Issue No. 7: Baseline Research and Biomarker Exploration

Introduction

It is believed that it will take at least 10 years for the drugs currently under development to begin practical use. By then, the overall healthcare system surrounding drugs is expected to differ significantly from the present. In this sense, drug development must always be conducted with a vision for next-generation healthcare. Currently, big pharma companies advocate a new concept: “not merely selling drugs as products but offering health solutions tailored to individual patients.” This indicates that the goal is not just getting drugs approved but also creating the right environment for their proper use. This trend was previously discussed under the title “From Drug Discovery and Development to Precision Medication.”1)

Whether it’s a pharmaceutical company or a broader healthcare enterprise offering comprehensive services, aiming to provide health solutions tailored to individuals requires considering not only medication but also lifestyle as a whole. This necessitates personalized interventions or approaches for each individual. Baseline research serves as the foundation for such broad and innovative studies. This essay aims to expand the concept of precision medication to include other forms of interventions, starting from baseline research.

The NIH Shift from “Precision Medicine” to “Research for All”

Francis Collins, who played a leading role in the Human Genome Project both in the United States and internationally, later became the director of the NIH in 2011. He spearheaded the Precision Medicine Initiative under the Obama administration.2) The term “precision medicine” gained widespread acceptance among researchers, becoming a buzzword. However, with the transition to the Trump administration, policies associated with the Obama name were systematically overturned. To navigate this, the NIH rebranded the “Precision Medicine Initiative” as the “All of Us Research Program,” aimed at promoting research “for all.”3) This strategic renaming likely helped secure funding to continue precision medicine initiatives without direct conflict with the Trump administration.

Launched in 2016, this program began with a budget of $200 million, comprising $130 million for the NIH’s cohort studies and $70 million for the National Cancer Institute. The NIH cohort study aims to register one million participants, collecting diverse datasets, including genomic data, to advance the understanding of health and disease.

In essence, the program seeks to extend the cancer-driven approach of precision medicine to other diseases. Its implementation leverages rapidly decreasing genome sequencing costs, electronic medical records, portable biosensors (wearables), and advanced data science techniques. Broad public participation is encouraged. This program is designed to observe diverse participants longitudinally and meticulously over time. As a result, “research for all” aspires to involve everyone, gathering data that reflects the diversity of the U.S. population beyond ethnic differences. Currently, approximately 200,000 participants are enrolled. A fundamental principle of this program is to regard participants not as “research subjects” but as “research partners,” reflecting a new zeitgeist.

Figure 1: The NIH renamed Precision Medicine to All of Us Research. This represents an effort to broaden the concept of precision medication to include responses to lifestyles and environments.

Figure 1: The NIH renamed Precision Medicine to All of Us Research. This represents an effort to broaden the concept of precision medication to include responses to lifestyles and environments.

Advancing Research on Precision Medication

As mentioned in the previous installment of this series on precision medication, the term “precision medication” signifies “diverse efforts to ensure the proper use of drugs.” The initial target disease for precision medicine, as defined by Collins and colleagues, was cancer.2) Consequently, the $70 million allocated to the National Cancer Institute from the All of Us initiative appears to have been used for cancer-focused precision diagnostics based on genome sequencing. Collins and his team stated that the next step after cancer in the realm of precision medicine is to expand research into “the right drug, the right dose, (at the right time) for the right patient” across other disease domains.2) In essence, this is the practice of precision medication. The next high-priority target for such research is cardiovascular disease. In this sense, the “All of Us Research Program” can be seen as a broader implementation of the principles of precision medicine or precision medication.

In Japan, systems related to cancer-focused precision diagnostics have been rapidly developing since spring last year. Several major companies now offer services for gene panel testing that detect somatic mutations in tumor tissue samples (Weekly Economist, March 12, 2019, p.31).

Beyond cancer, Japan also has a pressing need for precision medication research in fields such as depression and dementia. Depression is a severe illness that spans all generations, from young people to the elderly. In cases of slow recovery, over ten different drugs may be used in various combinations over extended periods. On the other hand, the aging population directly correlates with an increase in dementia patients. However, no effective treatment drugs exist yet, leaving doctors with only options that are expected to “slow disease progression.” Even so, the application of such drugs heavily relies on the individual knowledge and experience of the attending physician.4) The Ministry of Health, Labour and Welfare has issued guidelines based on expert meetings.5) However, considering the era of genome sequencing and precision medicine, the foundation for translational research—collecting and analyzing clinical cases for objective discussions—appears insufficient.

Diseases involving mental health and the mind face challenges in identifying objective indicators (biomarkers) for differential diagnosis and evaluating drug effectiveness.6) This makes it difficult to select patients for whom a particular drug will be effective and confirm its efficacy. Following these mental and neurological diseases, chronic complex diseases such as cardiovascular disorders, obesity, hypertension, diabetes, and kidney diseases—common among the working-age population—are likely to become targets for precision medication research. Some of these are referred to as lifestyle-related diseases. Additionally, chronic conditions like rheumatoid arthritis, multiple sclerosis, allergy-related diseases, and gout, which require prolonged treatment, are also included.

The Need for Large-Scale Research Studies

In summary, across all disease domains, it is clear that (1) more detailed classification of patients and (2) more frequent and longitudinal measurements of patients are necessary. Classifying patients requires comparisons with others, and more detailed classifications demand larger comparison groups. This is especially evident in genome sequencing.

One reason the NIH’s “All of Us Research Program” emphasizes increasing participant numbers is precisely this need. In true American fashion, efforts are also being made to recruit participants from specific ethnic groups, such as Hispanics, to investigate racial differences. Additionally, the NIH has noted that the number of male and female subjects in biomedical experiments should be balanced to account for sex differences.7) Even large-scale studies have started examining gut microbiota, with efforts underway to establish baseline values for comparison.8) In Japan, the Japan Microbiome Consortium (JMBC) is building a gut microbiota database based on healthy individuals.

The concept of precision medication is intricately linked to precision medicine. While the foundational elements for its implementation are in place, achieving greater efficacy requires advancements in measurement and analytical technologies. Genome sequencing was the pioneering technology, but other molecular measurement techniques—such as those for RNA (transcripts), proteins, secondary metabolites, epigenetic factors, and gut microbiota—need to transition from basic research to clinical practice.9),10) Given its connection to gut microbiota, one particularly promising area is the expansion of metabolomics (comprehensive analysis of secondary metabolites) into clinical applications.

Extending the Concept of Precision Medication to Non-Pharmacological Interventions

There are various ways to speculate about the future of healthcare and drug development. In Japan, one of the most fundamental challenges is addressing the needs of an aging population. In this demographic, chronic and untreatable conditions often overlap. As a result, non-pharmacological interventions—such as diet, supplements, exercise, sleep, meditation, mindfulness, and other stress-relief techniques—along with various lifestyle adaptations, become increasingly important. In this context, attention must also be paid to factors like housing, workplace, living environment (ambient environment), and maintaining social connections to avoid isolation. These considerations are particularly critical for mood disorders, depression, and dementia, where the complementary role of non-pharmacological approaches is increasingly recognized.

These interventions or strategies that do not require prescriptions are collectively referred to as “Non-Pharmacological Interventions (NPI).” Like pharmaceuticals, it is essential to evaluate the efficacy and risks of these interventions. However, many believe that non-pharmacological interventions do not require the same stringent safety regulations as pharmaceuticals. This has led to concerns about the widespread promotion of anecdotal evidence of effectiveness while neglecting reports of harm.11) This issue is particularly prevalent with supplements. Paradoxically, from a scientific perspective, verifying the efficacy and risks of food is more challenging than with regulated pharmaceuticals because the chemical components in food are numerous, while drug approvals typically focus on single compounds.

In any case, the methodologies for verifying the efficacy and safety of non-pharmacological interventions are modeled after those developed and refined in the field of drug development. Specifically, regarding diet, the concept of “Personalized Nutrition” has emerged as an extension of personalized medicine built upon advances in genome sequencing. (We translate this concept as “tailored dietary advice for individuals.”)

The fundamental idea is to develop methodologies for measuring and assessing the effects of dietary components on the body, similar to drugs. These assessments include impacts on gut microbiota, specific cells or pathways, gene expression (epigenetics), proteins, and metabolites at the molecular level. A well-known example of such research involves studying the effects of diet on blood glucose levels and managing diabetes.12) Initiatives like these have sparked interest in research aiming to provide personalized dietary advice for individuals, particularly in countries like Israel and parts of Europe.13)-15) The “Food4Me” project is one such example.16)

In both precision medication and precision nutrition, having measurable indicators corresponding to the targeted condition can facilitate the identification of effective interventions. These measurable indicators are known as biomarkers. Once identified, these biomarkers can serve as guides to appropriately control interventions. In this regard, reducing the cost of metabolomics analysis is highly desirable.

Figure 2: Non-pharmacological interventions are diverse. From the perspective of 'intake of substances from outside the body,' there are commonalities in molecular-level impact analyses between food, drugs, and environmental chemicals. In Japan, the 'Four Food Group Scoring Method,' a quantified approach to dietary advice, was proposed by Aya Kagawa, the founder of the Kagawa Nutrition University.

Figure 2: Non-pharmacological interventions are diverse. From the perspective of ‘intake of substances from outside the body,’ there are commonalities in molecular-level impact analyses between food, drugs, and environmental chemicals. In Japan, the ‘Four Food Group Scoring Method,’ a quantified approach to dietary advice, was proposed by Aya Kagawa, the founder of the Kagawa Nutrition University.

The Original Model: Aerobics

As the saying goes, “food and medicine come from the same source,” highlighting the commonalities in research methodologies between personalized medicine (precision medication) and personalized nutrition. Exercise, however, is somewhat distinct in approach. For exercise, personalized prescriptions have been known for over half a century. The pioneer in this field is Kenneth H. Cooper. As a physician specializing in exercise physiology, Cooper developed a program for retraining pilots deemed unfit for duty while serving in the U.S. Air Force. His research culminated in the 1968 publication of the book *Aerobics*, which popularized the concept.17)

The practical theory behind his work was straightforward: physical fitness (necessary for pilot duties) could be measured by Maximum Oxygen Consumption (MOC), which correlated well with the distance a person could run in 12 minutes. Based on these findings, he categorized physical fitness into five levels and devised a system where exercises such as running, swimming, and cycling were converted into points. The guideline was simple: achieving 30 points of exercise per week would maintain fitness. In this system, MOC or the distance achieved in a 12-minute run served as a biomarker, while the various exercises became interventions. Their implementation could then be quantified into points, allowing individuals to compare activities such as running, swimming, cycling, or tennis and choose exercises suited to their preferences.

As a result, aerobics evolved from a retraining program for pilots into a widely accepted health regimen for the general public. However, over time, others commercialized aerobics by combining it with dance, distorting its original purpose. This skewed image spread to Japan in the 1980s and became entrenched. Despite this, the original aerobics remains a successful model for research into non-pharmacological interventions.

Advances in analytical and measurement technologies could enable the development of similarly accessible models for other interventions. For example, the discovery of clock genes has encouraged innovative approaches to interventions, as they provide insights into meal timing and sleep impacts. Moreover, efforts are underway to apply advanced brain measurement techniques like functional MRI (fMRI) and sophisticated data interpretation methods such as network identification to practices like mindfulness meditation for stress management. Additionally, research into olfaction aims to stimulate the brain via scent molecules to improve mild cognitive impairment.18) There is also growing interest in the effects of environmental factors, such as polluted air and forest bathing, though much research to substantiate these diverse interventions remains a task for the future.19)

Regardless of the intervention, its development requires the following: advancements and democratization (commoditization) of technologies for measuring physiological states and responses, systems to garner broad collaboration, and an environment that supports data science for managing and meaningfully utilizing the vast data generated.

Figure 3: The original aerobics program quantified personalized exercise prescriptions, representing the first successful model. Today, Cooper advises eight rules for a healthy lifestyle: maintaining a moderate weight, eating a healthy diet, exercising almost daily, taking appropriate supplements, avoiding smoking, drinking in moderation, managing stress, and undergoing regular comprehensive health check-ups.

Figure 3: The original aerobics program quantified personalized exercise prescriptions, representing the first successful model. Today, Cooper advises eight rules for a healthy lifestyle: maintaining a moderate weight, eating a healthy diet, exercising almost daily, taking appropriate supplements, avoiding smoking, drinking in moderation, managing stress, and undergoing regular comprehensive health check-ups.

Baseline and Biomarkers

So far, we have reviewed the current state of personalized approaches to healthcare, including the appropriate use of medications, as well as non-pharmacological interventions such as diet and exercise. These approaches align with the broader efforts exemplified by the NIH’s shift from “Precision Medicine” to “Research for All,” as discussed earlier. This movement reflects a zeitgeist symbolizing next-generation healthcare, a spirit that seems to be shared not only in the United States but also across other developed regions like the EU and Japan. A fundamental issue that has emerged in this context is understanding the normal or reference values within populations, including racial differences, and identifying deviations from these baselines that may signify a transition from health to disease. These reference values or normal states are referred to as the “baseline.” In clinical research or drug efficacy studies, the term “baseline” often denotes the state before the introduction of a specific intervention, such as medication, and is used to signify the “starting point.”

To determine baselines as reference values associated with health and disease, it is necessary to select target populations or individuals, identify key indicators or features, and measure them. These characteristics serve as diagnostic or therapeutic markers, referred to as biomarkers. Biomarkers can range from molecular-level indicators such as genes, proteins, and secondary metabolites, to physiological metrics and systemic traits known as phenotypes. The effort to establish baselines on a large scale within human populations is embodied in Verily’s “Project Baseline,” which will be discussed next.

The Ambitious “Project Baseline”

As the time seems ripe, researchers who have been at the forefront of human genome sequencing and omics studies are now turning their attention to baseline research, aiming to establish reference values and normal ranges in humans. Leading this effort appears to be Verily, a subsidiary of Google, through its ambitious “Project Baseline.” (Technically, Google is a subsidiary of Alphabet Inc., making Verily a sister company under Alphabet. For convenience, we will refer to Verily as part of Google in this context.)

According to Verily’s website, its initiatives are categorized into four areas: (1) Measurement (Sensors), (2) Interventions, (3) Development of Platforms and Tools for Health (Health Platforms & Population Health Tools), and (4) Precision Medicine. Project Baseline falls under the category of Precision Medicine. The project aims to collect longitudinal data from an initial cohort of 1,000 participants, ultimately expanding to 10,000 individuals over four years.

Interestingly, this initiative is part of the “All of Us” research program, the successor to the NIH’s renamed Precision Medicine Initiative. Within this framework, Verily’s research project, launched around 2016, involves collaboration with institutions such as Stanford University, Duke University (School of Medicine), and the American College of Cardiology. Its initial focus areas are cancer and cardiovascular diseases.

As with other health-related research projects by Google, publicly available information about Project Baseline is limited. Nonetheless, some insights can be gathered from magazines and newspaper articles.21),22) For instance, a cancer researcher at Stanford University, whose teenage son died of a brain tumor, has high hopes for the project to develop techniques for early cancer detection by analyzing changes in blood, stool, and urine samples. At Duke University in North Carolina, an interdisciplinary team is actively encouraging participation from Black, Hispanic, and Asian communities. As of now, 2,000 participants have registered for the project.

Meanwhile, Google is also developing various sensor-enabled devices, such as smartwatch-like sensors, shoes with motion sensors, sensor-equipped contact lenses, and spoons. These developments suggest that Google may be using Project Baseline as a testing ground for data collection via wearable technologies. This initiative has also attracted interest from major pharmaceutical companies, including GlaxoSmithKline (GSK), Johnson & Johnson, and Sanofi.

Figure 4: The ultimate goal of baseline research is to record the continuum of life from conception to death (E.J. Topol, *Individualized Medicine from Prewomb to Tomb*, Cell, 157:241-253, 2014). The duration of each stage varies. For instance, adolescence, previously considered a seven-year period, is now thought to last twice as long (L. Steinberg, *Age of Opportunity*, HMH, 2014).

From Epidemiological Studies to Large-Scale Genomic Research

Epidemiological studies have a long history, particularly in addressing the spread of diseases within specific regions, such as in infectious disease control. More recently, large-scale studies have been conducted to address the rising prevalence of non-communicable diseases, commonly referred to as lifestyle-related diseases. Among these, prospective cohort studies, which involve the long-term collection and analysis of health and medical data from residents of regions with minimal population inflow and outflow, are well-known for their unique characteristics. The most famous example is the Framingham Heart Study, initiated in 1948 in Framingham, Massachusetts, with the involvement of the U.S. National Heart, Lung, and Blood Institute. This study utilized multivariate data analysis to estimate risk factors and preventive measures for ischemic heart disease. In Japan, a notable example is the cohort study conducted since 1961 in Hisayama, Fukuoka Prefecture, with the involvement of Kyushu University. Another large-scale study in Japan is the “Japan Public Health Center-based Prospective Study on Cancer and Cardiovascular Diseases (JPHC Study),” which began in 1990 with the participation of the National Cardiovascular Research Center.23)

With advances in genome sequencing technology, the interpretation of genomic data has required larger sample sizes. This has led to the initiation of massive studies involving thousands, tens of thousands, or even millions of participants. Notable examples include Iceland’s nationwide genomic data collection (approximately 300,000 individuals), managed by the private company deCODE Genetics (which later faced bankruptcy), the U.S. NIH’s initiative involving one million participants, and the UK’s Genomics England project aiming to sequence 100,000 genomes. Alongside genome sequencing, large-scale studies have also been conducted to perform comprehensive analyses of proteins and metabolites in numerous individuals. In Japan, population-based research projects closely related to baseline studies and genomic data include the Tohoku Medical Megabank Project and a study involving residents of Kumejima Island in Okinawa, led by the University of the Ryukyus.

Hopes and Concerns About Large-Scale Research From a Consumer Perspective

Large-scale studies involving human populations are likely to become more prevalent in the future. Among these, the NIH’s *All of Us* program and Verily’s *Project Baseline* appear to have four distinct, forward-looking features:

  1. They broadly invite participation from the general public,
  2. They aim to utilize wearable devices and IoT for convenient body measurements,
  3. They strive to create platforms enabling various participatory healthcare-related research initiatives,
  4. They emphasize the application of ICT, particularly data science, throughout the entire project.

Some have remarked that “For Google, *Project Baseline* is akin to creating Google Maps for healthcare.” Such forward-looking initiatives are accompanied by both expectations and concerns. Below are two key concerns.

The first concern is the potential for excessive testing of participants, which might result in providing too much information and inadvertently fueling unnecessary anxiety. For instance, experts in chemical analysis and medical statistics have warned that studies involving long-term observations of healthy individuals (repeated testing) may fail to contribute to disease prevention. Instead, such studies risk detecting insignificant health issues or causing harm through unnecessary interventions.24) One of the examples they criticized was the “Hundred Person Wellness” project by Leroy Hood and colleagues, which involved detailed observation of 100 individuals over 10 months.25) Another target of their criticism was *Project Baseline*. Their argument draws on more than 30 years of preventive cancer screenings, where high-density observational studies utilizing multi-omics technologies risk leading to over-diagnosis and over-treatment, which may ultimately fail to benefit participants.

The second concern involves the potential exposure of Japanese health data and information to global private corporations or even hostile foreign entities. Google’s endeavor to create a healthcare equivalent of Google Maps poses the risk of this information being shared with other private companies or stolen by adversarial nations. Additionally, within Japan, there is the possibility that such data could be used for surveillance or as a reference for conscription. Even if one assumes the best intentions and dismisses these fears, it is preferable that health data related to Japanese individuals be collected with their consent and utilized for their benefit. This is particularly crucial because the unique attributes of the Japanese population, shaped by their lineage in human history, clearly influence health and disease outcomes.26)

The Critical Role of Participants

The rapid advancement of technology and societal changes are accelerating the realization of next-generation healthcare. Large-scale studies like the NIH’s *All of Us Research Program* and Google’s *Project Baseline* bring concerns and uncertainties from the participants’ perspective, while experts may have various opinions about research design, interpretation of results, and their utility. Such expert discussions may lead to a reevaluation of current clinical research practices and methodologies, such as those registered with FDA’s *ClinicalTrials.gov*.

Equally important, however, is the perspective of participants. This involves considering how these initiatives can benefit those who choose to take part. For example, participants might gain access to trusted primary care physicians, specialized medical institutions for complex diseases like cancer, or systems that allow them to securely store, access, and utilize their medical records with professional support when needed. Integrating infrastructure to support such benefits into large-scale research projects may be a valuable addition.

Studies like NIH’s *All of Us Research Program* and Google’s *Project Baseline* are likely to inspire similar initiatives in Japan. In such research, forming partnerships with participants is crucial. Ideally, participants would become increasingly active and engaged through their involvement in these projects. Such participants could be referred to as proactive participants, emphasizing their active role in contributing to the research. The key challenge, then, is determining how to encourage the general public and patients to become proactive participants.

The Combination of Wearables and Machine Learning

A fundamental tool that appears well-suited for such purposes is wearable devices. These are portable, wireless-enabled instruments capable of easily measuring various physiological states related to health. Wearables are also referred to as Digital/Mobile/Wireless Sensors. With the widespread adoption of smartphones—essentially handheld computers—wearables have seamlessly integrated into IoT (Internet of Things) environments.

Previously, wearables were primarily regarded as accessories for fitness enthusiasts. However, they are increasingly being considered for applications in clinical trials by pharmaceutical companies, monitoring health conditions outside of medical facilities, and research initiatives like *Project Baseline*.27)-29) Wearables are particularly useful for chronic pain tracking and the continuous observation of mental and emotional health conditions.30)

What is currently driving greater interest in wearables is the advancement of research using machine learning techniques to analyze repeated measurements and make critical predictions about physical health. Significant results have already been reported in predicting heart failure (atrial fibrillation),31),32) and similar applications are being explored for conditions like stroke and epilepsy.

In both Google’s *Project Baseline* and the NIH’s *All of Us Research Program*, wearables, along with smartphone apps, serve as key tools for data and information collection from participants. These programs collect data on physical activity, sleep, weight, heart rate, nutrition, and hydration, alongside survey responses, electronic medical records, physical measurements, and blood and urine tests. Wearables thus become essential tools for individual participants to contribute data.

In January of this year, the NIH announced the launch of the *Fitbit Bring-Your-Own-Device (BYOD) Project* as part of the *All of Us* initiative, emphasizing the use of Fitbit devices and services. This initiative also includes a research team from the Scripps Research Translational Institute, led by Eric Topol, a strong advocate of these tools.

As such, wearables have become indispensable tools in platform-based research involving large participant numbers. The repeated measurements collected are recognized for their suitability in AI-driven predictive studies using machine learning. Wearables also support community formation among participants and facilitate the rapid exchange of knowledge and information. Consequently, wearables are increasingly seen as strategic tools for next-generation healthcare and foundational devices poised to lead the creation of significant new markets.

Conclusion

Since the successful completion of the Human Genome Project at the beginning of this century, organizations such as the NIH, which played a leading role in the project, have identified predictive, personalized, preventive, and participatory approaches as the defining characteristics of the next generation of medicine, driven by advances in genome sequencing. The concept of precision medicine was proposed and further developed into the NIH-led *All of Us Research Program*. While details remain unclear, research initiatives like Google’s *Project Baseline* appear to represent early attempts to realize next-generation healthcare.

Similar endeavors are already underway in Japan. These large-scale studies, referred to as baseline research, bring both hope and concern. To mitigate these concerns and amplify the potential benefits, it is essential to foster a broader understanding of the significance of such research. Furthermore, the creation of a “research community” that includes not only healthcare professionals but also stakeholders from business, government, NGOs/NPOs, and local residents is necessary. By establishing such research communities as platforms, Japan can address challenges related to its aging population and declining birthrate. Through various experiments grounded in these platforms, it is hoped that genuine innovations in next-generation healthcare will emerge.


References

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  3. Official documents regarding the *All of Us Research Program* are available on the NIH website:
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  4. H. Oda, *Scientific Dementia Diagnosis: 5 Lessons*, Sign, 2018.
  5. Guidelines for the Proper Use of Medications for the Elderly (Draft), *Meeting Materials of the Expert Panel on Appropriate Medication Use for the Elderly*, Ministry of Health, Labour and Welfare, January 25, 2019.
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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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