Integrating pharmacokinetics, pharmacodynamics and pharmacogenomics to predict outcomes in antibacterial therapy.

In the field of pharmacogenomics, genetic information is utilized to explain variations in drug response between individuals. In many instances, information on single nucleotide polymorphisms (SNPs) of genes encoding enzymes that are important in the metabolism of drugs can help to explain pharmacokinetic variability. The systemic exposures to antibiotic drugs that are achieved as a result of these variations have differing pharmacodynamic effects on both the patient and the pathogen, which, in some cases, may manifest as exposure-related toxicity. The pharmacodynamic effect of the drug on the microbial pathogen is, in part, determined by the evolutionary genetic history of the microbe itself. In the science of microbial pharmacokineticspharmacodynamics (PK-PD), particular antibiotic exposures are related to outcomes such as microbial kill and resistance suppression, which can often be demonstrated in preclinical models. In this review, evidence is presented to demonstrate that in addition to drug exposures, host genetic polymorphisms may lead to gene products that modulate microbial response to antibiotic exposure. Population distributions of SNPs that explain some of the pharmacokinetic and pharmacodynamic variability of drugs are described in the literature with increasing frequency. These data, in addition to data on the distribution of indices of drug susceptibility in clinical isolates of pathogens and PK-PD exposure-effect relationships, can be integrated in Monte Carlo simulations, such that antibiotic doses with the highest probability of killing the pathogen and the least probability of causing dose-related toxicity can be predicted. The result is a rational starting point in drug regimen design. Such regimens can then be compared with standard therapies in randomized clinical trials.