Dynamic <i>In Vitro</i> PK/PD Infection Models for the Development and Optimisation of Antimicrobial Regimens: A Narrative Review
The antimicrobial concentration–time profile in humans affects antimicrobial activity, and as such, it is critical for preclinical infection models to simulate human-like dynamic concentration–time profiles for maximal translatability. This review discusses the setup, principle, and application of v...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/13/12/1201 |
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| Summary: | The antimicrobial concentration–time profile in humans affects antimicrobial activity, and as such, it is critical for preclinical infection models to simulate human-like dynamic concentration–time profiles for maximal translatability. This review discusses the setup, principle, and application of various dynamic <i>in vitro</i> PK/PD infection models commonly used in the development and optimisation of antimicrobial treatment regimens. It covers the commonly used dynamic <i>in vitro</i> infection models, including the one-compartment model, hollow fibre infection model, biofilm model, bladder infection model, and aspergillus infection model. It summarises the mathematical methods for the simulation of the pharmacokinetic profile of single or multiple antimicrobials when using the serial or parallel configurations of <i>in vitro</i> systems. Dynamic <i>in vitro</i> models offer reliable pharmacokinetic/pharmacodynamic data to help define the initial dosing regimens of new antimicrobials that can be developed further in clinical trials. They can also help in the optimisation of dosing regimens for existing antimicrobials, especially in the presence of emerging antimicrobial resistance. In conclusion, dynamic <i>in vitro</i> infection models replicate the interactions that occur between microorganisms and dynamic antimicrobial exposures in the human body to generate data highly predictive of the clinical efficacy. They are particularly useful for the development new treatment strategies against antimicrobial-resistant pathogens. |
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| ISSN: | 2079-6382 |