A stochastic formulation of the gompertzian growth model for in vitro bactericidal kinetics: parameter estimation and extinction probability.
Time-kill curves have frequently been employed to study the antimicrobial effects of antibiotics. The relevance of pharmacodynamic modeling to these investigations has been emphasized in many studies of bactericidal kinetics. Stochastic models are needed that take into account the randomness of the mechanisms of both bacterial growth and bacteria-drug interactions. However, most of the models currently used to describe antibiotic activity against microorganisms are deterministic. In this paper we examine a stochastic differential equation representing a stochastic version of a pharmacodynamic model of bacterial growth undergoing random fluctuations, and derive its solution, mean value and covariance structure. An explicit likelihood function is obtained both when the process is observed continuously over a period of time and when data is sampled at time points, as is the custom in these experimental conditions. Some asymptotic properties of the maximum likelihood estimators for the model parameters are discussed. The model is applied to analyze in vitro time-kill data and to estimate model parameters; the probability of the bacterial population size dropping below some critical threshold is also evaluated. Finally, the relationship between bacterial extinction probability and the pharmacodynamic parameters estimated is discussed.
Ferrante
L
L
Institute of Biochemical Biotechnologies, School of Medicine, Polytechnic University of Marche, Ancona, Italy. L.Ferrante@univpm.it
Bompadre
S
S
Leone
L
L
Montanari
M P
MP
D000900:Anti-Bacterial Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics*; D018407:Bacterial Physiological Phenomena / Q000187:drug effects*; D049109:Cell Proliferation / Q000187:drug effects; D015169:Colony Count, Microbial / Q000379:methods; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D007700:Kinetics; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D013269:Stochastic Processes
D000900:Anti-Bacterial Agents