Development of Novel Metal-Thiosemicarbazone Complexes Targeting HT-116 Cell Line for Anti-Colon Cancer Using in Silico Methods
Abstract
In silico approaches were employed to develop a series of novel metal-thiosemicarbazone complexes to inhibit colon cancer (HT-116). The compounds were identified using quantitative structure-property relationship (QSPR) models based on stability constants (logbeta ${ }_{11}$). Molecular descriptors, including 0-3D molecular and quantum parameters, were generated using the PM7 semi-empirical quantum mechanics method. Two techniques, multivariate linear regression (MLR) and artificial neural network (ANN) were used to build the quantitative structure and property relationship (QSPR). The developed QSPR $_{\text {Mlr }}$ model involves $M W, 3 N$, Hmax, Total Energy, and SssCH2 descriptors. The statistical values of the model were pointed out: $R^{2}{ }_{\text {train }}=0.888, R^{2}$ adj $=0.875, Q^{2}$ Loo $= 0.850$, and $\text{SE}=1.126$. Two QSPR ${ }_{\text {ann }}$ models were constructed with architectures $\mathrm{I}(5)-\text{HL}(9)-\mathrm{O}(1)$ and $\mathrm{I}(5)-\text{HL}(4)-\mathrm{O}(1)$, both showing impressive regression parameters. These models were rigorously evaluated using an independent dataset to ensure high predictive capability. A total of 15 new metal-thiosemicarbazone complexes were designed based on the models. The stability constants of these complexes were calculated using Applicability Domain (AD) and Outlier techniques. Among the 15 new complexes, 9 passed drug-likeness analyses according to Lipinski and Ghose rules. Finally, docking simulations…
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Evidence Hub reads this as a simulation / modelling, done in cell cultures (in vitro) and computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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