Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments
Accurate pharmacokinetic–pharmacodynamic (PK-PD) models of biofilm treatment could be used to guide formulation and administration strategies to better control bacterial lung infections. To this end, we developed a detailed pharmacodynamic model of <i>P. aeruginosa</i> treatment with the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/8/2316 |
| _version_ | 1797585367310794752 |
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| author | Swarnima Roychowdhury Charles M. Roth |
| author_facet | Swarnima Roychowdhury Charles M. Roth |
| author_sort | Swarnima Roychowdhury |
| collection | DOAJ |
| description | Accurate pharmacokinetic–pharmacodynamic (PK-PD) models of biofilm treatment could be used to guide formulation and administration strategies to better control bacterial lung infections. To this end, we developed a detailed pharmacodynamic model of <i>P. aeruginosa</i> treatment with the front-line antibiotics, tobramycin and colistin, and validated it on a detailed dataset of killing dynamics. A compartmental model structure was developed in which the key features are the diffusion of the drug through a boundary layer to the bacteria, concentration-dependent interactions with bacteria, and the passage of the bacteria through successive transit states before death. The number of transit states employed was greater for tobramycin, which is a ribosomal inhibitor, than for colistin, which disrupts bacterial membranes. For both drugs, the experimentally observed delay in the killing of bacteria following drug exposure was consistent with the sum of the diffusion time and the time for passage through the transit states. For each drug, the PD model with a single set of parameters described data across a ten-fold range of concentrations and for both continuous and transient exposure protocols, as well as for combined drug treatments. The ability to predict drug response over a range of administration protocols allows this PD model to be integrated with PK descriptions to describe in vivo antibiotic response dynamics and to predict drug delivery strategies for the improved control of bacterial lung infections. |
| first_indexed | 2024-03-11T00:06:02Z |
| format | Article |
| id | doaj.art-144173d0b9a044ca8d3dcfa8259ea7a7 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T00:06:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-144173d0b9a044ca8d3dcfa8259ea7a72023-11-19T00:22:00ZengMDPI AGBiomedicines2227-90592023-08-01118231610.3390/biomedicines11082316Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial TreatmentsSwarnima Roychowdhury0Charles M. Roth1Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USAAccurate pharmacokinetic–pharmacodynamic (PK-PD) models of biofilm treatment could be used to guide formulation and administration strategies to better control bacterial lung infections. To this end, we developed a detailed pharmacodynamic model of <i>P. aeruginosa</i> treatment with the front-line antibiotics, tobramycin and colistin, and validated it on a detailed dataset of killing dynamics. A compartmental model structure was developed in which the key features are the diffusion of the drug through a boundary layer to the bacteria, concentration-dependent interactions with bacteria, and the passage of the bacteria through successive transit states before death. The number of transit states employed was greater for tobramycin, which is a ribosomal inhibitor, than for colistin, which disrupts bacterial membranes. For both drugs, the experimentally observed delay in the killing of bacteria following drug exposure was consistent with the sum of the diffusion time and the time for passage through the transit states. For each drug, the PD model with a single set of parameters described data across a ten-fold range of concentrations and for both continuous and transient exposure protocols, as well as for combined drug treatments. The ability to predict drug response over a range of administration protocols allows this PD model to be integrated with PK descriptions to describe in vivo antibiotic response dynamics and to predict drug delivery strategies for the improved control of bacterial lung infections.https://www.mdpi.com/2227-9059/11/8/2316pharmacodynamicscompartmental modeldrug diffusionbiofilm |
| spellingShingle | Swarnima Roychowdhury Charles M. Roth Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments Biomedicines pharmacodynamics compartmental model drug diffusion biofilm |
| title | Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments |
| title_full | Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments |
| title_fullStr | Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments |
| title_full_unstemmed | Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments |
| title_short | Pharmacodynamic Model of the Dynamic Response of <i>Pseudomonas aeruginosa</i> Biofilms to Antibacterial Treatments |
| title_sort | pharmacodynamic model of the dynamic response of i pseudomonas aeruginosa i biofilms to antibacterial treatments |
| topic | pharmacodynamics compartmental model drug diffusion biofilm |
| url | https://www.mdpi.com/2227-9059/11/8/2316 |
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