Jinqiu Yin(1), Linda B.S. Aulin(1), Piet H. van der Graaf(1,2), Pyry A.J. Valitalo(3), J. G. Coen van Hasselt(1)
(1).Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands; (2).Certara QSP, Canterbury, UK; (3).Quantitative Clinical Pharmacology, Orion Corporation, Finland
Introduction:
Bacterial meningitis is a life-threatening condition associated with a high mortality. In adults, Streptococcus pneumoniae remains the most common cause for bacterial meningitis[1]. Typically antibiotic treatment of pneumococcal meningitis is based on treatment with penicillin and third-generation cephalosporins[2]. However, resistance to penicillin and cephalosporins is increasing[3] in which case vancomycin in combination with rifampicin or cefotaxime is recommended[2].
The blood-brain barrier (BBB) can impact exposure of antibiotics to the central nervous system (CNS)[4]. Consequently, the rate and extent of drug distribution into the CNS needs to be accounted when optimizing antibiotic treatments of bacterial meningitis. Furthermore, predicting antibiotic exposure in the brain may be relevant in preventing CNS-associated adverse drug reaction[5,6]. Various dose regimens to treat resistant bacterial meningitis have been proposed, including the continuous intravenous administration of vancomycin[7] and intrathecal injection of vancomycin[8]. To further optimize these dose regimens of meningitis in specific patient populations, quantitative characterization of the kinetics of CNS exposure as well as bacterial growth kinetics should be considered.
Objectives:
We aim to develop a pharmacodynamic model to characterize to exposure-response kinetics of Streptococcus pneumoniae to vancomycin that can be coupled to a previously developed CNS PBPK model in order to optimize vancomycin dose regimens for S. Pneumoniae associated bacterial meningitis[9,10]. The current analysis focuses on the development of the development of the pharmacodynamic model.
Methods:
Experimental data: We extracted time-kill data from previous publications featuring 7 bacterial strains[11-13]. The studies described static in vitro experiments with S. Pneumoniae with various concentrations of vancomycin.
Model development: We used a nonlinear mixed effect modeling to analyze the in vitro time kill data. We evaluated natural bacterial growth both as non-capacity limited growth with a net growth constant, and as capacity limited growth using a logistic growth function. Both the incorporation of a persistent and tolerant sub-population was evaluated. The drug effect was evaluated as a linear slope and as an Emax function. Additionally, inclusion of random effects for variability between time kill time courses and strains (ISV) was evaluated for all relevant parameters. The developed time kill model was subsequently linked to the previously developed CNS PBPK model to allow for treatment optimization of vancomycin.
Results:
The developed time kill model contained two sub-populations of bacteria, one non-growing non-drug sensitive persistent population (BP) and one drug-sensitive (BS) with growth following a logistic function modeled with a net growth constant (Knet) and a capacity limiting term (Bmax). The data did not support a more mechanistically plausible drug tolerant sub-population. The number of Bs and Bp at start of experiment was estimated as BS0 and BP0 respectively. The drug effect was included as an Emax model. Parameter estimates were: BS0 3.35 log CFU/mL (RSE 3%, ISV 100%), Knet 0.445 h-1 (RSE 14%), Bmax 11.2 log CFU/mL (RSE 3%, ISV 21%), Emax 4.87 (RSE 23%, ISV 100%), EC50 0.731 mg/L (RSE 34%, ISV 39%).
Conclusions:
We quantified the time-kill dynamics S. pneumonia to vancomycin which allowed integration with a previously published model of CNS pharmacokinetics, resulting in a strong modeling framework to optimize S. pneumoniae meningitis. In future, the work will be further extended with de novo time kill data and combination treatments.
References:
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[10] Yamamoto Y, Välitalo PA, Wong YC, et al. Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach. Eur J Pharm Sci.2018, 112:168-179.
[11] GA Pankuch MRJ. Study of comparative antipneumococcal activities of penicillin G, RP 59500, erythromycin, sparfloxacin, ciprofloxacin, and vancomycin by using time-kill methodology. Antimicrob Agents Chemother. 1994,38(9):2065-72.
[12] IR Friedland MP, Shelton S. Time-kill studies of antibiotic combinations against penicillin-resistant and -susceptible Streptococcus pneumoniae. J Antimicrob Chemother. 1994,34(2):231-7.
[13] Felmingham D, Foxall P, O’Hare M, Grüneberg R. The bactericidal activity of vancomycin and teicoplanin against Streptococcus pneumoniae. Scand J Infect Dis Suppl. 1990,72:20-5.
Reference: PAGE 27 (2018) Abstr 8749 [www.page-meeting.org/?abstract=8749]
Poster: Drug/Disease Modelling - Infection