Publicação
Creation of databases of ageing-related drugs and statistical analysis and applied machine learning for the prioritization of potential lifespan-extension drugs
| Resumo: | Over the last few centuries, the success of modern medicine has consistently increased the average life expectancy of mankind. This extended longevity came a paradigm-shift: multimorbidity is now our top concern, instead of the immediate fatal diseases (e.g. infections) of the past. The aged populations currently observed in developed countries, are already having negative recursions in the social state ideal and are expected to spread to the rest of the world. The scientific solution to this predicament lies in developing anti-aging therapies. In the recent decades, the idea that aging is not a fixed biological process was challenged and thoroughly refuted. There are now more than a thousand different genes known to alter lifespan in model organisms, and simple lifestyle interventions like a caloric restriction diet prolong the lifespan of non-human primates. Unfortunately, the discoveries made so far are yet to be translated into meaningful human anti-aging therapies. In this work, we offer several scientific contributions to help mitigate the looming aging crisis. Our most prominent contribution is the creation of the DrugAge database (http://genomics.senescence.info/drugs/). This unparalleled resource systematically compiles information regarding drug lifespan assays that increased the lifespan of model organisms. DrugAge is free, manually curated and is composed of 1316 entries featuring 418 different compounds from studies across 27 model organisms. We used the information provided on DrugAge to: train an algorithm for the prediction of the anti-aging potential of new compounds; conduct the functional enrichment of DrugAge; compare DrugAge with the known anti-aging genes; show that gender does not influence the performance of anti-aging compounds in model organisms. A separate section is dedicated to applying drug repurposing to accelerate the discovery of antiaging drugs in humans. After matching a meta-repository of drug-gene interactions with the known anti-aging genes in model organisms, we found 16 drugs with significant potential to affect the aging process. Two drug combinations are suggested to be tried in model organisms. |
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| Autores principais: | Barardo, Diogo Gonçalves |
| Assunto: | Ciências Naturais::Ciências Biológicas |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Over the last few centuries, the success of modern medicine has consistently increased the average life expectancy of mankind. This extended longevity came a paradigm-shift: multimorbidity is now our top concern, instead of the immediate fatal diseases (e.g. infections) of the past. The aged populations currently observed in developed countries, are already having negative recursions in the social state ideal and are expected to spread to the rest of the world. The scientific solution to this predicament lies in developing anti-aging therapies. In the recent decades, the idea that aging is not a fixed biological process was challenged and thoroughly refuted. There are now more than a thousand different genes known to alter lifespan in model organisms, and simple lifestyle interventions like a caloric restriction diet prolong the lifespan of non-human primates. Unfortunately, the discoveries made so far are yet to be translated into meaningful human anti-aging therapies. In this work, we offer several scientific contributions to help mitigate the looming aging crisis. Our most prominent contribution is the creation of the DrugAge database (http://genomics.senescence.info/drugs/). This unparalleled resource systematically compiles information regarding drug lifespan assays that increased the lifespan of model organisms. DrugAge is free, manually curated and is composed of 1316 entries featuring 418 different compounds from studies across 27 model organisms. We used the information provided on DrugAge to: train an algorithm for the prediction of the anti-aging potential of new compounds; conduct the functional enrichment of DrugAge; compare DrugAge with the known anti-aging genes; show that gender does not influence the performance of anti-aging compounds in model organisms. A separate section is dedicated to applying drug repurposing to accelerate the discovery of antiaging drugs in humans. After matching a meta-repository of drug-gene interactions with the known anti-aging genes in model organisms, we found 16 drugs with significant potential to affect the aging process. Two drug combinations are suggested to be tried in model organisms. |
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