Desenvolvimento e otimização de estratégias analíticas visando o diagnóstico de COVID-19 em amostras de urina

Abstract

COVID-19 (Coronavirus Disease) is now a part of a group of diseases that represents an important threat to public health, mainly in tropical countries and with a large percentage of low-income populations, as is the case from Brazil. COVID-19 was the cause of the recent global pandemic that maintained a state of emergency for approximately 3 years. The disease is a viral infection caused by the new SARS-CoV-2 that shares symptomatic manifestations with other diseases, such as the ones caused by Arboviroses and Influenza for example, despite requiring different treatments. In this context, assertive laboratory diagnosis tests that are accessible to the affected population is necessary. Therefore, the investigation of new analytical strategies applicable to the diagnosis of COVID-19 is the proposal of this work. Optimizations involving collection, preparation and storage of biological samples were initially studied, followed by the development of a series of analytical methods using a multiplatform instrumental approach in association with data science for the investigative study of metabolites present in urine of patients affected by COVID-19 infection in comparison with healthy individuals, generating diverse and comprehensive data sets. Segmented methods for analyzing organic acids, amino acids and creatinine by capillary electrophoresis were optimized and validated., as well as comprehensive approaches using liquid chromatography coupled to highresolution mass spectrometry and infrared spectroscopy using computational tools and experimental design aimed at searching for biomarkers. Qualitative results obtained by capillary electrophoresis with ultraviolet detection applied to numerous groups of urine samples with an accuracy of 1.22% and by infrared spectroscopy were applied to build machine learning prediction models for COVID-19 with an accuracy of 77.9% and 81.0%, respectively, useful as analytical screening routines. Quantitative analysis of creatinine in urine by capillary electrophoresis with UV detection using internal standard calibration with automated injection protocol were validated, obtaining analytical precision of 2.43%. The use of capillary electrophoresis associated with mass spectrometry was also evaluated. The applicability of the methods to different samples and other analytical fronts are also discussed throughout the text.