Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance
Abstract
:1. Introduction
2. Components of Semi-Mechanism PK/PD Model
2.1. Bacterial Growth Model
2.2. Antibacterial Effect Model
2.2.1. Persistent Resistance
2.2.2. Pre-Existing Resistance
2.2.3. Adaptive Resistance
3. Methods for the Development of Semi-Mechanism PK/PD Model
4. The Factors Affecting Model Establishment
4.1. Inoculum Effect
4.2. Host Response
4.3. The Types of Pharmacodynamic Data for the Model
5. Application of Model in Dosing Regimen, Combination Therapy, and Determination of Breakpoint
5.1. Dosage Regimen
5.2. Combination Therapy
5.3. PK/PD Breakpoint and Cutoffs
5.4. Prediction the Kinetic of Bacterial in Guts
6. Overlook
- Regulation. The official regulations need to be published, which will play the role of encouragement and guidance.
- Education. It is very important to tell the modelers how to establish a model and judge the model. It is an efficient way to acquire the relevant knowledge from the tutorial. Rowland et al. summarized the inception, maturation, and future vision about Pharmacometrics and Systems Pharmacology. Twenty representative particles over the past 10 years were outlined [66]. Besides the tutorials, the software company and the public training courses also can offer some guidance. For example, many detailed courses can be found on the Metrum research group (www.metrumrg.com (accessed on 6 January 2022)).
- Share. It is critical and necessary to publish the model code for the subsequent model applications. This will help modelers to learn the programming languages. Of course, the excellent forms of programming languages are also important. Mathematical models are widely used in various fields that would require more competent modelers.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Antimicrobial Class | Drug | Bacteria | Resistant Species | References |
---|---|---|---|---|
Aminoglycosides | Gentamicin | Staphylococcus aureus | Adaptive resistance | [42] |
Pseudomonas aeruginosa Acinetobacter baumannii | Adaptive resistance | [43] | ||
Escherichia coli | Adaptive resistance Persistent resistance | [31] | ||
Pseudomonas aeruginosa | Pre-existing resistance | [44] | ||
Tobramycin | Pseudomonas aeruginosa | Persistent resistance | [24] | |
Pre-existing resistance | [45] | |||
Beta-lactams | Benzylpenicillin Cefuroxime | Streptococcus pyogenes | Persistent resistance | [18] |
Meropenem | Pseudomonas aeruginosa | Adaptive resistance | [30] | |
Pre-existing resistance Persistent resistance | [46] | |||
Pre-existing resistance Persistent resistance Adaptive resistance | [47] | |||
Ertapenem | Escherichia coli | Pre-existing resistance Persistent resistance | [48] | |
Ceftobiprole | Staphylococcus aureus | Persistent resistance | [49] | |
Cefuroxime | Escherichia coli | Persistent resistance | [34] | |
Fluoroquinolones | Moxifloxacin | Streptococcus pyogenes | Persistent resistance | [18] |
Staphylococcus aureus | Pre-existing resistance Adaptive resistance | [50] | ||
Ciprofloxacin | Staphylococcus aureus | Pre-existing resistance | [51] | |
Pseudomonas aerugeinosa | Adaptive resistance | [52] | ||
Escherichia coli | Pre-existing resistance Persistent resistance | [22] | ||
Enrofloxacin | Escherichia coli | Pre-existing resistance | [28] | |
Macrolides | Erythromycin | Streptococcus pyogenes | Persistent resistance | [18] |
Polymyxin | Colistin | Pseudomonas aeruginosa | Adaptive resistance Persistent resistance | [53] |
Pre-existing resistance | [54] | |||
Chloramphenicols | Florfenicol | Pasteurella multocida Mannheimia haemolytica | Persistent resistance | [55] |
Tetracyclines | Eravacycline | Escherichia coli Acinetobacter baumannii | Adaptive resistance | [56] |
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Mi, K.; Zhou, K.; Sun, L.; Hou, Y.; Ma, W.; Xu, X.; Huo, M.; Liu, Z.; Huang, L. Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance. Pharmaceutics 2022, 14, 246. https://doi.org/10.3390/pharmaceutics14020246
Mi K, Zhou K, Sun L, Hou Y, Ma W, Xu X, Huo M, Liu Z, Huang L. Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance. Pharmaceutics. 2022; 14(2):246. https://doi.org/10.3390/pharmaceutics14020246
Chicago/Turabian StyleMi, Kun, Kaixiang Zhou, Lei Sun, Yixuan Hou, Wenjin Ma, Xiangyue Xu, Meixia Huo, Zhenli Liu, and Lingli Huang. 2022. "Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance" Pharmaceutics 14, no. 2: 246. https://doi.org/10.3390/pharmaceutics14020246
APA StyleMi, K., Zhou, K., Sun, L., Hou, Y., Ma, W., Xu, X., Huo, M., Liu, Z., & Huang, L. (2022). Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance. Pharmaceutics, 14(2), 246. https://doi.org/10.3390/pharmaceutics14020246