HUBUNGAN KUANTITATIF STRUKTUR-AKTIVITAS (HKSA) TURUNAN DITIOKARBAMAT SEBAGAI KANDIDAT SENYAWA ANTIKANKER HATI

• Main Author: Christian Prawiro, Joshua
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/44326
• Institution: Institut Teknologi Bandung

Liver cancer or hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths worldwide. Liver cancer is commonly caused by hepatitis B (HBV) or C (HCV) virus. Treatment with surgery or organ transplantation still has many constraints. The aim of this study was to determine the best QSAR equation of dithiocarbamate derivatives and candidates of new derived compounds with lower IC50 predictive value and optimal interaction with KEAP1 protein receptors. This study analyzed statistically by using Quantitative Structure-Activity Relationship (QSAR) method on training set that have anticancer activity in liver cancer cells (Bel-7402). The training set of 42 compounds was designed with GaussView 5.0.8 and geometrically optimized with Gaussian 09W. The physicochemical properties of the optimized compounds were calculated through calculation of descriptor with MOE 2014.09. The statistical analysis was performed using SPSS Statistics 22.0 to obtain the best QSAR equation: log IC50 (?M) = ?37,126 (± 8,236) + 0,425 (± 0,062) x AM1_dipole ? 4,736 (± 0,925) x AM1_HOMO ?0,02 (± 0,005) x ASA_H + 0.019 (± 0.009) x vol. The design of the new derivatives was based on the Topliss scheme and the Craig curve to obtained 15 compounds with lower IC50 prediction value than the parent compound (compound 27f or 2-[5-(1H-pyrrol-3-yl)- 1,3,4-oxadiazol-2-yl] ethyl [(pyridin-3-yl) methyl] carbamodithioate). Molecular docking with AutoDock 4.2.6 was performed to see the interaction of new derivatives with KEAP1 protein receptors. Docking results were analyzed with Discovery Studio Visualizer 3.5. Biphenyl and phenanthrene derivatives had lower IC50 prediction value than compound 27f and can interact optimally with binding site of KEAP1 protein receptors to be candidates for liver anticancer compounds.