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Bio-Synergy Research Center
Knowledge-Based Systems Biology Platform for Natural Product Engineering

by Doheon Lee


According to the World Population Ageing 2013 Report by the United Nations (UN), population ageing is already a global phenomenon. UN further emphasized the major social and economic consequences, which includes fiscal pressures on social healthcare systems. To cope with this situation, the paradigm of healthcare is shifting from disease ‘treatment’ to ‘prevention’. The convergence of information technology (IT) and biotechnology (BT) will bring a paradigm shift of healthcare, and presents a P4 medicine; Personalized, Preventive, Predictive and Participatory. ‘Personalized’ for customized medicine which is in accordance with individual’s genetic and physiological characteristics, ‘preventive’ for avoiding or delaying disease onsets with proactive efforts; ‘predictive’ for reliable prediction of disease risks, accurate early diagnosis and prognosis of diseases; ‘participatory’ for active participations of patients, families, and communities for healthcare.

Development of new medicinal agents is essential among many technological innovations to realize the P4 medicine. However, new drug development processes most often face limitations. Current target-based drug development starts with identification of target proteins, hits, and leads. Discovered novel compounds will have to undergo the selection for optimal drug performance, and hence compounds are subjected to several selection phases during clinical trials. In this clinical trial process, tens of thousands compounds are screened but less than ten compounds are finally selected for clinical trials. In population studies, clinical trials are the most costly and time-consuming processes, and they often require tedious work to complete and achieve successful drug development. Over the last 18 years, the cost to developing a new drug have been increasing, but the number of approved-drug keeps declining as shown in Figure 1.

One of the most promising approaches to overcome the current limits of new drug development is to utilize natural products with known empirical efficacy, i.e. reverse pharmacology. Reverse pharmacology is a study that reveals the mechanisms of active and effective compounds including herbal extracts, and to further develop the identified active ingredients into drug candidates or formulations through preclinical and clinical research. Development of new natural product drug is considered as a breakthrough for drug development since not only the toxicity of the natural products are known, but also applying reverse pharmacology on natural products can reduce the drug development time and cost. Due to these advantages, Traditional Chinese Medicine (TCM) and Traditional India Medicine (Ayurveda) are gaining great interests in drug development research fields. Also, the National Center for Complementary and Integrative Health (NCCIH), European Scientific Cooperative On Phytotherapy (ESCOP), and Bio-Synergy Research Center (BSRC) are conducting intensive research on natural product medicines.

Natural product-based drugs can be classified into two categories, natural product-derived compounds (NPC) and natural product extracts (NPE). NPC means pure compounds derived from natural products. Aspirin from willow trees and Taxol from pacific yew trees are famous examples of NPC drugs. On the other hand, NPE means mixture of natural compounds. Pharmaton from Ginseng and Tebonin from Ginko, licensed Boehringer Ingelheim and Schwabe, respectively, are famous examples of NPE. Recently, US Food and Drug Administration (FDA) has approved two NPE drugs, Veregen and Fulyzaq. Currently, it has been known that more than 100 NPE drug candidates are in the FDA clinical trial stages. Though the attentions and interests for NPE drugs are increasing significantly due to shorter time and lower cost of development in comparison to the synthetic drugs, there are two major intrinsic problems to resolve.

Firstly, the standardization of the products is difficult as they are mixtures of many compounds. Knowledge about efficacies of natural products in the field of traditional medicine comes from prescriptions which are usually combinations of multiple herbs. Those herbs contain many active compounds as well. Specific compound composition in a given prescription is determined by many factors including the growing conditions of the component herbs and extraction methods of the prescription. Due to this complexity, it is difficult to expect a uniform compound composition for different production batches. To solve this issue, global pharmaceutical companies have cultivated natural products based on Good Agricultural Practices (GAP) and have processed them according to Good Manufacturing Practices (GMP). FDA place more requirements for standardization including chemical equivalence and biological equivalence verifications on top of them.

Secondly, complex interactions between multi-components from natural products and multiple targets in human bodies should be considered, as NPE drugs consist of multiple compounds by definition. It is expected that their mild efficacy with lower toxicity comes from such multi-component and multi-target interactions. Yet, the number of required experiments increases exponentially as the number of compounds becomes larger. If we rely on cell-based or animal-based experiments solely to analyze the effect of multiple compounds, the time and cost would become simply infeasible. Many research groups suggest to adopt information and computational models to alleviate this situation by predicting promising combinations and reducing the number of required experiments.

Bio-Synergy Research Center Overview

Bio-Synergy Research Center aims to develop knowledge-based systems biology platforms for natural product engineering, where multi-component and multi-target (MCMT) interactions between natural products and human bodies are explored in order to develop natural product-based drugs and functional foods. To achieve this goal, the research center has been developing fusion of information technology (IT) and biotechnology (BT), shortly IT-BT fusion, which includes computerized virtual human system, bioinformatics, systems biology and multi-omics integration. It is also developing high-throughput phenotype-based material screening, metabolomics-based standardization of natural materials, genome-scale molecular target discovery, and clinical trial protocols applicable to natural product-based drugs and functional foods.

Bio-Synergy Research Center was established in 2013 as a Korean national project, whose duration is ten years. It consists of five collaborating research groups depicted in Figure 2. Virtual Human Systems group is developing a computerized virtual human system to predict and analyze multi-component and multi-target (MCMT) effects of natural product-based materials. Natural Product Materials group is developing technologies for high-throughput phenotype-based material screening and metabolomics-based standardization of natural materials. Molecular Target Discovery group is developing genome-scale molecular target identification technologies for effective natural product compounds in vitro and in vivo. Multi-Omics Bio-Markers group is conducting genomics, transcriptomics, proteomics, and metabolomics experiments to explore mechanisms of natural products in cell and animal models. It is also developing multi-omics analysis technologies to integrated different levels of omics information. Human Studies group is developing clinical trial protocols applicable to natural product-based drugs and functional foods, and actually applying those protocols to functional foods.

Technology Core

Bio-Synergy Research Center has developed an innovative bio-research workflow called CONET, which stands for Collaborative Natural Product Exploration Task shown in Figure 3.

CONET is a workflow system, which combines the essential technologies of natural product engineering, molecular target discovery, biomarkers discovery and advanced clinical research, centered on a virtual human system CODA. Equipped with this combined information, CONET can analyze the effects that multi compounds from natural products have when acting on multi targets.

CODA, which stands for Context-Oriented Directed Association, is a computerized virtual human system containing organ-specific and organ-to-organ biological relation networks. To build CODA, vast amounts of biological knowledge have been acquired from biological databases and literatures on the levels of molecular, cellular, tissue and organ information. Furthermore, novel experimental information produced by four collaborating groups within the center is being integrated into the CODA knowledge base. CODA is the world’s largest virtual map of the human body. Using CODA, the efficacy of drug and its side effects can easily be found because we can look into the clinical mechanisms of drug actions as if looking into a real human body. In addition, the mutual interactions between multi-compound drugs can be systematically analyzed.

Another important element in the CONET workflow is COCONUT (Compound Combination-Oriented Natural Product Database with Unified Terminology), a standardized natural product database that focuses on compound combinations. COCONUT is comprehensive of all the information that has been standardized into international codes on prescriptions, herbs, compounds, targets and phenotypes from traditional herbal medicines, functional foods and conventional drugs information. By applying cutting edge data mining technology to the vast amounts of material information stored in COCONUT, compound combination patterns called COCOA (Compound Combination-Oriented Association), whose synergistic clinical effects are expected, are generated systematically. The CONET workflow provide an integrated platform to develop natural product drugs and functional foods in the use of COCOA rules, which are verified by CODA, cell, animal and clinical experiments.

After COCONUT suggests a promising COCOA rule, which needs to be verified, Natural Product Materials group prepares experimental natural product extracts which contain most of effective compounds shown in the COCOA rule. It is done by specifying components of natural products and verifying their effects using high speed graduation system and metabolite analysis technology. Molecular Target Discovery group identifies molecular targets of the effective compounds specified in the COCOA rule. Multi-Omics Bio-Markers group conducts cell-based and animal-based experiments with natural product extracts prepared by Natural Products Materials group. Each omics experimental data is comprised into a multi-omics network and is integrated into CODA virtual human system. Human Studies group applies the natural product extracts, only when they are allowed to clinical studies, to voluntary human subjects. Their experimental data is also integrated into the CODA virtual human system.

To give an illustrative example, suppose that we want to determine which natural products are effective in the treatment of diabetes and how these natural products interact with human bodies using the CONET workflow. Let us assume that data mining for diabetes has been carried out on COCONUT, and it was suggested that, as a particular COCOA rule, “The combination of Gomisin N, Schisandrin and Astragaloside” is effective in blood sugar reduction. Natural Product Material group comes to learn from COCONUT that ‘Gomisin N and Schisandrin are contained in Schisandra, and Astragaloside in the Milk Vetch root’. The group produces the OHGSA fraction through applying the natural products isolation technology that enables the maximum extraction of these three effective compounds. Molecular Target Discovery group identifies the molecular cell targets of the efficacious Gomisin N, Schisandrin and Astragaloside compounds that have appeared in the COCOA rule. The mechanisms of action of these three efficacious compounds are analyzed via CODA by Virtual Human Systems group. CODA shows the results of the reduction in blood sugar and, one by one, what the reactions of particular proteins and metabolites will be in the process of reducing blood sugar. In addition, it presents the synergistic effects of the three efficacious compounds.

Multi-Omics Bio-Markers group measures changes to the genes, proteins and metabolites related to the mechanisms of action of drugs suggested by the CODA system. Human Studies group measures human physiological changes through non-invasive experiments such as blood and urine testing, by administering the in vivo experimental food materials using the Schisandra and the Milk Vetch root. The experimental results are compared with the inferred CODA results and the knowledge base of CODA is updated in cases of conflicts.

The updated CODA explains the mechanisms of the COCOA rule, as well as the combination of “Gomisin N, Schisandrin and Astragaloside I” that is effective to blood sugar reduction. Furthermore, it can identify and determine any effect dependent on the genetic makeup of the person. A customized new natural product medicine and functional food effective in curing and preventing diabetes can thus be developed by means of the validated COCOA rule.


In addition to reducing the time and cost of developing new natural product drugs and functional foods, CONET improves the chances of success when compared to the traditional drug development process. Bio-Synergy Research Center will continuously update its natural product database to make world’s largest natural product database. CODA will be extended and updated with verification of effectiveness of natural products to become the world’s largest virtual human map resource. Bio-Synergy Research Center will serve as national and international technology infrastructures where personalized natural product-based drugs and functional food are explored and developed.

About the Author

Doheon Lee, Ph.D.
Director, Bio-Synergy Research Center
Professor, Department of Bio and Brain Engineering, KAIST

Doheon Lee received the B.S., M.S., and Ph.D. degrees in computer science from Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1990, 1992, and 1995, respectively. He was a visiting professor at Stanford University, Indiana University, Translational Genomics Research Institute (TGEN) and Univ. of Texas at Austin, USA. Currently, he is a professor in Department of Bio and Brain Engineering, KAIST, and the director of Bio-Synergy Research Center (BSRC), a Korean national project where over 30 principal investigators are collaborating for natural product bioinformatics and systems biology. He is also a technical advisor for CRS Diogenes SRL, Italy. He was an Associate Editor for ACM Transactions on Internet Technology for nine years. He is also serving Computers in Biology and Medicine, International Journal of Data Mining in Bioinformatics, and Healthcare Informatics Research as an Editorial Board Member. He is a co-founder of ACM International Workshop on Data and Text Mining for Biomedical Informatics. He has published over 200 academic papers in bioinformatics, systems biology, and data mining.

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