title: Discordant minimum inhibitory concentration analysis: a new path to licensure for anti-infective drugs.
abstract: BACKGROUND
  Evaluation of anti-infective drugs for licensure often relies on a noninferiority (NI) design where new drug B is noninferior to comparator drug A if the difference in success rates is reliably not worse than some fixed margin. The margin must be based on historical studies that estimate the magnitude of the benefit of drug A over placebo. This approach hampers drug development because the obligatory evidence for margin determination is often nonexistent.
  
  
  OBJECTIVE
  To develop a new method for licensure of anti-infective drugs when there is no historical evidence for reliable construction of the NI margin.
  
  
  METHODS
  The minimum inhibitory concentration (MIC) measures the minimum amount of drug that it takes to inhibit growth of bacteria in vitro. Patients who are infected with bacteria that have a low MIC to a given drug are expected to have good outcomes when treated with that drug. Thus, a differential effect of drug B versus drug A, if it exists, is likely to occur in patients whose pathogens have discordant MICs (e.g., low MIC for drug B, high MIC for drug A, or vice versa). A new paradigm for licensure of anti-infective drugs is proposed where a clinically acceptable NI margin is selected and licensure supported if the NI margin is met and B is reliably demonstrated superior to A in a subset of patients whose paired MICs favor B. The requirement for some evidence of superiority encourages a study that is carefully designed and executed.
  
  
  RESULTS
  Simulations indicate that the approach shows promise in realistic settings, provided adequate data are available. A simulated example illustrates use of the methods.
  
  
  CONCLUSIONS
  If the data have small sample size, weak MIC/success relationship, or high correlation between MIC-A, MIC-B, this procedure will have poor power.
  
  
  CONCLUSIONS
  Discordant MIC analysis may offer a novel path to licensure for certain anti-infective drugs.
authors.affiliation: aBiostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.
authors.forename: Dean
authors.initials: D
authors.lastname: Follmann
authors.forename: Erica
authors.initials: E
authors.lastname: Brittain
authors.forename: John H
authors.initials: JH
authors.lastname: Powers
mesh_terms: D000890:Anti-Infective Agents / Q000592:standards*; D019540:Area Under Curve; D017277:Drug Approval; D004341:Drug Evaluation / Q000379:methods*; D006801:Humans; D008826:Microbial Sensitivity Tests / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D012107:Research Design; D018401:Sample Size
chemical_list: D000890:Anti-Infective Agents