Introduction
This series is based on the recognition that “drug development is entering a major turning point,” and its primary theme is to explore responses to this shift. In the previous articles1)2), we discussed responses to the sweeping waves of ICT, including big data, artificial intelligence, and cloud technologies. Drug development is conceived with a vision of healthcare 10 to 15 years into the future, where these drugs will be utilized. From this perspective, this article will examine research using simple model organisms, particularly in conjunction with the remarkable advancements in ICT utilization.
Movements by AstraZeneca and Google
Before delving into the main topic, let me introduce two recent noteworthy developments. The first is related to the review of pharmaceutical R&D by AstraZeneca, a major European and American big pharma company. This effort is based on the “5R framework” for R&D, which was published in a well-known journal in the drug development field, Review3),4). AstraZeneca began a strategic review in 2011, recognizing that their R&D productivity during the 2005–2010 period was lower than that of their peers. The foundation of this review was the concept of selecting the right target, the right tissue, the right safety, the right patient, and the right commercial potential—referred to as the “5R framework.” They claim the effectiveness of these efforts is evidenced by the increase in the success rate of drug candidates completing Phase III trials, which rose from 4% during the 2005–2010 period to 19% in 2012–2016.
Their efforts involve exploring more appropriate measures at each stage of the R&D pipeline, appearing as an accumulation of modest, iterative improvements that are difficult to summarize in a single phrase. These include narrowing development targets, conducting deeper research, emphasizing companion diagnostics, and selecting the right patients. Underlying this is a focus on pursuing scientific rigor, prioritizing collaborations, and leveraging new technologies such as genome sequencing, precision medicine, and artificial intelligence. Such movements suggest that there is no panacea for innovation in drug development today.
The second development involves Google’s (renamed Alphabet) foray into the “healthcare and drug development business.” In 2013, Google established Calico, a research company aimed at “developing methods to address life extension from a long-term perspective.” At the time (September 30, 2013), Time magazine shockingly reported on it as “Google’s attempt to solve death.” However, Calico’s subsequent activities have been shrouded in secrecy.
Meanwhile, Alphabet partnered with Verily Life Sciences LLC, a company focused on healthcare innovation5). On Verily’s website, it is mentioned that they have initiated the Project Baseline study in collaboration with multiple universities6). These movements by big pharma and large ICT companies seem to provide hints for predicting the trends of next-generation healthcare and drug development. Furthermore, these new developments are not unrelated to this article’s theme of research using simple model organisms.
What Are Simple Model Organisms?
Molecular biology emerged as a discipline that unified the previously separate fields of botany and zoology. A guiding principle of molecular biology research has been to “use the simplest organism in which a phenomenon can be observed” as the material for investigation. In practice, genetic analysis methods were developed through studies of fruit flies and bacteria (E. coli) and their parasitic viruses (phages). Sea urchins and nematodes were used to trace 3D cell aggregates and determine cell lineages during multicellular development. Research on the brain and nervous systems, which are distinctive to humans, has utilized not only humans and mice but also smaller model organisms such as nematodes, flies, and zebrafish.
In the 1970s, Benzer (Seymour Benzer) began using fruit flies (Drosophila melanogaster) for behavioral research, while Brenner (Sydney Brenner) introduced nematodes (Caenorhabditis elegans, C. elegans) for developmental studies. In his early research proposals, Brenner wrote, “We aim to study the developmental processes of multicellular organisms, and for this, we have chosen C. elegans as a promising organism. It is small, with a short life cycle, easy to culture, amenable to large-scale handling like microorganisms, and allows detailed analysis of cell lineages and patterns. Furthermore, its cell count is low enough to permit genetic analysis.”7).
Shortly afterward, zebrafish (Danio rerio), a small tropical fish recognized for similar advantages, emerged as a model organism. Pioneers such as Streisinger (George Streisinger) and Nüsslein-Volhard (Christiane Nüsslein-Volhard) spearheaded its use8). These organisms became the three pillars of simple experimental animals. Initially, however, they were perceived by those involved in medicine and drug development as mere toys for basic biologists. This perception shifted during the 1990s with the Human Genome Project, which established the sequencing of entire genomes, including these experimental organisms and humans, and solidified the concept of genomic medicine.
Species | Size (m) | Gene Count | Neuron Count | Generation Time (days) | Offspring Count |
---|---|---|---|---|---|
E. coli, Yeast | 10-6 | 100 | 100 | 10-2 | 100 |
C. elegans | 10-3 | 103 | 102 | 100 | 102 |
Fruit Fly (Drosophila melanogaster) | 10-3 | 103 | 105 | 101 | 102 |
Zebrafish | 10-2 | 103 | 107 | 102 | 102 |
Mouse | 10-1 | 104 | 108 | 102 | 101 |
Human | 100 | 104 | 1010 | 104 | 100 |
Distinctive Experimental Styles
Simple model organisms as experimental materials exhibit the following characteristics:
- They are well-suited for genetic analysis, with established techniques and systems for efficiently creating, storing, and supplying mutant strains.
- Their short lifespans allow for rapid repetition of experiments. Additionally, their short time to reach maturity and high offspring numbers make them ideal for research.
- The early stages of their development can be observed and tracked with optical microscopes, and data and knowledge bases on their cellular aggregates and cell lineages have been established.
- Specialized equipment has been developed to efficiently conduct experiments involving many individuals, such as testing the effects of compounds or other stimuli on the body (Chemical Biology) or observing behavioral changes.
- Abundant data and knowledge have been accumulated on genes, proteins, and intracellular signaling pathways (Signal Pathway/Network), enabling analysis from evolutionary and developmental perspectives while allowing mutual comparison and reference.
- The research community is mature, with manuals and tutorials available for relatively quick and low-cost acquisition of experimental techniques, facilitating the rapid development of research personnel.
- They are particularly well-suited for the application of computational tools (ICT).
Since the turn of the century, these characteristics have been leveraged in advanced research on genes, proteins, and the pathways they involve, as well as in the development of models that share molecular-level similarities with human diseases. These insights can naturally be applied to drug development, where phenotypic screening and drug repurposing (exploring new applications for existing drugs) have been actively pursued9)-14). Such screening studies often serve as the starting point for elucidating the molecular mechanisms of diseases. Similar approaches have also been applied to safety and toxicity evaluations of chemical substances15).
Today’s research using simple model organisms has evolved into strategic initiatives that build foundational network environments, combining and cross-referencing multiple model organisms rather than focusing on a single preferred organism. “The Model Organisms Screening Center for the Undiagnosed Diseases Network” is an example of such a project16). This project is part of the NIH-supported strategic research initiative known as the Undiagnosed Diseases Network (UDN), which aims to support research on rare and undiagnosed diseases17). The project connects an integrated center, seven clinical sites, two sequencing facilities, model organism screening centers, a metabolomics core facility, and a biobank through a research network (UDN). Baylor University, which plays a key role in utilizing simple model organisms within this network, has its clinical division affiliated with the Department of Molecular and Human Genetics, a globally recognized and large-scale genetics facility. This NIH initiative reveals a cross-species translational research strategy, aiming to translate findings from studies on simple model organisms and mice into clinical applications18).
Figure 1: Research environment integrating simple model organisms, human-derived cells, and computational models. The computational model serves as the backdrop for all experimental systems and is interconnected with them.
Research Using Other Animal Models
In addition to the commonly studied simple model organisms, there are foundational and applied research efforts that effectively utilize the characteristics of other organisms. One well-known phenomenon in honeybee (Apis mellifera) colonies is how female larvae, which would typically develop into worker bees, grow into queen bees when fed royal jelly. Masaki Kamakura elucidated this process, identifying that it is caused by a component of royal jelly called royalactin19). This research also confirmed that administering royalactin to fruit flies resulted in larger individuals resembling “queen flies.”
There is also research that uses fruit flies as a model for mosquitoes. Mosquitoes transmit many diseases through blood-feeding on humans and animals. While the U.S. Army developed a mosquito-repelling compound called DEET, concerns about its safety for humans (particularly soldiers) persisted. Pinky Kain and colleagues conducted a study where they screened a database of chemical compounds (natural substances) known to be safe for humans using computer-aided structure-activity relationship analyses. They identified several candidate compounds effective against flies and subsequently tested these compounds on fruit flies as models. The study verified the repellency effects on flies and identified compounds that were also effective against mosquitoes20).
Thus, nematodes, fruit flies, and zebrafish serve as model organisms for studying human diseases and safety. At the same time, they are also experimental animals for evaluating the environmental impact of insecticides, repellents, herbicides, and unintended chemical pollutants on insects and aquatic organisms21),22).
Google’s Research on Health and Longevity
Since its striking debut, Calico’s research activities, shrouded in secrecy, have only recently begun to be unveiled through publications released last year and this year. Last year’s paper focused on the genetic and pathway mechanisms of longevity using nematodes23). This study, authored by three researchers including Cynthia Kenyon, Calico’s vice president overseeing aging research, was conducted collaboratively between Calico and the University of California, San Francisco. It investigated nematodes with mutations in the daf-2 gene, known for extending lifespan beyond that of wild types. These nematodes exhibit severe signs of senescence as they approach the end of their extended lifespan. The researchers found that these symptoms were caused by colonies of E. coli, provided as food, forming in their intestines. They reported that mitigating this issue, for example by feeding the nematodes dead E. coli, improved their senescence symptoms in the final stages of life. This finding could potentially contribute to addressing the challenges of extending healthy lifespans, a significant issue in developed countries, including Japan. Kenyon is a renowned pioneer in genetic and pathway research related to longevity using nematodes24),25), making this study a natural extension of her previous work.
This year’s paper, published in *eLife*, took a different direction by examining health and longevity in naked mole-rats26). The core finding was based on a detailed examination of past research records on these rodents, which are unusual for their small size and extraordinary lifespan of up to 30 years. The study concluded that these animals do not exhibit an increase in mortality rate as they age, essentially maintaining their health. This paper did not include Kenyon as a co-author but was instead co-authored by three researchers affiliated solely with Calico. One of them, R. Buffenstein, has spent over 30 years studying these rodents as model organisms, suggesting that Calico recruited her to advance their research on longevity using this species.
There is considerable evidence suggesting that research on health and longevity intersects with cancer research at the molecular level. Perhaps for this reason, Calico also recruited Hal Barron, a prominent cancer researcher. Barron, who had served as the Chief Marketing Officer (CMO) at Roche after its acquisition of Genentech, was subsequently recruited by GlaxoSmithKline from Calico. Barron may have viewed health and longevity research as a roundabout path to cancer research, given the rapid advancements in the latter.
In comparison to Calico’s secretive beginnings, Verily’s objectives in its Baseline Study are easier to comprehend. According to its website, Verily’s projects fall into four categories: sensors, interventions, development of health platforms and population health tools, and precision medicine5),6). The Baseline Study is part of the precision medicine initiative. These projects encompass not only conventional healthcare (as seen in Japan’s health insurance system) but also efforts toward disease prevention and maintaining wellness, broadly covering the entire spectrum of healthcare.
As one of Verily’s initiatives, the Baseline Study aims to quantitatively define what it means to be “healthy.” Specifically, it seeks to measure individuals’ health status quantitatively and track how such states transition to various disease conditions. The initial goal is to collect longitudinal data from 1,000 participants, eventually expanding to 10,000 over four years. Stanford University and Duke University (School of Medicine) are also involved, with particular focus on cancer and cardiovascular diseases.
Verily’s precision medicine initiatives include collaboration with the NIH’s “All of Us Research Program.” This program, which succeeds the Precision Medicine Initiative launched during the Obama administration, involves a large cohort study recruiting one million participants for genomic medicine research. The name was changed during the Trump administration, but the project’s essence remains intact.
These insights, though based on limited information, suggest that Google is simultaneously advancing molecular biological research on health and longevity using simple model organisms and conducting the Baseline Study on health status among diverse populations. The latter heavily relies on simple measurement and analysis tools for collecting and analyzing longitudinal data. Various pharmaceutical companies appear to be collaborating on specific projects. While general drug development research unfolds between these two polar realms of research, Google’s health and longevity efforts remain in their early stages from the perspective of drug development.
Future Research Areas and Strategies
Biomedical research, grounded in molecular biology, intertwines the foundational scientific goal of understanding life with the applied goal of understanding disease mechanisms at the molecular level to develop pharmaceuticals. Many fundamental biological phenomena have been discovered through studies of simple model organisms and animals, evolving into applied technologies. Deciding where and how to allocate research resources between basic biological research and translational research is therefore a challenging task.
Major pharmaceutical companies showed interest in basic research until the 1970s. However, as governments began heavily funding universities and public research institutions, foundational studies linked to drug target discovery were increasingly delegated to academia and startups27). This trend is also evident in Japan.
Despite this, the reliability and utility of research using simple model organisms or model animals, including mice, as substitutes for humans are often criticized28). For simple model organisms, in particular, their small size and simplicity make it difficult to create disease models suited to specific conditions. Similarly, evaluating a compound’s effects may not simply involve observing the same phenotype in humans and the model organisms. This is also true for experiments using human iPS cells or organoids, where a robust knowledge base is needed to properly translate and interpret results.
Such a knowledge base includes accumulated data on genomes, genes, gene expression, proteins, metabolites, molecular networks linking these elements, and the similarities and differences in tissues and organs across species. Strategic use of ICT is a prerequisite for advancing this field. Research on healthy aging and anti-aging necessitates such strategic approaches.
One noteworthy initiative from this perspective is the work of the Bioinformatics Group within the European Molecular Biology Laboratory (EMBL), based in the UK. In 2011, they announced the establishment of a computational biology infrastructure for aging research29). Subsequently, they published research combining findings from model organisms with computational techniques to explore anti-aging drugs through drug repositioning30). This group is well-known for developing comprehensive compound-related databases for drug discovery, such as ChEMBL. One of its leaders, Janet M. Thornton, is a prominent figure in bioinformatics.
Utilizing their rich data and knowledge base on organisms and compounds, they ranked known drug candidate compounds by their binding sites across various animals. Techniques from cheminformatics, such as Lipinski’s Rule of Five, were employed to identify potential anti-aging compounds. These compounds were then tested on simple model organisms such as nematodes and fruit flies as part of their research scenario. The business of science related to anti-aging research has been emerging since the 1990s when aging-related genes began to be identified31). One of its key attractions is that interventions like calorie restriction and pharmaceuticals discovered through this research seem promising for preventing or treating major non-communicable diseases common in developed countries, such as cancer, metabolic disorders, and Alzheimer’s disease.
Nrf2/SKN-1 Pathway: Coping with Oxygen Toxicity
Alongside anti-aging research, the importance of model organisms is increasingly recognized in studies of the Nrf2/SKN-1 pathway, a sensory circuit involved in antioxidant activity. Nrf2, along with Aryl Hydrocarbon Receptors (AhR), known as dioxin receptors, and nuclear receptors associated with metabolic syndrome, functions as a receptor that responds to external substances and energy intake. Normally bound to Keap1 outside the nucleus, Nrf2 translocates into the nucleus upon binding to target compounds. Inside the nucleus, it interacts with the Antioxidant Response Element (ARE) on DNA to promote the expression of phase II drug-metabolizing enzyme genes. These enzymes, through a feedback loop, help address external substances detected by the Keap1/Nrf2 complex32). This feedback circuit and its components were initially discovered in mice and humans but were later identified in nematodes, where the homolog is known as SKN-133). Similar receptors and circuits have also been found in fruit flies and zebrafish.
Currently, a theory suggests that Nrf2 evolved as a safety mechanism to cope with atmospheric oxygen across diverse organisms, from fungi to metazoans34). Furthermore, Nrf2 is recognized as a core member of a critical network involved in three health-related domains: dietary components, diseases, and safety (toxicity)—encompassing food, medicine, and toxins. Research in this area progresses from the perspectives of drug discovery, health, and safety. Consequently, the utility of simple model organisms in this field is becoming increasingly evident.
Utilizing ICT
There is growing interest in leveraging machine learning, artificial intelligence, and broader automation technologies, including robotics, in biomedical research. Simple model organisms are highly compatible with ICT applications, or automation technologies, which significantly contribute to improving the productivity of biomedical research as a whole8),35).
In the early 1980s, we conducted compound screening experiments using nematodes, attempting to capture the number of individuals and their behavior (movement trajectories on plates) with television cameras and computers. Simultaneously, we observed the early embryonic development of nematodes using optical (Nomarski differential interference) microscopy, storing the images as digital data, and identifying the three-dimensional positions of cell nuclei. Using molecular graphics technology, we modeled the 3D aggregates of cells constituting early embryos36) (Figures 2, 3). With the remarkable advancements in computing and imaging technologies since then, many similar initiatives have already been commercialized.
Figure 2: 4D tracking system for nematode embryonic development. Optical tomographic images are stacked (3D) and arranged along the time axis.
Figure 3: Video model of early embryonic development aligned with the three-world model. The entire embryo can be observed from any angle, and selecting a specific cell provides access to its intracellular network data.
One of the benefits of such research and development is providing a platform for mutual understanding among experimentalists, clinicians, and ICT specialists. Today, effective and efficient execution of biomedical research, drug discovery, and healthcare (services) necessitates collaboration and mutual understanding among interdisciplinary teams, including ICT experts. In this context, research and education using simple model organisms, preceding studies on humans or rodents, serve as valuable opportunities for fostering collaboration and talent development.
For instance, Calico once recruited Daphne Koller, a star in AI research from Stanford University. Similarly, the EMBL Bioinformatics Group in the UK, led by Thornton, integrates simple model organism research with bioinformatics and cheminformatics, exemplifying an advanced research style in drug development30). Kain and colleagues’ work on identifying mosquito-repellent compounds is another example of a fusion study combining computational structure-activity correlation with simple model organisms20). Additionally, in Manchester, UK, there are initiatives to promote biology education using fruit flies37). In Japan, there is room for more discussion on advanced research combining simple model organisms and ICT, as well as on efforts to cultivate talent in this field.
Conclusion
Model organisms, particularly simple model animals, once played a pioneering role in molecular biology, advancing new frontiers by determining the complete cell lineage in development or mapping the entire neural circuitry. However, they are no longer exclusive tools for specialized biomedical research. Today, studies involving genome-to-gene, RNA, protein, secondary metabolites, and pathway identification using various simple model organisms have become the cornerstone of biomedical research. Moreover, comparative analyses between model organisms and humans have become standard practices in the field. Leveraging their advantages, such as rapid and low-cost precision genome analysis, phenotypic screenings are widely conducted in drug discovery, antioxidant research, anti-aging studies, and efforts to extend healthy lifespans. Additionally, organizing such data into databases for integrated analysis is a common approach, employing diverse AI and analytical techniques, including machine learning.
The next strategic research domain involving simple model organisms is likely to focus on animal design using technologies such as genome editing and direct reprogramming. This encompasses the fundamental biological questions of fate determination mechanisms and control, which relate to evolution and development, as well as the practical expectations of regenerative medicine. However, this theme touches on the creation of new organisms through artificial design—an area that could be described as “Genesis Technology.” Research in this domain necessitates broad discussions on safety and ethical considerations. Here again, the role of simple model organisms is anticipated to be crucial.
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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.