The Pharmacodynamics And Pharmacokinetics Research Of Antibacterial Agents Of Quinolones

Quinolones is a kind of antibacterial synthesized artificially. There are thousands of compounds of quinolones have been synthesized since 1962, and now there are dozens drugs of quinolones appear on maket. Antibacterial quinolones had a wide clinical application because of its high activity, good pharmacokinetics quality and low toxicity. But quinolones can redounds many toxicity and side effect, so it is necessary to devise and search better drugs of quinolones, which have higher activity, better PK quality and lower toxicity. Study the relationship of pharmacodynamics and pharmacokinetics quality of quinolones, action mechanisms and the molecule structure plays crucial roles in the design of more potent drugs. In this paper, a correlative analysis is given between the pharmacodynamics/pharmacokinetics parameters and the molecule structure parameters by use of quantum chemistry and neural network. 110 compounds are selected and their minimum inhibition concentrations to Staphylococcus aureus and Escherichia coli have been determined. The 18 molecule structure parameters have been calculated: total energy, binding energy, isolated energy, electronic energy, core-core energy, heat of formulation, point dipole moment, dipole moment, 7-net charge, surface area, volume of molecule, hydration energy, parameter of distant water, refractive index of molecule, polarizational rate, mole weight of molecule, EHOMO and ELUMO. We firstly analyzed the relativity of the 18 parameters, and distribute the relativity coefficient larger than 0.75 to a same group. There are 9 groups at all. Then, we use the method of try, choose the 6 parameters for the input of net to build networks separately. The 6 parameters is heat of formulation, dipole moment, 7-net charge, surface area, hydration energy and EHOMO. The network's structural parameters are described as: the maximal cycles are 5000, the nodes of the first-hidden layer are 20 and 18, the ones of second-hidden layer are 32 and 28, target error is 0.02, original learning rate is 0.01 and its increasing ratio is 1.05 and its decreasing ratio is 0.8, the momentum factor is 0.9. By this network, 100 compounds are selected stochastically as the training gather and the residual 10 ones as the prediction gather for validation our network. The correct ratio of being learned and predicted of the minimum inhibition concentrations to Staphylococcus aureus is 64% and 70%; the correct ratio of being learned and predicted of the minimum inhibition concentrations to Escherichia coli is 74% and 60%. On the other hand, 21 compounds are selected to study the relationship between the molecule structure parameters of quinolones and PK parameters: Cmax, AUC, T1/2. Equally, we calculate the 18 molecule structure parameters of the 21 compounds and choose 6 parameters for input of the net: dipole moment, 7-net charge, volume of molecule, hydration energy, parameter of distant water and EHOMO. The other parameters of network is: the maximal cycles are 5000, the nodes of the first-hidden layer are 16, the ones of second-hidden layer are 26, target error is 0.01, original learning rate is 0.01 and its increasing ratio is 1.05 and its decreasing ratio is 0.8, the momentum factor is 0.9. 20 compounds are selected stochastically as the training gather and tested by leave-one-out method. The 3 pharmacokinetics parameters of the leave one have been predicted. The result shows, The correct ratio of being learned of the 3 PK parameters is 85%, 75% and 95%; The correct ratio of being predicted by leave-one-out of the3 pharmacokinetics parameters is 70%, 60% and 80%; and the relatively error of being predicted of the 3 pharmacokinetics parameters is 18.44%, 14.46% and –8.25%. All the results show the model between the pharmacodynamics/pharmacokinetics parameters and the structure parameters of quinolones molecules is reasonable, and we can use the networks to predicte the pharmacodynamics/pharmacokinetics parameters of antibacterial quinolones. Based on the results, we have analyzed the relationship of 6 stru...