Introduction: Quinolones are the mainstay of treatment for Salmonella typhi (S. typhi) infections but there is growing concern about resistance to older quinolones & poor understanding of quinolone /S. typhi pharmacodynamics. We have developed a new approach to modeling kill curve data and have applied it to describing the pharmacodynamics of gatifloxacin vs. S. typhi.
Methods: Log-phase cultures (107-8 CFU/mL) of S. typhi (MIC=0.5 mg/L) were exposed to gatifloxacin at 0, 0.5, 1, 2, 4 & 8xMIC; bacterial counts (CFU) were obtained serially over 24h. Time-course of CFU was fit to a pharmacodynamic model with capacity-limited bacterial growth, 1st-order rate constant for death (Kd) & a Hill-type function in which gatifloxacin enhanced Kd. The total CFU was represented by a mixture model, with up to 4 sub-populations differing in gatifloxacin susceptibility. Each kill curve was fit individually & later simultaneously, using Adapt II. Akaike’s Information Criterion was used to determine the number of sub-populations and which parameters would be allowed to vary between kill curves.
Results: The final model had inter-kill curve variance in maximum velocity of growth & 4 sub-populations. The 1st 2 sub-populations were 99.99% of the initial CFU with sensitivities < 1xMIC & the other 2 sub-populations were each <0.01% of total CFU, with sensitivities of 2.3 & 4.4xMIC. The gatifloxacin Emax was a 17-fold increase in Kd. Goodness of fit was excellent with an overall r2=0.96 (Observed=1.01*Fit-0.15).
Conclusions: This approach to pharmacodynamic modeling of in vitro data will give better insight into the activity of gatifloxacin vs. different S. typhi sub-populations & aid in determining regimens which minimize therapeutic failure due to the development of resistance.
American Society for Clinical Pharmacology and Therapeutics (ASCPT), Miami Beach, Florida, March 2004
By Olanrewaju O. Okusanya, Alan Forrest, Brent M. Booker, Patrick F. Smith, Sujata M. Bhavnani, Paul G. Ambrose