New Bacterial Targets and Computational Methods Against Bacterial Resistance

The increasing number of resistant strains of pathogenic bacteria results in a growing number of infections becoming harder to treat. The over and misuse of antibiotics have caused the emergence and spread of multidrug resistant “superbugs” by selecting against sensitive organisms. An example that highlights the problem of multidrug resistant bacteria is the recent report by ABC news of a Nevada woman who died following septic shock caused by the bacteria K. pneumonaiae¹. This bacterium was among the carbapenem-resistant Enterobacteriaceae (CRE) and was resistant to all available antibiotics in the U.S. In 2013, the CDC characterized CRE infections as an urgent threat, meaning the bacteria are an "immediate public health threat that requires urgent and aggressive action." Exacerbating the problem of drug resistance is the scaling down of funding allocated to new antibacterial development by the pharmaceutical industry due to increased cost and low return on investment compared to other groups of medications that are used for life such as cholesterol lowering medications. However, despite the growth of multidrug resistant bacteria and scaling down of funding towards it, there is still hope. The cost of developing new antibiotics can be reduced by focusing on the well validated bacterial targets and by utilizing the available computational resources to efficiently maximize the number of successful leads that make it to the market as new antibiotics. The focus in this article is on simple and cost efficient strategies to develop novel antibiotics or to revive old ones. To assist in this effort, presented in this article is a review of computational techniques and strategies that can be employed to develop safe and effective novel antibacterial therapies. This is followed by a review of resistance mechanisms in bacteria and validated bacterial targets amenable for drug design.