TY - JOUR
T1 - Antibiotic pharmacokinetic/pharmacodynamic modelling
T2 - MIC, pharmacodynamic indices and beyond
AU - Rao, Gauri G.
AU - Landersdorfer, Cornelia B.
N1 - Funding Information:
Funding: This work was supported by the National Institute of Allergy and Infectious Diseases, award number R01AI146241 to G.G.R. and the Australian National Health and Medical Research Council Ideas grant GNT1184428 to C.B.L. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Australian National Health and Medical Research Council.
Publisher Copyright:
© 2021 Elsevier Ltd
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - The dramatic increase in antimicrobial resistance and the limited pharmacological treatment options highlight the urgent need to optimize therapeutic regimens of new and available anti-infectives. Several in-vitro and in-vivo infection models are employed to understand the relationship between drug exposure profiles in plasma or at the site of infection (pharmacokinetics) and the time course of therapeutic response (pharmacodynamics) to select and optimize dosage regimens for new and approved drugs. Well-designed preclinical studies, combined with mathematical-model-based pharmacokinetic/pharmacodynamic analysis and in-silico simulations, are critical for the effective translation of preclinical data and design of appropriate and successful clinical trials. Integration with population pharmacokinetic modelling and simulations allows for the incorporation of interindividual variability that occurs in both pharmacokinetics and pharmacodynamics, and helps to predict the probability of target attainment and treatment outcome in patients. This article reviews the role of pharmacokinetic/pharmacodynamic approaches in the optimization of dosage regimens to maximize antibacterial efficacy while minimizing toxicity and emergence of resistance, and to achieve a high likelihood of therapeutic success. Polymyxin B, an approved drug with a narrow therapeutic window, serves as an illustrative example to highlight the importance of pharmacokinetic/pharmacodynamic modelling in conjunction with experimentation, employing static time-kill studies followed by dynamic in-vitro or in-vivo models, or both, to learn and confirm mechanistic insights necessary for translation to the bedside.
AB - The dramatic increase in antimicrobial resistance and the limited pharmacological treatment options highlight the urgent need to optimize therapeutic regimens of new and available anti-infectives. Several in-vitro and in-vivo infection models are employed to understand the relationship between drug exposure profiles in plasma or at the site of infection (pharmacokinetics) and the time course of therapeutic response (pharmacodynamics) to select and optimize dosage regimens for new and approved drugs. Well-designed preclinical studies, combined with mathematical-model-based pharmacokinetic/pharmacodynamic analysis and in-silico simulations, are critical for the effective translation of preclinical data and design of appropriate and successful clinical trials. Integration with population pharmacokinetic modelling and simulations allows for the incorporation of interindividual variability that occurs in both pharmacokinetics and pharmacodynamics, and helps to predict the probability of target attainment and treatment outcome in patients. This article reviews the role of pharmacokinetic/pharmacodynamic approaches in the optimization of dosage regimens to maximize antibacterial efficacy while minimizing toxicity and emergence of resistance, and to achieve a high likelihood of therapeutic success. Polymyxin B, an approved drug with a narrow therapeutic window, serves as an illustrative example to highlight the importance of pharmacokinetic/pharmacodynamic modelling in conjunction with experimentation, employing static time-kill studies followed by dynamic in-vitro or in-vivo models, or both, to learn and confirm mechanistic insights necessary for translation to the bedside.
KW - AUC
KW - Bacteria
KW - MIC
KW - Modelling
KW - PD
KW - PK
UR - http://www.scopus.com/inward/record.url?scp=85108663277&partnerID=8YFLogxK
U2 - 10.1016/j.ijantimicag.2021.106368
DO - 10.1016/j.ijantimicag.2021.106368
M3 - Review Article
C2 - 34058336
AN - SCOPUS:85108663277
SN - 0924-8579
VL - 58
JO - International Journal of Antimicrobial Agents
JF - International Journal of Antimicrobial Agents
IS - 2
M1 - 106368
ER -