Prediction of Ropinirole Urine Level: An Application of the Adaptive-Network-Based Fuzzy Inference System (ANFIS) in Pharmaceutical Analysis

Abstract

Background: Ropinirole is a non-ergot dopamine agonist indicated for Parkinson disease and restless leg syndrome. The adverse effects associated with its use are nausea (40-69%) and dizziness, etc. Objective: In order to decrease its dose-dependent adverse effects and monitoring its levels for each individual patient, a more sensitive and cost-effective monitoring techniques were in demand. Methods: Microextraction technique using hollow fiber has been introduced for the analysis of agents at trace levels, which was coupled with HPLC-UV in this study. This sample preparation technique was used to determine the trace level of ropinirole in urine samples. The experiments were designed using Minitab and the results were optimized using MATLAB software. The method was simply and easily implemented by applying a pH gradient of 2 (acceptor phase) and 9 (donor phase) and n-octanol as the organic solvent, entrapped in the pores of the hollow fiber. Other factors affecting the preconcentration and microextraction such as stirring rate, temperature, and salt addition were optimized. Results: Under optimum conditions, the following results were obtained: Preconcentration Factor (PF): 122; Limit of Detection (LOD): 0.0010 mg L-1; Limit of Quantitation (LOQ):0.0031 mg L-1; R2:0.994; RSD: 1.15%(interday) and 1.5% intraday; and R: 12.61%. Conclusion: The advantage of using MATLAB was that it provided the optimum range instead of the optimum points for each parameter, enabling us to predict the conditions required for microextraction of similar drugs, needless to do extra experiments.