Olamufloxacin mesilate

A quantitative structure-activity relationship (QSAR) study on a set of 66 structurally-similar 6-fluoroquinolones was performed using a large pool of theoretical molecular descriptors. Ab initio geometry optimizations were carried out to reproduce the geometrical and electronic structure parameters. The resulting molecular structures were confirmed to be minima via harmonic frequency calculations. Obtained atomic charges, HOMO and LUMO energies, orbital electron densities, dipole moment, energy and many other properties served as quantum-chemical descriptors. A multiple linear regression (MLR) technique was applied to generate a linear model for predicting the biological activity, Minimal Inhibitory Concentration (MIC), treated as negative decade logarithm, (pMIC). The heuristic method was used to optimize the model parameters and select the most significant descriptors. The model was tested internally using the CV LOO procedure on the training set and validated against the external validation set. The result (Q 2 ext = 0.7393), which was obtained on an external, previously excluded validation data set, shows the predictive performances of this model (R 2tr = 0.7416, Q 2 tr = 0.6613) in establishing (Q)SAR of 6-fluoroquinolones. This validated model could be proficiently used to design new 6-fluoroquinolones with possible higher activity.

Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (μg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.