Bioscience: Lost in Translation? (book excerpt)

Barker Bioscience Lost in Translation Cover.jpeg

At the heart of this book is a mystery. Our everyday experience is of accelerating innovation in many areas that touch our lives. In information technology (IT) and services, we live in an exponential age. But in the area of perhaps our greatest interest—our health—innovation seems stubbornly slow. Before we dive into the mystery further, let us step back and ask some basic questions. What is true innovation? And are we really living in an innovative society?

Richard Barker, Bioscience—Lost in translation?: How precision medicine closes the innovation gap?, Oxford University Press, 2016

Introduction: Are we living in an innovative 21st century society?

I’m writing a book on innovation on a 21st century product—a sleek laptop—sitting at a 20th century product, a desk designed by someone described as a ‘prolific architect and innovator’, Osvaldo Borsani. So perhaps we should start with a question: What makes a product innovative? The MacBook Air, with its slim profile, large sharp screen, and well-designed keyboard, looks innovative, although we can clearly trace its evolution from the desktop computers that preceded it. It combines a great deal of technology in a small space and results from a rapid sequence of incremental improvements since the first personal computer, the breakthrough innovation. Is the desk innovative? Well, the gently curved shape is practical, so I can reach everything without moving my seat. It has a cunning way of locking the drawers. And a small bookshelf built into it—but at the back, where I can’t reach from here. But in most respects, this desk is just a place to sit down and write, and its basic features—flat top, four legs, drawers to put things in—haven’t changed for centuries. In that basic sense, this desk, attractive as it is, isn’t really an innovation, other than aesthetically. Which, of course, is a benefit.

So what is innovation? Innovation, as I will use the term, is the ability to turn new science and technology into practical things—products and services— that are better, faster, or cheaper solutions to human need. The only potential innovations that really count are ones that are translated into beneficial impact for the ultimate customer. So are we living in innovative times? I have heard commentators like Gary Kasparov, the chess champion, and Peter Thiel, the entrepreneur venture capitalist, say that the conservative nature of our institutions is too often stifling our innovative drive. But it is undeniable that, in many areas of human need, we have seen innovation deliver benefits better, faster, and often also cheaper. The smartphone sitting beside me is seen by most of us as a major breakthrough, even if it actually is mainly the bundling of past innovations—many of which are 20– 30 years old—into one single, portable, ingenious device. And this smartphone is just the tip of a veritable iceberg of IT innovation, with wireless networks, cloud computing, apps, and GPS tracking all underpinning its success.

 Figure 1.1 Growth in Internet users and traffic. Source: Data from Internet Live Stats, http:// www.internetlivestats.com/, accessed 01 Oct. 2015; Cisco Visual Networking Index (VNI). ‘Global IP Traffic Forecast 2009– 2014’, http:// www.cisco.com/ c/ dam/ en_us/ about/ ac78/ docs/ Cisco_VNI_Global_and_APAC_IP_Traffic_Forecast.pdf, accessed 01 Oct. 2015. Copyright © 2009 Cisco VNI.

Figure 1.1 Growth in Internet users and traffic. Source: Data from Internet Live Stats, http:// www.internetlivestats.com/, accessed 01 Oct. 2015; Cisco Visual Networking Index (VNI). ‘Global IP Traffic Forecast 2009– 2014’, http:// www.cisco.com/ c/ dam/ en_us/ about/ ac78/ docs/ Cisco_VNI_Global_and_APAC_IP_Traffic_Forecast.pdf, accessed 01 Oct. 2015. Copyright © 2009 Cisco VNI.

Across the board, innovation in IT is speeding ahead, constantly accelerating in its pace. The gap in time from the appearance of a new device or service and its widespread adoption is reducing with every new generation. The cost per new unit of capability is falling at a rate that is faster than exponential. Computer speed is a case in point: the number of calculations per second per $ 1000 of computer investment has risen from 1 in the 1950s to 10 000 000 000 today—10 to the power of 10 in my lifetime. This has been powered by steady advances in a single basic technology, the microchip. Since it was first published in 1965, Moore’s Law—the doubling of the density of transistors on a microchip every 2 years—has been followed faithfully by the semiconductor industry. And we have yet to see the real impact of optical or quantum computing! At the current course and speed, it is estimated that, by around 2045, computing will deliver (in 1 second for $ 1000) 1026 calculations, roughly the number performed by all the human brains on the planet. Since its inception, the Internet has also grown exponentially. As the result of the subsequent invention of the World Wide Web opening up millions of sources of content to billions of users, it now reaches more than a third of the world’s population (Figure 1.1). This in turn has driven a truly astonishing growth in traffic, which has virtually doubled every year, to the mind-numbing level of 3.6 million terabytes per month (Figure 1.1). By the way, just 10 terabytes could hold the whole Library of Congress (itself holding more than 30 million books).

IT patents, too, are rising exponentially, promising yet further practical advances, from smartphones that advise and monitor us in real time, in all areas of our life, to artificial intelligence to make sense of all the data we are accumulating. The IT specialists IDC estimate a 50% increase year on year, so that we will be the lucky generators of 40 zettabytes of data by 2020, more than 50 times the amount we have today. This data will increasingly be unstructured data—including videos, images, voice records, blogs—in fact anything that doesn’t come fitted within a structured database. These growing stores of data will in turn drive further innovations to mine and make sense of it all. The speed of change in information businesses is breathtaking in relation to 20th century experience, when products and industries lasted for decades. Film-based photography originated in the 1820s, but digital photography replaced it in 10 years. In an ironic twist of fate, digital photography was first developed by Steven Sasson in Kodak, the company it went on to bankrupt. In the process, the technology has gone from delivering 0.01 of a mega-pixel for $ 10 000—via a device weighing 1.7 kg—to 10 mega-pixels for $ 10 and 0.014 kg! In short, 1000 times the resolution for 1000 less in cost and weight. A billion-fold improvement in performance. The GPS receiver, that we are now all rely on (although we may have no idea of how it functions) began life in 1981, costing $ 119 900 and weighing 24 kg. Today, the same functionality is delivered for less than $ 5 in a device that is less than a centimetre across. As well as quantity, the quality of what is delivered to consumers advances also. Devices—from HD TV screens to embedded cameras in tablet computers—have become steadily more impressive in performance. Companies involved in the digital revolution also grow (or disappear) more and more rapidly. Remember Netscape? ‘Exponential’ is a favourite word in Silicon Valley, and many of these companies and their technologies exhibit exponential growth, at least for a while. A few, of course, remain decade after decade, adapting as best they can to the changing environment. From the first successful mainframe computer in the 1960s, IBM developed Big Blue to defeat the world’s leading chess player Gary Kasparov in 1997. Now its artificial intelligence successor, Watson, can mine sources on a vast variety of subjects and come up with findings that no human being has the brain capacity or time to uncover. It can also beat any human contestant at Jeopardy. I recently heard a talk by Peter Diamantis (the founder of the X-Prize). Included in his long list of ‘exponential technologies’ were: sensor networks, robotics, 3D printing, nano-materials, and artificial intelligence. Also, interestingly, synthetic biology, of which more later. So, in anything touched by the digital revolution, we are seeing innovation at a rate never before seen. In that sense, 21st century society is certainly very innovative. However, we see science-driven innovation in other, less obviously ‘digital areas’. The energy sector, long dominated by decades-old oil extraction, coal mining, and turbine technology, has seen fracking, solar energy, and electric cars all arrive in earnest in the last 10 years. Electric vehicle sales are taking off, brands proliferating and costs falling: the US sales are up more than six-fold over the last 3 years. The emergence of green energy as a competitive force has also been dramatic, with solar energy moving from a useful source for remote installations in tropical areas to a mass production technology, with field after field in temperate parts of Europe now covered with solar panels. As the efficiency of photovoltaic cells has improved from 2% in 1955 to 40% today, the cost dropped from $ 1785 to $ 1.25 per watt, following the solar energy version of Moore’s Law— Swanson’s Law. Moving from energy production to its consumption, after languishing for decades, the electric car is now entering a phase of exponential growth, propelled by environmental concerns and efficiency savings. Even the highly conservative area of food has seen the application of chemistry by chefs such as Heston Blumenthal to create dishes never seen or eaten before. And many restaurant menus throughout the world contain creative fusions of cuisines old and new. On a more industrial scale, genetically engineered crops, controversial as they are in some parts of the world, are delivering more affordable food to millions. So information processing, communications, food, transportation, and energy— some of the areas of greatest human need— are certainly feeling the touch of innovation. As the underlying technology advances, it feeds through to a rapid development of new products and the delivery of greater consumer benefit, often at an accelerating pace. And all of this translates into rapidly falling prices per unit of performance. The net result of exponential innovation is often major disruption of industries: Uber, the world’s largest taxi company, owns no vehicles; Facebook, the world’s most popular media owner, creates no content; Alibaba, the most valuable retailer, has no inventory; Airbnb, the world’s largest accommodation provider, owns no real estate. Of course, all of these new businesses are internet-enabled. Exponential progress in innovation comes when three major factors combine. Governments lay the foundation by targeted investments in both basic science and early development, just as the US government did in the early days of the internet. Then start-ups and subsequently major corporations pour investment into R& D to develop sequential generations of products. And then consumers adopt and upgrade these, seeing ever-increasing benefit at lower or steady prices. Peer prompting encourages them to replace good products with even better ones. Of course, there remain many conservative industries (when did you last see a major change in French bakeries, for example?), but for many areas of life rapid change seems now to be the norm.

Is this happening in life sciences?

When we turn to the life sciences and their impact on medicine, the pattern is not just different but startlingly so. While customers for IT innovation, for example, are enjoying faster, better, and cheaper products simultaneously, in biomedicine this is far from the case. Too often we are seeing products that make a fairly marginal impact, take many years to arrive, and end up costing more. The delay between a basic bioscience breakthrough and its widespread impact on human health stays stubbornly at 15–20 years. The cost of new drugs and devices is spiralling upwards, not downwards. Moreover, with some exceptions, the impact on human health they bring is more incremental than revolutionary. Most products alleviate symptoms or extend life by a few months, rather than reverse disease or cure it. So customers for life science innovation—patients and payers alike—are not seeing products that arrive faster nor cheaper, and they are often not so much better either. This is certainly not for want of trying, and investing. The pharmaceutical industry, for example, invests in R&D at twice the rate (in terms of percentage of sales) of the IT industry. Many companies spend 15–20% of their revenues on R&D, year after year. Medical device R&D continues to rise rapidly, now standing at $ 23bn annually. Despite the heavy expenditure, the number of pharmaceutical products reaching the market has remained fairly stable, at 25– 35 a year (as measured by Food and Drug Administration [FDA] approvals). So the productivity measure of products per unit of investment has fallen dramatically. My Centre for the Advancement of Sustainable Medical Innovation (CASMI) colleague Jack Scannell coined the phrase ‘Eroom’s Law’ to describe this phenomenon: that the products delivered per unit of investment has demonstrated the reverse of Moore’s law—exponential decline [1]. This is not just over the last 20 years, but for more than 50, and remains even when we adjust for the delay between investment and products appearing (Figure 1.2). The result of this dramatic loss of productivity is that the investment required per new drug has reached several billions of dollars. Matthew Herper of Forbes recently calculated the cost for individual major companies, which varied from $ 3.3bn to $ 13.4bn [2]! Academic studies confirm the trend: the average cost, including the cost of money over time, has risen exponentially. The most recent (December 2014) figure for this from the group that has tracked it over many years, the Tufts Center for the Study of Drug Development, is $ 2.6bn [3]. The difference between this figure and the simple ratio per major company is consistent with the fact that smaller companies manage to spend less per new drug than most of the larger companies. So the contrast in innovation productivity with other industry sectors couldn’t be sharper! This is one of the major factors that has led to steady price rises for new drugs. Payers are proving increasingly reluctant to fund products they see as having modest value for high cost, even though they understand that most of the $ 2.6bn is the cost of failures: products that never make it to the market. We will analyse the productivity problem in much more depth in Chapter 3. Now, what makes these trends so interesting is that they are in startling contrast to the rapid progress being made in the underlying science of human biology, the subject of Chapter 2. Genomics, proteomics, structural analysis, systems biology, cell biology, and medical imaging—the list of new tools and discoveries goes on. In all these basic biological science areas, we have seen huge advances over the last 20 years. These are reflected in the almost exponential rise in publications in life science journals, and the steady increase in patents filed. So, while scientific inputs have multiplied, outputs—in terms of products and patient benefit—have not.

 Fig. 1.2 Exponential decline in pharmaceutical products produced per unit of investment. Reproduced with permission from Scannell JW, Blanckley A, Boldon H, and Warrington B. Diagnosing the decline in pharmaceutical R& D efficiency. Nature Reviews Drug Discovery, Volume 11, pp. 191– 200, Copyright © 2012 Macmillan Publishers Ltd.

Fig. 1.2 Exponential decline in pharmaceutical products produced per unit of investment. Reproduced with permission from Scannell JW, Blanckley A, Boldon H, and Warrington B. Diagnosing the decline in pharmaceutical R& D efficiency. Nature Reviews Drug Discovery, Volume 11, pp. 191– 200, Copyright © 2012 Macmillan Publishers Ltd.

The mystery deepens. The period in which many of today’s most successful drugs were conceived—one that some call the ‘golden age’ of drug discovery—was not blessed with most of what now know of human biology, and most of the tools we can now use to probe it. We knew precious little about the complex gene control mechanisms, how proteins interact together in cellular networks, and how cells communicate. Sometimes all we knew was that a particular protein was somehow involved in disease, that the drug could inhibit or stimulate its activity, and that early experiments in animal models of human disease had seen a good response. In some cases, we simply found serendipitously that a drug that was being investigated for disease A actually had strong activity in disease B. Twenty years on, we can sequence genes rapidly, profile their expression, determine protein structures, map protein networks, create huge libraries of promising compounds and probe their effects in high throughput experiments. So why has our innovative output not also increased by leaps and bounds?

 Fig. 1.3 The innovation gap.

Fig. 1.3 The innovation gap.

In contrast, we see a widening ‘innovation gap’ (Figure 1.3). This is the central question I will tackle in this book: what is it about the medical sciences and their application to achieve patient benefit that has defied the ‘better, faster, cheaper’ pattern of innovation seen in so many other sectors—and can anything be done about it? We will look at the quality of basic science, efforts to translate it into potential products, and the process of development via clinical trials. We will examine the tests applied by regulators and payers to understand if they are reasonable or could be made more receptive to high value innovation. We will also explore the various barriers that even approved innovations can face in their uptake by conservative health systems, cautious clinicians, and confused patients. We will look not only at the origination of products but their spread through the system. This is remarkably slow, requiring 10–15 years in many cases, unless a significant incentive or driving programme is in place. The English National Health Service (NHS) itself quotes 17 years as a typical time! 4 So, despite huge advances in the underlying science, product origination is increasingly costly, R& D productivity has steadily fallen and those products that do emerge take many years to be adopted. A depressing pattern, in contrast to so many other science-driven sectors. Some might find this comparison very simplistic, and say: of course medicine is different—it’s more complex, deals with life and death issues, and should properly progress slowly and cautiously. But these people are unlikely to be patients with severe, life-limiting conditions. The basic question is this: does our slow and costly medical innovation process simply reflect basic knowledge gaps or intractable aspects of human biology, or are they a reflection of human choice—how research is organized, products developed, innovation rewarded, risks assessed, regulations drafted, and users engaged? While there are indeed some huge gaps in our basic knowledge of biology, and we can never afford to be cavalier in our handling of medical innovation, I will seek to show that the majority of factors that inhibit the medical innovation process are subject to human choice. So, if we understand our choices and their implications we can change them. That is the good news.

 Table 1.1 Goals of stakeholders in medical innovation

Table 1.1 Goals of stakeholders in medical innovation

One complexity is undeniable. Medical innovation involves an unusually wide range of stakeholders: academic researchers, biopharmaceutical companies, device and diagnostics manufacturers, regulators, health technology assessment (HTA) agencies, politicians, clinicians, payers (public and private), and last, but of course far from least, patients and those who care for them. They all have different goals, both in terms of their basic needs and the improvements they would like to see (Table 1.1). An effective innovation process must satisfy the needs and concerns of all. Throughout this book I will seek to reflect these different perspectives, both in the specific case examples I discuss and in terms of general principles for the future.

What approach am I taking?

Firstly, the problem has many causes and therefore needs several different and complementary solutions. Most problems are a function of the overall system, not specific players: we need to avoid the ‘blame game’. Some complain about academics uninterested in turning their discoveries into useful therapies. Some lay responsibility for disappointing productivity at the door of conservative regulators. Some level their criticism at avaricious pharmaceutical companies unwilling to invest in difficult disease areas. Others blame short-sighted HTA agencies unwilling to recognize the future value of innovation as they attempt to control prices. Still others point to the conservatism of clinicians. In contrast, I will assume all the players are seeking to play their roles according to their mission and strengths and that we should look to change how they all interact in what is increasingly called the ‘innovation ecosystem’. For although all have a critical role to play—academics, companies, regulators, reimbursement agencies, doctors, and payers— they do not couple together to form an effective system. They leave what I describe as major ‘gaps in translation’. In the course of the book, I will also challenge a number of popular myths about the innovation process. No one person will hold all these beliefs, but collectively they impede our progress:

  • Academic science produces all the major innovations; then companies patent them and reap all the rewards.
  • The products that are in the pipeline are mainly ‘me-toos’ of marginal value.
  • Life science companies are sworn competitors and will never cooperate.
  • Patents are the basic problem: if we abolished them we would get more innovation, more cheaply.
  • Regulators are much more conservative than the companies with whom they deal.
  • The medical innovation business, because of its complexity, is best left to the professionals: patients can contribute little. 
  • Doctors always know best and are able to keep up with their fields by reading medical journals.
  • Patients often do not comply with their medications because they simply forget.

The subject of this book—creating a ‘sustainable ecosystem’—sounds academic and theoretical. But it’s far from that, for at least two pressing reasons. First, many of the people that fund the life sciences from governments to company investors—are seeking greater returns, in terms of practical innovations and valuable health advance. And, despite the exciting scientific headlines, it is the delivery of practical outcomes that determines long-term investor confidence. The whole innovation enterprise is in jeopardy if disappointed investors, public and private, cut back on the research that fuels it. One key measure of confidence is the amount the global pharmaceutical industry spends annually on R&D: this rose steadily in the 1990s and early 2000s, reaching $136bn by 2011. It actually fell slightly in 2012, and has now resumed growth, but at a much reduced level. The most important reason to close the innovation gap waits for treatment in doctors’ surgeries, outpatient departments, and nursing homes across the world: patients with unmet need.

There are literally thousands (at least 7000) of untreated rare diseases, scores of still-fatal cancers, the steady neurodegeneration of millions of Alzheimer’s and Parkinson’s sufferers, and the human tragedy of premature deaths from such unconquered diseases as muscular dystrophy and amyotrophic lateral sclerosis (ALS). Most treatments for chronic conditions such as chronic obstructive pulmonary disease (COPD), heart failure, osteoarthritis, and diabetes we have today deal with the symptoms, not the disease itself, let alone its prevention. The work of medical innovation is vital and far from over. In tackling the issue of the innovation gap, the book’s chapters flow as follows. Hopefully by this point (Chapter 1) you are persuaded there is a problem to tackle. Chapter 2 reviews the dramatic scientific advances that have occurred in the last 20 years and should be forming the basis of medical innovations today and tomorrow. Chapter 3 will analyse the ‘gaps in translation’ that are preventing the full benefit being received of this revolution in bioscience. Chapter 4 will scan a host of examples of both past success and failure to seek to distil lessons on what can and should change about the innovation process. Chapter 5 is the core of the book’s conclusions and recommendations on how we should reform and refashion medical innovation. It lays the scientific foundation for seven steps to sustainability, with their major unifying theme of precision medicine. However, the environment needs to be right for the changes to occur and be embraced by wider society: Chapter 6 will review aspects of this environment and point to supportive changes needed in both policy and practice. Part of the argument for change, both in the process and the environment will be a sense of the prize: Chapter 7 will describe the impact of both the enabling technologies and disease programmes we could see in the next 20 years if we get the changes right. Finally, in Chapter 8 we will step back and survey the overall ‘innovation ecosystem’ and the revolutionary changes to roles and relationships that the changes will imply. Although I’ve taken pains to emphasize the challenges we are facing in turning medical discoveries into medical advance, I want to strike a very positive note at the outset. There are promising signs of a turnaround beginning. The last 2– 3 years have seen a resurgence in new products approved by the FDA: most of these have been successful because they are better targeted. There is much debate on how sustainable this recovery is, but it has restored investor confidence, at least in the small and medium-sized enterprise (SME) sector. There have been more than 120 initial public offerings (IPOs) of biotech companies, and more established companies with products in Phase 3 trials have been able to sell themselves to the majors at very high prices. However, investor sentiment in biotech is notoriously fickle, and much depends on the success of the pipeline. The only way this recovery in investor confidence can be maintained is for promising products to make it through to approval, reimbursement, and use. The challenges at each of these stages have not changed. I also see many examples of the various players in this drama beginning to take new stances and play new roles. There is a rapid increase in what is called ‘pre-competitive’ collaboration between companies. They are collaborating on areas that they realize are impossible—or just very inefficient—for them to pursue alone. Regulators in many parts of the world are showing a new level of flexibility and responsiveness in dealing with novel types of innovation or therapies in areas of high unmet need. HTA agencies are asking searching questions about their methodology. Payers are prepared to consider new types of commercial arrangements that reward innovators for impact, not just supply of product.

Perhaps most importantly, patient organizations are leaving their comfort zone of highlighting need and lobbying for treatment to actually design new therapies and participate actively in the innovation process. It is always the patient with most at stake, and industry, the regulators, the clinicians, and the payers ignore them at their peril—and the politicians know their votes count! So, with early promising signs of change in attitudes as well as practice in many quarters, it is a good time to review what we need to do to create sustainable innovation.

Some final comments before we start

This is not a ‘how to’ book on the basic principles of the discovery and development of medical technologies. There are some useful accounts of this subject [5], [6], [7]. There is also an excellent and provocative recent book on the specific challenges of drug research by Tomas Bartfai, a lifelong drug discoverer, that I commend to readers [8]. I will analyse many case examples of innovation, but my goal is to go beyond anecdotes and wherever possible to take a robust scientific approach to the problem. My colleague Rob Horne of UCL coined the useful term ‘innovation science’ for the study of the innovation process itself, the factors that affect it, and the stakeholders for whom they are important. My intent in the pages that follow is to lay some of the foundations of a new discipline of ‘medical innovation science’ for researchers and innovators in both public and commercial R&D organizations. Understanding this science will also have value for policy-makers and for the leaders of health systems. They desperately need faster, better, and cheaper innovation, if they themselves are to be sustainable. They will not be able to cost-cut their way to sustainable healthcare, as I have pointed out elsewhere9. Readers will notice there is proportionately more diagnosis and prescription in the book for the translation gap in pharmaceuticals than for other parts of the life science sector. This reflects both the level of debate and the seriousness of the issues. But wherever possible I will compare and contrast the situation in pharmaceuticals with that in medical devices, in vitro and in vivo diagnostics, and in digital health, or devote sections to these vital elements of the sector. Finally, I will summarize at the end of each chapter its key conclusions, and how they lead into the next chapter. This is intended to help readers who may, for example, be familiar with the science (they may wish to skip Chapter 2), or with the barriers to translation (Chapter 3), and wish to move straight to 

the case studies or my proposed solutions. Summary of Chapter 1 In a world caught up in ever more rapid technological advance, biomedical innovation remains stubbornly slow and unproductive, as measured by output (benefit to patients) over input (investment). This is despite very rapid progress in the underlying science, and it puts at risk the very enterprise of life sciences, in which society and investors place so much faith. We need a scientific study of the gaps in translation, and radical thinking to bridge them. In Chapter 2, we review the breathtaking advances in basic bioscience and related technologies over the last 20 years—advances that have the potential to lead to major innovations, if we can reform the innovation process.

References

  1. Scannell JW, Blanckley A, Boldon H, Warrington B. 2012. Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery, 11: 191– 200.
  2. Matthew Herper, Forbes Magazine
  3. Tufts Center for the Study of Drug Development report, dated 18 November 2014
  4. NHS England, ‘Health and high quality care for all, now and for future generations: Frequently asked questions’
  5. Burns LR. 2012. The Business of Healthcare Innovation, Second Edition, Cambridge: Cambridge University Press.
  6. Rang HP. 2006. Drug Discovery and Development, Oxford: London: Elsevier.
  7. Barker R, Darnbrough M. 2007. The role of the pharmaceutical industry. In: Kennewell PD, Moos WH, Kubinyi H, et al. (eds), Comprehensive Medicinal Chemistry II, vol. 1, pp. 527– 52.
  8. Bartfai T, Lees GV. 2013. The Future of Drug Discovery: Who Decides Which Diseases to Treat? London: Academic Press.
  9. Barker R. 2011. 2030— The Future of Medicine: Avoiding a Medical Meltdown, Oxford: Oxford University Press.