title: The use of machine learning methodologies to analyse antibiotic and biocide susceptibility in Staphylococcus aureus.
abstract: BACKGROUND
  The rise of antibiotic resistance in pathogenic bacteria is a significant problem for the treatment of infectious diseases. Resistance is usually selected by the antibiotic itself; however, biocides might also co-select for resistance to antibiotics. Although resistance to biocides is poorly defined, different in vitro studies have shown that mutants presenting low susceptibility to biocides also have reduced susceptibility to antibiotics. However, studies with natural bacterial isolates are more limited and there are no clear conclusions as to whether the use of biocides results in the development of multidrug resistant bacteria.
  
  
  METHODS
  The main goal is to perform an unbiased blind-based evaluation of the relationship between antibiotic and biocide reduced susceptibility in natural isolates of Staphylococcus aureus. One of the largest data sets ever studied comprising 1632 human clinical isolates of S. aureus originated worldwide was analysed. The phenotypic characterization of 13 antibiotics and 4 biocides was performed for all the strains. Complex links between reduced susceptibility to biocides and antibiotics are difficult to elucidate using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data.
  
  
  RESULTS
  In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to different structural families, is associated to multidrug resistance. We have consistently found that a minimum inhibitory concentration greater than 2 mg/L for both biocides is related to antibiotic non-susceptibility in S. aureus.
  
  
  CONCLUSIONS
  Two important results emerged from our work, one methodological and one other with relevance in the field of antibiotic resistance. We could not conclude on whether the use of antibiotics selects for biocide resistance or vice versa. However, the observation of association between multiple resistance and two biocides commonly used may be of concern for the treatment of infectious diseases in the future.
authors: INESC-ID/IST, Technical University of Lisbon, Lisbon, Portugal. jrcoelho@kdbio.inesc-id.pt
authors: Joana Rosado
authors: JR
authors: Coelho
authors: João André
authors: JA
authors: Carriço
authors: Daniel
authors: D
authors: Knight
authors: Jose-Luis
authors: JL
authors: Martínez
authors: Ian
authors: I
authors: Morrissey
authors: Marco Rinaldo
authors: MR
authors: Oggioni
authors: Ana Teresa
authors: AT
authors: Freitas
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003663:Decision Trees; D004202:Disinfectants / Q000494:pharmacology*; D024881:Drug Resistance, Bacterial; D008826:Microbial Sensitivity Tests; D013211:Staphylococcus aureus / Q000187:drug effects*
chemical_list: D000900:Anti-Bacterial Agents; D004202:Disinfectants