Theoretical Study of Octreotide Derivatives as Anti-Cancer Drugs using QSAR, Monte Carlo Method and formation of Complexes

QSAR investigations of a series of 25 Octreotide drugs were performed using multiple linear regression (MLR) and artificial neural network (ANN) as modelling tools and simulated annealing (SA) and genetic algorithm (GA) as optimization methods. The obtained results were compared with each other and with the results of MLR-MLR approach in terms of correlation coefficient (R-2) and root mean of square error (RMSE), from which the ANN-GA was found to be the best scheme. It was revealed that the activity of derivatives of Octreotide drugs depends majorly on HATS7v, SIC5, Mor24u, RDF010m, EEig06r, E1u, EEig02x descriptors in gas phase. The best Octreotide derivative (according to -log IC50) was exposed to reaction with Cu, Zn, Fe using B3lyp/lanl2dz to investigate the stability of the formed complexes, from which the copper complex was perceived to be the most stable one. In addition, CORAL software was employed to study the derivatives using the Monte Carlo method and correlation coefficient, cross-validated correlation coefficient and standard error of the model were checked out. It was concluded that simultaneous utilization of QSAR and Monte Carlo method can lead to a more comprehensive understanding of the relation between physicochemical, structural and theoretical molecular descriptors of drugs to their biological activities.