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Pharmacology

Semimechanistic Modeling of Eravacycline Pharmacodynamics Using In Vitro Time-Kill Data with MIC Incorporated in an Adaptive Resistance Function

Ken Nguyen, Timothy J. Bensman, Xiaohui (Tracey) Wei, Jason N. Moore
Ken Nguyen
aExperimental Therapeutic Branch, Department of Clinical Pharmacology, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
bDivision of Infectious Disease Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
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Timothy J. Bensman
bDivision of Infectious Disease Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
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Xiaohui (Tracey) Wei
bDivision of Infectious Disease Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
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Jason N. Moore
bDivision of Infectious Disease Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA
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DOI: 10.1128/AAC.01308-20
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ABSTRACT

Effective bacterial infection eradication requires not only potent antibacterial agents but also proper dosing strategies. Current practices generally utilize point estimates of the effects of therapeutic agents, even though the actual kinetics of exposure are much more complex and relevant. Here, we use a full time course of the observed in vitro effects to develop a semimechanistic pharmacokinetic-pharmacodynamic model for eravacycline against multiple Gram-negative bacterial pathogens. This model incorporates components such as pharmacokinetics, bacterial life cycle, and drug effects to quantitatively describe the time course of antibacterial killing and the emergence of resistance. Model discrimination was performed by comparing goodness of fit, convergence diagnostics, and objective function values. Models were validated by assessing their abilities to describe bacterial count time courses in visual predictive checks. The final model describes 576 bacterial counts (expressed in log10 CFU per milliliter) from 144 in vitro time-kill experiments with low residual error and high precision. We characterize antibacterial susceptibility as a function of the MIC and adaptive resistance. In doing so, we show that the MIC is proportional to initial susceptibility at 0 h and the development of resistance over the course of 16 h. Altogether, this model may be useful in supporting dose selection, since it incorporates in vitro pharmacodynamics and clinically observed individual drug susceptibilities.

  • This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.
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Semimechanistic Modeling of Eravacycline Pharmacodynamics Using In Vitro Time-Kill Data with MIC Incorporated in an Adaptive Resistance Function
Ken Nguyen, Timothy J. Bensman, Xiaohui (Tracey) Wei, Jason N. Moore
Antimicrobial Agents and Chemotherapy Aug 2020, 64 (9) e01308-20; DOI: 10.1128/AAC.01308-20

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Semimechanistic Modeling of Eravacycline Pharmacodynamics Using In Vitro Time-Kill Data with MIC Incorporated in an Adaptive Resistance Function
Ken Nguyen, Timothy J. Bensman, Xiaohui (Tracey) Wei, Jason N. Moore
Antimicrobial Agents and Chemotherapy Aug 2020, 64 (9) e01308-20; DOI: 10.1128/AAC.01308-20
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KEYWORDS

in silico
in vitro
modeling
pharmacodynamics
pharmacology
semimechanistic

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