PURPOSE: The application of artificial @entity1274 in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial @entity1274 (CPANNs), combined with genetic algorithm (GA) for prediction of @entity1771 ( @entity22717 ) serum levels based on identified factors important for its prediction. METHODS: The study was performed on 118 @entity22717 measurements obtained from 78 adult @entity302 @entity1 . @entity1 were on stable @entity22717 dosing regimen for at least 7 days; therefore, steady-state was assumed. @entity22717 serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the @entity1 on @entity22717 levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. RESULTS: Data for training included 88 measured @entity22717 concentrations, while remaining were used for validation. Among all factors tested, @entity22717 dose, renal function ( @entity470 ) and @entity2049 dose significantly influenced @entity22717 level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were -6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. CONCLUSIONS: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict @entity22717 concentrations and to adjust relative importance of identified variability factors in population of adult @entity302 @entity1 .  This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Application of Counter-propagation Artificial Neural Networks in Prediction of XXXX Concentration in @entity1 with @entity302 .

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients'] @entity470 :: ('1956', 'Gene') :: ['eGFR'] @entity22717 :: ('CHEBI:63631', 'Chemical') :: ['TPM'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural networks'] @entity1771 :: ('MESH:C052342', 'Chemical') :: ['topiramate'] @entity302 :: ('MESH:D004827', 'Disease') :: ['epileptic'] @entity2049 :: ('MESH:D002220', 'Chemical') :: ['carbamazepine']
@entity1771 :: (MESH:C052342,Chemical) :: ['Topiramate']
