Christoph Hethey (1,2), Sebastian G. Wicha (2,3), Charlotte Kloft (2,3), Wilhelm Huisinga (2,4)
(1) Institut für Biochemie und Biologie, Universität Potsdam; (2) Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling; (3) Institute of Pharmacy, Dept. Clinical Pharmacy & Biochemistry, Freie Universität Berlin and (4) Institut für Mathematik, Universität Potsdam
Objectives: Ribosomes are a central constituent of bacterial growth and as well of bacterial growth inhibition alike. They are targets of multiple antibiotic classes, and yet the intracellular ribosomal concentration (IRC) is commonly ignored in most pharmacodynamic modelling approaches for antibiotics. Our objective was to develop a simple bacterial growth model to analyse time-kill curve data, which explicitly describes intracellular ribosomal accumulation and dilution processes.
Methods: We used data for S. aureus exposed to the protein biosynthesis inhibitor linezolid. Observations on single cell level in drug-free control experiments [1] were combined with time-kill curve data, in which lag and exponential phase cultures were analysed [2]. A unified model was developed to describe the growth dynamics of both cultures during drug exposure. Following a mechanistic approach, we integrated drug effects on the cellular level and linked the perturbed cellular characteristics to population growth. Visual data exploration, parameter estimation, model diagnostics and simulations were performed in Matlab 2015a.
Results: The initial value for IRC naturally accounted for delayed growth kinetics observed in control experiments during the lag phase. Over time, IRC increased and approached a quasi steady state in the exponential phase. On the observed lag phase bacteria, the drug potency was initially decreased by approximately one order of magnitude in comparison to the exponentially growing culture. The IRC scaled linearly with the potency of linezolid and thus factored in the differences of antibacterial effects on lag and exponential phase cultures.
Conclusions: Cellular characteristics, such as IRC, deliver powerful support when modelling bacterial population growth. The lag phase, as an emergent observation on the population level, can be interpreted as the result of differences on the cellular level. A mechanistic integration of drug effects paves the way to predict drug-drug interactions when modelling bacterial growth kinetics.
References:
[1] Martin, S. E., & Iandolo, J. J. (1975). Translational Control of Protein Synthesis in Staphylococcus aureus. Journal of Bacteriology, 122(3), 1136–1143.
[2] Wicha, S. G., Kees, M. G., Kuss, J., & Kloft, C. (2014). Pharmacodynamic and response surface analysis of linezolid or vancomycin combined with meropenem against Staphylococcus aureus. Pharmaceutical Research, 32(7), 2410–2418.
Reference: PAGE 25 (2016) Abstr 5705 [www.page-meeting.org/?abstract=5705]
Poster: Drug/Disease modeling - Infection