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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Published

31-08-2022

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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