Akademik Veri Yönetim Sistemi
Journal of Pharmaceutical and Biomedical Analysis, cilt.270, 2026 (SCI-Expanded, Scopus)
The objective of the present work is to develop a voltammetric method for the determination of entacapone (ENT) in the presence of levodopa (LEV) and carbidopa (CAR) in synthetic mixtures and pharmaceutical formulations, using a multivariate approach. ENT is an inhibitor of Catechol-O-methyltransferase (COMT), used in the treatment of Parkinson's disease as an adjunct for the combination therapy with LEV and CAR. With the optimized parameters in a pH 3.0 Britton-Robinson buffer, the quantitative analyses were assessed by the root mean square error in prediction (RMSEP), as well as the root mean square error of calibration (RMSEC) and the relative error of prediction (REP). The Radial Basis Function Artificial Neural Network (RBF-ANN) and Partial Least Squares regression (PLS) were applied for the determination of ENT in the presence of LEV and CAR. In the RBF-ANN model, the REP of 0.87 and RMSEP of 2.14×10−7 are obtained for the test data when quantifying the ENT. The RBF model, as a nonlinear calibration method, was successful for predicting ENT concentration. To evaluate the validity of the recommended method, a commercial pharmaceutical tablet (Stalevo ® 200 mg) was analyzed. RBF model has the ability to overcome difficulties such as non-linearity and overlaps in the voltammograms.