Seleção e caracterização de biomarcadores por phage display e desenvolvimento de plataformas imuzoenzimática e biofotônica para detecção da doença hepatite delta

Abstract

Hepatitis Delta virus (HDV) is a peculiar virus, commonly classified as a satellite virus agent of hepatitis B virus (HBV) due to its dependency to complete the replication cycle. In Brazil, between 2000 and 2021, a total of 4,259 cases of Delta hepatitis were reported, with the majority (73.7%) of cases reported from the North region. HDV infection is the most serious form of viral hepatitis, presenting a higher risk of developing cirrhosis and rapid progression to hepatocellular carcinoma compared to HBV mono-infection. Therefore, HDV diagnostic testing is recommended for all patients with chronic HBV infection. This thesis is composed of two chapters relating to the development of platforms for the diagnosis of HDV. In the first chapter, the application of Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (ATR-FTIR) supported by artificial intelligence algorithms as a tool for screening hepatitis Delta is presented. In a cohort of 359 patients, this biophotonic platform using 10 µL of serum allowed rapid, portable, sustainable discrimination, without the use of reagents, of patients with positive serology for hepatitis Delta in comparison to control subjects negative for hepatitis B and hepatitis C with negative serology for antibodies against HBV (anti-HBs), control subjects negative for hepatitis B and hepatitis C with positive anti-HBs serology, and patients with hepatitis B presenting, respectively, with an accuracy of 80%, 82%, and 74% supported by Support algorithms of Machine Vectors (SVM). The second chapter describes the use of Phage Display technology for the selection of HDV-mimetic molecules. The product of this selection allowed the construction of a recombinant protein called HDAgProt, which when applied on an immunoenzymatic platform to detect anti-HDV in comparison to patients with hepatitis B presented a sensitivity of 74.71%, specificity of 97.85%; an area under the curve (AUC) of 0.8906. The specific analysis of patients with active HDV infection based on RT-qPCR testing showed an increase in the detection rate with a sensitivity of 88.00%, specificity of 98.92%, and AUC of 0.9411 compared to patients with hepatitis B. The set of data from this doctoral thesis indicates that the sustainable biophotonic platform without the use of reagents supported by artificial intelligence algorithms and the selection of molecules using Phage display technology inserted in immunoenzymatic tests have great potential for detecting hepatitis delta (HDV).