Little is known regarding the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics and biofilms yet bacterial biofilms account for over 80% of all important human infections, including cystic fibrosis (CF). The development of effective therapies to counter biofilm infections has been called one of the most pressing challenges in anti-bacterial drug development by the NIH. P. aeruginosa undergoes extensive genetic adaptation in the CF lung over time, allowing it to persist despite intense, repeated courses of antimicrobial treatment. Thus the current goal of antimicrobial therapy is to suppress infection and not cure. Guidelines for the treatment of P. aeruginosa airway infection are based upon PD analyses of bacteria grown in planktonic (e.g. liquid) culture which cannot be extrapolated to biofilms. Additionally, different classes of antibiotics are known to target differnt subpopulations within the biofilm. The long-term goal of this study is to identify the antibiotic combinations, doses, and schedules needed to eradicate P. aeruginosa biofilms in patients with CF through the application of PD on bacterial biofilms. Traditional PK/PD studies quantify antimicrobial effect based upon simple counts of """"""""live"""""""" or """"""""dead."""""""" The unique spatial and temporal approach to PD modeling proposed in this study will allow simultaneous visualization and quantification of the exposure response relationship of antibiotics on heterogeneous subpopulations within the biofilm in real-time and the associated effect on resistance evolution. The goal of Aim 1 is to optimize the design of an innovative dynamic in vitro biofilm PK/PD model that can simulate in vivo antibiotic concentration-time profiles on P. aeruginosa biofilms under continuous culture conditions.
In Aim 2, this model will be used to determine the biofilm PD targets of meropenem and tobramycin, administered alone and in combination, on a unique collection of genetically identical P. aeruginosa isolates longitudinally-collected from CF patient over a period of 35 years thus allowing for comparisons in PD targets between isolates causing early vs. late disease and acute vs. chronic infection.
In Aim 3, mathematical models describing the relationship between antibiotic activity and biofilm killing will be developed. We have assembled an international, multi-disciplinary research team with expertise in PK/PD, microbial biofilms, CF, and mathematical modeling. Results from this study will be used to investigate new antibiotic regimens for maximal bio- film killing. This translational study will provide an innovatve new framework for the investigation of antimicrobial PD that accounts for the genetic adaptations and phenotypic diversity observed in P. aeruginosa biofilms and will enable the discovery of alternative antimicrobial dosing strategies and drug combinations specifically targeting P. aeruginosa biofilms in CF with the ultimate goal of improving clinical care. Results of this study will have broad applications toward other biofilm-forming organisms (e.g., Staphyloccocus aureus) which are important causes of human infections such as endocarditis, orthopedic-implant infections, and catheter-related infections.
The proposed research is relevant to public health because bacterial biofilms make up over 80% of important human infections, including cystic fibrosis, yet current in vitro pharmacodynamic models used for antibiotic dose optimization do not account for the role of biofilms in antimicrobial resistance. Current guidelines for antimicrobial treatmen of P. aeruginosa lung infection in cystic fibrosis can only suppress infection and not cure. Systematic pharmacodynamic profiling of antibiotics on P. aeruginosa biofilms may lead to antibiotic dosing regimens directed towards the biofilm and improve clinical outcomes in patients with cystic fibrosis.
|Haagensen, Janus; Verotta, Davide; Huang, Liusheng et al. (2017) Spatiotemporal pharmacodynamics of meropenem- and tobramycin-treated Pseudomonas aeruginosa biofilms. J Antimicrob Chemother 72:3357-3365|
|Verotta, Davide; Haagensen, Janus; Spormann, Alfred M et al. (2017) Mathematical Modeling of Biofilm Structures Using COMSTAT Data. Comput Math Methods Med 2017:7246286|
|Haagensen, Janus A J; Verotta, Davide; Huang, Liusheng et al. (2015) New in vitro model to study the effect of human simulated antibiotic concentrations on bacterial biofilms. Antimicrob Agents Chemother 59:4074-81|
|Huang, Liusheng; Haagensen, Janus; Verotta, Davide et al. (2014) Determination of meropenem in bacterial media by LC-MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci 961:71-6|