title: Predicting and preventing antimicrobial resistance utilizing pharmacodynamics: Part I gram positive bacteria.
abstract: BACKGROUND
  Antimicrobial resistance is a potentially inevitable consequence of widespread use of antibiotics in the healthcare system. An enhanced understanding of pharmacodynamic (PD) targets that prevent antimicrobial resistance development will improve currently availably therapies and help to guide future drug development strategies. Current in vitro methods to predict bacterial resistance to antimicrobials consist of serial dilution experiments, determination of the mutant prevention concentration (MPC), mutant selection window (MSW), and human simulated pharmacodynamics studies. Clinical trial data and real -world surveillance studies can help validate or disprove in vitro modeling.
  
  
  METHODS
  This review will discuss methods of predicting development of resistance and how the use of pharmacodynamics can reduce or eliminate the emergence of resistance among Staphylococcus aureus, Streptococcus pneumoniae, and Enterococcus species.
  
  
  CONCLUSIONS
  Pharmacodynamic targets can be used successfully to guide antimicrobial therapy to prevent resistance development. Currently, PD targets do not take into consideration horizontal resistance gene transfer and various factors may lead to different PD targets based on sites of infection. Further research is necessary to guide future drug development strategies and optimize new drug therapies.
authors.affiliation: a Department of Pharmacy , Hartford Hospital , Hartford , CT , USA.
authors.forename: Kristin E
authors.initials: KE
authors.lastname: Linder
authors.affiliation: b Center for Anti-infective Research and Development , Hartford Hospital , Hartford , CT , USA.
authors.forename: David P
authors.initials: DP
authors.lastname: Nicolau
authors.affiliation: a Department of Pharmacy , Hartford Hospital , Hartford , CT , USA.|c Department of Pharmacy Practice , University of Connecticut School of Pharmacy , Storrs , CT , USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Nailor
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000494:pharmacology*; D015195:Drug Design; D024881:Drug Resistance, Bacterial / Q000235:genetics*; D006094:Gram-Positive Bacteria / Q000187:drug effects* / Q000235:genetics; D016908:Gram-Positive Bacterial Infections / Q000188:drug therapy / Q000382:microbiology; D006801:Humans; D008962:Models, Theoretical; D009154:Mutation
chemical_list: D000900:Anti-Bacterial Agents
keywords: Daptomycin; linezolid; macrolides; quinolones; vancomycin