Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach.

The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.

CORAL: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical.

The rate constants (K(OH)) of reactions between 78 organic aromatic pollutants and hydroxyl radical were examined. Simplified molecular input line entry system was used as representation of the molecular structure of the pollutants. Quantitative structure-property relationships was developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the subtraining, calibration, and test sets. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r(2)) for the test sets of the four random splits are 0.75, 0.91, 0.84, and 0.80. Using the Monte Carlo method CORAL software generated the optimal descriptors for one-variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The current data were compared to previous results and discussed.

Aconitum and Delphinium alkaloids of curare-like activity. QSAR analysis and molecular docking of alkaloids into AChBP.

Early studies have shown that some of diterpenoid alkaloids, found in highly toxic plants of the genera Aconitum and Delphinium, act at neuronal nicotinic acetylcholine receptors (nAChRs) and exhibit potent N-cholinolytic activity. In the current study, GA-MLRA and GA-PLS approaches have been used to build QSAR models to predict N-cholinolytic activity measured in vivo (blockade of neuromuscular conductivity, BNMC and third eyelid relaxing activity, TYRA) and in vitro (suppression of frog's abdominal straight muscles on acetylcholine, SAM) for a series of diterpenoid alkaloids. Random splitting of a data set (five trials in total) produced QSAR models of a good level of correlation between experimental in vitro/in vivo and calculated N-cholinolytic activity expressed as log(1/ED(50)) with following average statistical parameters: log BNMC (r(2) = 0.87, s = 0.14, q(2) = 0.82), log TYRA (r(2) = 0.80, s = 0.29, q(2) = 0.67), log SAM (r(2) = 0.84, s = 29, q(2) = 0.64). QSAR results suggest descriptors accounting for H-bond capability of molecules influence all three type of N-cholinolytic activity with additional contribution of steric and reactivity features as identified for TYRA and SAM data, respectively. The alkaloid-receptor complexes were further analyzed by means of AutoDock Vina docking program using the binding site of MLA complexed with AChBP (homolog of the ligand binding domain of nAChRs) as template. All compounds were shown to be well fitted in the binding pocket of native MLA with good correlation exhibited between their ED(50) and AutoDock Vina binding free energy. An analysis of the possible factors significant for the ligand recognition has been enhanced by comparative docking studies performed for structurally related lycoctonine-type alkaloids (lappaconitine and aconitine) that are known to bind to voltage-gated Na(+) channel, but not to nAChRs.

Molecular modelling and QSAR analysis of the estrogenic activity of terpenoids isolated from Ferula plants.

The relationship between chemical structure and estrogenic activity in a series of terpenoid esters with aromatic and aliphatic acid substituents isolated from Ferula plants, was studied. The fragments of the terpenoid structure that are potentially responsible for estrogenic activity were revealed. A quantitative structure-estrogenic activity study has been carried out using the QSAR approach with use of data derived from quantum-chemical calculations as well as data generated from three-dimensional structures of terpenoids. A number of molecular descriptors was obtained from the density functional theory (DFT) at the B3LYP/6-31G(d, p) level of calculation. Comparative analysis of the quantum-chemical computational data was also performed to confirm hypothesis concerning importance of the distance between the oxygen of alcohol hydroxyl group and the functional group in the para-position of the benzene ring (the hydroxyl or methoxy group). Use of the Genetic Algorithm in the QSAR analysis allowed the structural and physicochemical parameters of the terpenoids responsible for estrogenic activity to be determined. A significant QSAR model was obtained with an r(2) value of 0.892. The resulting model showed a reliable dependence of estrogenic activity of the terpenoids on such parameters as molecular shape, number of phenolic groups, surface polarity and the energy of the highest occupied molecular orbital.