Issue No. 5: Urgent Need for Building Co-Creation Platforms and Developing ICT Talent

Introduction

This series is based on the recognition that “drug development is entering a major turning point,” with its main theme being how to address this transition. In Issue 3, it was mentioned that “pharmaceutical companies are increasingly exploring business models that go beyond merely selling ‘products’ as drugs to providing ‘health solutions’1).” The previous issue outlined the impact of the Second Internet Revolution and a new wave of artificial intelligence on this trend2), showcasing various response strategies. Here, we discuss two urgent challenges in the broader context of drug development: the importance of building a “co-creation platform” to drive healthcare innovation and training “ICT specialists,” whom we refer to as “D2K Scientists.”

The Path from Drug Development to Health Solution Provision

For pharmaceutical companies to evolve their business models from simply “developing drugs” to “providing health solutions,” innovation is naturally essential. This topic was hypothetically explored in Issue 4 of this series3). Figure 1 and Table 1 summarize these considerations and are reproduced here for reference. The leftmost column of the table lists key innovation areas: (1) responding to open collaboration, (2) studying proper drug use, (3) researching non-drug interventions, (4) building new relationships with consumers and patients, and (5) revising the pipeline. These points highlight critical aspects of innovation.



Figure 1: Major pharmaceutical companies are working to evolve their business models from drug development to providing health solutions. This transition necessitates embracing innovation. Here, “Healthcare” refers broadly to the English term “Healthcare” and is not limited to “Wellness.” It naturally includes healthcare services under national health insurance systems.

Table 1: Innovation Challenges for Evolving Pharmaceutical Companies into Health Solution Providers3)
EpG: Epigenetics, GxE: Interaction between Genome and Environment, GOP/N: Genome-Omics-Pathway/Network, PHR: Personal Health Record, EMR: Electronic Medical Record, PGx: Pharmacogenomics, TGx: Toxicogenomics, NGx: Nutrigenomics.
Innovation Challenges Specific Issues Biomedical Challenges Information and Computational Techniques
Responding to Open Innovation Establishing platforms for rapid adoption of new discoveries and technologies Absorbing and utilizing new discoveries and technologies
Advancing from Genome to EpG and GxE research
Developing near-human model systems
Building co-creation platforms
Sharing information and knowledge
Collaborating on talent development
Research on Proper Drug Use Selecting appropriate drugs for individuals
Studying optimal timing and dosage of drug use
PGx, TGx research
Biomarker discovery
Research on appropriate polypharmacy
Chronobiology studies
N of 1 studies and Point of Care implementation
GOP/N approach
Integration of PHR and EMR
Data analysis and pattern recognition
Knowledge processing and cognitive computation
Natural language processing, control theory
Research on Non-Drug Interventions Studying the molecular basis of the effects and risks of lifestyle factors such as diet, exercise, sleep, and mental control PGx, TGx, NGx research
Biomarker and health marker discovery
Chronobiology studies
N of 1 studies and Point of Care implementation
GOP/N approach
Integration of PHR and EMR
Data analysis and pattern recognition
Knowledge processing and cognitive computation
Natural language processing, control theory
Building New Relationships with Consumers and Patients Promoting patient participation in drug R&D
Supporting participatory healthcare
Reducing participation barriers
Addressing ethical, legal, and social issues
Building co-creation platforms
Point of Care + N of 1 studies
Building co-creation platforms
Sharing information and knowledge
Providing learning opportunities
Data analysis and pattern recognition
Pipeline Revision Collaboration with external organizations Innovation in specific areas Full integration of ICT/IoT

The term “innovation” is widely used in business and media, often expressing the expectation of breakthroughs achieved through scientific discoveries, technological advancements, or new methodologies. However, the essence of innovation in healthcare lies in whether it makes a difference in service delivery. General discussions of innovation often reflect the expectation that investing in new insights or technologies will improve healthcare services, representing a push-driven innovation. In contrast, the innovation challenges outlined in this table propose action goals from the perspective of service recipients, considering whether such services can be realized. This approach can be termed “pull-driven innovation.”

Supporting push-driven innovation is relatively straightforward for traditional government agencies, academic societies, and academia because it aligns with existing divisions of science, technology, and administrative jurisdictions. However, pull-driven innovation often requires combining different scientific insights and technologies, making it difficult to garner support from academic societies or government agencies, especially at the start. Consequently, securing funding for such activities is also challenging.

For pharmaceutical companies striving to evolve their business models from drug development to providing health solutions, the first hurdle may be correctly recognizing the differences between push-driven and pull-driven innovation. From this perspective, two challenges emerge: building new relationships with patients and consumers, and leveraging information and computational techniques (ICT). Clearly understanding these challenges appears to be the initial gateway for pharmaceutical companies as they transition from drug makers to healthcare service providers.



Figure 2: Transition from push-driven innovation through science and technology to pull-driven innovation from a service perspective. The left side represents push factors, while the right side represents pull factors. The key here is patient-centered thinking. Artificial intelligence as a computational algorithm is related to advances in the upper-level software.

Why Must Next-Generation Healthcare Be Participatory?

Among the two challenges, “building new relationships with patients and consumers” was discussed in a previous issue, so we will first cite that discussion here1):
“… In Western countries, there has been a rapid shift in mindset toward recognizing consumers and patients as stakeholders in drug development. Voices advocating for listening to patients, encouraging their participation, and evaluating efficacy and harm from their perspective have grown louder, leading to numerous discussions and experimental projects funded by both public and private sectors4).
This concept is encapsulated in the phrase, ‘Patient involvement can become the blockbuster of drug development’5). The idea that humans are not just large guinea pigs but partners in biomedical research has already been widely accepted among genome researchers6) (reference numbers correspond to those in this issue).”
It appears that this trend of considering patients as partners in drug development has already been well-established in Western countries.

This trend is natural when considering the advancements in biomedicine. Genomic medicine, as proclaimed by progress in genome sequencing and accompanying omics (comprehensive analysis technologies), aims for precision medicine, personalized medicine, and even N of 1 studies—clinical research or trials focused on specific individuals. Individuals participating in large-scale genomic sequencing and interpretation studies, while part of a vast data pool, gain lifelong access to valuable genetic data related to their germline DNA. Therefore, personalized medicine inherently leads to participatory medicine.

Additionally, the major threat to healthcare in advanced countries like Japan, the US, and Europe is the increasing prevalence of complex chronic diseases associated with an aging population. For such conditions, drug-only solutions are insufficient. Integrated approaches, including non-pharmacological interventions (NPI), are necessary, with behavior change, such as altering lifestyle, serving as the foundation. This requires the cultivation of willpower. Unlike prescribed medications, the choice of NPIs is left to individual consumers or patients. However, the information and knowledge required to make informed decisions are scattered and often derived from advertisements. Thus, guidance is essential for wise decision-making.

On the other hand, the rapid advancement of wearable and wireless devices—capable of connecting to smartphones and collecting personal health data—has created an environment where individuals can easily gather, store, and analyze their data in the cloud1). This domain is often referred to as Digital Health. Particularly for complex chronic diseases, supplementing clinical data collected at medical institutions with such personal data clearly has significant potential7). Therefore, the conditions necessary for next-generation healthcare to be participatory are now in place.

Sources of Information and Knowledge for Patients and Consumers

However, for patients and consumers to become key stakeholders in healthcare, it is not an easy task. Even with the willingness to participate, significant barriers hinder their involvement as crucial contributors to healthcare. These barriers can be summarized as a lack of information and knowledge, human resources, and financial support for activities. While each is a critical issue, advancements in ICT are playing a dramatic role in lowering these barriers.

First, regarding information and knowledge, it is essential for the general public to access reliable information and knowledge about the benefits and risks of food, health supplements, and pharmaceuticals; the proper use of medicines; and the advantages and risks of lifestyle choices such as smoking, exercise, sleep, and meditation (now often referred to as “mindfulness”). Additionally, understanding the safety (toxicity) of chemical substances in the environment is vital for healthcare8-11). However, in Japan, the development of public research institutions related to regulatory science—focused on generating information and knowledge for utilizing scientific and technological advancements—lags significantly behind institutions pursuing new discoveries and technologies. As a result, the asymmetry of information and knowledge between healthcare service providers and recipients appears particularly pronounced among advanced countries.

It is also essential to recognize that such information and knowledge are inherently incomplete and subject to revisions driven by scientific and technological progress. Therefore, continuous research and learning are crucial. However, due to differences in the regulatory responsibilities of government agencies, there are often invisible barriers within the research community (e.g., academic societies). From the perspective of service recipients, it is difficult to claim that these entities are well-coordinated.

To resolve the asymmetry of information and knowledge, two efforts are required: ensuring access to information and enabling patients and consumers to actually obtain it. Regarding healthcare information and knowledge, much of it is already available, at least in English. This is thanks to the widespread use of the internet and the efforts of the United States and the EU to promote information accessibility online. While information is accessible (primarily in English), addressing the lack of human resources to assist in utilizing this information remains an unresolved challenge.

The Necessity of Co-Creation Platforms

To address the lack of human resources, including the challenges discussed earlier, support is required from researchers in academia, government-affiliated institutions, and professionals providing services (e.g., clinicians). Specifically, this involves the backing of public research institutions and academic experts dedicated to studying the science, technology, and societal frameworks necessary for applying scientific advancements to healthcare. In Japan, these include institutions similar to the NIH, FDA, NIEHS (National Institute of Environmental Health Sciences), and EPA in the United States. Examples include the National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), the National Institute of Health Sciences (NIHS), the National Institute of Genetics (NIG), the National Institute for Environmental Studies (NIES), the National Institute of Advanced Industrial Science and Technology (AIST), the Food Research Institute, and the National Institute of Occupational Safety and Health (JNIOSH).

If this network of national research institutions includes academics involved in education, researchers from foundation-based research organizations, and corporate researchers, a community of government, academic, and industry professionals can be established. If this connection is guided by the needs of general consumers, patients, and their support organizations such as NPOs/NGOs, it opens the possibility of fostering pull-driven innovation.

Generally, government, academic, and industry researchers tend to gravitate towards push-driven approaches, focusing on new discoveries and technologies. Moreover, when they apply for research funding, they often become constrained by the existing frameworks of government agencies or academic societies. Consumers and patients, however, are free from such constraints and can envision solutions based purely on their desires, asking, “What if things were this way?” To achieve these goals, access to specialized information, technical expertise, facilities, and funding is required. A platform to discuss how to combine these resources without being restricted by existing organizational structures is essential. Such a community, led by consumers and patients, is more efficient. This forms the fundamental idea of a co-creation platform. Establishing such a foundation is a prerequisite for implementing “participatory healthcare,” where patients and consumers take active roles.

Moreover, the practices carried out by such a researcher community align with experimental initiatives aimed at leveraging scientific and technological advancements to maintain human health—essentially translational research. As is well-known, the major challenge for NPOs/NGOs is securing adequate funding. However, this obstacle can potentially be addressed within such a community, opening new pathways forward.

The Importance of Equal Partnership

The fundamental difference between experimental initiatives like this and previous healthcare efforts lies in whether an equal partnership exists between service providers and recipients. For example, numerous initiatives have aimed to revolutionize healthcare through ICT, but in most cases, patients and consumers have not been treated as equal partners. This reflects the pervasive paternalism of physicians in clinical settings12).

Another critical flaw in past initiatives is their independence, leading to limited ripple effects on other similar projects and their inability to adapt to the rapid progress in biomedicine and ICT. The significant limitation of such initiatives often stems from a lack of “imagination,” resulting from the exclusion of patients and consumers as equal partners. This is problematic because they are ideally positioned to drive pull-driven innovation, an area where service providers, including pharmaceutical companies, typically struggle.

Ultimately, pull-driven innovation requires a community that includes not only government, academia, and industry but also NGOs and NPOs led by patients and consumers. Building new relationships with patients and consumers through such a community-based foundation may be the most prudent way for pharmaceutical companies to advance innovation. This forms the basis of my hypothesis. Taking this concept further leads to the creation of a co-creation platform based on government-academia-industry-NGO/NPO collaboration.



Figure 3: Conceptual diagram of the co-creation platform. It assumes the participation of diverse stakeholders.

Co-Creation Platforms and Rapid Learning Environments

Let us consider a concrete vision of such a platform. This platform serves as a societal foundation supporting the practice of participatory healthcare. Its primary purpose is to provide an environment where stakeholders with diverse roles, expertise, and strengths can collaborate smoothly, effectively, and efficiently. Naturally, it assumes the use of the (inter)net or cloud environments, encompassing both cloud computing and crowdfunding. Thus, cooperation from ICT/IoT experts is indispensable.

For such an organization to function effectively, dialogue among participants, information sharing, and fostering continuous learning habits among members are prerequisites. Beyond administrative document management, it is essential to create an environment that facilitates the collection, organization, and utilization of knowledge in rapidly advancing fields like biomedicine and ICT. However, supporting such initiatives is often the Achilles’ heel of Japanese academic institutions, necessitating special efforts. One such effort involves building rapid learning environments in particularly critical areas of participatory healthcare.

A noteworthy example of such efforts is Rapid Learning Oncology, particularly the well-known initiative CancerlinQ by ASCO (American Society of Clinical Oncology)13). Similar efforts are needed in research on gut microbiota and their role in health and disease, whether for drug-dependent therapies or non-drug interventions. Such measures are also crucial for other fields experiencing exponential growth in knowledge, such as brain and mental health research. Technically, this is not an attempt to bring AI as a specialist into healthcare but an effort to utilize all available ICT, including AI, to support the rapid learning of all stakeholders in participatory healthcare—from general consumers and patients to clinical experts and basic researchers. This type of research aligns with the concept of Augmented Intelligence, where human intelligence is enhanced through machines.



Figure 4: Research on gut microbiota (human symbiotic microorganisms) is expanding explosively. Alongside oncology, it is becoming a priority for rapid learning.

Expectations for the Co-Creation Platform

Consumers can expect the following functionalities from this community:

  1. Guidance to reliable information and knowledge managed by public research institutions and universities, including fundamental biomedical knowledge and clear explanations forming the foundation of healthcare.
  2. Guidance on issues related to collecting and managing information and data when consumers participate in health-related research, particularly technologies ensuring privacy and security, as well as information on ethical, legal, and social issues (ELSI).
  3. Access to knowledge about the effectiveness and risks of direct-to-consumer (DTC) genetic and genome testing, which has rapidly gained popularity in Japan. Additionally, guidance on the accuracy (reliability) and safety of physiological and chemical laboratory tests that individuals can conduct using DTC services.
  4. Guidance on the transparency and reliability of algorithms in mobile health apps designed for healthcare purposes, particularly the reliability and effectiveness of advice for decision-making on interventions, and information on standards for certifying such devices for medical use.
  5. Guidance on the reliability of research into the effects and risks of ordinary foods, health supplements, and nutraceuticals, including the scientific validity and concerns regarding health claims for food components. This includes information on the scientific evaluation of Japan’s functional food labeling system and specific dietary approaches like low-carbohydrate diets, high-fat diets, or calorie-restricted diets, examining their utility, risks, and reliability from an individual perspective. More generally, guidance on research reliability related to environmental factors contributing to health and disease states, following the Genome x Environment = Traits (G x E = T) paradigm.
  6. Advice on pre-research planning to ensure the quality of consumer-led participatory healthcare research (Citizen Science), even with expert collaboration. This includes guidance on acquiring necessary knowledge to review protocols for registering research outcomes, such as those listed in the U.S. FDA’s ClinicalTrials.gov.

Please refer to Figure 3 for examples of target participants invited to join this platform.

The Modern Temple of Asclepius and ICT Specialists Working There

I would like to propose the utilization of information and computational techniques (ICT), which are indispensable for driving pull-driven innovation to realize next-generation healthcare. My proposal is rooted in experiences from over 30 years ago14).
At that time, I was effectively leading a research group at the Tokyo Metropolitan Institute of Medical Science, which integrated two laboratories focused on epidemiology and medical engineering, utilizing computational techniques upon request from physician-researchers. Prior to joining the institute in 1976, I had been working on applying pattern recognition, expert systems, and control theory to medical diagnostics and treatment. After joining, I expanded my work to include statistical analysis, waveform analysis, image analysis, toxicity prediction of chemical substances, molecular graphics, phenotype screening of nematodes, and embryonic development tracking systems (4-D image analysis), as either commissioned research from clinical researchers or as independent system development and fundamental biological experiments.

This expansion of scope was not entirely by choice, and I began to feel that our group’s goals were becoming chaotic. It was then that the idea emerged: “Our aim is to build an environment where any data encountered in medicine or healthcare can be processed by computers.” From then on, we referred to our activities as the Asclepius Project, inspired by the notion of creating a “modern Temple of Asclepius.” This idea was based on the story I had read: “In ancient Greece, people who recovered from illness wrote down their symptoms and dedicated them to the Temple of Asclepius. Hippocrates, regarded as the father of medicine, is said to have analyzed these records.” Whether this story is true remains unverified, but the essence of introducing computers into medicine—to record all illnesses digitally and generate accurate, preferably optimal, knowledge for diagnosis and treatment—remains unchanged.

However, at the time, digitizing all data encountered in medicine and healthcare was no easy task. Ironically, the easiest data to input were image records, despite being the most voluminous. The most challenging were the textual records in medical charts. Today, however, digital medicine has become a reality. Yet, in Japan, there still appears to be a shortage of expert groups capable of effectively handling these data computationally. The critical point is that the goal should not be to integrate big data, machine learning, or AI into traditional healthcare frameworks. The real objective is to reconstruct the very structure of healthcare itself using networks, IoT, cloud computing, and other ICT.

This distinction can be clarified by considering military operations. Healthcare, after all, is a battle against disease. Modern military forces operate under a comprehensive Command & Control system. While powerful weapons are essential, it is even more critical to determine how, when, and where to use them and to establish systems for appropriately allocating personnel and resources. Healthcare organizations, however, are far less straightforward than military hierarchies. In healthcare, decision-making often falls to those responsible for individual scenarios rather than being dictated from the top, resembling guerrilla warfare. While this analogy may be somewhat crude, it serves as a mental exercise to understand the complexities involved.

What Kind of Professionals Are Needed?

When considering healthcare innovation from the perspective of leveraging current and future ICT, a significant challenge is how to train or secure ICT specialists who can lead such efforts. These specialists will be involved in advising physicians and other staff (paramedical professionals) on decision-making and actions to improve the quality of care. At the core of their role is the ability to extract knowledge from data related to clinical care (from data to knowledge; D2K) and apply it back into services. This role, which could be called Translational Data Science, involves high-level judgment supported by extensive experience—an art, in essence. Their work encompasses various information and computational techniques, such as data handling, data analysis, and modeling. Future hospitals will likely need to employ a significant number of such data science specialists as regular staff or establish partnerships with expert groups for ongoing, stable collaboration.

These specialists will collaborate with healthcare professionals, particularly physicians, to provide expertise in areas such as measurement, data analysis, and evaluation techniques that underpin Evidence-Based Medicine (EBM) or Evidence-Based Supplementation (EBS). EBM and EBS are, needless to say, the “Holy Grails” of clinical medicine, drug development, preventive medicine, and the study of health foods and supplements. Thus, these specialists require sufficient biomedical knowledge and communication skills to engage in deep dialogue with clinicians and basic researchers. Unlike fields such as number theory, which has seen young geniuses like Abel and Galois, statistics is said to produce few prodigies due to its reliance on experience. Similarly, developing the data science expertise described above cannot be rushed. Like clinicians, they need to gain experience over time, which is why the process takes considerable effort.

Such specialists will be in demand not only in clinical settings but across all areas related to healthcare. They may be referred to as D2K Scientists in Biomedicine, Pharmaceuticals, and Nutrition (BioMedPharma & Nutrition). To foster pull-driven innovation in healthcare, it is imperative to train these specialists as quickly as possible while simultaneously creating attractive career opportunities for them.



Figure 5: The initial concept of a modern Temple of Asclepius based on ICT. Developed by the author’s team at the Tokyo Metropolitan Institute of Medical Science as the “Computing Center for Medicine of Tomorrow” (circa 1985).

Key Strategic Areas

It is evident that a large number of D2K scientists must be trained quickly to enable a full transition from current healthcare to next-generation healthcare. However, without deliberate effort, such developments are unlikely to gain significant momentum. One way to accelerate this process is to concentrate efforts on areas where translational research is most urgently needed, achieving tangible results. In military terms, this involves creating a “beachhead” in specific disease domains. The following areas are strong candidates:

Cancer:
The first area to consider is oncology. In fact, the initial goal of the well-known Precision Medicine initiative was oncology. Over recent years, advancements in NGS (next-generation sequencing) for detecting somatic mutations in cancer tissue and the development of antibody drugs and immune checkpoint inhibitors have highlighted the urgent need to establish systems for delivering the right treatments to the right patients15).
Gut Microbiota:
Another critical domain involves the detection of human symbiotic microorganisms, including gut microbiota, and their relationships with health and disease. Rapid advances in sequencing technologies have enabled the comprehensive study of microbiota (all microorganisms in a given environment) and microbiome (the entire genetic material within those microorganisms). Research has also expanded to investigate metabolites produced by these microorganisms and how their transport via the bloodstream affects inter-organ interactions. The starting point for such research often involves metabolism-related diseases associated with obesity and type 2 diabetes, which are linked to immunity and inflammation. However, the scope of this research has rapidly expanded to include diseases affecting the liver, kidneys, cardiovascular system, skin, and central nervous system. The necessity of translating basic research findings into clinical applications swiftly is well recognized, but the shortage of D2K science specialists remains a significant challenge16-17).
Brain and Mind:
Another major domain includes diseases involving the brain and mind, such as depression and dementia. Alzheimer’s disease, in particular, stands as a symbolic and challenging target in this field. Alongside cancer, this domain represents one of the most ambitious areas of drug development. Moreover, it is highly attractive to researchers in cognitive science and cognitive computing, such as those specializing in pattern recognition and artificial intelligence18-20). The initial collaboration between biomedical researchers and engineers in this field began shortly after World War II, and a second wave of interest emerged in the 1980s. We are currently witnessing a third wave of collaboration, and many computational scientists are deeply interested in this fusion domain. The emblematic focus of this interest is Connectome research, which explores neural networks. A critical task in this area is to create opportunities for biomedical researchers to engage with and harness the enthusiasm of computational experts.

Conclusion: The Pharma Crisis as a Crisis for Researchers

In Japan’s industrial and science and technology budgeting landscape, there are invisible yet rigid structures. However, these structures have started to erode, at least within competitive industries. Japan’s industries have traditionally maintained relationships of rivalry and mutual support. In the ICT industry, which emerged and rapidly grew after the internet became available to the public in 1994, such structures appear weaker. Nevertheless, in many industries where these structures were once strong, rapid changes akin to “industrial structural liquefaction” seem to be underway. The automotive and home appliance sectors are prime examples.

Large pharmaceutical companies, aiming to externalize everything but funding and management expertise, appear to be dramatically reshaping the traditional structure of the pharmaceutical industry. Such transformations—mergers, acquisitions, outsourcing, and stock buybacks—may be showcases for managerial skill21). However, for many employees, particularly researchers, these changes have created challenging conditions to adapt to. The so-called Pharma Crisis, once discussed as a crisis for the pharmaceutical industry, is not a crisis for its executives but rather one for its researchers. Addressing this crisis has become a new and pressing challenge for individuals working in the field.

This essay focused on the theme of innovation. The concept of “innovation” was introduced by Austrian economist J. A. Schumpeter. Schumpeter argued that capitalism requires constant innovation, driven not by large corporations but by startups. This idea was also emphasized by Naoki Komuro in his book *Innovation for Capitalism* (Nikkei BP, 2000). Schumpeter was close to the father of Peter Drucker, a prominent figure in management theory, and Drucker often encountered Schumpeter during his childhood. Drucker frequently referenced healthcare and hospitals in his books on management, emphasizing that the core of healthcare service management is considering “what the patient thinks.” He also recounted how IBM’s founder, Thomas Watson, declared early on that they were not in the business of selling machines but rather in the business of “information processing.” Drucker also predicted the advent of the Knowledge Society well in advance. When I was in my 30s, working at a medical research institute in Tokyo, I realized, thanks to reading Drucker’s works, that “the role of medical research institutions is to make a difference in healthcare services.” (For an overview of his key ideas, see P. F. Drucker, *The Essential Drucker*, Harper, 2001.)

The environment surrounding researchers is becoming increasingly volatile and difficult to navigate. However, understanding the underlying trends may provide a glimpse into the future as it unfolds. I hope this essay has offered some useful insights or mental exercises to help grasp this reality.


References

  1. Tsuguchika Kaminuma. “Changing Drug Development: Looking Toward 2020,” Pharma Plaza, 3:3-7, 2016.
  2. Tsuguchika Kaminuma. “How to Address the New Wave of the Internet Revolution and Artificial Intelligence?” Pharma Plaza, 6:4-9, 2017.
  3. Tsuguchika Kaminuma. “Can the Drug Development Pipeline Be Reevaluated?” Pharma Plaza, 4:4-9, 2016.
  4. M. Anderson, K. K. McCleary, “From Passengers to Co-pilots: Expanding Patient Roles,” Sci. Transl. Med. 7, 291fs25 (2015); “On the Path to a Science of Patient Input,” Sci. Transl. Med. 8, 336ps11 (2016).
  5. Quoted from L. Kish, “The Blockbuster Drug of the Century: An Engaged Patient,” HL7 Standards (28 August 2012), http://healthstandards.com/blog/2012/08/28/drug-of-the-century/.
  6. J. Kaye et al., “From Patients to Partners: Participant-Centric Initiatives in Biomedical Research,” Nature Reviews Genetics, 13:372-376, 2012.
  7. J. C. Kvedar, “Digital Medicine’s March on Chronic Disease,” Nature Biotechnology, 34(3): 239-256, 2016.
  8. Yoshitaka Tsubono. “Nutritional Epidemiology,” Nankodo, 2001.
  9. Yoshitaka Tsubono. “Food and Cancer Prevention,” Bungei Shunju, 2002.
  10. Chikako Uneyama, “A Comprehensive Guide to ‘Health Foods’,” Nihon Hyoronsha, 2016.
  11. Kuniko Takahashi, “The Truth and Lies of ‘Health Foods’,” Kodansha (Bluebacks), 2016.
  12. E. Topol, *The Patient Will See You Now*, Basic Books, 2015; *The Creative Destruction of Medicine*, Basic Books, 2012; E. Topol, S.R. Steinhubl, A. Torkamani, “Digital Medical Tools and Sensors,” JAMA, 313(4): 353-354, 2015.
  13. R. S. Miller, “CancerLinQ Update,” Journal of Oncology Practice, 12(10), 2016.
  14. Tsuguchika Kaminuma.. *Medical Innovation and Computers*, Iwanami Shoten, 1985.
  15. E. R. Mardis, “The Translation of Cancer Genomics: Time for a Revolution in Clinical Cancer Care,” Genome Medicine 2014, 6:22.
  16. E. Stulberg et al., “An Assessment of US Microbiome Research,” Nature Microbiology, Vol. 1, Jan. 2016.
  17. E. M. Bik, “The Hoops, Hopes, and Hypes of Human Microbiome Research,” YALE JOURNAL OF BIOLOGY AND MEDICINE 89: 363-373, 2016.
  18. G. Deco and M. L. Kringelbach, “Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders,” Neuron, 84(5): 892-905, 2014.
  19. O. Sporns and R. F. Betzel, “Modular Brain Networks,” Annu Rev Psychol. 67: 613–640, 2016.
  20. R. D. Mill, T. Ito, and M.W. Cole, “From Connectome to Cognition: The Search for Mechanism in Human Functional Brain Networks,” NeuroImage, Online, 2017.
  21. Tsuguchika Kaminuma; Yukio Tada; Masami Horiuchi; *The Future of Drug Development: Breaking Through R&D Crises*, Nikkei BP, 2014 (Translation of Bartfai T and Lees GV, *The Future of Drug Discovery: Who Decides Which Diseases to Treat?* Elsevier/Academic Press: Amsterdam, 2013).
PROFILE
Tsuguchika Kaminuma

Tsuguchika Kaminuma

Born in Kanagawa Prefecture, Japan, in 1940. Educated at International Christian University, Yale University, and the University of Hawaii. Received a Ph.D. in Physics. Since 1971, has worked at Hitachi Information Systems Research Institute, the Tokyo Metropolitan Institute of Medical Science, and the National Institute of Health Sciences. Conducted research in pattern recognition, medical artificial intelligence, medical information systems, bioinformatics, and chemical safety. In 1981, founded an industry-government-academia research exchange organization (now CBI Society) aimed at theoretical drug design. Later engaged in interdisciplinary human resource development at Hiroshima University and Tokyo Medical and Dental University. Established the NPO Cyber Bond Research Institute in 2011.

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