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ABSTRACT: Danshensu is an active water-soluble component from Salvia Miltiorrhiza, which has been demonstrated holding multiple mechanisms for the regulation of cardiovascular system. However, the relative contribution of danshensu to its multiple cardiovascular activities remains largely unknown.
To develop an artificial neural network (NN) model simultaneously characterizing danshensu pharmacokinetics and multiple cardiovascular activities in acute myocardial infarction (AMI) rats. The relationship between danshensu pharmacokinetics (PK) and pharmacodynamics (PD) were evaluated using contribution values.
Danshensu was intraperitoneally injected at a single dose of 20mg/kg to AMI rats induced by coronary artery ligation. Plasma levels of danshensu, cardiac troponin T (cTnT), total homocysteine (Hcy) and reduced glutathione (GSH) were quantified. A back-propagation NN model was developed to characterize the PK and PD profiles of danshensu, in which the input variables contained time, area under plasma concentration-time curve (AUC) of danshensu and rat weights (covariate). Relative contribution of input variable to the output neurons was evaluated using neuron connection weights according to Garson's algorithm. The kinetics of contribution values was also compared and was validated using bootstrap resampling method.
Danshensu exerted significant cTnT-lowering, Hcy- and GSH-elevating effect, and these marker profiles were well captured by the trained NN model. The calculation of relative contributions revealed that the effect of danshensu on the PD marker could be ranked as cTnT>GSH>Hcy, while the effect of AMI disease on the PD marker could be ranked in the following order: cTnT>Hcy>GSH. The activity of transsulfuration pathway was quite obvious under the AMI state.
NN is a powerful tool linking PK and PD profiles of danshensu with multiple cardioprotective mechanisms, it provides a simple method for identifying and ranking relative contribution to the multiple therapeutic effects of the drug.
Journal of ethnopharmacology 09/2011; 138(1):126-34. · 2.32 Impact Factor