Application of Physicochemical and DFT Based Descriptors for QSAR Study of Camptothecin Derivatives

DFT studies of camptothecins cytotoxicity IV — active and inactive forms of irinotecan

Structures of irinotecan (CPT-11) in neutral lactone, neutral carboxyl, and anionic carboxylate forms in singlet ground states and of their complexes with Cu(II) in doublet ground states are optimized using B3LYP/6-311G* treatment. Metal ion affinities (MIA), Cu charges and Laplacians of Cu-ligand bond critical points of possible CPT active sites are evaluated. The formation of Cu(II) complexes with the anionic carboxylate ligand leads to the release of CO2 that can cause a decrease in the concentration of the active lactone form due to equilibria between all forms of the drug. MIA values and electron density transfer to Cu increase in the sequence lactone < neutral carboxyl < anionic carboxylate. Both neutral forms of irinotecan exhibit lower MIA values than those of camptothecin, unlike the anionic carboxylate form.

DFT studies of camptothecins cytotoxicity III: camptothecin, irinotecan and SN-38

Geometries of camptothecin, irinotecan, SN-38, and of their hypothetical Cu(II) complexes were optimized at the B3LYP/6-311G* level of theory. Their electron structure, evaluated in terms of Mulliken population analysis and Quantum Theory of Atoms-in-Molecule, was subsequently related to in vitro cytotoxicity. Electron density transfer from the relevant active sites to Cu decreases in the sequence irinotecan > SN-38 > camptothecin. The absolute values of their metal-ligand interaction energies exhibit the same trend. Discrepancy with the least relative in vitro cytotoxicity of irinotecan can be explained by differences in its pharmacokinetics.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Aim and Objective:

A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods:

A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

:

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results:

QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion:

The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.