Modeling and optimization of malondialdehyde (MDA) absorbance behavior through response surface methodology (RSM) and artificial intelligence network (AIN): An endeavor to estimate lipid peroxidation by determination of MDA

Abstract

Abstract Oxidative stress (OS) associated with reactive oxygen species (ROS) attacks many biomolecules, leading to cell death, lipid peroxidation (LP), and physiological mechanism damage in the human body because of its reaction with nucleotides, membrane lipids, and proteins. Malondialdehyde (MDA) is one of the end products of polyunsaturated fatty acids peroxidation. The elevated level of MDA is widely known as a biomarker for the indirect measurement of OS during LP. Although several derivatizations' approaches and successful commercial kits are available, their analytical validity and reliability in clinical studies require further attention to obtain robust quantitation and high‐quality data. Also, re‐evaluation and development of new analytical methods with high sensitivity and selectivity in response to free MDA and total MDA, DNA and protein adducts are not well‐characterized. Here, response surface methodology (RSM) and artificial neural network (ANN) were proposed to model, predict, and optimize MDA detection. This paper introduced a novel, simple, and fast modeling method using a new reagent (para methoxy aniline or PMA) for the first time to convert MDA to an adduct which could be detectable by UV‐Vis spectroscopy. Based on the interactions between MDA and PMA at times and temperatures, RSM and artificial intelligence network (AIN) were then applied to forecast the absorbance rate of MDA‐PMA. The optimum mode for the highest absorbance rate of MDA‐PMA is at the temperature of 74.99°C with a PMA concentration of 499.87 μM and MDA concentration of 49.98 μM. The best structure with five hidden layers, including 5,5,5,9,4 neurons with the transfer function logsig for each layer, was selected for the MLP‐ANN model. The maximum error rate of the MLP‐ANN model was 2.08%.Our new approach is an improvement over existing assay kits, overcoming limitations such as time, temperature, and reliability with a bright future to target increased MDA level in human samples.