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Title: Design Synthesis Characterization and In Vitro Anticancer Activity of Thiosemicarbazone Derivatives
Researcher: Ms Niharika Gokhale
Guide(s): Dr. Ravindra Pal Singh
University: Suresh Gyan Vihar University
Completed Date: 2018
Abstract: Virtual screening through docking and redocking of all 250 thiosemicarbazone derivatives exhibited comprehensive structure-based molecular information. Study of results revealed the atoms, groups and subgroups participated in RNR inhibition. Top 20 candidates were selected based on their descending order of re-rank scores and discussed in detail for their molecular interactions with RNR. Novel substituted derivatives of thiosemicarbazone were synthesized and coded under names TSC-1 to TSC-20.Selected 20 compounds substituted Thiosemicarbazons were synthesized by conventional method. The synthesis of designed compounds was carried out by the reaction of amantadine and carbon disulphide, followed by reacting the intermediate with the mixture of carboxaldehyde, thiosemicarbazide and Sulfuric acid. All compounds were in conformity with the structure envisaged. The structures were proved on the basis of UV, IR, H1, C13 NMR and Mass spectroscopy.All the synthesized compounds were evaluated for In-vitro activity by MTT assay. The IC50 values of synthesized compounds were calculated and compared with standard drug doxorubicin. It was found that amongst all the tested compounds Tsc-1, Tsc-2, Tsc-5, Tsc-7, Tsc-11, Tsc-12, Tsc-15, Tsc-16 and Tsc-18 gives better effect on MCF-7 cell lines.QSAR studies have been performed in present investigation with multiple linear regressions (MLR) analysis and Support vector machine (SVM). SVM under Gaussian Kernel Function yielded non-linear QSAR models. Four relevant descriptors calculated are identified as LOGP values. Chosen descriptors are MACCS151 (Molecular ACCess system), X5A average connectivity index chi-5, GATS5e corresponds to Geary autocorrelation lag 5, [MlogpPRX] Moriguchi based lipophilicity descriptor (proximity effect).Internal validations of QSAR models have been achieved using R2CV (LOO), PRESS, SDEP and Y-Scrambling. Dataset was processed in SVM using Linear, Polynomial and Gaussian kernel functions Gaussian kernel function yielded appreciable
Appears in Departments:Department of Pharmacy

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