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La fasciolosis es una trematodosis de gran relevancia en medicina humana y veterinaria. Los resultados de la presente tesis ponen de manifiesto la importancia de la selección adecuada de adyuvantes para obtener respuestas de anticuerpos frente a epitopos ocultos y sientan las bases para un diseño racional de antígenos quiméricos destinados a la vacunación de distintas especies. De manera complementaria, se evalúa una nueva metodología para mejorar la especificidad los métodos ELISA que emplean antígenos recombinantes expresados en E. coli.

ELISA-based methods of detecting Fasciola cathepsins in feces are powerful techniques for diagnosing infections by F. hepatica and F. gigantica. In the last decade, the in-house MM3-COPRO ELISA and its commercial version BIO K 201 (BIO X Diagnostics, Belgium) have been recognized as useful tools for detecting early infections by such trematodes and for monitoring the efficacy of anthelmintic treatments in human and animal species, as they provide some advantages over classic fecal egg counts. However, the sensitivity of MM3- COPRO ELISA can sometimes be compromised by the high variability in the concentration of cathepsins in fecal samples throughout the biological cycle of Fasciola (mainly in cattle) and by differences in the between-batch performance of peroxidase-labeled anti-mouse IgG polyclonal antibodies. To prevent such problems, we investigated whether the incorporation of a commercial streptavidin-polymerized horseradish peroxidase conjugate, in order to reveal bound biotinylated monoclonal antibody MM3, can improve the sensitivity of the MM3-COPRO ELISA. We observed that inclusion of this reagent shifted the previous detection limit of the assay from 0.6 ng/mL to 150 pg/mL and that the modified test is able to identify infection in cows harboring only one fluke. Moreover, we demonstrated that maximal OD values can be achieved with short incubations (30 min each step) at RT with shaking, rather than standard incubations, which significantly accelerates the diagnostic procedure. Finally, we did not find a significant correlation between coproantigen concentration and parasite burden in cattle, which may be due to the low parasite burden (1–10 adult flukes) of the animals used in the present study. As the usefulness of the classic MM3-COPRO test for detecting animal and human infections has already been demonstrated, it is expected that the improvements reported in this study will add new insights into the diagnosis and control of fasciolosis, We have previously reported how the combined use of mAb MM3 with polyclonal antibodies obtained from rabbit immunized with Fasciola hepatica excretory-antigens led to the development of the in-house MM3-COPRO ELISA and its commercial version BIO K 201 (BIO X Diagnostics, Belgium), which are widely used to detect human and animal infections caused by F. hepatica. After more than a decade in use, both tests have proven to be useful tools for specifically detecting Fasciola infections, although it has also been found that: i) the conditions of use of the commercial test in some field studies did not enable the sensitivity obtained with the in-house test to be reached, and ii) the batches of the secondary reagent (peroxidase-labeled anti-mouse antibodies) currently available for use in the in-house test do not perform the same as previous batches. To solve these problems, we provide data showing that the incorporation of an enhancement system consisting of streptavidin-polymerized horseradish peroxidase conjugate greatly improved the sensitivity of the MM3-COPRO ELISA and enabled reduction of the incubation time. These modifications enabled the detectability of the assay to be 150 pg/mL, thus enabling detection of infection in animals harboring only one fluke

[Abstract] Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc.) to study the relationship between the socioeconomic status and the disease. In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used. In this work, the first artificial neural network (ANN) model is proposed for the prediction of HAART cocktails, to halt AIDS on epidemic networks of U.S. counties using information indices that codify both biomolecular and several socioeconomic factors. The data was obtained from at least three major sources. The first dataset included assays of anti-HIV chemical compounds released to ChEMBL. The second dataset is the AIDSVu database of Emory University. AIDSVu compiled AIDS prevalence for >2300 U.S. counties. The third data set included socioeconomic data from the U.S. Census Bureau. Three scales or levels were employed to group the counties according to the location or population structure codes: state, rural urban continuum code (RUCC) and urban influence code (UIC). An analysis of >130,000 pairs (network links) was performed, corresponding to AIDS prevalence in 2310 counties in U.S. vs. drug cocktails made up of combinations of ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found with the o

[Abstract] This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.