Métodos alternativos ao uso de animais de laboratório em pesquisas cardiovasculares: uma revisão sistemática

Abstract

Cardiovascular health research plays a crucial role in promoting the well-being of society, as heart disease represents a leading cause of morbidity and mortality worldwide. In Brazil, around a third of deaths are caused by cardiovascular diseases. Over the years, several discussions have been initiated about clinical research on laboratory animals and animal rights. In 1959, the concept of the "3Rs Principle" emerged, the 3Rs refer to reduction, refinement and replacement. This commitment aims to reduce the number of animals used in experimental procedures, improve procedures to reduce animal discomfort and suffering, and replace the use of animals whenever possible. Therefore, several alternative methods have been investigated for replacing animals in research, such as in vitro and in silico techniques, computational modeling and epidemiological studies. The application of alternative approaches not only involves resolving ethical issues, but also presents advantages in terms of financial cost, time and sustainability, in addition to having the potential to provide results that are more relevant to human beings. The methodological strategy chosen to carry out this research was a systematic literature review with a qualitative approach. 12 articles were trained that show recent updates on alternative methods to the use of animals in cardiovascular research. After discussing several articles that used alternative methods to the use of laboratory animals in cardiovascular research, we can conclude that the alternative method of choice will depend on the objective of the research. As with in vivo research, trained methods have their advantages and disadvantages, such as technical limitations and the need for validation. However, it is possible to see that the results obtained in studies with alternative methods are superior and, often, superior when compared to animal studies due to their potential for agility of results, low economic cost, great diversity of data and maximization of the potential complexity of research.