Application of an ELISA-elution assay to dissociate digoxin-antibody complexes in immunoaffinity chromatography

Development of Indirect Competitive Enzyme-Linked Immunosorbent Assay and Lateral-Flow Immunochromatographic Strip for the Detection of Digoxin in Human Blood

Mouse-mouse hybridoma cell lines producing stable, highly specific monoclonal antibodies with good affinity for the cardiac glycoside digoxin (DIG) were established to construct an indirect enzyme-linked immunosorbent assay and lateral-flow immunochromatographic strip to detect DIG in human blood. The hapten DIG was coupled to bovine serum albumin or chicken ovalbumin by sodium periodate oxidation. The highest sensitivity and specificity antibody had a median inhibitory concentration (IC₅₀) of 0.45 ng/mL, a linear range of detection of 0.293-0.7 ng/mL, and low cross-reactivity with several DIG analogues. The cut-off value of the lateral-flow immunochromatographic strip was 5 ng/mL when the strip was tested with human blood. The immunochromatographic lateral flow strip test provides a quick and convenient method for determining DIG in plasma which can be visually observed in only 5 min to promote rational drug use.

ASSESSMENT OF EFFECTIVE BLOOD CONCENTRATION READINGS FROM CLINICAL DATA ON PATIENTS WITH HEART FAILURE DISEASES AFTER DIGOXIN INTAKE: A PROJECTION BASED ON THE INVERSE PROBLEM ALGORITHM

In this study, a projection of effective blood concentration (EBC) readings of digoxin is made using the inverse problem algorithm based on clinical data for patients with heart failure diseases. Seven factors, including body surface area (BSA), blood urine nitrogen (BUN), creatinine, sodium (Na), potassium (K), magnesium (Mg) ion readings, and mean arterial pressure (MAP) were compiled with nonlinear regression fit to develop a projection function having 29 terms obtained from an inverse problem algorithm via the default function run in STATISTICA. Accordingly, data collected from the clinical 168 heart failure patients were normalized to be included in same domain range ([Formula: see text]1 to +1), and then calculated by the specific algorithm to optimize the numerical solution to evaluate EBC readings of digoxin. The evaluated first-order regression fit owned an optimal loss function ([Formula: see text]) coupled with correlation coefficient [Formula: see text] = 0.892 and variance of 89.20%. Furthermore, 45 patients having similar clinical syndromes were also adopted to verify the projection and implied with high agreement. The BUN factor dominated the projection and defined as the most significant coefficient in the analysis, and K ion, MAP, BSA, and Mg ion factors exhibited minor contributions to the projection. The repeated trials to lower number of factors from seven to a smaller number (namely 6, 5, 4, 3, 2, and 1) for simplifying method but resulting with unaccepted outcomes, with high loss function values and low linearity. However, the algorithm held its accuracy to handle the verified data that were out of the original bounds. The proposed algorithm demonstrated a useful analysis to handle the drug administration in pharmaceutical field.