Inversely Calibrated Curvilinear Artificial Neural Network Model for Simultaneous Assay of Ternary Cardiovascular Drug Mixture

Novel chemometric design, tailored for pre-clinical multiple drug screening, goals for bioanalytical future scope. A highly sensitive, non-linear multivariate Artificial Neural Network (ANN) is developed and applied for simultaneous spectrophotometric determination of three commonly concomitant cardiovascular drugs in laboratory made mixtures and spiked human plasma samples. Ticagrelor, Irbesartan and Hydrochlorothiazide have been simultaneously quantified in the curvilinear ranges of 0-30 µg/mL, 0-10 µg/mL and 0-3 µg/mL respectively. Highly overlapping Near UV absorption spectra of three drugs, in the region of 215-280 nm, have been recorded 1-nm range in synthetic ternary mixtures and trained iteratively. By inversely relating the concentration matrix (x-block) with its corresponding absorption one (y-block), gradient-descent back-propagation ANN calibration could be computed and optimized. All proposed mathematical modeling was manipulated using MATLAB® 2007, reaching down to sixth order exponential Mean Square Error, MSE. To validate, independent set of ternary synthetic mixtures has been constructed and examined, where excellent recovery results has been obtained. Furthermore, application of suggested model to varying ratios synthetic ternary mixtures as well as spiked plasma samples has resulted in accurate, precise and robust estimations with no background interference. ANN method was compared to a reference HPLC method; Student’s t-test and F-variance ratio were calculated and showed insignificant difference. This chemometric approach is an eco-friendly green assay, time-saving, and economic method. It initiates a pathway for clinical drug screening through affordable spectroscopic instrumentation.