Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling

Wu, Benjamin; Sy, Sherwin K. B.; Derendorf, Hartmut

doi:10.1007/978-0-387-75613-4_4

Cited by 16 publications

(16 citation statements)

References 43 publications

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“…Measurement of MIC is associated with substantial uncertainty and variability because of the two-fold dilution technique used for its assessment and the interpretation as implied binary response, which considers efficacy only above MIC and no killing below MIC. Furthermore, MIC varies across patient populations and bacterial strains, and may change with time in the same patient [7]. Thus, MIC-based approaches ignore the dynamics of bacterial killing with time.…”

Section: Pk/pd Indicesmentioning

confidence: 99%

“…The underlying assumption of this model is that the microbial population is homogenous having the same turnover rate constants, which is an oversimplification of the real scenario as microbial populations are usually known to be heterogeneous with multiple subpopulations with distinct metabolic states and drug resistance profiles [7]. …”

Section: Bacterial Growth Kineticsmentioning

confidence: 99%

“…It can also be used for quantitative comparisons of multiple drug candidates to identify lead candidates and support go-no go decisions [7]. …”

Section: Development Of a Translational Pk/pd Model For Antibioticsmentioning

confidence: 99%

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Translational PK/PD of anti-infective therapeutics

Rathi

Lee

2016

Drug Discovery Today: Technologies

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Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.

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Section: Pk/pd Indicesmentioning

confidence: 99%

Section: Bacterial Growth Kineticsmentioning

confidence: 99%

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Translational PK/PD of anti-infective therapeutics

Rathi

Lee

2016

Drug Discovery Today: Technologies

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“…Most PK/PD models of antibiotic action [reviewed, e.g., in (1)(2)(3)] are deterministic: the main equations concern averages, e.g., the average number of CFU such as bacteria. Time dependent averages are typically computed by integrating coupled nonlinear first order ordinary differential equations [reviewed, e.g., in (4)].…”

Section: Pk/pd Modelingmentioning

confidence: 99%

Stochastic Process Pharmacodynamics: Dose Timing in Neonatal Gentamicin Therapy as an Example

Radivoyevitch

Siranart

Hlatky

et al. 2015

AAPS J

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ABSTRACT. We consider dosing regimens designed to cure patients by eradicating colony forming units (CFU) such as bacteria. In the field of "population" pharmaco-kinetics/dynamics (PK/PD), interindividual variability (IIV) of patients is estimated using model parameter statistical distributions. We consider a more probabilistic approach to IIV called stochastic process theory, motivated by the fact that tumor treatment planning uses both approaches. Stochastic process PD can supply additional insights and suggest different dosing regimens due to its emphasis on the probability of complete CFU eradication and its predictions on "pure chance" fluctuations of CFU number per patient when treatment has reduced this integer to less than~100. To exemplify the contrast between stochastic process PD models and standard deterministic PD models, which track only average CFU number, we analyze, neglecting immune responses, neonatal intravenous gentamicin dosing regimens directed against Escherichia coli. Our stochastic calculations predict that the first dose is crucial for CFU eradication. For example, a single 6 mg/kg dose is predicted to have a higher eradication probability than four daily 4 mg/kg doses. We conclude: (1) neonatal gentamicin dosing regimens with larger first doses but smaller total doses deserve investigation; (2) in general, if standard PK/PD models predict average CFU number drops substantially below 100, the models should be modified to incorporate stochastic effects more accurately, and will then usually make more favorable, or less unfavorable, predictions for front boosting ("hit hard early"). Various caveats against over-interpreting the calculations are given.

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“…Mechanistic mathematical models, including experimentally validated pharmacodynamic/pharmacokinetic models, describe antibiotic effects better than simple MIC measurements (19)(20)(21)(22)(23). For example, the shape of the antibiotic dose-response curve is very important for the microbiological efficacy of antibiotic treatment regimens at high (treatment) levels (19).…”

mentioning

confidence: 99%

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration

Greenfield

Shaked

Marrs

et al. 2018

Antimicrob Agents Chemother

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Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient () that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [κ]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well ( ≥ 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ≤ κ ≤ 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the , indicating the importance of fitness differences between strains for determining the MSC. The MSC can be>1 order of magnitude lower than the MIC, typically by the factor This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

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Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling

Cited by 16 publications

References 43 publications

Translational PK/PD of anti-infective therapeutics

Translational PK/PD of anti-infective therapeutics

Stochastic Process Pharmacodynamics: Dose Timing in Neonatal Gentamicin Therapy as an Example

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration

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