Development and optimization of a flexible enzyme-based platform for the colorimetric quantification of metabolic disease-related salivary biomarkers

Abstract

Early detection aims at timely treatment to improve patient's fate. To fulfill this, the identification and validation of appropriate biomarkers, as well as the development of rapid, simple, sensitive and low-cost methodologies are crucial. These factors would enable the identification of biomarkers in rural and remote locations or where sophisticated equipment and highly trained personnel are not available. Among the current techniques for this purpose, enzyme-based colorimetric platforms stand out as a great alternative due to their properties including low cost, simplicity, flexibility, specificity, and adjustable sensitivity. These platforms are the core of diagnostic kits and point-of-care devices that fully comply with the aforementioned characteristics. Saliva is an emerging biofluid that contains multiple useful molecules for the non-invasive diagnosis of diseases. Its collection is carried out through a simple and painless process that either does not require qualified personnel or even by self-sampling. Therefore, saliva stands out as an alternative to blood due to its great potential for diagnostic purposes. The first stage of this work aims to develop and optimize a multi-enzymatic platform for the colorimetric quantification of salivary glucose. The methodology tests two different dyes to compare the results and demonstrate the versatility of the system for glucose quantification in both buffer conditions and human saliva samples in which concentrations are up to 100 times lower than those found in blood. The second stage of this study focused on demonstrating the flexibility of the multi-enzyme platform. This was performed by modifying simple parameters such as pH buffer, incubation time, as well as the concentration of the enzymes and 3,3’,5,5’-tetramethylbenzidine (TMB). These modifications enabled the adaptation and optimization of this platform for the detection and quantification of clinically relevant biomolecules, such as galactose, uric acid and 1,5-anhydroglucitol in buffer conditions. Overall, results suggest that this platform is useful for measuring the proposed biomarkers at concentrations found in different biofluids, both conventional (blood and urine) and unconventional (saliva, sweat and tears). Thus, this work is the basis of a platform that could be further adapted for the quantification of other clinically relevant biomolecules. However, more studies are required to demonstrate its correct operation in terms of accuracy, precision, and specificity with the biofluids.