Meng Gu, Tingjie Guo, Coen van Hasselt
Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
Background and objectives: Bacteriophages, or phages, are viruses capable of infecting and replicating within bacterial cells, and ultimately lysing them. Phage therapy has emerged as a promising therapeutic strategy to combat bacterial infections associated with antimicrobial resistance. However, there is currently a lack of understanding of how differences in bacteriophage-specific characteristics affect their pharmacodynamic, which is crucial for designing effective dosing strategies. The specific objectives of this analysis were (1) to evaluate how phage-specific properties influence empirical exposure-response (ER) relationships, and (2) to determine if phage-specific properties should be considered in design of dosing schedules of phage-based treatments, using a mechanism-based theoretical model for bacteria-phage interactions.
Methods:
Model definition: A theoretical delay differential equation model which considered uninfected bacteria (Bu), infected bacteria (Bi), phage (P) densities were implemented using the R package deSolve [1]. Bacteria-associated parameters included the maximum bacterial growth rate constant (µmax) and capacity (Bmax). Phage-specific parameters included the latent period between infection and lysis (τ), the number of phages released per lysing bacterial cell (b), general phage decay (d), where the phage-specific adsorption rate (β) was described according to the following relationship: β=βmax*(1-P/P50), where βmax was the maximum adsorption rate, and P50 represents the phage density corresponding to half of the maximum adsorption rate. The model was defined according to the following differential equations:
dBu/dt=µ*Bu–β*P*Bu.
dBi/dt=β*P* Bu-β(t-τ)*P(t-τ)*Bu(t-τ).
dP/dt=β(t-τ)*P(t-τ)*Bu(t-τ)*b–β*P* Bu–d*P.
Simulation scenarios: To investigate how phage-specific parameters impact the observed ER relationship, we simulated CFU with a single dose of phage (10-5 PFU/mL to 103 PFU/ml) with different phage-specific parameters. We evaluated the impact of different τ (0.4-1.6 h), b (40-160), and βmax (10-7.52-10-7.85 h-1). Further, to evaluate the impact of phage-specific properties on dosing schedules (amount, interval), we studied the same ranges of phage parameters and fractionated the daily phage dose, previously used in the single-dose simulations, into 1 to 4 daily administrations over a 72-hour treatment period.
For both scenarios, we assumed an initial bacterial inoculum of 107 CFU/mL. We calculated the multiplicity of infection (MOI) at the start day of treatment, i.e., the ratio of the phage dose and the bacterial inoculum amount, as phage dose index. For the single-dose scenario, we fitted empirical asymmetric ER sigmoidal functions (parameters E0, ED50, Emax, Hill, gamma) to observe CFU counts at end of treatment (24 h) over phage dosages. For the dosing schedule simulations, we also evaluated the observed change in CFU at the end of the treatment (72 h) to quantify how differences in phage-specific parameters impact treatment effects.
Results:
Empirical exposure-response relationships: An asymmetric ER was observed across most of the evaluated parameter values. We found that an increased βmax leads to an enhanced maximum drug effect. Between βmax and ED50, we observed a unique U-shaped relationship. Finally, an increased burst size affected both ED50 and Emax, enhancing overall bacterial suppression.
Dosing schedules: We found that the total daily phage dose rather than dosing intervals was the main driver for bacterial suppression at end of treatment. As expected, βmax was essential for the magnitude of bacterial suppression. The impact on the bacterial suppression attributable to burst size was found to be reduced when the latent period is increased. For phage with a lower βmax, we found that the ER did not exhibit a monotonic escalation with escalating doses, although this effect can be mitigated by increasing dose frequency.
Conclusions: Our analysis underlies the need to consider phage-specific properties in defining optimal dosing strategies for phage therapy. Our model-based approach may be used to guide rational design of phage-based dosing schedules. Next steps will focus on incorporation of phage resistance development and antibiotic-phage combination treatments.
References:
[1] Leclerc QJ, Lindsay JA, Knight GM. Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: Killers and drivers of resistance evolution. PLoS Comput Biol. 2022 Nov 30;18(11):e1010746. doi: 10.1371/journal.pcbi.1010746. PMID: 36449520; PMCID: PMC9744316.
Reference: PAGE 32 (2024) Abstr 11258 [www.page-meeting.org/?abstract=11258]
Poster: Drug/Disease Modelling - Infection