ABSTRACT
The main parameters used today to indicate whether the use of an antimicrobial will have a reasonable probability of success are the classifications “resistant” (R) and “susceptible” (S), based on the minimum inhibitory concentration (MIC), either directly by various dilution methods or by disk diffusion. For clinicians, this categorization is important, because the choice of therapy is often guided by these reports from the clinical microbiology laboratory. Supposedly, if a microorganism is categorized as S to an antimicrobial agent, there is a reasonably good probability of success when the patient is treated with that antimicrobial agent, while failure of therapy is more likely when an isolate is categorized as R. The “intermediate” (I) category is used for various purposes, but mainly to indicate a degree of uncertainty in response or dose dependency. However, the criteria used for categorization are less clear, and the meanings of S, I, and R have varied over time and place. Over the last decade, pharmacodynamics (PDs) has started to play a major role in distinguishing the S, I, and R categories. Because concentration-effect relationships became increasingly apparent and could be described in a meaningful manner, drug exposures indexed to MIC that result in a high probability of clinical success could be ascertained. In addition, it was increasingly appreciated that not all patients are created equal and that large differences in pharmacokinetic (PK) behavior between patients do exist. The use of a statistical technique called Monte Carlo simulation (MCS) (1,2) is now utilized to account for PK variation inherent in human populations. In this chapter, we will discuss the meaning of S, I, and R in a historical context as well as provide the current view, based on PK-PD
relationships. Both the European Committee on Antimicrobial Susceptibility Testing (EUCAST) (3) and the Clinical and Laboratory Standards Institute [CLSI, formally known as the National Committee for Clinical Laboratory Standards (NCCLS)] consider PK-PD data in the selection of susceptibility breakpoints (4).
