Less than a century after Fleming's discovery of penicillin, the World Health Organization declared in its 2014 antibiotic resistance report¹ the beginning of the post antibiotic era. From a projection announced in a recent UK review², the number of worldwide victims related to antimicrobial resistance could be equal to 10 million a year by 2050 if the trend is not reversed. The worrying spread of multiresistant and panresistant superbacteria requires a strong commitment by the scientific community to research new tools of treatment and prevention against pathogens in order to stem a health emergency with a potential disastrous economic and social impact. A rich space to venture in search of new antimicrobials is nature. Historically, natural products have accounted for more than half of all therapeutic agents, and today, natural products are the inspiration for most new drugs on the market. Activity of plant-derived antimicrobials is widely described in the literature; to illustrate this it is enough to mention polyphenolic extracts. Other natural antimicrobials are derived from animal sources (such as lactoferrin, chitosan and several peptides) and others, still, from microbial sources. Antibiogram analysis, in all technical variants, plays a basic role in the research of new antimicrobials. A new alternative method for predicting the effectiveness of new molecules in combination (adjuvant therapy) is machine learning. The proposed collection has the purpose of updating the scientific literature on this topic, as well as disclosing the evolution and innovation of the microbiological techniques used to assay microorganisms’ susceptibility towards antimicrobials.

1.WHO. (2014). Antimicrobial resistance: global report on surveillance. Geneva, Switzerland.

2. O'Neill J. (2014). Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Review on Antimicrobial Resistance. London, UK