Discovery and Analysis of Biological Patterns to Enhance Drug Repurposing Strategies

• Autores: Belén Otero Carrasco
• Directores de la Tesis: Alejandro Rodríguez González (dir. tes.) , Juan Pedro Caraça-Valente Hernández (codir. tes.)
• Lectura: En la Universidad Politécnica de Madrid ( España ) en 2024
• Idioma: inglés
• Tribunal Calificador de la Tesis: Ernestina Menasalvas (presid.) , Aurora Pérez Pérez (secret.) , Fernando M. Delgado-Chaves (voc.) , José Alberto Benítez Andrades (voc.) , Raquel Abalo Delgado (voc.)
• Programa de doctorado: Programa de Doctorado en Software, Sistemas y Computación por la Universidad Politécnica de Madrid
• Menciones:
  • Internacional
• Calificación: Sobresaliente Cum Laude
• Enlaces
  • Tesis en acceso abierto en: Archivo Digital UPM
• Resumen

  • The development of new treatments through de novo drug development is a lengthy and expensive process. As a result, drug repurposing has emerged as a promising alternative. Drug repurposing involves using an existing pharmaceutical compound to treat a different disease than the one it was originally developed for. This approach allows for the development of treatments in a more timely, safe, and cost-effective manner. It also offers the potential to identify effective drugs for diseases that have received limited investment, such as rare diseases. In recent years, the massive exploitation of data through machine learning has led to the development of promising computational strategies for drug repurposing. However, these advances could be significantly improved with a deeper insight into the reasons behind the successful cases of drug repurposing to date. Examining the biological patterns behind these favourable cases could facilitate the development of more effective repurposing strategies. It is also noteworthy that there are differences in the response to drugs and the number of side effects that occur when taking a drug between the male and female sexes. This is another variable of great interest that should be included in the search for new treatments through drug repurposing. The current thesis has focused on three lines of research that address these problems. Firstly, a set of computational drug repurposing methods have been developed with the objective of identifying potential treatments for a set of rare diseases. The methods were based on the biological similarity between non-rare diseases with treatment and rare diseases, with the focus on different biological elements such as genes, proteins, or biological pathways. This methodology enables the identification of potential drugs for all the rare diseases under study. Secondly, a computational strategy was developed to detect cases of drug repurposing based on biological pathways and to extract all the characteristics that differentiate these cases. The results obtained permit the implementation of a novel drug repurposing strategy that places greater emphasis on the significance of biological pathways, as opposed to the conventional paradigm that is based on the concept of one-drug-one-target-one-disease. Subsequently, distinctive patterns were discerned within the amino acid sequences of drug target proteins in lung cancer. This was undertaken with the objective of demonstrating the potential relevance of these patterns in other related diseases. As a result, we proposed a novel repurposing strategy. Finally, a molecular model was developed to predict the efficacy and adverse reactions of a set of anticancer drugs as a function of biological sex. The results indicated that all the drugs studied exhibited a sex bias on these variants. The identification of novel cases of drug repurposing and understanding mechanisms that give rise to them represent a pivotal advance in the field of study. The search for new strategies based on alternative methods allows the acquisition of valuable information that contributes to the discovery of new treatments. Moreover, it is crucial to consider the variability that exists between the male and female sexes in terms of response to drugs to achieve a more personalized, effective, and safe medicine.