Kajian Hubungan Kuantitatif Struktur Aktivitas (QSAR) Potensi Inhibitor Enzim -Amilase dan -Glukosidase dari Minyak Kayu Putih (Eucalyptus camaldulensis Dehnh) - Tiara Ajeng Listyani; Kusumaningtyas Siwi Artini (Sehat Rakyat: Jurnal Kesehatan Masyarakat, 2025)
Diabetes Mellitus is a major endocrine disease and a global health problem characterized by increased blood glucose levels. Active compounds found in the essential oil of Eucalyptus camaldulensis Dehnh. have been reported to potentially act as in vitro inhibitors of the α-amylase and α-glucosidase enzymes, making them attractive candidates for antidiabetic agents. This study aimed to identify and predict the potential of derivative compounds through a computational approach using the Quantitative Structure-Activity Relationship method. The research methodology included structural modeling, geometry optimization, calculation of molecular descriptors, statistical analysis using multilinear regression with a backward approach in SPSS software, model validation, and new compound design. The best validated QSAR model equation was obtained with satisfactory statistical criteria (n=29; r=0.89; r²=0.79; SE=0.25; F-calculated/F-table=15.20). The selected descriptors with the most significant influence included LUMO energy, dipole moment, and estimated surface area, yielding the final equation: Log K = - 709,312 + 122,620·qC1 + 42,819·qC4 + 51,232·qC5 + 35,743·qC6  – 722,345·qO1 - 211,214·qO2. Based on this model, 3-(4-(ethylamino)butyl)-5-hydroxy-1H-pyrazole-4-carbonitrile, was designed and demonstrated significant potential as an inhibitor. This compound exhibited a predicted Log IC50 value of 2.75 (equivalent to an IC50 of 0.02 µM), which is substantially more potent than the IC50 value of the reference compound (30.58 µM). These results indicate that the novel compound has great potential as a candidate for an oral α-amylase and α-glucosidase inhibitor preparation for the management of DM. Further in vitro and in vivo validation studies are necessary to confirm these computationally predicted biological activities