Novel Pharmacokinetic/Pharmacodynamic Parameters Quantify the Exposure–Effect Relationship of Levofloxacin against Fluoroquinolone-Resistant Escherichia coli

Seeger, Johanna; Guenther, Sebastian; Schaufler, Katharina; Heiden, Stefan E.; Michelet, Robin; Kloft, Charlotte

doi:10.3390/antibiotics10060615

Cited by 5 publications

(10 citation statements)

References 40 publications

(51 reference statements)

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“…The use of the PK-PD indices in these ways has undoubtedly been very helpful across the range of areas where they have been applied, as they have provided a way to semi-quantitatively describe the exposure-response relationship of antibiotics. However, it is increasingly recognised that there are a number of important limitations associated with the use of these traditional PK-PD indices (Seeger et al, 2021a;Friberg, 2021;Landersdorfer and Nation, 2021). Some of the limitations relate to the nature of the nonclinical experimental models and approaches that are used to establish what is concluded to be the most predictive index, while other limitations arise from the use, in each of the indices, of the MIC as a measure of the antibacterial "potency" of the antibiotic.…”

Section: Antibiotic Pk-pd: Looking Backmentioning

confidence: 99%

“…MIC is included as the "PD" component in each of the three traditional PK-PD indices, but it is important to recognise that it has a number of limitations that go well beyond the fact that it takes a day or more for its estimation (Wen et al, 2016;Mouton et al, 2018a;Bader et al, 2018;Mouton et al, 2018b;Mouton, 2018;Seeger et al, 2021a;Kowalska-Krochmal and Dudek-Wicher, 2021). The MIC is a very crude in vitro measure of the "potency" of an antibiotic that is determined in a matrix bearing little resemblance to the physiological milieu at an infection site in a patient.…”

Section: Mic: a Highly Problematic Measure Of Antibacterial Pd Activitymentioning

confidence: 99%

“…This problem is minimized in animal infection model or dynamic in vitro infection model studies aimed at elucidating the predictive PK-PD index and target values for various magnitudes of bacterial kill by ensuring that several bacterial strains are included in the study to inform setting of PK-PD targets and breakpoints (Zhao et al, 2016;Mouton et al, 2018b;Jorda and Zeitlinger, 2020). However, an inaccurate estimate from a single determination of MIC for an isolate from a particular patient is problematic in the clinical application of MIC-based PK-PD targets within the framework of what has been described as therapeutic drug monitoring (TDM) (Mouton et al, 2018b;Roberts et al, 2019;Märtson et al, 2020;Seeger et al, 2021a;Goutelle et al, 2021;Jorgensen et al, 2021;Moser et al, 2021). Thus, if an isolate from an infected patient returns an MIC estimate from an assay which has a typical error range of two dilutions (i.e., potentially a 4-fold range of reported MIC values), it follows that the PK-PD driven target plasma concentration for that patient would vary 4-fold if the PK-PD index for the antibiotic is fAUC/MIC or fC max /MIC.…”

Section: Mic: a Highly Problematic Measure Of Antibacterial Pd Activitymentioning

confidence: 99%

“…Although the traditional PK-PD indices have undoubtedly been helpful in facilitating preclinical development of antibiotics, their translation into clinical studies and ultimately in proposing dosage regimens for various categories of patients, the indices are associated with several substantial limitations as discussed above. The need to capture the full time-course of both the PK of the antibiotic and the PD of its antibacterial effect, without reliance on MIC, has been increasingly recognised; fortunately, moves in this direction have already commenced (Seeger et al, 2021a;Friberg, 2021;Landersdorfer and Nation, 2021;Wicha et al, 2021).…”

Section: Antibiotic Pk-pd: Moving Forwardmentioning

confidence: 99%

“…One approach involved the development of novel MICindependent PK-PD metrics based upon quantification of the cumulative area under the antibiotic concentration-time curve and the cumulative area between the growth control and the bacterial-killing and -regrowth curves from in vitro time-kill experiments (Seeger et al, 2021a). While the potential applicability of this approach warrants further examination, it is not clear how other factors that may influence clinical outcome (e.g., immune function, severity of overall illness) can be incorporated.…”

Section: Antibiotic Pk-pd: Moving Forwardmentioning

confidence: 99%

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Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Landersdorfer

Nation

2021

Front. Pharmacol.

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Within a few years after the first successful clinical use of penicillin, investigations were conducted in animal infection models to explore a range of factors that were considered likely to influence the antibacterial response to the drug. Those studies identified that the response was influenced by not only the total daily dose but also the interval between individual doses across the day, and whether penicillin was administered in an intermittent or continuous manner. Later, as more antibiotics were discovered and developed, antimicrobial pharmacologists began to measure antibiotic concentrations in biological fluids. This enabled the linking of antibacterial response at a single time point in an animal or in vitro infection model with one of three summary pharmacokinetic (PK) measures of in vivo exposure to the antibiotic. The summary PK exposure measures were normalised to the minimum inhibitory concentration (MIC), an in vitro measure of the pharmacodynamic (PD) potency of the drug. The three PK-PD indices (ratio of maximum concentration to MIC, ratio of area under the concentration-time curve to MIC, time concentration is above MIC) have been used extensively since the 1980s. While these MIC-based summary PK-PD metrics have undoubtedly facilitated the development of new antibiotics and the clinical application of both new and old antibiotics, it is increasingly recognised that they have a number of substantial limitations. In this article we use a historical perspective to review the origins of the three traditional PK-PD indices before exploring in detail their limitations and the implications arising from those limitations. Finally, in the interests of improving antibiotic development and dosing in patients, we consider a model-based approach of linking the full time-course of antibiotic concentrations with that of the antibacterial response. Such an approach enables incorporation of other factors that can influence treatment outcome in patients and has the potential to drive model-informed precision dosing of antibiotics into the future.

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Section: Antibiotic Pk-pd: Looking Backmentioning

confidence: 99%

Section: Mic: a Highly Problematic Measure Of Antibacterial Pd Activitymentioning

confidence: 99%

Section: Mic: a Highly Problematic Measure Of Antibacterial Pd Activitymentioning

confidence: 99%

Section: Antibiotic Pk-pd: Moving Forwardmentioning

confidence: 99%

Section: Antibiotic Pk-pd: Moving Forwardmentioning

confidence: 99%

See 3 more Smart Citations

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Landersdorfer

Nation

2021

Front. Pharmacol.

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Forecasting antimicrobial resistance evolution

Rolff,

Bonhoeffer,

Kloft

et al. 2024

Trends in Microbiology

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Prediction of Compound Plasma Concentration–Time Profiles in Mice Using Random Forest

et al. 2023

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Pharmacokinetic (PK) parameters such as clearance (CL) and volume of distribution (Vd) have been the subject of previous in silico predictive models. However, having information of the concentration over time profile explicitly can provide additional value like time above MIC or AUC, etc., to understand both the efficacy and safety-related aspects of a compound. In this work, we developed machine learning models for plasma concentration–time profiles after both i.v. and p.o. dosing for a series of 17 in-house projects. For explanatory variables, MACCS Keys chemical descriptors as well as in silico and experimental in vitro PK parameters were used. The predictive accuracy of random forest (RF), message passing neural network, 2-compartment models using estimated CL and Vdss, and an average model (as a control experiment) was investigated using 5-fold cross-validation (5-fold CV) and leave-one-project-out validation (LOPO-V). The predictive accuracy of RF in 5-fold CV for i.v. and p.o. plasma concentration–time profiles was the best among the models studied, with an RMSE for i.v. dosing at 0.08, 1, and 8 h of 0.245, 0.474, and 0.462, respectively, and an RMSE for p.o. dosing at 0.25, 1, and 8 h of 0.500, 0.612, and 0.509, respectively. Furthermore, by investigating the importance of the in vitro PK parameters using the Gini index, we observed that the general prior knowledge in ADME research was reflected well in the respective feature importance of in vitro parameters such as predicted human Vd (hVd) for the initial distribution, mouse intrinsic CL and unbound fraction of mouse plasma for the elimination process, and Caco2 permeability for the absorption process. Also, this model is the first model that can predict twin peaks in the concentration–time profile much better than a baseline compartment model. Because of its combination of sufficient accuracy and speed of prediction, we found the model to be fit-for-purpose for practical lead optimization.

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Novel Pharmacokinetic/Pharmacodynamic Parameters Quantify the Exposure–Effect Relationship of Levofloxacin against Fluoroquinolone-Resistant Escherichia coli

Cited by 5 publications

References 40 publications

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Forecasting antimicrobial resistance evolution

Prediction of Compound Plasma Concentration–Time Profiles in Mice Using Random Forest

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