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The world is facing a significant increase in infections caused by drug-resistant infectious agents. In response, various strategies have been recently explored to treat them, including the development of bacteriocins. Bacteriocins are a group of antimicrobial peptides produced by bacteria, capable of controlling clinically relevant susceptible and drug-resistant bacteria. Bacteriocins have been studied to be able to modify and improve their physicochemical properties, pharmacological effects, and biosafety. This manuscript focuses on the research being developed on the biosafety of bacteriocins, which is a topic that has not been addressed extensively in previous reviews. This work discusses the studies that have tested the effect of bacteriocins against pathogens and assess their toxicity using in vivo models, including murine and other alternative animal models. Thus, this work concludes the urgency to increase and advance the in vivo models that both assess the efficacy of bacteriocins as antimicrobial agents and evaluate possible toxicity and side effects, which are key factors to determine their success as potential therapeutic agents in the fight against infections caused by multidrug-resistant microorganisms.

The “-omics” era has brought a new set of tools and methods that have created a significant impact on the development of Metabolic Engineering and Synthetic Biology. These fields, rather than working separately, depend on each other to prosper and achieve their individual goals. Synthetic Biology aims to design libraries of genetic components (promoters, coding sequences, terminators, transcriptional factors and their binding sequences, and more), the assembly of devices, genetic circuits and even organism; in addition to obtaining quantitative information for the creation of models that can predict the behavior of biological systems (Cameron et al., 2014 ). Metabolic engineering seeks for the optimization of cellular processes, endemic to a specific organism, to produce a compound of interest from a substrate, preferably cheap and simple. It uses different databases, libraries of components and conditions to generate the maximum production rate of a desired chemical compound and avoiding inhibitors and conditions that affect the growth rate and other vital functions in the specific organism to achieve these goals; metabolic fluxes manipulation represents an important alternative (Stephanopoulos, 2012 ).

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