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From published literature, the combined properties of Curcumin and Piperine infers that the two molecules are potential preventative agents. The nutraceutical combination of Curcumin and Piperine may aid in breast cancer prevention by enhancing Curcumin’s anti-inflammatory and antioxidant effects. In this paper, we demonstrate the following analysis on these two nutraceutical drug molecules in the effort to replicate these inferences in-silico: Drug Synergy Analysis, Molecular Docking and Simulation of Dose-Response curves. We have predicted the synergy (i.e., the effect of two drug molecules when administered simultaneously) between the two molecules on the basis of the Bliss’ Additivity Score. These scores have been predicted by devising a deep learning model using neural networks to determine that the two molecules of interest are synergetic in nature (i.e., one molecule improves the effectiveness of the other). The data used for this model has been extracted from the DrugComb database and filtered to having drug combinations under the MCF7 cell line. The drug molecules are converted from its original SMILES representation to the ECFP6 molecular fingerprint format prior to training the data with the output feature as the Bliss’ score. In this work we also study the interactions between the molecules of interest and protein targets which are of interest (TRPV1 and STAT3) via Molecular Docking. From this, we have been able to determine the binding affinity of the two ligands of interest by docking simultaneously. We have also identified the coordinates of the binding sites of the respective ligands and other potential binding sites for effective binding. From the binding free energies simulated from molecular docking, we have derived the IC50 values using the Cheng-Prusoff Equation. From these IC50 values we have derived the monotherapy dose-response curves with respect to the activation of each the protein targets and the molecules of interest over a range of molar concentrations (0.01um to 100um) using the 4 – parameter logistic curve equation. Furthermore, we have devised a 3D heatmap indicating the combined effectiveness of Curcumin and Piperine from the 4 – parameter log – logistic curve of the Loewe’s Additivity Model over each of the protein targets in focus.
From published literature, the combined properties of Curcumin and Piperine infers that the two molecules are potential preventative agents. The nutraceutical combination of Curcumin and Piperine may aid in breast cancer prevention by enhancing Curcumin’s anti-inflammatory and antioxidant effects. In this paper, we demonstrate the following analysis on these two nutraceutical drug molecules in the effort to replicate these inferences in-silico: Drug Synergy Analysis, Molecular Docking and Simulation of Dose-Response curves. We have predicted the synergy (i.e., the effect of two drug molecules when administered simultaneously) between the two molecules on the basis of the Bliss’ Additivity Score. These scores have been predicted by devising a deep learning model using neural networks to determine that the two molecules of interest are synergetic in nature (i.e., one molecule improves the effectiveness of the other). The data used for this model has been extracted from the DrugComb database and filtered to having drug combinations under the MCF7 cell line. The drug molecules are converted from its original SMILES representation to the ECFP6 molecular fingerprint format prior to training the data with the output feature as the Bliss’ score. In this work we also study the interactions between the molecules of interest and protein targets which are of interest (TRPV1 and STAT3) via Molecular Docking. From this, we have been able to determine the binding affinity of the two ligands of interest by docking simultaneously. We have also identified the coordinates of the binding sites of the respective ligands and other potential binding sites for effective binding. From the binding free energies simulated from molecular docking, we have derived the IC50 values using the Cheng-Prusoff Equation. From these IC50 values we have derived the monotherapy dose-response curves with respect to the activation of each the protein targets and the molecules of interest over a range of molar concentrations (0.01um to 100um) using the 4 – parameter logistic curve equation. Furthermore, we have devised a 3D heatmap indicating the combined effectiveness of Curcumin and Piperine from the 4 – parameter log – logistic curve of the Loewe’s Additivity Model over each of the protein targets in focus.
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