Translational modeling of antibacterial agents

Abstract

The golden era of antibiotic discovery almost 60 years ago seems to be an interlude in the eternal battle against bacteria; the spread of resistant bacteria has led to the emergence of highly resistant pathogens and hard-to-treat infections. In vitro infection models have made it possible to gather time-course data regarding antibiotic and bacteria interaction. The main drawback of these in vitro systems is that they do not take into account the host immune system, drug-protein binding, and characteristics of the infection site. Hence, animal studies are important to gain insights into the host response and the progression of infection. Modeling antimicrobials has the distinct advantage wherein the target is readily accessible since the bacterial load can be quantified in both in vitro and in vivo experimental conditions. The increasing knowledge base about molecular mechanisms of interactions between antibiotics and antibiotic targets, coupled with advanced experimental techniques and computational capability, makes the development of mechanism-based PK/PD models incorporating receptor binding and bacterial physiology possible. This new generation of mechanism-based PK/PD models will enable the exploration and increased understanding of the underlying complex mechanisms of the infectious process. The transition toward systems-based approaches requires the ability to integrate diverse data types and experimental platforms. Most approaches for translating preclinical antimicrobial research focus only on antibiotic activity and interactions between the drug and bacteria. Mathematical modeling can assist in this process by integrating the behavior of multiple components into a comprehensive network-based model, and by addressing questions that are not yet accessible to experimental analysis.