Issue No. 2: From Drug Discovery and Development to Precision Medicine

Introduction

Recently, three acquisition reports in the pharmaceutical industry have drawn significant attention: Takeda Pharmaceutical’s acquisition of Shire (a biopharmaceutical company), Merck’s (additional) acquisition of Foundation Medicine (specializing in precision oncology), and Amazon’s acquisition of PillPack (an online prescription drug delivery service). Takeda’s move, while large in scale, aligns with the strategic acquisitions often seen among major Western pharmaceutical companies. Merck’s action reflects its intensified focus on precision oncology. These acquisitions likely came as little surprise to those in the pharmaceutical industry. However, Amazon’s acquisition sent shockwaves, as it signaled the company’s potential entry into the pharmaceutical sector.

Major pharmaceutical companies are now moving beyond the traditional “one size fits all” approach of promoting specific drugs and expanding their vision to offer health solutions under the banner of “Beyond the Pills.” However, this emerging market is expected to attract strong global ICT companies like Google and Apple. Among these, Amazon—a retail giant—is viewed with particular caution, as its entry has the potential to significantly reshape the pharmaceutical industry. The primary battleground in this evolving market may not be drug discovery but ensuring the proper use of medications.

What we are witnessing today is likely just the tip of the iceberg—merely the surface of a massive transformation underway. The traditional structure of the pharmaceutical industry is clearly beginning to dissolve. Yesterday’s rivals are becoming today’s partners, and today’s partners may become tomorrow’s competitors. These dynamics reflect a new spirit of the times, which could be encapsulated by the term “precision medicine,” a concept that forms the central theme of this essay.

Drug Discovery and Development

In Japan, the term “drug discovery” (sōyaku) has been commonly used among pharmaceutical research and development professionals since the 1980s. This term corresponds to the English concept of “Drug Discovery and Development,” specifically referring to the efforts leading up to a new drug’s approval. Later, another term, “drug development” (ikuyaku), also became widely used among Japanese pharmaceutical and pharmacological experts. According to the Japan Pharmaceutical Manufacturers Association (JPMA), “drug development” encompasses post-market activities such as follow-up research, improvements to existing drugs, and the development of new drugs inspired by these efforts¹⁾.

Examples include transforming sublingual nitroglycerin into a skin patch to address angina attacks occurring late at night or early in the morning, or the ongoing discovery of new indications for aspirin over its more than 100-year history. Based on this definition, research and development efforts that repurpose what were initially considered side effects into new indications (Drug Repositioning) could also be considered part of drug development. A striking success story in this regard is Viagra. In any case, the Japanese concept of “drug development” seems to be encompassed within what is internationally referred to as “Drug Development.”

The Concept of Precision Medicine

The term “precision medicine” refers directly to the “various efforts to ensure the appropriate use of medications.” In simple terms, it entails creating systems to use already approved drugs appropriately—and optimally whenever possible. This concept is often summarized as efforts to ensure the “right drug, right dose, (at the right time) to the right patient”^2,3). The goal is to design and implement such prescriptions effectively.

Typically, those who develop drugs and those who use them are different groups. Depending on the definition, there is often an intermediary—those who prescribe the drugs—between production and usage. While numerous experts are involved in the drug development process, many others are also engaged in the delivery and usage stages. Generally, the responsibility for medication usage lies with physicians or individuals (patients). Other experts and stakeholders may provide advice, knowledge, or opinions, but they are not directly responsible for how medications are used.

Pharmaceutical developers and manufacturers, responsible for providing accurate information, deliver necessary data through materials like package inserts for prescription drugs. Pharmacists play a role in understanding and mediating this information, while government agencies oversee or guide these processes. However, even these knowledge providers do not always have sufficient information about the contexts or backgrounds in which medications are used, or about the individuals using them. Clinical trials for new drug approvals typically do not include every population, requiring caution when prescribing drugs to pregnant women, children, or the elderly. Information on comorbidities, other medications being used, diet, lifestyle, and the physical characteristics or conditions of the user is often incomplete. For example, elderly patients with multiple chronic conditions frequently receive numerous unrelated prescriptions, potentially leading to complex interactions⁴⁾.

Historically, pharmaceutical companies have prioritized physicians as their most important customers, relying heavily on sales representatives (Medical Representatives, MR, or Sales Representatives, SR) to promote their products. However, the complexity of precision medicine lies not in the drugs themselves but in the foundational knowledge required to understand drug mechanisms at the molecular level, awareness of the patient’s physical condition, and the development of clinical systems that integrate this information. Costs are another consideration. For example, the use of biomarkers—biological indicators that help determine whether a drug is appropriate—can make medication use more precise but also introduce additional costs. This brings pharmacoeconomics into the discussion.

Ultimately, precision medicine involves making complex clinical and individual decisions appropriately. Mathematical approaches, such as control theory, were proposed for addressing these challenges as early as the 1970s⁵⁾. While the basic principles remain unchanged, techniques for measuring the attributes and states of the human body have advanced significantly. These advancements are led by genomic analysis (genomics) and various accompanying omics fields (e.g., transcriptomics, proteomics, metabolomics). Techniques for examining genetic characteristics and genomic properties relevant to drug use are known as pharmacogenomics (PGx), a central area of research for precision medicine.

Pharmacogenomics

Around the time the Human Genome Project was nearing successful completion, A. Roses, then at GSK (GlaxoSmithKline), proposed forming a consortium of pharmaceutical companies to comprehensively identify genetic variations related to drug responsiveness through SNP (Single Nucleotide Polymorphism) analysis⁶⁾. The necessity of investigating genetic variations related to drug response (Pharmacogenomics) was also raised by other researchers. This concept later evolved into collaborative research initiatives, leading to the establishment of databases and knowledge bases to facilitate the broad utilization of findings⁷⁾. Subsequently, efforts to apply this data and knowledge in clinical practice have spread across advanced countries engaged in drug development^8),9). A notable example of such efforts can be seen in reports from the Mayo Clinic in the United States^2),10).

The foundation of precision medicine lies in defining the appropriateness of medication usage, which is assessed based on two criteria: safety (i.e., the absence of harm) and efficacy (i.e., therapeutic effectiveness). For the purposes of this discussion, issues such as drug quality or societal concerns like “preventing misuse of medical narcotics” are excluded. Thus, the goal is to provide the safest and most effective treatment at the lowest possible cost for the user. When medications are used as a treatment, this issue is fundamentally addressed within the realm of ADME research.

ADME stands for Absorption, Distribution, Metabolism, and Excretion, describing the process by which drugs are absorbed into the body, transported, chemically altered by enzymes in the liver, and eventually excreted. The mechanisms involved in ADME and the interactions between drugs and their “targets” or “receptors” in the body are generally treated separately. The former is the domain of Pharmacokinetics (PK), while the latter falls under Pharmacodynamics (PD). The key players in pharmacokinetics are drug-metabolizing enzymes, notably the Cytochrome P450 family, collectively referred to as CYP. These enzymes metabolize drugs in the liver. Conversely, the effectiveness of a drug depends on its ability to act on (or bind to) the intended target molecule or site within the body. Sometimes, drugs may interact with unintended targets, referred to as “off-target” effects.

Off-target interactions may lead to adverse effects. For instance, the HLA (Human Leukocyte Antigen) protein is associated with Stevens-Johnson Syndrome (SJS), a severe adverse reaction affecting the skin and eyes. The study of genetic predisposition to chemical safety issues, including adverse drug reactions, falls under Toxicogenomics (TGx). Given that drug usage often involves some risk of adverse effects, there may be methodological overlap between PGx and TGx. The distinction between a drug and a toxin often depends on the dosage, leading to the combined study of ADME and toxicity, referred to as ADME-Tox.

Similarly, the study of genetic characteristics affecting biological responses to food is known as Nutrigenomics (NGx). Whether a compound is a drug, a nutrient, or a toxin, the molecules they interact with inside the body are predominantly proteins, which are produced through the expression of corresponding genes. Genetic polymorphisms can lead to variations in drug absorption, metabolism, and efficacy. Thus, it is essential to identify the genes related to each drug. This knowledge, referred to as “Drug-Gene Pairs,” often focuses on genes like CYP and HLA. For example, when treating a specific disease (X) with a drug (Y), it may be recommended to examine a specific gene (Z) in the patient. In this context, Z is known as a genetic biomarker. Information on biomarkers and Drug-Gene Pairs can be accessed via the FDA website (refer to Table 1 and 2 in Reference 10). The exploration of biomarkers leveraging advancements in genomic analysis techniques remains a critical area in pharmacogenomics.

Appropriate Use of Anticancer Drugs

The field of oncology has seen some of the most active advancements in the pursuit of precision medicine. While anticancer drugs have historically been used alongside surgery and radiation therapy, the introduction of immune checkpoint inhibitors, such as CTLA-4 and PD-1 antibody drugs, has brought significant attention to this area. One notable example is the anti-PD-1 antibody, Opdivo (Nivolumab). These novel immunotherapy drugs, which target the immune system to control cancer, have shown dramatic effectiveness in certain patients. However, they are highly expensive and often yield varying results, including severe side effects or lack of efficacy in others. This underscores the urgent need for research into appropriate patient selection, drug combinations, and usage strategies in this domain¹¹⁾.

Cancer is primarily a disease caused by genetic mutations. Tumor tissues containing cancerous cells often reveal mutations known as somatic mutations. The relationship between these mutations and the genetic variations identified through germline testing (e.g., those commonly advertised as Direct-to-Consumer, DTC tests) or comprehensive whole-genome sequencing remains insufficiently understood. Furthermore, these complexities are not adequately explained to patients or the general public.

Around May of last year, U.S. agencies such as the FDA and NIH began to issue recommendations for administering anti-PD-1 antibody drugs when specific mutations (e.g., mismatch-repair deficiencies) are detected in DNA from tumor tissues¹²⁾. These recommendations often rely on panel tests that screen for approximately 100 known genes. Consequently, while new immunotherapy drugs for cancer continue to be developed, a parallel race has emerged to advance genetic testing techniques that identify somatic mutations reflecting tumor progression¹³⁾.

Although Japan initially led in the basic research and development of PD-1 antibodies, it lagged in establishing clinical systems for their use. However, since last year, efforts have intensified, including designating “Core Hospitals for Cancer Genomic Medicine” and “Cooperative Hospitals for Cancer Genomic Medicine,” as well as mandating the registration of cancer genomic data with the “Center for Cancer Control and Information Services” within the National Cancer Center. In oncology, efforts toward precision medicine are advancing rapidly, encompassing basic research, translational studies, and clinical practice frameworks.

Expanding Applications to Other Diseases

The current advancements in cancer treatment exemplify the forefront of precision medicine grounded in genomic technologies²⁾. These developments are now extending to various other disease areas, including cardiovascular diseases¹⁴⁾, advanced kidney diseases¹⁵⁾, chronic pain¹⁶⁾, and even the treatment of depression¹⁷⁾. Such progress reflects the ongoing evolution of research in genetic polymorphisms and pharmacogenomics.

As a result, biomarker discovery—key indicators for diagnosis and treatment—has become a focus in various disease areas. Pharmacogenomics is evolving from general discussions to deep, disease-specific analyses. Simultaneously, biomarker discovery is broadening its scope, moving from molecular-level indicators such as genes and proteins to psychological indicators like pain perception and mood disorders.

Figure 3: Workflow of Precision Oncology. Adapted from M. Doherty et al., Precision medicine and oncology, Ann Oncol. 27(8):1644-1646, 2016, Fig. 2.

Gut Microbiota and Personalized Pharmacology: Pharmacomicrobiomics

Since the turn of the century, advancements in genome sequencing technologies, particularly metagenomic analysis, have unveiled the critical role of human symbiotic microbiota, exemplified by gut bacteria, in health and disease. This rapidly expanding and deepening field is profoundly impacting both medical research and clinical practice¹⁸⁾.

Gut bacteria metabolize dietary components to produce a range of metabolites, such as short-chain fatty acids (SCFAs) including acetate, butyrate, and propionate. These metabolites circulate through the bloodstream, reaching organs such as the liver, kidneys, blood vessels, heart, and brain. In addition to the bloodstream, the nervous system also mediates inter-organ communication. It is becoming increasingly evident that these interactions influence drug metabolism and efficacy. This emerging understanding has led to the adoption of the term “Pharmacomicrobiomics,” which combines pharmacology and microbiomics^19),20).

Symbiotic bacteria in humans are not limited to the gut but are also widely present on the skin and other body sites. For example, oral bacteria are known to cause periodontal diseases and are linked to systemic conditions²¹⁾. This supports the health benefits of oral hygiene practices such as tooth brushing. Additionally, lifestyle factors like exercise have been reported to influence the composition of gut microbiota²²⁾.

Notably, cutting-edge cancer immunotherapies such as immune checkpoint inhibitors have been shown to be influenced by the gut microbiota composition of patients²³⁾. This suggests that combining cancer immunotherapy with appropriate dietary interventions or fecal microbiota transplantation could enhance treatment outcomes. This area represents one of the forefronts of research in personalized pharmacology.

Figure 4: The Environment Influencing Drug Action in Humans. Humans coexist with a vast microbiota, including gut bacteria. Investigating drug responses at the molecular level through pharmacogenomics requires integration with nutrigenomics, toxicogenomics, and microbiomics to understand dietary, toxic, and microbial interactions.

Impact of Portable Biosensing Devices

To achieve personalized pharmacology, it is crucial to understand the attributes and conditions of the individuals using the medication. Attributes refer to inherent traits that do not change over time, such as gender or genomic data obtained from germline cell analysis, representing a person’s innate characteristics. In contrast, parameters like body weight and temperature vary with physiological conditions. Alongside the proliferation of smartphones—practically handheld computers—portable, compact, wireless-enabled biosensors (Wearable/Wireless Sensors or Wearables) capable of measuring diverse health-related metrics have emerged as consumer products. This has spurred interest in self-quantification (Quantified Self) and DIY Healthcare (Do It Yourself Healthcare).

According to one of the pioneers, Larry Smarr, consumers can measure 60 to 70 parameters on their own, such as body weight, body fat, body temperature, blood pressure, heart rate, and ECG²⁴⁾. That observation was made around 2012, but these devices have since advanced significantly²⁵⁾. Moreover, there has been an explosion in smartphone applications (Apps) that either complement these devices or operate independently. However, these products and free software tend to lose user interest quickly among general consumers. Additionally, critical tests like blood analysis (beyond glucose measurements) often require specialized services, which can be prohibitively expensive.

Recently, attention has shifted to how these portable devices can be utilized by pharmaceutical companies and healthcare providers^26-28). These digital devices are well-suited for longitudinal (repeated) measurements, making them highly promising for managing chronic conditions characterized by persistent pain or mood disorders. Consequently, there is growing interest in integrating personally collected data (Personal Health Records, PHR) with electronic medical records (EMRs) maintained by hospitals and clinics. Together, these are leveraged as Real-World Data (RWD) to advance Digital Medicine²⁹⁾.

Discussion on Redefining Pipelines and Their Relation to Personalized Pharmacology

At the end of last year, two reports were published regarding the activities of NCATS (National Center for Advancing Translational Sciences), a research institute under NIH that leads efforts in Translational Research (TR)^30),31). These reports proposed a conceptual framework for “Drug Discovery, Development, and Deployment Maps (4DM),” reflecting NCATS’ guiding philosophy. The map, a draft created through discussions led by NCATS director Christopher P. Austin, involved pharmaceutical companies, academic institutions, and experts from NGOs in the field of translational medicine. The draft is publicly accessible on the NCATS website.

The 4DM represents the processes of drug research, development, and subsequent efforts to ensure proper usage. Traditionally, pharmaceutical companies have visualized these processes as linear “pipelines,” depicted as straightforward (Chevron-style) flowcharts. However, the NCATS team expressed concern that such representations mislead new entrants to the pharmaceutical industry about the complexity of drug development. To address this, NCATS proposed a conceptual roadmap to encourage broader discussion, emphasizing that drug development is not a simple, unidirectional process but rather an intricate web of specialized tasks interconnected by numerous feedback loops³²⁾.

Indeed, if pharmaceutical research and development are viewed as a linear assembly line, research related to proper drug usage, such as pharmacogenomics (PGx), might appear as an activity confined to the “Drug Deployment” stage of the NCATS map. However, this separation risks creating the impression that PGx is a disconnected field, unrelated to drug discovery. In reality, there is significant overlap in the techniques and data applied in both domains. For instance, studies on the binding mechanisms of drugs to target biomolecules during development are closely related to post-market investigations of observed side effects.

This example highlights the potential pitfalls of oversimplifying the intricate relationships between specialists and methodologies involved in various stages of pharmaceutical research and development. Fragmenting these processes could ultimately lead to reduced productivity. This issue also intersects with the pros and cons of dividing pharmaceutical company divisions into separate entities. The “Pharma Crisis” appears to encompass both managerial and research management challenges. NCATS’ initiative seems to address these very concerns, aiming for a more integrated approach.

Bioinformatics and Computational Chemistry for Personalized Pharmacology

The history of research into personalized pharmacology is long. The terminology and concepts of ADME (Absorption, Distribution, Metabolism, and Excretion) were familiar to pharmaceutical and toxicological researchers long before the Human Genome Project began in the 1990s. However, these concepts were less familiar to researchers focusing on molecular biology or recombinant DNA technologies (biotech) that emerged in the 1980s and aimed to revolutionize drug discovery. ADME-related research techniques were more relevant to preclinical safety studies using animal models and human clinical trials, often regulated under specific guidelines, rather than the early stages of drug discovery involving the identification of biological targets and small molecule optimization. This type of research is often categorized under Regulatory Science, a term that started gaining prominence around 1990.

In 1981, we established a research group, the predecessor of today’s CBI Society, advocating for the application of computational chemistry and bioinformatics in drug development and toxicological studies. Promoting computational toxicology was also a key objective. However, interest in computational toxicology related to ADME was extremely limited in Japan. In the late 1990s, as an advisor for international research on chemical safety (IPCS), I even drafted internal documents to highlight the importance of computational approaches, addressing concerns from the Ministry of Health about the growing influence of computational toxicology in Europe and the U.S.

As the Human Genome Project neared its conclusion, the importance of knowledge bases related to biological pathways and networks became widely recognized. Around the turn of the century, significant funding was allocated to projects that integrated computational approaches and animal testing in drug research. Collaborative studies between the U.S., Europe, and Japan, regions with strong drug development capabilities, became more common. Concurrently, pharmacogenomics began to advance, utilizing genomic and other omics data to comprehensively organize molecular-level knowledge on drug interactions with biological systems, encompassing efficacy and adverse effects.

For example, determining where and how much of a drug is distributed in the body involves ADME. However, the levels of enzymes that metabolize or transport the drug and the target proteins are influenced by individual phenotypic variations. Even within a single individual, the expression levels of messenger RNA (mRNA) and the resulting proteins vary across tissues and organs, affecting drug efficacy. Therefore, databases like the Genotype-Tissue Expression (GTEx) project, which catalog mRNA expression levels of genes with SNPs across human tissues, have been developed. The GTEx Portal by the Broad Institute is a notable example.

The core challenge of computational drug discovery lies in docking studies, which analyze the binding interactions between drug molecules and their biological targets. Similarly, pharmacogenomics employs techniques like these to identify biomarkers and Drug-Gene Pairs^33),34). Examples include three-dimensional docking studies examining interactions between drug molecules and metabolic enzymes like CYP or HLA proteins³⁵⁾. Recent research has also investigated the genetic polymorphisms of GPCRs (G protein-coupled receptors), the most prominent drug targets, to deduce structural variations and analyze differences in drug binding³⁶⁾. These efforts are expanding to include comprehensive studies on genetic and structural diversity in known drug targets, such as kinases in cancer or nuclear receptors in metabolic diseases. Challenges also lie in analyzing the inhibition of protein-protein interactions by small molecules. Advances in technologies like cryo-electron microscopy are expected to facilitate these studies, enabling investigations into genetic polymorphisms and resulting structural changes in proteins.

D2K Science to Support Decision-Making for Precision Medicine

As previously mentioned, pharmacogenomics has evolved to integrate genomic data with other omics knowledge, consolidating molecular-level insights into drug interactions with biological systems, including efficacy and adverse effects. The ultimate goal is to establish a foundational infrastructure for healthcare services that can utilize these data, information, and knowledge to assist clinical decision-making¹⁰⁾. This effort has expanded into international collaborations between healthcare providers, such as hospitals, and foundational research institutions specializing in pharmacogenomics.

Such knowledge bases have also become vital intellectual resources for pharmaceutical companies during drug development. Many drug development projects are halted due to insufficient efficacy. Therefore, identifying trial participants (responders) who are likely to respond well to candidate compounds early in the development process is crucial³⁷⁾. Drug development costs escalate sharply in later stages, making early-phase decisions critical. Moreover, identifying potential adverse effects early can help mitigate risks.

The true stage for precision medicine lies in clinical practice. Determining whether a drug is being used—or has been used—appropriately relies on clinical records, which serve as the basis for such evaluations. However, obtaining or compiling the necessary data for these investigations poses significant challenges.

Clinical decision-making for precision medicine requires leveraging both preemptively accumulated knowledge about the drugs being used and insights into patient-specific drug responses. Pharmacogenomics focuses on generating preemptive knowledge, making it possible to compile foundational data and predictive insights for clinical use. However, patient-specific data must be collected during actual encounters and within the context of clinical practice, not as part of formal research. In this sense, while the knowledge of Drug-Gene Pairs compiled by pharmaceutical companies, hospitals, and research institutions is essential, it is not sufficient. Expanding the adoption of genetic tests listed in Drug-Gene Pairs remains a priority.

Several other factors influence drug efficacy, including circadian rhythms, the presence of other medications, diet, and the surrounding ambient environment, such as gut microbiota and air quality (Figure 4). As seen with gut microbiota, there are underexplored research areas that could significantly impact pharmacogenomics. While research in pharmacogenomics continues to expand and deepen, the quantity and quality of knowledge, information, and data needed for precision medicine are growing exponentially, making it increasingly challenging for clinical practice to incorporate these advances effectively.

Recent efforts in pharmaceutical development have focused on integrating real-world data (RWD)—clinical records from actual medical practice—with rigorously controlled trial data to support clinical research. Interest is also growing in developing pharmacokinetic-pharmacodynamic-pharmacoeconomic models (PK-PD-PE models) that incorporate cost considerations³⁸⁾. Additionally, the potential of patient-participatory digital health solutions is being explored.

The guiding principle for computational applications in precision medicine can be traced back to Norbert Wiener’s cybernetics³⁹⁾. As the foundation of control theory, it includes methodologies like linear programming (LP), quadratic programming (QP), and dynamic programming (DP). QP underpins machine learning techniques such as support vector machines (SVM), while DP is used in algorithms for sequence alignment of nucleotides or amino acids. DP has also been applied to optimize gout treatment regimens⁵⁾. However, research aimed at optimizing clinical decision-making for drug administration remains relatively underexplored.

One challenge is quantifying concepts like “appropriate” or “optimal” in decision-making. These values may differ between service providers (physicians) and recipients (patients). Another practical challenge is the sheer number of factors (variables) to consider simultaneously. For example, a breast cancer specialist may account for around 200 factors when making medical decisions, according to a lecture by an expert in the field.

The challenges outlined above represent only a fraction of what D2K scientists encounter when supporting decision-making for precision medicine. Building systems capable of rapidly presenting all necessary factors and data to ensure appropriate decisions requires an extraordinary level of effort.

The Future of Precision Medicine

This article has provided an overview of the concept of precision medicine. Here, we summarize the key points and discuss the future. First, let us consider the English translation of “適薬” (tekiyaku). The goal of precision medicine is encapsulated by the three Rs: “the right drug, at the right dose, to the right patient.” In essence, it is about “the practice of appropriate drug use.” Dr. Francis S. Collins, a leader in Precision Medicine and director of the NIH, has stated, “The initial goal of Precision Medicine is Precision Oncology, followed by the implementation of ‘the right drug at the right dose to the right patient’ across more disease areas, leveraging pharmacogenomics.”³⁾ This second objective, envisioned for the near future, directly aligns with the concept of precision medicine.

The word “medicine” carries dual meanings: medical care and pharmaceuticals. Generally, Precision Medicine refers to personalized healthcare. However, precision medicine specifically aims to refine drug usage. In this sense, it can be regarded as “another form of Precision Medicine.” Moreover, as discussed earlier, precision oncology stands at the forefront of precision medicine research. The methodologies developed here are likely to be applied to other disease areas in the future.

The study of precision medicine represents an example of “science and technology applied to improve our lives.” In this sense, it is the essence of Translational Research (TR). However, the importance of TR has only recently been recognized in the biomedical field, with dedicated research organizations being established. Genomic decoding technologies and ICT, which drive innovation in biomedical science and healthcare, also serve as foundational technologies for TR. Today, significant advancements in pharmacogenomics, based on these two technologies, are leading the way in precision medicine research.

The next goal for precision medicine is to shift its focus toward consumers and patients. Within Big Pharma, terms like “Beyond the Pills” and “Patient-Centric” have become mantras. This reflects an ambition to “provide solutions for health” rather than simply selling “drugs as products.” Thus, precision medicine research represents a frontier for pharmaceutical companies.

Conclusion: A Participatory Healthcare Perspective

Today, consumers are increasingly interested in addressing health and disease through non-pharmacological interventions (NPIs) such as diet, exercise, sleep, and lifestyle modifications, which do not require a physician’s prescription. This trend has led to research aimed at providing “personalized dietary advice,” or Personalized Nutrition. Such efforts will likely draw on Nutrigenomics (NGx) as a foundational science. Consumers are also becoming more aware of the impacts of environmental chemicals, including tobacco smoke, within their surroundings and living environments. This is where Toxicogenomics plays a pivotal role.

In Japan, drugs are regulated by the Ministry of Health, Labour, and Welfare (MHLW), food by the Ministry of Agriculture, Forestry, and Fisheries (MAFF), and the safety of environmental chemicals by multiple government agencies. This creates administrative barriers to supporting research that spans Pharmacogenomics, Nutrigenomics, and Toxicogenomics. However, from a molecular science perspective, food, drugs, and toxins are interconnected. Therefore, research into personalized nutrition, appropriate drug use, and proper handling of toxins should transcend these administrative divisions. We refer to this integrated approach as the “trinity of food, drug, and toxin health sciences.” Raising consumer awareness of the importance of such research and encouraging their involvement, even in small ways, represents the next frontier for precision medicine “beyond drugs.”

Acknowledgment: The author expresses sincere gratitude to the reviewers of this manuscript for their valuable comments.