Mapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network
ABSTRACT A neural network (NN) pharmacodynamic model was developed that correlates the inhibition of ex vivo platelet aggregation by orbofiban, an oral glycoprotein IIb/IIIa antagonist, with the administered dose and patient characteristics. Data were obtained from a Phase-II dose-finding study of orbofiban in patients presenting with acute coronary syndromes. A back-propagation NN was designed to predict drug effect measured at predose and 4 and 6 h on treatment days 1, 28, and 84 (nine responses/patient). The training set consisted of patients for whom complete response profiles were reported (n = 67), and remaining patients were included in the validation data set (n = 47). The concentration-effect relationship was described additionally using a population direct-effect inhibitory sigmoidal model, and a comparison of the predictive performances of both models was performed. The final NN reasonably described orbofiban pharmacodynamics from sparse data sets without specifying a structural model or drug concentrations. Despite considerable inter-patient variability in response-time profiles, the population model revealed a strong correlation between drug concentration and effect and exhibited greater precision than the NN model. Although the population model showed greater precision, these results suggest that NNs may be useful for individualizing pharmacotherapy when drug concentrations are relatively unpredictable or unavailable.
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ABSTRACT: The wire bonding process is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to improve the wire bonding process capability. In this study, an integrated system is proposed to identify and control parameters in the wire bonding process in order to achieve high level performance and quality. First, an experimental design with Taguchi method is applied to identify the critical parameters in the wire bonding process. Then, an ANN is used to establish the nonlinear multivariate relationships between wire boning parameters and responses. Finally, a GA is adopted to find the most desired parameter settings by using the output of ANN as the fitness measure. Another popular method, response surface method, for parameter design problems is conducted for comparison purpose. Results of this comparison demonstrate the effectiveness of the proposed approach.International Journal of Advanced Manufacturing Technology 08/2006; 30(3):247-253. DOI:10.1007/s00170-005-0083-0 · 1.78 Impact Factor
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ABSTRACT: The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.Current Medicinal Chemistry 07/2012; 19(25):4289-97. DOI:10.2174/092986712802884259 · 3.72 Impact Factor
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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. DOI:10.1016/j.jep.2011.08.069 · 2.32 Impact Factor