The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus Aureus

PLoS One. 2013;8(2):e55582. doi: 10.1371/journal.pone.0055582. Epub 2013 Feb 19.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Anti-Bacterial Agents / pharmacology*
  • Artificial Intelligence
  • Cluster Analysis
  • Decision Trees
  • Disinfectants / pharmacology*
  • Drug Resistance, Bacterial
  • Microbial Sensitivity Tests
  • Staphylococcus aureus / drug effects*

Substances

  • Anti-Bacterial Agents
  • Disinfectants

Grant support

This work was supported by FCT (INESC-ID multiannual funding) through the PIDDAC Program funds and under project PEst-OE/EEI/LA0021/2011 and the FP7 Cooperation Work Programme: Food, Agriculture and Fisheries, and Biotechnologies, KBBE-227258 (BIOHYPO project). Quotient Bioresearch received part-funding from the European Union in the scope of BIOHYPO project. The work of J. R. Coelho was supported by the PhD grant SFRH/BD/33719/2009 from Fundação para a Ciência e a Tecnologia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.