A. Farooq (1), S.C. Nang (2), H.H. Yu (2), H. Wickremasinghe (2), M. Han (2), Y.W. Lin (2), J. Li (2), S.G. Wicha (1)
(1) Dept. of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany, (2) Biomedicine Discovery Institute, Infection and Immunity Program, Department of Microbiology, Monash University, Clayton, Victoria 3800, Australia
Objectives: Emerging resistance to last-line antibiotics in multidrug-resistant K. pneumoniae has led to exploration of phage therapy as promising, non-traditional alternative. However, combining antibiotics with phages remains understudied. We used quantitative modelling and simulation methods to characterize the synergy of novel phage pK8 in combination with polymyxin B (PMB).
Methods: The mcr-1-carrying K. pneumoniae strain was classified as resistant to PMB (MIC 8 mg/L). The in vitro static time-kill experiments (TKEs) were conducted over 24 h involving K. pneumoniae at starting inoculum ranging from 2 – 8.5 log10 CFU/mL treated with a various doses of PMB (2 – 16 mg/L) and phage pK8 at different multiplicity of infections (MOIs of 0.00001 – 10; phage particles per bacterium) [1]. Nonlinear-mixed-effects modeling was conducted in Monolix® 2023R1 to develop a longitudinal PKPD model.
The antimicrobial effect of PMB was assessed using a slope or maximum effect (Emax) model, while the antimicrobial effect of phage pK8 was described using an infection-lysis model derived from Delattre et al. [2]. Following a semi-mechanistic approach, we tested interaction models based on the findings of Han et al., showing phage-PMB synergistic inhibition of multiple central metabolism pathways of bacteria. We tested combined effect models either based on suppression of resistance against phages, or re-sensitization of PMB in combination. Therefore, we implemented the general pharmacodynamic interaction (GPDI) model [3] on the adaptive resistance rate of susceptible bacteria (suppression of resistance to phages) or a derived slope effect model based on the initial MOI (re-sensitization of PMB when phages present). The best model was chosen by the Akaike information criterion (AIC) [4] and inspection of the graphical model fit.
Results: In TKE, the tested K. pneumoniae strain showed resistance to PMB alone with no observable killing effect even at 16 mg/L, resulting in an effect model estimated to approach zero. The phage pK8 alone showed rapid initial killing of bacteria with effect sizes being higher for higher initial MOIs, and regrowth of bacteria occurred in every tested scenario. One combination scenario with a MOI close to 1 showed rapid killing without regrowth. All scenarios showed a multiplication of phages to a stationary phase.
The final model consisted of three bacterial compartments representing susceptible (S), phage-resistant (R) and phage-infected-bacteria (I) and one phage compartment addressing free phage particles (P). The model described the binding of free phage particles to susceptible bacteria, followed by a lysis of bacteria and the release of a certain number of phage particles per bacterium. The PD interaction model addressing a re-sensitization of PMB in combination with phages outperformed the interaction model based on the suppression of resistance to phages (AIC: 1782 vs. 1817). The developed model described both the observed bacterial and phage count in mono and combination scenarios from low to high MOIs well, while also identifying the interaction-type of the combination.
Conclusions: Phage-PMB combination showed synergistic PD interactions, however this synergy was only observable when the MOI was not lower than 1. Han et al. showed that besides no observable bacterial killing of PMB alone, an outer membrane depolarization was observed, which most likely plays a significant role in the synergistic effect. Our developed longitudinal PKPD model successfully assessed and quantified the synergy between phage pK8 and PMB using a reoccurring PMB effect following the idea of collateral sensitivity. Simulation studies with different dosing regimen resulting in dynamic concentrations of PMB will be performed to guide the translation of static TKE into dynamic TKE experiments and eventually an in vivo evaluation.
References:
[1] Han, M. L. et al. Comparative metabolomics revealed key pathways associated with the synergistic killing of multidrug-resistant Klebsiella pneumoniae by a bacteriophage-polymyxin combination. Computational and Structural Biotechnology Journal 20, 485–495 (2022).
[2] Delattre, R. et al. Combination of in vivo phage therapy data with in silico model highlights key parameters for pneumonia treatment efficacy. Cell Reports 39, (2022)
[3] Wicha, S. G., Chen, C., Clewe, O. & Simonsson, U. S. H. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nature Communications 8, 1–11 (2017).
[4] Akaike, H. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19, 716–723 (1974).
Reference: PAGE 32 (2024) Abstr 10867 [www.page-meeting.org/?abstract=10867]
Poster: Drug/Disease Modelling - Infection