Jamu Informatics: A New Perspective in Jamu Research

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1 Jamu Informatics: A New Perspective in Jamu Research Department of Statistics, Bogor Agricultural University Farit Mochamad Afendi, Rudi Heryanto, Latifah Kosim Darusman, Nur Hilal A. Syahrir, Rizal Bakri, Nurul Qomariasih fmafendi@ipb.ac.id Abstract The paradigm of drug development has been shifted from one-drug one-target into multi drug network target. TheIndonesian traditional medicines, Jamu,is composed fromseveral plants. It provides a promising source in developing drug addressing this new paradigm. In this paper, we propose a concept of Jamu informatics as a tool in understanding Jamu as well as guidance in developing a new Jamu formula. We provide our implementation of Jamu informatics started from modeling the ingredients of Jamu as to estimating the efficacy of Jamu. We found that the developed model performs well in predicting Jamu efficacy given the information of its ingredients. Given the model, a new Jamu formula targeted for type-2 diabetes is developed and tested to Zebrafish.The experiment results show that the new Jamu formula is promosing and showing better performance compared to negative control. The ingredient composition of the new Jamu formula is then optimized using the response surface optimization. Finally, we evaluate the working mechanism of the new formula using network pharmacology. 1. Introduction With the growing understanding of complex diseases, the focus of drug discovery has shifted away from the wellaccepted one-drugone-target mode, to a new multi-drug network-target mode, aimed at systemically modulating multiple targets [1]. Jamu, as well as other traditional medicine, already use multi-drug network-target concept which refers to the comprehensive analysis for therapeutic effects of herbal formula on the basis of identification of the network-target underlying a given diseases as well as target network of given herbal formula [2]-[5]. This concept offers a promising approach in developing Jamu as anew potential drug. In Indonesia the research on Jamu focus more on pharmacy or chemical aspect. Preclinical pharmacological studies have been carried out with extracts and isolated compounds, and even a few clinical studies are available. However, Jamu is still largely not evidence-based from a clinical perspective [6]. Since 2010, the the Ministry of Healthof Indonesian is regulating Jamu scientification programthrough Ministry of Health regulation number002/menkes/per/i/2010. The program attempt to push research on Jamu from empirical-based to evidence-based. However, understanding the scientific basis of Jamu formulae as well as other traditional medicine at the molecular level and from a system perspective is still one of great challenges for evidence-based medicines [7][8]. To address this challenges, we propose Jamu informatics concept as a multidisciplinary research field involving statistics, pharmacy, genomics, and computer scienceto model the formulation pattern, elucidate the efficacy mechanism on Jamu, as well as provide a guidance in formulating Jamu. Jamu informatics collaborate pharmacy as subject matter core engine, statistics to build a model the phenomenon, computer science to cover computational aspect, and genomics to exploit deepen the research down to molecular aspect. Those combined field in Jamu informatics aim to provide evidence statistically based in developing and modernization of Jamu as well as contribute new insights into the current drug discovery field. In this work, we integrate our previous studies into a Jamu informatics platform as a newperspectiveinjamuresearch. To address the challenges in the study of Jamu, the integration between the knowledge of plant usage and existing omics data, including metabolomics relating Jamu have been developed into Jamu Database [9]-[11]. In this mini-review, we discuss the usage of Jamu Database asa search engine for use in metabolomics research. We further explain the method in modeling ingredients of Jamu, developing a new Jamu formula based on that ingredient model, optimization of ingredients composition, and elucidating molecular aspect of Jamu. 2. Jamu Database Molecular biological data has rapidly increased with the recent progress of the omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data CICSJ Bulletin Vol.34 No.2 (2016) 47

2 [9]. Integration between the knowledge of plant usage and existingomics data, including metabolomics, is a major challenge forthe usage of systemsbiological approaches to understandingthe effects of components in medicinal plants on human health[7][10][11].the previous study reviewed the usage of KNApSAcK Family Databaseto comprehensively understand the medicinal usage of plants based upon traditional and modern knowledge by adding database system the selected herbal ingredients i.e., the formulas of Kampo and Jamu, omics information in plants and humans, and physiological activities in humans [10][11]. Jamu data could be obtained from the KNApSAcK Family database in JAMU Database (Fig. 1).JAMU Databaseconsists of 5,310 Jamu formulae encompassing 550 medicinal plants and 797 producers.it focus on commercial Jamu registered at the National Agency on Drug and Food Control (NA-DFC) of Indonesia. Information provided such as name of Jamu, producers, ingredients/formulae,andjamu efficacy (Fig. 2).Jamu Database integrated The Metabolomics DB system which consists of the KNApSAcKMetabolomics Search Engine and the species metaboliterelationship DB (KNApSAcK Core DB). KNApSAcK Core DBallows users to retrieve a list of candidate metabolites that correspond to a particularmolecular weight on a mass spectrum. From such data, it is possible to obtain information regarding individual metabolites [11]. Thus, we can obtainthe metabolite which are contained in medicinal plants in Jamu formulae. In the JAMU DB, we can retrieve the formulae from the names of medicinal plants,and also the names of medicinal plants from formula names. Jamu data in The KNApSAcK Family DBs which have been enriched by addition of the Metabolite Activity DB, as well as information related to metabolites in various fields, such as omics sciences, particularly metabolomics, nutrigenomics and foodomics, can assist the scientist/ researcher to develop Jamu as potential drug due to data-intensive or data-driven biological discovery is required to facilitate comprehensive research. 3. Modeling ingredients of Jamu Jamu is prepared from a mixture ofseveral plants. The plants are chosen so that the Jamu has the desiredefficacy. As a result, the composition of the plants used in Jamu formuladetermines the efficacy. Thus, it is interesting to model the ingredients ofjamu, i.e. the constituent plants, and use this model to predict efficacy.partial Least Squares Discriminant Analysis (PLS-DA), a statistical modelfor classification and discrimination based on Partial Least Square Regression(PLSR), is suitable for this analysis because a large number of plantsare used in Jamu, whereas Jamu efficacies can be grouped into categoriesor classes. In this method, the plants used in each Jamu medicine served asthe predictors, whereas the efficacy of each Jamu provided the responses. The data structure used for PLS-DA is as follows. The data matrix X in X-block contains plant usage status. The dimension of matrix X is (I J), where I is the number of Jamu and J is the number of plants. If the predictor X is constructed only in binary data, the availability of information about Jamu products, which generally do not state in detail the mixing ratio of the plants used. Each cell x j (i=1,2,,i; j=1,2,,j) is set to 1 if Jamu i uses plant j, and is set to 0 otherwise. The Y-block represents the 9 efficacy groups, thus the dimension of data matrix Y is (I x 9). Each cell y il (l = 1, 2,, 9) is set to 1 if Jamu i is classified into efficacy group l, and is set to 0 otherwise. This model able to predict Jamu efficay at rate of 71.6% overall correct classification [12]. We further extend the PLS-DA model into multiway version of PLS-DA namely N-PLS-DA by adding plant s pharmacological activity as new dimension in X-block [9]. The analysis show that some activities related to one or two efficacies (hence we call them as specific activity) while other activities related to many efficacies (we call them as general activity) (Fig 3). This finding lead us to a hypothesis regarding Jamu formulation that plant used in Jamu formula should have four activities: one activity related to the disease representing the specific activity; the other three activities are analgesic, antimicrobial, and anti-inflammatory representing the general activity. 4.Developing New Jamu Formula We evaluate the previous hypothesis regarding Jamu formulation by developing new Jamu formula for Type 2 Diabetes (T2D) [13]. T2D is characterized by high blood glucose levels due to insufficient insulin secretion, insulin resistance, and impaired insulin action [14]. From this, the specific activity related to T2D disease is hypoglycemic, an activity that lowering the glucose level. Thus, our new formula should consist plants with hypoglycemic, analgesic, antimicrobial, and antiinflammatory. 48

3 Figure 1. KNApSAcK Family Database contains Jamu Database. KNApSAcK Family Database contains Jamu Database Figure 2. JAMU Database. Medicinal plants and formulae can be obtained from the JamuDatabases The steps in developing new Jamu formula is as follows. First, for each of those four activities we identify plants having that activity. Second, from the list in first step we keep only plants that easy to obtain. Third, a new Jamu formula is developed by choosing one plant for each activity from the filtered list and combine them as one formula. The process of combining plants is repeated to make another Jamu formula. In our study, we generate 50 such combinations representing 50 Jamu formula. Fourth, we use the combination as input to our PLS-DA model and predict the efficacy. From this steps we obtain new Jamu formula predicted useful for T2D consists of Bitter Melon or Momordica charantia(plant with analgesic activity), Sembung or Blumeabalsa mifera (has antimicrobial activity), Ginger orzingiber officinale (has anti-inflammatory activity), and Bratawalior Tinosporacrispa (has a hypoglycemic activity). The new Jamu formulas consist of 4 plants plants were tested using zebrafish as an animal model. Zebrafish has similarities of psychologyand genetics with mammalian [15] as well as proving that grown zebra fish is good enough to use as a model of diabetes [16]. Before testing on zebrafish, the selected new Jamu formula made with various specific compositions using mixture experiment. The composition of different Jamu that are formed as many as 20 groups of Jamu and each group was tested on zebrafish as test animals. From the experiment we found that the Zebrafish glucose levels decreased after feeding Jamu. Figure 3. Clustergram of pharmacological activity against Jamu efficacy Figure 4 shows that the new Jamu formula with various compositions plants can reduce blood glucose levels. Compared with the negative control group using Duncan Multiple Range Test, several Jamu formula seffectively lower blood glucose levels are the Jamu formula 1, 4, 8, and 20. This finding showing that the usage of the ingredients model in developing new Jamu formula is promising. Figure 4. Performance of new Jamu formula in reducing the blood glucose levels in Zebrafish. In X-axis, the number represents the new Jamu formula whereas CN is the negative control. The height of the bar provides the blood glucose level of Zebrafish for each condition. The red color indicates that the blood glucose level of the corresponding Jamu formula is statistically different from negative control using Duncan Multiple Range Test. 5.Optimization of ingredients composition Previous experiment is conducted using the mixture experiment which focus on the composition of plants in Jamu formula. We then optimized the plants composition using the concept of response surface methodology [17]. Response surface methodology is a combination of mathematical and statistical techniques used to create a model and analyze a response Y influenced by several independent variables X in order to optimize the response [18].The method begin with development of Y model using the input of X. The model consist of first and second order form to accommodate main effect as well as interaction effect among the input X. CICSJ Bulletin Vol.34 No.2 (2016) 49

4 In this context, the X-block is plants composition in Jamu formula which consists of 4 independent variables, each representing the composition of one plant in Jamu formula (X1, X2, X3 and X4 for Sembung, Bitter Melon, Ginger, and Bratawali, respectively). However, due to the nature of the mixture composition, one of the X variable is linear combination of the other 3 X variables. Therefore, X4 is dropped from themodel development stage. The proportion of X4 in Jamu formula can be obtained from 1 subtracted with sum of the proportion of the other 3 X variables.in addition, the Y variable is the reduction of blood glucose level. The results of previous mixture experiment provide the input for this analysis. The implementation of response surface optimization in our analysis showing that the model consist of second order form indicating the the efficacy of Jamu formula is a result of interaction among plants used in the formula. Furthermore, we found that the optimum point of the Jamu formula consist of X1, X2, X3, and X4 with proportion of , , , and , respectively. 6. Molecular aspect of Jamu formula Based on advances in chemical biology and network science, network pharmacology is a special new approach to drug discovery. This approach involves the application of network analysis to determine the most important set of proteins in the disease, and then chemical biology to identify molecules or compounds which are capable of targeting the protein set. Network pharmacology differs from conventional medicine approach, which is generally based on highly specific targeting of a single protein [19]. Analysis of pharmacological networks has several uses. The first is to predict target protein of plant s active compound blended in herbal medicine formula. It was taken using the concept of DrugCIPHER method. This method is a review of knowledge about the use of pharmaceuticals. More focus [20] defines this method as a framework for network-based computing which includes pharmacological space, genomics space, and relatedness on both. Pharmacological space is a representation of the similarities among compounds which is based on similarities in chemical structure (drugcipher-cs) and the similarity of the therapeuticfunction (drugcipher-ts) whereas the genomics space explain the interaction among proteins in the body (drugcipher-gr). So the link between two spaces will generate close relationship between the compound and the target protein which is calculated based on three linear regression models which assume a linear correlation between the three combinations. The second use is to evaluate any active compounds from medicinal plants associated with a particular disease. This can be performed by evaluating the protein target profile similarities among the plants active compound and synthetic drug. Thus, if the protein target profile of plant compound has close similarities with the protein target profile of synthetic drug compounds, both are involved to a particular disease. We implement this concept to our new formula for T2D using the following steps. First, we make a list of active compound of plants used in our new formula. The list is obtained from KNApSAcK Database as well as Database of Natural Product. We also make a list of synthetic drug for T2D from DrugBank Database. Second, we apply drugcipher to predict protein targeted by the plants active compounds as well as synthetic drug from step 1. Third, we perform simultaneous cluster analysis among drug (consist of plants active compounds and synthetic drug) and their protein target. In Figure 5 we provide the dendrogram from the clustering analysis. The plants active compounds and synthetic drug (column) are clustered into two clusters. The left cluster contains all synthetic drug and some of the active compounds whereas the right cluster contains only active compounds. The active compounds grouped in the left cluster can be viewed as active compound related to curing T2D due to their target protein similarity with the synthetic drug. Focusing on the left clustered, we then create a network connecting plants active compound and synthetic drug. The connection among those elements is established based on their target protein similarity. Figure 5. Simultaneous clustering among plants active compounds and synthetic drug (column) and their protein target The plants active compounds are connected to the synthetic drug if their target protein profile similarity is larger than threshold of 0.5 (Fig 6). The picture shows that the active compound of Bratawali and Ginger are suspected associated to 50

5 synthetic compounds in a protein activity which causes diabetes.this findings consistent with the response surface optimization that proportion of Bratawali and Ginger are the largest among 4 plants. It is assumed that both plants act as main ingredients in the new Jamu formula. predicted using drugcipher (row). Figure 6. Network of synthetic drug for T2D (red) and plants active compounds (yellow). More specifically, the B and J node of plants active compounds stands for Bratawali and Ginger, respectively. The third use of network pharmacology is to measure the synergistic effect between the active compounds by utilizing the character of proteins that are targeted by the active compounds. One analysis used namely Network-based targets Identification of Multicomponent Synergy (NIMS). NIMS is a computational approach toidentify synergistic agent partner at the molecular level. NIMS integrates two aspects based on the networking of target protein topology features and the active ingredient in common phenotype (disease) of the active ingredient of targetprotein. Synergy scores between the two pairs of active ingredients obtained from multiplication TopologyScore (TS) and Agent Score (AS). Implementing this concept to our new Jamu formula we obtain the following results synergy score (Fig 7). The results of the synergy score above is to indicate that the active ingredient (Y axis) from Bratawali has a great opportunity to synergize with the active ingredient (X axis) contained in Ginger. Figure 7. Synergy score among active compounds. 6. Conclusion Jamu informatics which involving statistics, computer science, genomics, and pharmacy offers a new perspective in research about Jamu. We provide implementation of this concept begin by modeling the ingredients of Jamu as predictor of Jamu efficacy. We found that the developed model performs well in predicting Jamu efficacy given the information of its ingredients. Given the model, new Jamu formula targeted for type 2 diabetes is developed and tested to Zebrafish. The experiment show that the new Jamu formula is promosing and showing better performance compared to negative control. The ingredient composition of the new Jamu formula is then optimized using the response surface optimization. The optimum point showing the Bratawali and Ginger has the largest proportion. Finally, we evaluate the working mechanism of the new formula using network pharmacology. We found that active compound of Bratawali and Ginger has highest target protein similarity with the synthetic drug for T2D. The active compounds from these two plants also showing synergy effect based on NIMS method. References [1] A. Masoudi-Nejad,Z. Mousavian, J. H. Bozorgmehr, Drug-target and disease networks: polypharmacology in the post-genomic era, In Silico Pharmacol., 1: 17, 2013 [2] S. Li, Framework and practice of network-based studies for Chinese herbal formula,journal of Chinese IntegrativeMedicine, 5(5), , [3] S. Li, Network systems underlying traditional Chinesemedicine syndrome and herb formula,current Bioinformatics,4(3), , [4] S. Li, Network target: a starting point for traditional Chinese medicine network pharmacology, Zhongguo Zhong Yao Za Zhi,36(15), , [5] S. Li, B. Zhang, N. Zhang, Network target for screeningsynergistic drug combinations with application to traditional Chinese medicine,bmc Systems Biology, 5(Suppl 1):S10, [6] Elfahmia, Herman J. Woerdenbagb, Oliver Kayser, Jamu: Indonesian traditional herbal medicine towards rational phytopharmacological use. JHerbal Medicine, 4(2), 51-73, [7] J. Qiu, Traditional medicine: a culture in the balance,nature,448(7150): , [8] R. Stone, Lifting the veil on traditional Chinese medicine, Science, 319(5864): , 2008 [9] F. M. Afendi, N.Ono, Y. Nakamura, K. Nakamura, L. K. Darusman, N. Kibinge, A. Hirai-Morita, K. Tanaka, H. Horai, Md. Altaf-Ul-Amin, S. Kanaya,Data Mining Methods for Omics and Knowledge of Crude MedicinalPlants toward Big Data Biology, Computational and Structural Biotechnology Journal, 4(5), e , [10] T. Okada, F. M. Afendi, Md. Altaf-Ul-Amin, H. Takahashi, K. Nakamura, S. Kanaya, Metabolomics of medicinalplants: the importance of multivariate analysis CICSJ Bulletin Vol.34 No.2 (2016) 51

6 of analytical chemistry data, Curr. Comput. Aided Drug Des,6, , [11] F. M. Afendi, T. Okada, M. Yamazaki, A. Hirai-Morita, Y. Nakamura,K. Nakamura, S. Ikeda, H. Takahashi, Md. Altaf-Ul-Amin, L. K. Darusman,K. Saito, S. Kanaya, KNApSAcK family databases: integrated metabolite plant species databases for multifaceted plant research,plant and Cell Physiology, 53(2), e1(1-12), [12] F. M. Afendi, L. K. Darusman, A. Hirai-Morita, Md. Altaf-Ul-Amin, H. Takahashi, K. Nakamura, K. Tanaka, S. Kanaya, Efficacy prediction of Jamu formulations by PLS modeling, Curr Comput Aided Drug Design,9(1), 46-59, [13] M. R. Nurishmaya. Pendekatan bioinformatika formula Jamu baru berkhasiat antidiabetes dengan Zebrafish (Danio rerio) sebagai hewan model, Undergraduate thesis, Bogor Agricultural University, [14] B.B. Kahn, Type 2 diabetes: when insulin secretion fails to compensate for insulin resistance, Cell, 92(5), , [15] A. Avdesh, M. Chen, M. T. Martin-Iverson, A. Mondal, D. Ong, S. Rainey-Smith, K. Taddei, M. Lardelli, D. M. Groth, G. Verdile, R. N. Martins, Regular Care and Maintenance of a Zebrafish (Danio rerio) Laboratory: An Introduction, J. Vis. Exp.,69(e4196), 1-8, [16] E. Shin, B.N. Hong, T.H. Kang, An optimal establishment of acute hyperglicemia zebrafish model,african Journal of Phamacy and Phamacology. 6(42), , [17] E. Maryati, Kajian penerapan metode permukaan respon pada formula Jamu antidiabetes dengan Zebrafish (Danio rerio) sebagai hewan model,undergraduate thesis, Bogor Agricultural University, [18] D. C. Montgomery, Design and Analysis of Experiments, 5th Edition, Willey: New York, [19] G. Zhang, Q. Li, Q. Chen, S. Su,Network pharmacology: a new approach for chinese herbal medicine research, Evidence-Based Complementary and Alternative Medicine, 2013, Article ID , 1-9, [20] S. Zhao, S. Li, Network-based relating pharmacological and genomic spaces for drug target identification,plos ONE,5(7), e11764, Farit Mochamad Afendi Farit Mochamad Afendi has an academic background of statistics and currently interests in jamu- informatics for Type 2 Diabetes which is implementation of statistical modeling and computation in Jamu (traditional herbal medicine). Address: Department of Statistics, Bogor Agricultural University, Indonesia, Biopharmaca Research University, Bogor Agricultural University, Indonesia Rudi Heryanto, Latifah Kosim Darusman Biopharmaca Research University, Bogor Agricultural University, Indonesia Nur Hilal A. Syahrir, Rizal Bakri, Nurul Qomariasih Department of Statistics, Bogor Agricultural University, Indonesia 52