01:33:32.960

Thank you. Barbara. Thank you all very much. This was really interesting.

01:33:32.960 - 01:33:55.960

And I want to actually continue in the line that we just were talking about the concept of a resistome is really interesting. And the fact that horizontal gene transfer really between microbiomes changes the way that we look at how agriculture works. And I can see this as a plant biologist. We plants share the soil microbiome with animals.

01:33:55.960 - 01:34:16.960

And so there are some really interesting possibilities and your conclusion that the lines between microbiomes are obliterated blurred. This is a really different way of looking at things. So antibiotic resistance is really daunting. And now it's really exceptionally daunting with this new way of with this way of looking at it.

01:34:16.960 - 01:34:33.960

So what do we do? What are the new practices that need to be implemented? What aren't we doing to address this way of looking at the microbiomes and the sharing of genes in a very different agricultural way. Thank you very much.

01:34:33.960 - 01:34:57.960

That's at the heart of the research in my laboratory right now. And in fact, by the way, the same phenomena can be observed in soils. That is the spreading of resistance genes from microbes that have been added in animal feces to the soils as well as resident bacteria in the soils. That was a study done by one of my graduate students.

01:34:57.960 - 01:35:32.960

I think we need to stop focusing on good and bad bacteria and good and bad drugs. We need to have a policy that looks at and focuses on controlling gene flow. And the way in which we do, for example, NARMS is still embedded in a very 1950s definition of bacteria and our relationship and animals relationship with bacteria. That unless it's really a bad bacteria and a vital drug that's involved, it's not really a problem.

01:35:32.960 - 01:36:04.960

And that does not really reflect what goes on in the microbial world. Thank you. And I would just add, however, that going back to drivers that antibiotic use came into food animal production because of a more efficient production of meat. And so as our human population grows and as we get older and as we get wealthier and as our diet preferences tend to ask for meat,

01:36:04.960 - 01:36:39.960

trying to find a sustainable food production system on this planet that would not... Right now we're looking at a serious reduction in rainforest. So land use and water use and what's happening around the whole area of food production is an incredibly missed one health opportunity to really look at it in a comprehensive framework and it's critical. What we're looking at in the next 10, 20, 30, 40 years is really critical.

01:36:40.960 - 01:36:58.960

The focus is on even down to the genes, but let's look at the driver. Where is this coming from? How can we change how we get our food? And it's only going to get more severe as the number of us continues to increase.

01:36:58.960 - 01:37:30.960

Well, actually we looked into that and I got all the registrations for all the drugs used in animal feeds from the FDA with the assistance of my senator. And in fact the evidence that there really is a growth promoting effect is highly dubious. And the one major study, we wrote a review of this, which was done by Purdue, which really was a double blind clinical trial saw no effect on growth, no effect on feed consumption and no effect on diseases.

01:37:30.960 - 01:37:48.960

So I'm operating right now that it's a rebuttable presumption that we need antibiotics at this point in time. So all the more important to look at how we're producing our food. Thank you. So can I just mention a couple of initiatives that are kind of in progress at national and international level.

01:37:48.960 - 01:38:32.960

The OAS produced guidelines on prudent use of antimicrobials, which will be reiterated kind of each year from feedback from their member countries. And action is being taken and Netherlands have taken a lot of action to reduce the use of antimicrobials in their agricultural system without an impact on productivity. And talking about livestock and farming in general, there's a progression of one health has been towards this sustainable crops livestock system, which takes really looks at minimizing the impact of agriculture on all systems, including climate, antimicrobial resistance, animal welfare, local kind of waste and environment.

01:38:32.960 - 01:39:11.960

And that's a really positive thing and that's been launched because people acknowledge that livestock do have a role to play in food. We're not going to stop farming livestock, but we can do it in more efficient and more ways that have less impact. And on the research side, there's quite a lot of research going on in the animal health side to look at developing or providing recommendations on the research that should be done to develop vaccines in animals that could lead to further reduction in the use of antimicrobials. And some of these production diseases could be tackled with vaccines rather than having to use antimicrobials.

01:39:11.960 - 01:39:46.960

Good, Susan. This is way outside my field and so I apologize if this is a naive question, but in this discussion, I see One Health as an umbrella program in some respects. And you've talked about animals without ever seeing what's meant by animals. And so I'm inferring that that's the animals that are of interest to the veterinary medicine community.

01:39:46.960 - 01:40:12.960

Maybe it's broader than that. That's part of my question. But the rest of my question is where are insects in this whole story? Because, for example, we talked about mosquitoes a lot yesterday in connection with Zika. There are parasitic insects. There are insects that live in soil. There are insects that live in water.

01:40:12.960 - 01:40:38.960

And it would seem to me that they're part of this science picture, and yet I didn't hear them mentioned at all. When I often speak of One Health, ten minutes didn't allow it. I have a slide of all the disciplines that are needed to work together on these issues. An entomologist, and there's no single discipline that has it all.

01:40:38.960 - 01:41:06.960

And that's why the One Health approach and a comprehensive framework and an organizer and a convener to bring all the different disciplines to the table that are needed. So whether insects fall in the animal world or in the environmental piece, they're there. And that expertise is needed for a variety of problems that we face.

01:41:06.960 - 01:41:30.960

So animals are quite broad, and on the clinical side, they can include companion animals. On the food production side, we've talked about that. There's wildlife. But it is one of the challenges on workforce. Any discipline actually is very fragmented. So, you know, as people frame complex problems broadly, it is somewhat of a challenge,

01:41:30.960 - 01:41:48.960

but it's a surmountable challenge to actually bring the right groups and the right, you know, the needed people to the table. But thank you for raising that. They're often overlooked. I was speaking with someone about Zika virus, and where do we go? And I said, well, I hope you have some entomologists and some vector control people at the table in the discussion,

01:41:48.960 - 01:42:13.960

because they're often forgot. So thank you very much for the question. Great. Our last question is Dan Shrag. We have only a minute or two left, so Dan will be his usual concise self. Thank you. I really appreciate all of your perspectives, and I'm very sympathetic to what you're trying to do. I'm the shadow PI of a grant from the Rockefeller Foundation for the Planetary Health Alliance at Harvard,

01:42:13.960 - 01:42:36.960

which is, I think, very much aligned with the One Health concept. I've got to say, in creating, it's not a research grant, it's a capacity-building grant to try to create a community working on planetary health, which is, I think, the same thing, more or less as One Health. And I'll tell you two problems that we're having, and I'd love to hear your perspective on them.

01:42:36.960 - 01:43:03.960

One is a problem of actually getting health scientists really interested in engaging with. There's lots of entomologists interested because it's an opportunity for funding, et cetera, but it's really hard to get the health scientists interested. I haven't seen that engagement yet. The second problem is one of a concern about bias, that most of the people in this field, and it's a very small number,

01:43:03.960 - 01:43:27.960

focus on examples where destruction of wildlife or habitat has negative health consequences. And there's a concern expressed by some people in the health community that what they're overlooking is all of the ways that, in fact, destruction of wildlife can actually have very positive health consequences also. For example, we know emerging diseases like Zika come from the interface.

01:43:27.960 - 01:43:48.960

There are lots of examples where it actually can work both ways. And there's this kind of teleology, this bias, and it makes some scientists afraid to be associated with this because it feels like a crusade, it doesn't feel like science. So I'd love your perspective on how to handle that because it's something that I'm struggling with right now.

01:43:48.960 - 01:44:13.960

Well, this is where I made the comment that sometimes where this has gone to environmental health scientists and human health experts, it's hard to get them into the same room to talk to one another. Recently, there's an upcoming meeting, the Consortium of the University's Global Health, and this year they're actually focusing on the Planetary Health Report, and we've convened a panel outside of the Plenary,

01:44:13.960 - 01:44:39.960

but a panel that is bringing, we've got the human global health, so people traditionally working on malaria, diarrheal disease, TB, HIV-AIDS, but we have a speaker who's going to be speaking of the planetary boundaries work and where we are in that. We have a speaker who's going to be speaking about women's reproductive health,

01:44:39.960 - 01:45:07.960

and one of our moderators for the session is Dr. Sam Myers, who's PI on a Rockefeller-funded Planetary Health Alliance work. So we're getting them together, but it has taken extra work and effort and nurturing to bring them together. So I think you've underscored the gap, which I was trying to allude to, and a challenge,

01:45:07.960 - 01:45:31.960

but I firmly believe that if we can keep putting the focus on that bottom tier and what is needed together to come up with sustainable solutions, we can overcome the biases and some of the other things that keep these folks apart. It's absolutely critical, funding is a part of it.

01:45:31.960 - 01:45:56.960

If you can get some of this joint funding or funding that doesn't pit mission against mission or you can't spend, we have money, but we can't give it to you or you have money, but we can't get it over here, and therefore the silos continue, that leads to it. But it's a serious problem, but I'm optimistic, like many of you here,

01:45:56.960 - 01:46:14.960

that there definitely is a challenge that can be overcome with appropriate attention and nurturing and support. All right, we must end now because we're a few minutes over our time, but I really want to thank the panel tremendously for coming and informing us about this and doing so in a very frank manner, so thank you.

01:46:14.960 - 01:46:39.960

We're going to take a break for about 12 minutes and we're going to resume and discuss research and cancer. We're now going to turn to a discussion of frontiers of cancer research. There are a lot of motivations for having this conversation today. First, there's just been extraordinary progress in cancer research

01:46:39.960 - 01:47:04.960

and the understanding of the underlying genetic and epigenetic changes that cause cancers and thereby identifying targets, great progress in immunotherapies, in particular other modalities for treating cancers that are complementary to targeting particular processes that go awry in cancer, and an increasing influx of people from chemistry and engineering

01:47:04.960 - 01:47:29.960

and other disciplines that are coming in with various different approaches. And we're really lucky today to have three speakers who can talk to us about this, and of course it's especially relevant because the President has declared a real focus on cancer research with his remarks in the State of the Union declaring a cancer moonshot that the Vice President is taking leadership of.

01:47:29.960 - 01:47:52.960

And so we're eager to hear today from Charles Sawyers, from Carl June, and from Tom O'Halloran. Charles Sawyers is the Chair of Human Oncology and Pathogenesis at Memorial Sloan Kettering Cancer Center and a tremendous expert in the development of cancer drugs. We have Carl June, who's Professor in Immunotherapy in the Department of Pathology and Laboratory of Medicine

01:47:52.960 - 01:48:24.960

at the University of Pennsylvania, and he is a tremendous leader in the field of immunotherapies in cancer. And we have Thomas O'Halloran, the Founding Director of the Chemistry of Life Processes Institute at Northwestern University. He's also the Director of the NCI Chicago Regional Physical Science Oncology Center. And he's really in a great position to talk to us about the roles of chemistry and physics and engineering coming together in new methodology.

01:48:24.960 - 01:48:48.960

So we couldn't imagine a better panel. Each of the speakers is going to take no more than 10 minutes, and perhaps last, and then we're going to get into general discussion. We'll start with Charles. Thank you, Eric, and the committee for inviting me. So I've had the occasion to meet with the Vice President or his team three times since the beginning of the year,

01:48:48.960 - 01:49:17.960

so I titled this Reflections on Munch Outs. I want to start with this slide, which is a concept that you're all familiar with. It's driven by genomics as having the ability to add precision to diagnosis of cancer and to guide treatment decisions. And I won't go through the flowchart, but this is something that I think has been adopted by most cancer centers in the academic world

01:49:17.960 - 01:49:42.960

and is rapidly becoming the way clinical treatment outside of clinical trials is starting to go. So let me just, at a very high level, remind you that cancer is a disease of mutations. You get a mutation. Every cell division results in mutations. These are due to the error rates in DNA polymerase. If that mutation is in the wrong location,

01:49:42.960 - 01:50:04.960

meaning the wrong base pair in a cancer gene or in the wrong cell, it has to be in a cell that has the potential to expand rather than just terminally differentiate and die. And there's this concept of timing. It turns out the incidence of cancer we all think goes up with age, but there's a certain point when you're at a certain age,

01:50:04.960 - 01:50:27.960

your risk of cancer is actually going down because you've sort of made it through the critical period. So we also have this concept of bad luck. Some of you may recall a lot of attention in the press a little over a year ago when Bert Vogelstein and colleagues published a really very compelling mathematical analysis of those first three bullets,

01:50:27.960 - 01:50:50.960

but this was the press and then there was another, those were two New York Times articles, then there was actually a quite nice piece in the cancer letter about the press misinterpreting things. So what do we know about these mutations? As I think probably everyone is aware, both at a national and international level,

01:50:50.960 - 01:51:13.960

the cancer research community made investments in deploying sequencing technologies to complete sequencing of thousands of cancers. So there's concepts like exomes, that's all of the coding regions. You know, just six, seven years ago, that was a very big idea and now it's routine. And then there is, of course, whole genome sequencing.

01:51:13.960 - 01:51:38.960

Roughly 10,000 cancers have been sequenced through these efforts and are all publicly available exomes, which have formed the basis for a lot of calculations of how many cancer genes there are. The second point here is that with those numbers, we can with confidence say that we've defined all of the common cancer drivers. And common in this case means within a tumor type and a tumor type means lung cancer or colon cancer or melanoma.

01:51:38.960 - 01:51:57.960

If it's present at 5% or more in that cancer, we know about it already. So what does that really mean? Well, it means that there is a list of cancer genes across tumor types in the, you know, depending on what you want to call, it's in the 200, 300 range of genes.

01:51:57.960 - 01:52:20.960

What you also find out is those same genes are mutated at a much lower frequency in other cancers. And this is this concept called the long tail. It's a very important concept in the clinical management of patients that's being exploited now through the clinical sequencing efforts. And let me give you a cartoon sort of to make this point.

01:52:20.960 - 01:52:40.960

There's a concept that you may have heard of in clinical trial lingo called basket studies. And these are trials in which patients are enrolled based on the presence of a mutation regardless of the histology or the site of origin of that cancer. Patients are treated by this in the same protocol in this basket approach.

01:52:40.960 - 01:53:07.960

And as a result, we learn, we can accrue these trials much faster and we learn that the drug works extremely well when the mutation is a certain context but less well in another context. It was very difficult to accrue patients to such trials if you don't have mutation profiling happening in patient populations. And just as an example of how recent this is at Sloan Kettering where I work,

01:53:07.960 - 01:53:29.960

we really invested this heavily just beginning two years ago and you can see the rate at which we're sequencing. I want you to point out that number at the bottom is 8,400 cases. I want you to contrast that with the 10,000 across an international consortium that took several years to develop. This is how fast this is moving.

01:53:29.960 - 01:53:52.960

Now, these aren't whole exomes, these are panels which is a detail. Now, this slide shows the impact of that test which is called impact. And that big dark gray line is the accrual to these trials and that dotted line sort of between November and March of 2013 is when we launched this assay and you can see the rate at which patients are accrued on these trials.

01:53:52.960 - 01:54:16.960

This is a very positive outcome for enrolling patients and getting effective drugs more efficiently and precisely delivered to patients. What I want to finish with though is, you know, that's a really great story, lots of excitement. It's the whole rationale behind precision medicine and cancer. But how many patients of all cancer patients are really benefiting from this?

01:54:16.960 - 01:54:30.960

So I have a couple of pie charts I want to walk you through. So we call these mutations when we sort of lock it down and say this is a cancer gene, we call it a driver. So how many cancers have known drivers? And it's about two-thirds.

01:54:30.960 - 01:54:48.960

About a third we sequenced an exome and we still don't really know what's causing that cancer. There's a project out there we call the cancer of unknown driver. We need to figure that out. Then of the ones who have a known driver on the right, I've split that roughly in half and these numbers are approximate.

01:54:48.960 - 01:55:04.960

Those are the, of those, how many do we have a treatment that we can offer that we believe is rational? About half. We call that druggable versus undruggable. So for the first pie chart, how do we improve on the unknown driver problem?

01:55:04.960 - 01:55:28.960

Well, we need to do more exome sequencing because then we will have mutations, we will discover mutations that are present at an even rarer frequency. And we need to do, I think, whole genome sequencing on some subset of these to find out if there are mutations outside of the coding region to explain these cancers. And there are enough examples precedent for that being an explanation. What do I think will happen?

01:55:28.960 - 01:55:46.960

We'll never get rid of completely this blue slice, but it will get smaller. And how do we address the problem of the undruggable drivers? This is a very large challenge. I think we need new chemistry and pharmacology and we can go through some of these details in the questions if you want.

01:55:46.960 - 01:56:09.960

But there are some very clear steps forward. Another strategy is to generate a map, what I call a map of cancer vulnerabilities. If you have a driver and you can't make a direct drug against it, perhaps you can take advantage of a dependency that the presence of that driver creates on another gene or pathway. That's known as synthetic lethality.

01:56:09.960 - 01:56:35.960

And then the third point is that the perfect drug actually is nucleic acid-based drugs because of the perfect match with the driver. But the problem has been delivery and I think this is a potentially solvable problem. A little note at the bottom is that I also want to, you know, I'm enthusiastic about this. Druggable does not mean cure and we need combination therapies to overcome resistance.

01:56:35.960 - 01:56:56.960

It's essentially an exact parallel argument as we heard with the antibiotic resistance. So I want to conclude, you know, with four sort of thoughts. We really have to leverage this explosive growth in clinical sequencing. We've gone into the data sharing mantra that the vice president has been talking about.

01:56:56.960 - 01:57:16.960

And I'm involved very intimately in one such data sharing effort called Genie. I believe we should, you know, do a large scale project. It has the sort of feel of a moonshot engineering style to define all cancer vulnerabilities. We need to invest in this new chemistry.

01:57:16.960 - 01:57:40.960

We'll hear from Carl, but this explosion of an immuno-oncology, including huge commercial investments by Biotech and Pharma, is happening, I think, on a background where we don't have a fundamental understanding of the basic cancer immunology underlying all of this. And there's a lot of challenges in the models to make those conclusions. And so I will stop there.

01:57:40.960 - 01:57:55.960

And there's two of my photo ops with the vice president. Where's the third? That was one of the most coherent tours of the horizon that I've heard. Thank you so much, Charles, for coming down and distilling it in that fashion.

01:57:55.960 - 01:58:19.960

So let's turn to Dr. June, who's going to talk to us, I presume, indeed, about cancer immunotherapy. An old idea that finally has its day. Well, thank you, Dr. Holden and Dr. Lander, and members of the commission, for the opportunity to participate in this. I think everyone here has been challenged or touched by cancer in some way.

01:58:19.960 - 01:58:45.960

My head actually was touched last week, and that's the reason for the patch on the top. What I'd like to do over this next few minutes is go over some of the excitement in cancer immunotherapy. And you'll see how it dovetails in with the precision medicine that Charles has just outlined. So cancer immunotherapy had a quite disappointing history for over a century.

01:58:45.960 - 01:59:12.960

It's not a new idea. But finally, some major advances occurred beginning in around 2010. And that was recognized by science in 2013 as the number one breakthrough in all of science and engineering. So it's now a field that is recognized after some time and making rapid progress.

01:59:12.960 - 01:59:40.960

When you look at the cancer epidemic for many years, heart disease was overrepresented as a cause of death. And it was only about 10 years ago when cancer actually became the number one cause of death for those under age 85. So we face this as a challenge in the United States. And worldwide it's an epidemic that we need to solve.

01:59:40.960 - 02:00:13.960

So cancer therapies traditionally have then looked at as a way to treat patients. And the end points of those trials that led to FDA approval was pushing out the median survival curve. Often as few as six weeks of lifetime extension could enable commercialization of a drug. What is unique about cancer immunotherapy is that it has pushed the survival, long-term survival out so that people live decades and maybe are cured.

02:00:13.960 - 02:00:30.960

And that's the appeal of immunotherapy is its long lasting effects. And where we're at now in the field is how do we combine therapies so that we can basically have everyone have long-term remission and cures. We're not there yet. So what is the case for immunotherapy?

02:00:30.960 - 02:00:46.960

The immune system is a living drug, if you will. The immune system, you know, a vaccine at age five can protect you at age 60 against chickenpox. So the immune system is a living drug. The cells that we give last.

02:00:46.960 - 02:01:09.960

They only need to be given once in many cases. And another advantage is that the immune system can evolve as a tumor may evolve so that it can respond to escape mechanisms, whereas single drugs given cannot do that. And the data so far is that immunotherapy can in fact cure some subsets of cancer.

02:01:10.960 - 02:01:38.960

I think the data is best at this point in melanoma and leukemia. So one of the hallmarks of cancer other than the mutations that lead to the growth abnormalities that you just heard about from Charles are that the cancer to become metastatic needs to escape the immune system. And that in immunologic terms is called tolerance. There are two mechanisms and approaches now in the field to overcome this.

02:01:38.960 - 02:02:07.960

One being so-called checkpoint therapies, antibodies that are antagonists of the systems in the immune system that normally shut things off. So these are ways, if you will, to take the breaks off. These first became FDA approved in 2011 and have now been extended to many kinds of cancers. The particular area that I've been interested in are the area of cell therapies, which can be given to patients as a form of transfusion.

02:02:07.960 - 02:02:32.960

A number of these are being developed for many kinds of cancer. And I would make the point that these should be synergistic based on the modeling data that we have at this point. So a so-called CAR T cell that many of you have heard about is a so-called, it's a synthetic molecule. It creates a T cell that does not exist in the natural immune system.

02:02:32.960 - 02:02:59.960

That's one point that really differentiates engineered cell therapies from standard vaccines. One can create an immune system that is more potent or has properties that didn't exist in nature. And in particular, the goal that we have is to make an immune system that will be resistant to the tumor microenvironment. For instance, the tumor microenvironment may have poor vascularization, may be hypoxic, and have a number of immunotoxic aspects there.

02:02:59.960 - 02:03:27.960

So the field is rapidly evolving in this area to overcome some of the subversion from the cancer system. So the first patients treated with CAR T cells were in 2010. Initially it was adults with refractory leukemias and then pediatric cases. Now hundreds of patients in our most recent assessment, more than 500 patients, have been treated with various forms of CAR T cells.

02:03:27.960 - 02:03:59.960

And so we've gone from small numbers of patients in 2010 to now where at least three companies have registration trials in place for various types of CAR therapies. And we know now that those initial responses have been durable. So they've been durable in both leukemia, lymphomas, myeloma, and melanoma. Where we don't know and where the future lies really is what their role will be in solid cancers, such as lung cancer.

02:03:59.960 - 02:04:24.960

These are the first examples of synthetic immunity using the principles of gene transfer to improve the immune response. They're living drugs. The first patients that we infused back in 2010 continue to have these CAR T cells in their body, and we can show that they're functional. And as I mentioned, the immune system can evolve with the tumor.

02:04:24.960 - 02:04:53.960

So if you will, it can encode a counter punch to help prevent relapses. And the data so far with five-year sort of follow-up indicates that in some cases patients are cured. One of our first patients with acute leukemia was with President Obama at the launching of the Precision Medicine Initiative last January, and she remains in remission now nearly four years after therapy.

02:04:53.960 - 02:05:23.960

So there's a deep pipeline of cancer immunotherapies shown in this review. Many different antibodies engineered drugs, vaccines, and cell therapies are being developed. The first T cell therapies are anticipated to have FDA approval in the next year or so. So there will be both many new therapies come on the market, and the real issue is going to be figuring out where the resistance mechanisms are and how to efficiently overcome those.

02:05:24.960 - 02:05:57.960

So we're at a point now in medical delivery where our healthcare system consists really of three pillars. One being the pharmaceutical industry, the other biotechnology industry that delivers recombinant proteins, and then medical devices that form the basis of our system. And where we're headed now is the new pillar being added, which will be cell therapies. And so that's just a new industry.

02:05:57.960 - 02:06:27.960

There are a number of issues specific to this, one being that at this point these cell therapies are personalized. They come from the patient's own cells, so they're so-called N of 1 therapies from the regulatory point of view. One major effort will be to develop universal cells so that they will be off the shelf at this point. Red blood cells are transfused from universal donors, and to engineer T cells that way is a realistic goal.

02:06:27.960 - 02:06:58.960

Another issue is what the reimbursement models will be for these kinds of more expensive cell therapies. Another point that I would like to bring up is that this is highlighting, in my view, meetings with the Vice President. We found that there's a shortage of trained scientists and clinicians in this area. For many years the field was not viewed as one having a future, and so translational research in cancer immunotherapy was ignored.

02:06:58.960 - 02:07:25.960

It's not different from other areas where we have a very strong pipeline in basic sciences. And then what we need to enhance, I think, is the intersection with clinical sciences. And that is there's an evolving new specialty in how to efficiently conduct first in human trials with these kinds of therapies and how to work with what may seem to be orthogonal therapies with precision medicines that may target basic molecular pathways.

02:07:26.960 - 02:07:51.960

So I'll sum up by saying that synthetic biology is adding to the toolbox of cancer therapies. The first engineered virus, a herpes virus, was approved by Amgen this year for metastatic melanoma. CAR T cells have pivotal trials underway for leukemias and lymphomas. There are combinational trials with many other agents where these appear to be synergistic.

02:07:51.960 - 02:08:13.960

So that means that there were thousands of potential combinations, and we need to understand how to efficiently conduct those trials. The Moonshot Initiative may accelerate this cure of cancer, and I thank you for your attention. Wow, another extremely helpful and coherent presentation. Thank you. This is really great.

02:08:13.960 - 02:08:38.960

I hope people watching this on the web will, you know, just come to the site and listen to these talks, because I think they do a beautiful job of summarizing the state of the whole field. Thank you. No pressure there, Dr. O'Halloran, but let's see if we can get a hat trick here. Well, I have to say that the very last slide that Carl presented made the second to last is just the perfect segue.

02:08:38.960 - 02:09:03.960

My simple thesis for you in these slides are going to take a very high-level view is that we can really accelerate the rate at which we make these kind of innovative discoveries to the rate at which I think society expects of the scientific community. We all are having friends die of cancer regularly in spite of the phenomenal progress in breast cancer

02:09:03.960 - 02:09:24.960

and prostate cancer and therapeutics and early diagnostics in those areas, but we're not going fast enough. So this is, my talk is about how to do that, and it really evolves around how to stimulate translation, and that's my thesis is that we bring in outsiders into the mainstream of cancer biology and oncology

02:09:24.960 - 02:09:45.960

and put these teams together in innovative ways with short-term goals and expect the best outcomes. So why should we expect that and how will we do it? Well, the NCI is one of the most successful organizations in the world in stimulating advances in human understanding of cancer and the treatment of it.

02:09:45.960 - 02:10:09.960

It is, by nature, a silo-driven process. The expertise to reach a cure or an application of immunotherapy takes 20 years of training and of investment and cross-checking and developing of understanding of molecular pathways. Molecular pathways is generally one of the hearts of the focus is a basic science research in NCI,

02:10:09.960 - 02:10:34.960

and the silo structure creates a set of incumbents. When I'm going to use two types of terms, incumbents are people that are experts in the field with a great depth of knowledge and experience and training in particular subsets of cancer biology. The outsiders are people that have interest ideas often orthogonal to what the normal approaches are,

02:10:34.960 - 02:10:49.960

and when they come into the field they bring revolutions sometimes and other times they bring nonsense. So the question is how do you optimize that process? I wasn't looking at anybody in the room when I said that. So how do you do that?

02:10:49.960 - 02:11:09.960

And so this kind of silo structure gives rise to really excellent communications between oncologists, molecular biologists, cancer biologists, immunologies, but all of these other parts of the structure there's not nearly enough communication and transfer of knowledge of rapid insights.

02:11:09.960 - 02:11:26.960

We're now looking at single molecules within cells and we're watching where they go. This is something we couldn't imagine five years ago. How can that help a cancer biologist think about a pathway? How can I help a drug discoverer think about optimizing their target?

02:11:26.960 - 02:11:43.960

Well, I would argue that the way we're going to do that is by having a much more extensive communication network. This is easy to draw. These kind of thickness of these lines often would represent the number of publications that have a joint co-authorship between people from tutor from fields.

02:11:43.960 - 02:12:01.960

And this is kind of an idealized vision of the world in the future. All sorts of hurdles, silos, buried costs and sunken costs in given technologies, language differences and culture differences, even how to do a simple control is so different between these fields.

02:12:01.960 - 02:12:20.960

So is it building a tower of babble? Well, I would say historically oncology has been absolutely dependent upon the physical sciences and the breakthroughs that have come from them. From using x-rays, radioactivity even within 20 years of Murray's first Nobel Prize,

02:12:20.960 - 02:12:41.960

we see these applications in cancer, radioimmunoassays. I just picked some Nobel Prizes here and I won't go through them. I think you can see and each of you can connect how they may have transformed the current practice of oncology. The problem is these types of translational events are incredibly slow,

02:12:41.960 - 02:13:05.960

much more than 20 years sometimes between when that fundamental discovery gets established and when it works. So what's different over that kind of 110-year period that I just showed you is that science is evolving culturally and at the highest impact papers. And this is a beautiful study from my colleagues at Northwesterns, Jones and Uzi

02:13:05.960 - 02:13:28.960

in the Kellogg School of Management. They analyzed over 20 million publications and looked at them as a function of time over the last 60 years and found that we're absolutely moving to the point of team-based science. The team-based science gives rise to the highest number of highly cited papers,

02:13:28.960 - 02:13:41.960

over 100 citations. These teams dominate 6-to-1 over a single investigator or a very singular discipline approach. So that's the future. But how do you build these teams, right?

02:13:41.960 - 02:13:58.960

And how do you catalyze this? And I would argue the moonshot is a phenomenal opportunity for the administration to come in and say, look, what we're doing now is the best that each of our groups and disciplines can do, but we're going to now put a connectivity layer over.

02:13:58.960 - 02:14:12.960

We're going to start catalyzing the cross-fertilization of ideas and focus those cross-fertilized ideas onto immediate goals in cancer. What is this? Well, this is another Brian Uzi publication.

02:14:12.960 - 02:14:32.960

He wondered where does creativity come from? And he asked a very simple question and he analyzed analytically prior relationships between collaborators. If they've published a bunch of papers before, they're going to have a high value on that lower axis. There are a lot of prior relationships.

02:14:32.960 - 02:14:43.960

And he plotted that against the experience of the team when they came into a project. They had very little experience. They were like a graduate student. They would be low on the other axis.

02:14:43.960 - 02:14:55.960

So what he found is, and what did he take? He took Broadway musicals. So he went back for 70 years of Broadway musicals. These are the same types of problems we face in science.

02:14:55.960 - 02:15:00.960

You have a librettist. You have a book. You have a composer. You have libretto.

02:15:00.960 - 02:15:21.960

You have all these creative people have to work together. And he said, well, how often did the team that had worked together on dozens of musicals come together to produce something? And then how often did they have a lot of experience? He took this kind of matrix and then he asked, well, what's the criterion of success?

02:15:22.960 - 02:15:45.960

And he looked at the box office revenues and he saw there was kind of a sweet spot in between having a bunch of prior relationships and having very few. And that sweet spot is there, is in the blue. So those were the most successful musicals. The critical reviews also had a sweet spot in this type of analysis.

02:15:45.960 - 02:16:09.960

And at the end of one of his favorite examples is West Side Story where a phenomenal team came together, but they said they needed some fresh blood when they were posing. And they went to an unproven guy 25 years old, Stephen Sondheim. And so this is the exact example of incumbents mixing with outsiders that we want to achieve in science.

02:16:09.960 - 02:16:37.960

And I would say this is not a brand new idea. In fact, if we go around this room, I would bet half of this panel was an outsider when they started in the community where they made a significant scientific impact. The National Cancer Institute and NSF, other agencies have experimented with these. I think we have some phenomenal success stories with, say, the Cancer Centers for Nanotechnology Excellent and the Physical Science Oncology Centers that the NCI has said.

02:16:38.960 - 02:17:07.960

But these are always fighting a headwind of funding of the silos. When the silos, which are very important farms for cancer research, are only getting funded at the 7% level, the outsiders are the first to take a hit. So this is where the opportunity comes that if the, through the process of the moonshot, that a real plan for sustainable breakthroughs could be built. And here now is the big issue.

02:17:07.960 - 02:17:30.960

The moonshot, I did not warm up to this analogy. It took me a long time. But within the period of eight years from Kennedy's announcement at Cape Canaveral that he was going to put a man on the moon to Apollo 11 in 1969, what was spent was about $152 billion in today's dollars. All right.

02:17:30.960 - 02:17:42.960

And that was a multi-year investment over time to get there. All right. So a billion dollars is a good start, but it has to be part of a vision. And then what should that vision do?

02:17:42.960 - 02:18:04.960

I would argue we create centers to begin training people to speak multiple languages across the disciplines, to think across physical science and quantitative biology and systems biology and synthetic biology, and bringing all these types of players together. How do you do that? It would have to be a multi-year program.

02:18:04.960 - 02:18:26.960

I think the national nanotechnology initiative, NIDERD, are examples of this that this group knows much better than I do. I would argue you have to integrate the incumbents and the outsiders as you do this. We can talk about themes in the discussion. I think immunotherapies, synbiology, early detection is so critical.

02:18:26.960 - 02:19:09.960

And is also right in the sweet spot of the materials and engineering communities that we're talking about as outsiders. Mining health data, et cetera, new materials to deliver drugs, right, to deliver those nucleic acids that we talked about and the previous speakers have talked about. I won't go through the national nanotechnology initiative, but it was started under the Clinton administration and about a billion dollars a year, and it's still going. And this had a profound, and this continued to have a profound influence on interagency research across the fields where nanotechnology can have a stunning impact on our national well-being.

02:19:09.960 - 02:19:32.960

This is my last slide and what I would recommend, and it is just kind of from my experiences in leading interdisciplinary teams. This is not only doable, but we have a lot of assets and a lot of templates. We know what works and what doesn't. We need teams of five and 10 people that integrate these outsiders and incumbents.

02:19:32.960 - 02:19:47.960

And I would argue if we really want to do a moonshot, we take that same period of time, take about that same amount of money. We could transform the practice and treatment of ecology treatment of cancer and its early diagnosis. Stop there. Great. Thank you.

02:19:47.960 - 02:20:03.960

So we now have time for a few questions. I think we've got, oh, 10, 10, 15 minutes or so. And Chad Mirkin and Dan Schrag have flags up, and Dan Schrag just had a leftover flag. Okay, so we Chad Mirkin.

02:20:03.960 - 02:20:23.960

Let me start by thanking all three of you. I thought it was absolutely fantastic and what an incredible diversity of ideas and I think spot on from starting with Charles in terms of, I believe, nucleic acids are going to be the next. Really. I believe in terms of what we do and in terms of what we're trying to accomplish on the cancer research side of things.

02:20:23.960 - 02:20:49.960

But I also believe what you said that there are major hurdles, understanding pathways. It's critical. It's the bread and butter of what we do in biology and medicine. But if you don't have in parallel efforts aimed at developing the right materials and the right ways of getting drugs where you need them to go, a lot of that's wasted.

02:20:49.960 - 02:21:07.960

And I didn't hear from both Charles and Carl, maybe by design, the way you guys set this all up, what you thought the path forward was in terms of really capitalizing on this. I look at things like antisense, S-I-R-N-A. Antisense has been around for 30 plus years. S-I-R-N-A for now 20 years.

02:21:07.960 - 02:21:37.960

We're just beginning to see drugs come online and the sad part is all of them are for diseases of the liver. That's because that's where these things get dumped. That's the lowest bar and we're missing this incredible opportunity. How do we use the moonshot to catalyze an effort that will really not just move the needle but create, for lack of a better term, a quantum leap in terms of how we go about developing therapeutics for cancer?

02:21:37.960 - 02:21:55.960

Chad, I think you're more qualified to answer that than I am. I'll just make a couple of points. The antisense got a bad rap for quite a while, but my understanding is maybe liver is the only indication. I know I is another one.

02:21:55.960 - 02:22:18.960

I'm hearing that the chemistry is much better. It just took time. Maybe this is the incumbent problem. Incumbents have trouble forgetting about the failures of the past and re-adopting once the kinks have been worked out.

02:22:18.960 - 02:22:30.960

I actually saw a presentation on antisense recently in a research context, very detailed. Everyone's rolling their eyes. We heard this 30 years ago. Instead of saying, wow, it looks like it's finally working.

02:22:30.960 - 02:22:48.960

Maybe that's a cultural asset. I didn't know what Tom was going to say and it resonated very much with me to bring in newbies and people in the material sciences. I believe in this nanotechnology concept. The nano initiative is what?

02:22:48.960 - 02:22:56.960

Eight to ten years old. Maybe that's just not enough time. Maybe it was just a few centers. I think there's clearly more there.

02:22:58.960 - 02:23:20.960

Carl, do you want to respond as well? I think it's a very similar analogy to what we've had in immunotherapy. I think the, for instance, delivery of nucleic acids is at that tipping point and the disruption is going to happen with these liver deliveries. It was just more complex than anyone assumed.

02:23:20.960 - 02:23:38.960

I think the great thing is the momentum will happen. The mixture of incumbents and outsiders will occur. I think we're going to see rapid, after more than maybe 30 years of stagnation. I think it's a really wonderful time to see that happen.

02:23:38.960 - 02:24:00.960

Bill Press and then Craig Mundie. One of the biggest incumbents of all is the FDA. I've heard at least two of you suggest that there's going to be a need for different kinds of clinical trials, different kinds of regulatory apparatus generally,

02:24:00.960 - 02:24:20.960

between combination therapies, personalized therapies, cellular therapies. Really a question to all three of you. If you were going to give the new director of the FDA advice on how does FDA have to change to support this moonshot, what would your advice be?

02:24:20.960 - 02:24:46.960

Well, I've met with the new FDA director actually and I'm very excited that he's finally approved. Honestly, I think the FDA is not a rate limiting step at all here. They very much understand a lot of what we've talked about today. They need, and they're interacting with the academic community for advice on how to set up the

02:24:46.960 - 02:25:07.960

guidances for sort of telegraphing to the commercial world, what they're going to set as the bars for approvals. They're also new incentives to push companies to start combination trials earlier, which we all need.

02:25:07.960 - 02:25:26.960

They include things as simple as instead of, you know, if it's a two drug combination, the old way was a forearm study, you know, standard of care A, B, A plus B. Now you can do standard of care A plus B. You can count A plus B as a single drug.

02:25:26.960 - 02:25:47.960

You have to do a little bit of safety on each one. There are also incentives to extend patent life for certain orphan or pediatric indications. So I guess my main point is the FDA is not really the problem in my view. But see what Carl says.

02:25:47.960 - 02:26:18.960

So I deal with a side of the FDA called CBER and then the off-of-cell and tissue and gene therapies. And I can tell you that they are widely regarded as leaders around the world in the development of new technologies. I mean, there are the critical issues are, you know, acceptance of basket trial designs that Charles mentioned. I think another issue is surrogate endpoints and when those can be accepted for rapid trial completion.

02:26:18.960 - 02:26:40.960

The FDA has been a leader in how cell therapies can be done. I think their next challenge is with genetic engineering, you know, issues such as CRISPR and so on. But I think they have a very good relationship with science balancing the regulatory parts with the science, which is so rapidly changing. So I would agree they're not the problem.

02:26:40.960 - 02:26:53.960

And our issue is learning how to work together with different siloed data sets. Great. Did you want to add something, Tom, on this? Basically, I think, you know, the issue is speed and innovation.

02:26:53.960 - 02:27:11.960

And we have to adapt more quickly to the novel materials and the novel biological tools that allow us to interrogate where they are and how they're working. And I think that's one of the critical things that the FDA could do better is to innovate more quickly. That's great. Craig Mundie.

02:27:11.960 - 02:27:32.960

Yeah. As I listened to all three talks, you can say there's certainly an undercurrent about the role of computing as it related to the genomics itself. But it seems like there's a lot more that computing and modeling, you know, may be able to do than was implied in the discussion. And even Tom, I think maybe you'll mention systems biology once.

02:27:32.960 - 02:27:56.960

But it seems a lot of these questions are the pathways, the interactions, you know, and other than the ad hoc, oh, you know, we have these two things and maybe they interfere with each other. That whole thing also seems to be underrepresented to me in the moonshot and even this discussion. So I was curious if you think that's true or not. There was a lot of talk about the physical sciences, et cetera.

02:27:56.960 - 02:28:28.960

But, you know, computer science seems broadly underrepresented in the way you described at least bringing these together. No, Craig, I think you hit on a critical part of this, the type of outside communities that need to get more fully integrated. I think these types of people come from applied math, from systems analysis, complexity analysis, looking at flow of airlines and clients through airports on a minute-to-minute basis, looking at patterns that takes large computational capacity and we keep every year getting more of it.

02:28:28.960 - 02:28:59.960

So we're actually able to do more modeling now than we actually know how to design the right questions to make sense out of the complexity of all the patient data we're getting. So I think it's a critical part of it. There's a fellow at Northwestern. I know Adelson-Modder in applied math and physics and basically is redoing a way we understand the Warburg effect by looking at it as a mass of protein production as opposed to any type of net energetic calculation.

02:28:59.960 - 02:29:25.960

So it's a really important need. I'll just take your point, but I don't want you to think that the biomedical research community has ignored this. At our center, we have computational biology, systems biology, and most others have. Whether or not it's resource at the level that it should be, I can't really say.

02:29:25.960 - 02:29:56.960

I don't have a good sense for that. What's happened, though, is the genomics, the clinical sequencing genomics efforts have created this huge demand for a certain type of computational person, which is not necessarily what you're getting at, but it's also an incredibly safe career path for a young mathematician. So we may be unfortunately sort of skewing people down that more applied pipeline when we could leverage them for some of these larger questions.

02:29:56.960 - 02:30:24.960

But even in your comment, computational biologists, that's all basically people who are using computers in the biology as they understand it. I was suggesting that people who are trained as computer scientists, not the people who use the computers, the people who design the computers, who design the networks, who figure out how all that stuff works, may actually also bring some skills to these things that you don't have.

02:30:24.960 - 02:30:50.960

And you won't discover it just coming at it from the application layer. And so I'm just saying, getting computer science people to come in and think about these networks, the way they think about computer networks, might actually inform some of these things in a way you would otherwise not find. Craig, if I just amplify on that, I think the key is that you have to start training them in the language and some of the philosophy of cancer.

02:30:51.960 - 02:31:12.960

That's a little bit of a road hoe, right? There is some investment in that. But if you look at the Moonshot, you look at the Kennedy's initiative, the average age of the person on the entire Apollo mission platform, average age, 28 years old, okay? So it was new thinkers. It was an equivalent of our postdocs, right?

02:31:13.960 - 02:31:30.960

Coming in and being the nuts and bolts in inventing stuff, accelerating the rate at which, you know, microtonization of electronics, transistor, printed circuits, creating a drive and a demand, they didn't know, they didn't have any training in that. They had to invent these languages as they go.

02:31:30.960 - 02:31:47.960

I think that's the kind of spirit we want to really inculcate in this with this opportunity. Very unusual to have this kind of money sitting in between normal ages. It has to go both ways. You can't just teach those guys about cancer. You better take some cancer guys and teach them some computing.

02:31:47.960 - 02:32:11.960

That's right. And that's what these centers do, these physical oncology, physical science oncology centers are able to do. We are officially at the end of the period, but I think we're okay because I think our public comments are just two today. So I might just ask Charles in particular, if you could tell us just a bit more about the Gini program that you and your colleagues have launched.

02:32:11.960 - 02:32:36.960

I think it's something that folks on PCAS would be interested about. So this is, it addresses the silo question. And basically, these institutions, a lot of cancer centers have used their own funds, philanthropic funds, etc., to build out this clinical sequencing infrastructure, which we've done because we believe it's good for our patients.

02:32:36.960 - 02:33:04.960

It's also good for our faculty. It's an incredible database of what I almost, I would call, it's maybe a little bit of a misnomer, but it's kind of a population science experiment in a way. And this is, you know, on a background of electronic medical record tracking of clinical phenotypes. So, you know, a couple of years ago it dawned on many of the people who were first doing this

02:33:04.960 - 02:33:32.960

that once you've done a couple of thousand and realized you haven't really cracked the problem, you need bigger numbers. So to cut to the chase, seven centers agreed to participate in a pilot to pool their data. It required an unbiased sort of neutral Switzerland to become the broker. And that was the, is the American Association for Cancer Research, which for many reasons was a good idea,

02:33:32.960 - 02:33:54.960

including the fact that I served as its president a couple of years ago. So I knew how much money was in the Treasury and could afford to at least get it started. But going through this, there's been a lot of learnings about what are hospital systems willing to share from the, you know, general counsel's point of view for risk, legal risk,

02:33:54.960 - 02:34:17.960

and what are the faculty members willing to share, you know, for all the reasons we hate about not wanting to give up the low-hanging fruit before they've had a chance to pluck it. But we came to a pretty good understanding and, I mean, for the nitty-gritty, all the genomic data from these seven institutions is pooled into a central database,

02:34:17.960 - 02:34:38.960

including a little bit of clinical annotation of the sample. So that, so it's now 17,000 patients' genomes in this data set. So now an investigator or clinical group at any one of these sites can say, you know, we've seen 10 people with this mutation, but we, you know, that's it here.

02:34:38.960 - 02:34:50.960

Has anyone else seen it? You can instantly find that out. And then if it's compelling enough question, you can ask headquarters to do a query out to each of the centers to collect a little bit more phenotyping data and so forth.

02:34:50.960 - 02:35:06.960

So that's, so those kind of projects are now happening. We have bigger visions as well. Part of this was just sort of to build the model for the ecological mix to happen at a comfort level that everyone was familiar with.

02:35:06.960 - 02:35:18.960

That's fantastic. And I think moving slowly but carefully to work out the issues is really important there. So well done. I know we've come to the end of our time.

02:35:18.960 - 02:35:44.960

It really was a great panel. The three of you just fulfilled the assignment in just a spectacular way. I'll reiterate that I hope people will navigate to the PCAST website and listen to these talks as many young people in particular thinking about the Cancer Moonshot because the educational value of each of the talks plus the vision was really a great combination.

02:35:44.960 - 02:36:04.960

So let's thank our speakers. And the last order of business is public comment at this meeting for which I'm going to turn to Bill Press to share that session. Thanks. We have two public comments today if they're present.

02:36:04.960 - 02:36:22.960

Is Ernesto Perez-Chinona here? Yes. Why don't you go to the front? And you'll have two minutes and I'll give you a 30-second warning.

02:36:22.960 - 02:36:34.960

I'm going to press the speaker button. Yeah. And Ernesto Perez-Chinona's topic is the postdoctoral community of the biomedical research science. Thank you so much.

02:36:34.960 - 02:37:18.960

Before joining the NCI as a postdoctoral fellow, I worked at Johns Hopkins, the University of Florida, and I recently graduated from the University of North Carolina, Chapel Hill. And I come to you as a private citizen today because I'm interested in the question, are American taxpayers getting enough bang for their buck from the current research communities? And while I do not know the answer to this question, I do believe that the efficiency of our laboratories to finish and publish projects is in decline. One thing that I found very striking from my graduating classes, the very few Americans who obtained their PhDs in biomedical sciences who were interested in pursuing a career in research.

02:37:18.960 - 02:38:06.960

Most people went on to consulting and public policy and a lot of private sector jobs, leaving, of course, a vast number of foreigners in our research labs who are also not necessarily interested in pursuing tenured track positions due to their struggles with the English language or sometimes the assimilation of the American culture. And so what it has culminated to is the vast wealth of data in our labs that goes unpublished sometimes. And I think that this level of discontinuity in our research projects is unprecedented and has made a very expensive and glitchy machine of what should be a 21st century efficient engine.

02:38:06.960 - 02:38:20.960

And so I just urge you to take up this matter and discuss. Thank you very much for your time. Thank you very much. Our second public comment is from Andrew McAbee. Is he the President?

02:38:20.960 - 02:38:45.960

Good. Dr. McAbee is Executive Director of the Association of American Veterinary Medical Colleges and his topic today is One Health. Thank you. Excuse me. The AAVMC represents colleges and faculties of veterinary medicine, excuse me, throughout North America, Europe and Australasia.

02:38:45.960 - 02:39:33.960

One Health has long been a high priority of ours and specifically with interest in interprofessional education in the health professions and transdisciplinary research approaches. AAVMC has participated with the Office of the Directorate NIH over the past two years to host a series of interagency meetings on One Health. We've been very pleased to have the participation of nearly a dozen NIH centers and institutes as well as representatives from CDC, FDA and USDA at those meetings. And we've been very gratified by the willingness to collaborate among those members and use a One Health approach to their issues.

02:39:33.960 - 02:40:15.960

We're also very excited about the recent introduction of the One Health Act of 2016 by Senator Al Franken, which would charge the nation's agencies to work together on identifying specific goals and priorities to help prevent and respond to animal disease outbreaks that threaten human health and well-being. Using a One Health approach requires that partners with different mandates collaborate and communicate effectively. And since federal agencies have different mandates, different cultures and even different values, a One Health approach will help bring them together to address some of our most challenging health threats.

02:40:15.960 - 02:40:46.960

And as PCAST recognized in the 2014 report to the President on combating antibiotic resistance, there is a need for One Health approach to addressing antibiotic resistance. The agriculture and animal health communities have accepted the responsibility and challenge to be an integral part in the reduction in mitigation of antibiotic resistance. Thirty seconds. However, agency boundaries and funding constraints currently limit the type of One Health approach that is needed.

02:40:46.960 - 02:41:22.960

The result is that federal agencies working on the animal and environmental-related goals, which has significantly lower funding, are struggling while those in human health are making more progress. In addition, we would advocate for public-private partnerships that include human health, animal health, and environmental health partners, which are necessary for the One Health approach that's recommended by PCAST. Thank you for your time, and I appreciate your leadership and interest in this One Health issue. Thank you very much, Mr. Chair.

02:41:23.960 - 02:41:44.960

Well, that brings us both to the end of our agenda and to the end of our time. So again, my thanks to the PCAST members, to all of the presenters, including the public commenters, to the wider audience, both in the room and on the web. We look forward to seeing you next time.