Identification Pharmacodynamic Interactions of Active Compounds of Diabetes Mellitus Type 2 Herbal Plants Using the Random Forest Method

Identifikasi Interaksi Farmakodinamik Senyawa Aktif Tanaman Jamu Diabetes Melitus Tipe 2 Menggunakan Metode Random Forest

Authors

  • M. Aiman Askari Department of Statistics, IPB University, Bogor, Indonesia
  • Farit M. Afendi Department of Statistics, IPB University, Bogor, Indonesia
  • Anwar Fitrianto Department of Statistics, IPB University, Bogor, Indonesia
  • Sony Hartono Wijaya Department of Computer Science, IPB University, Bogor, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i2p245-260

Keywords:

chemical similarities, pharmodinamic interaction, random forest, side effect similarity, target protein connectedness

Abstract

Drug-drug interactions is defined as the modification of the effect of a drug as a result of another drug given simultaneously or with an interval or when two or more drugs interact so that the effectiveness or toxicity of one or more drugs changes. Pharmacodynamic interactions are one type of interaction that needs special attention because these interactions work directly on the body's physiological systems and compete on the same receptors so that they can be antagonistic, additive, or synergistic. The use of medicinal plants is becoming an alternative because in addition to their relatively safer side effects, medicinal plants consisting of active compounds are appropriate in treating degenerative metabolic diseases triggered by mutations in many genes. As in the case of polypharmacies, interactions of active compounds in medicinal plants can also lead to phapharmodynamic interactions. Therefore, it is also necessary to identify the active compounds so that it can then be known whether the interaction of the compounds will be beneficial or detrimental. In this study, pharmacodynamic identification was applied to Diabetes Mellitus Type 2 medicinal plant compounds by using the independent variables Target Protein Connectedness (TPC), Side Effect Similarity (SES), and Chemical Similarities (CS) using Random Forest classification method. From a search of various databases, 21 active compounds were obtained and then only 100 compound interactions could be calculated as independent variables. With an accuracy value and AUC of 0,96, there were 93 pairs of compounds that interacted pharmacodynamically and the remaining 7 did not interact.

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References

Afendi, F. M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K., … others. (2012). KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. Plant and Cell Physiology, 53(2): e1–e1.

Altaf-Ul-Amin, M., Tsuji, H., Kurokawa, K., Ashahi, H., Shinbo, Y., & Kanaya, S. (2007). A density-periphery based graph clustering software developed for detection of protein complexes in interaction networks. 2007 International Conference on Information and Communication Technology, 37–42. IEEE.

Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008). Drug target identification using side-effect similarity. Science, 321(5886): 263–266.

Chapman, J. (2008). Chapman and Hall dictionary of natural products. CRC Press, Hampden Data Services Ltd.

Dong, J., Cao, D.-S., Miao, H.-Y., Liu, S., Deng, B.-C., Yun, Y.-H., … Chen, A. F. (2015). ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics, 7(1): 1–10.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8): 861–874.

Hasnita, H., Afendi, F. M., & Fitrianto, A. (2020). Perbandingan beberapa metode klasifikasi dalam memprediksi interaksi farmakodinamik. Indonesian Journal of Statistics and Its Applications, 4(1): 11–21.

Huang, J., Niu, C., Green, C. D., Yang, L., Mei, H., & Han, J.-D. J. (2013). Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Computational Biology, 9(3): e1002998.

Katno, P. S., Prapti, I., Rahmawati, N., & Mujahid, R. (2008). Tingkat Manfaat, Keamanan dan Efektifitas Tanaman Obat dan Obat Tradisional. Balai Penelit Tanam Obat Tawangmangu.

Merle, L., Laroche, M.-L., Dantoine, T., & Charmes, J.-P. (2005). Predicting and preventing adverse drug reactions in the very old. Drugs & Aging, 22(5): 375–392.

Mulia, I. (2017). Model Prediksi Interaksi Senyawa Protein Menggunakan Fungsi Kemiripan dan Fingerprint Klekota-Roth (PhD Thesis). Bogor Agricultural University (IPB).

Nurishmaya, M. R. (2014). Pendekatan Bioinformatika Formulasi Jamu Baru Berkhasiat Antidiabetes dengan Ikan Zebra (Danio rerio) sebagai Hewan Model.

Syahrir, N. H. A., Afendi, F. M., & Susetyo, B. (2016). Efek sinergis bahan aktif tanaman obat berbasiskan jejaring dengan protein target. Jurnal Jamu Indonesia, 1(1): 35–46.

Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., & Bryant, S. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37(suppl_2): W623–W633.

Winata, H. M., Afendi, F. M., & Fitrianto, A. (2019). Peningkatan akurasi klasifikasi interaksi farmakodinamik obat berbasis seleksi pasangan obat takberinteraksi. Indonesian Journal of Statistics and Its Applications, 3(3): 247–259.

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Published

2022-08-31

How to Cite

Askari, M. A., Afendi, F. M., Fitrianto, A., & Wijaya, S. H. (2022). Identification Pharmacodynamic Interactions of Active Compounds of Diabetes Mellitus Type 2 Herbal Plants Using the Random Forest Method: Identifikasi Interaksi Farmakodinamik Senyawa Aktif Tanaman Jamu Diabetes Melitus Tipe 2 Menggunakan Metode Random Forest. Indonesian Journal of Statistics and Its Applications, 6(2), 245–260. https://doi.org/10.29244/ijsa.v6i2p245-260

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