Healthcare-associated infections (HAIs), previously called hospital infections, are a severe public health problem and can develop either as a direct result of medical or surgical treatment or in contact with a healthcare setting. These infections include central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia (VAP), and surgical site infections. Among the pathogens related to HAI, the group of bacteria is the one that stands out. More than 2 million HAIs occur each year in the USA (Stone et al., 2005), with 50–60% being caused by antimicrobial-resistant bacteria. In 2014, the World Health Organization (WHO) published the report “Antimicrobial resistance: global report on surveillance” (WHO, 2014), warning of the growing increase in antimicrobial resistance in the world. Antimicrobial resistance among hospital pathogens has increased at alarming levels, both in developed and developing countries. It is estimated that there will be a worldwide spread of untreatable infections both inside and outside hospitals. According to a bulletin published in 2017 by WHO (WHO, 2017), 12 major antibiotic-resistant bacteria deserve attention and urgently need more research and development (R&D) of new and effective antibiotic treatments. Gram-negative bacteria are the most involved in HAI (carbapenem-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae family), and R&D on new antibiotics against these is considered to be of critical priority (WHO, 2017).
Given the potential severity of multidrug-resistant (MDR) bacteria and the lack of treatment options, identifying and implementing effective strategies to prevent such infections are urgent priorities.
The integration of mathematical, statistical, and computational methods for biological data analysis to discover new therapeutic targets for any bacteria is exceptionally relevant. The combination of bioinformatics, systems modeling, and heterogeneous data integration can be a powerful tool for this purpose. At Physiotarget, we employ state-of-the-art methods and technologies to characterize MDR bacteria and identify the best treatment options.
References
Stone, P. W., Braccia, D., Larson, E. (2005). Systematic review of economic analyses of health care-associated infections. Am. J. Infect. Control 33, 501–509. doi: 10.1016/j.ajic.2005.04.246
WHO (World Health Organization) (2014). Antimicrobial resistance: global report on surveillance. Geneva: World Health Organization.
WHO (World Health Organization) (2017). Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. Geneva: World Health Organization.