Carrera de Biotecnología

Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/34800

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    Evaluación de posibles dianas terapéuticas para el cáncer de colon aplicando Pipeline Bioinformático como base para el diseño de fármacos asistido por computador
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2023-09) Jácome Campos, Mario Fabricio; Galarza Galarza, Cristian Fernando
    Colon cancer is a common type of malignant tumor in the gastrointestinal tract, accounting for about 13 percent of all tumors. Its development is influenced by epigenetic changes, genetic and environmental factors. However, current methods for diagnosing and treating this disease are often expensive, making access to treatment difficult for many patients. To identify the genes that are expressed or inhibited in colon cancer, differential expression analysis was performed using R. These genes were contrasted with databases such as: OMIM, Malacards, Harmonizome, and KEGG. The most relevant genes are grouped according to their biochemical alteration, with this information biological interaction networks are created through Cytoscape to identify proteins that are altered within the disease process. For the identification of the targets, the DrugBank database was used, through DoGSiteScorer the drugable sites were identified, virtual screening is performed through MTiopenScreen to find possible ligands that present high binding affinity. The interaction networks obtained from STITCH allow the identification of the drug that is linked to the metabolic pathways to analyze the protein-ligand interactions. The drug etoposide was identified, and its pharmacokinetic and toxicity properties are assessed by bioinformatics predictions using QSAR VEGA and pkCSM models. The toxicity and hepatotoxicity were found to be negative, suggesting that this could be a viable candidate for the development of drugs targeting the SMAD2 and CASP9 genes.
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    Identificación de posibles dianas terapéuticas en Alzheimer utilizando pipeline bioinformático combinado para aplicarlas al diseño de fármacos asistidos por computadora
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2023-09) Barriga Sanchez, Luis Leonardo; Galarza Galarza, Cristian Fernando
    Bioinformatics has a potential biotechnological use because it allows the study of genetically complex diseases using different tools. The aim of this project was to analyze transcriptomic data related to Alzheimer's disease (AD) for the identification of potential therapeutic targets and computer-aided drug design. This was done through the application of a multilevel analysis of biological interaction networks, molecular docking, and evaluation of the pharmacokinetic properties of selected drugs. The analyses indicated a total of 507 and 478 genes with high and low expression, respectively, present in brains with different stages of AD. The results of the protein-protein interaction and miRNA-miRNA network analysis identified only one biomarker, hsa-mir-34a-5p, for the diagnosis of AD and asymptomatic AD in cancer patients. Survival analysis revealed that CDK6 and CD44 are potential therapeutic targets for AD. Additionally, the established pipeline also identified CCND1 as another potential therapeutic target. The drug-gene interaction networks reflected four potential drug candidates for repurposing in the treatment of AD: Estradiol, Trichostatin A, Doxorubicin, and Dorsomorphin. Molecular docking demonstrated that Estradiol and Trichostatin A can inhibit the CCND1 protein, while Dorsomorphin induces inhibition of CDK6, because of their free energy of binding is lower than the reference. The evaluation of pharmacokinetic properties indicated that only Trichostatin A can be used for the treatment of AD in humans.
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    Predicción de proteínas de novo mediante inteligencia artificial para la inhibición del factor NF-κB en el cáncer gástrico
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2024-08) Laverde Lomas, Diego Martin; Galarza Galarza, Cristian Fernando
    In this research, highlights the limited effectiveness of current treatments for gastric cancer and the role of AI tools in the development of new personalized strategies, through the prediction of de novo proteins aimed at inhibiting the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) associated with this type of cancer. Thirty proteins were predicted, with a similar composition to those stored in standard and experimental databases. Their stability and folding capacity were analyzed based on the energies from intra and intermolecular interactions by molecular dynamics simulations. Furthermore, a molecular docking process was performed between several genes or transcription factors regulated by NF-κB and the predicted proteins, different thermodynamic variables such as Gibbs Free Energy, Dissociation Constant, Enthalpy, Heat Capacity, and Entropy were calculated and compared with respect to the complex of Inhibitor Alpha of kappa B and the protein composed of the p65 and p50 subunits of NF-κB to verify the affinity of their protein-protein interaction and structural conformation state. Finally, the affinity and selectivity of the resulting interactions were evaluated, concluding that the de novo proteins predicted by artificial intelligence are viable for the generation of new treatments and the design of new drugs to treat gastric cancer.
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    Propuesta de una quimioteca de compuestos relacionados estructuralmente y diseñados para el tratamiento la enfermedad de Huntington
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2024-08) Sanchez Guayta, Kevin Joel; Galarza Galarza, Cristian Fernando
    Huntington's disease (HD) is an inherited neurodegenerative disorder that affects movement, cognition and behaviour. It is characterised by uncontrollable chorea movements, difficulty speaking and swallowing, memory and thinking problems, and mood swings. HD progresses over time and usually results in death 15-20 years after diagnosis; treatment focuses on controlling symptoms. Medications can help relieve chorea movements, muscle stiffness and psychiatric problems. Physiotherapy and speech therapy may also be helpful. Research into HD is ongoing, with the goal of finding new therapies and, ultimately, a cure. Scientists are using various methods, such as bioinformatics and computer science, to identify genes and proteins that may be involved in the disease. In this study, these methods were used to identify 20 elite genes potentially implicated in HD. The researchers also identified several promising therapeutic targets, including DRD2, PDE10A and CNR1. These findings provide new insights into the molecular basis of HD and may lead to the development of new treatments. HD research is encouraging and there is steady progress towards understanding and treating this devastating disease. Scientists are working hard to find a cure, and with continued support, it is hoped that one day this goal will be achieved.