211. Defining Optimal Sampling Times for Cefepime Therapeutic Drug Monitoring in Clinical Practice

Abstract Background Clinicians performing beta-lactam therapeutic drug monitoring (TDM) lack evidence on when levels should ideally be drawn after a dose. Herein, we define the optimal timing (i.e., optimal sampling) for cefepime using real-world TDM data to validate our approach.Figure 1.External validation of the Bayesian prior non-parametric population PK model Methods De-identified data from two centers performing routine cefepime TDM were extracted by InsightRX and served as an external validation cohort. Plasma cefepime was quantified using validated LC-MS/MS assays for TDM and dosing was protocolized at each site. CRRT and ECMO patients were included but other dialysis patients were not. Bias (MPE) and precision (RMSE) of a non-parametric prior were assessed. Multiple-model optimal (MM-opt) sampling strategies were estimated for the first 24 hours of treatment. To mirror clinical practice, one- and two-sample designs were evaluated. Dose and covariate values informed optimal sampling times. Bayesian PK exposures were compared using all samples, trough-only sampling, or using a single optimally timed sample. AUCs were calculated from the posteriors. For fT >MIC analysis, the MIC was fixed at 8 mg/L. We used Pmetrics 2.1.1 for R.Figure 2.Distribution of MM-optimal sampling times in external validation data using n=1 sample Results 116 patients (42% female; median age, CRCL, and weight: 62 years, 76 mL/min, and 80 kg, respectively) contributed 235 levels. The PK model demonstrated acceptable bias and precision (-6% MPE, 30.9 RMSE) as a prior for estimating exposures from the TDM data (Fig1). For a one-sample approach, the most common MM-opt sampling times varied (Fig2) but were often a mid-point or trough. In the two-sample approach, sample one was often a mid-point and sample two was often a trough (Fig3). First 24-hr AUC and fT>MIC did not significantly differ using all available samples for analysis vs. limiting sampling to a single optimized time point vs. limiting sampling to a trough-only approach (P >0.05 for all comparisons; Fig4).Figure 3.Distribution of MM-optimal sampling times in external validation data using n=2 samples Conclusion Optimal cefepime sampling times depended on dosing regimen, and renal disposition. When limited to a single sample, optimal sampling times for cefepime TDM were often midpoint/trough levels, but when two samples were obtained the optimal sampling times were often a mid-point followed by a trough. Estimation of PK and PK/PD exposures was not significantly worse when using a validated Bayesian prior and a trough-only sampling approach.Figure 4.First 24 hours PK and PK/PD exposure estimates from Bayesian analysis of all external validation data and Bayesian analysis of n=1 optimally timed samples from the same population Disclosures Nathaniel J. Rhodes, PharmD MS, Apothecademy, LLC: Advisor/Consultant Brandon Smith, MD, PharmD, Melinta Therapeutics: Advisor/Consultant|Shionogi, INC: Advisor/Consultant Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support Jasmine Hughes, PhD, InsightRX: Employee|InsightRX: Stocks/Bonds (Private Company) Maria-Stephanie Hughes, PharmD, InsightRX: Employee of company|InsightRX: Stocks/Bonds (Private Company) Fekade B. Sime, PhD, S.Aust., Gilead: Grant/Research Support|Pfizer: Grant/Research Support Patrick J. Kiel, PharmD, Amgen: Employee|Amgen: Stocks/Bonds (Public Company) Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|Basilea: Advisor/Consultant|Cidara: Advisor/Consultant|DoseMe: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Lykos: Advisor/Consultant|Roche: Advisor/Consultant|Third Pole Therapeutics: Advisor/Consultant|Xelia: Advisor/Consultant

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Open Forum Infectious Diseases
Published
2025-01-29
DOI
https://doi.org/10.1093/ofid/ofae631.069
Primary Topic
Antibiotics Pharmacokinetics and Efficacy
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article
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article

211. Defining Optimal Sampling Times for Cefepime Therapeutic Drug Monitoring in Clinical Practice

Patrick J. Kiel, Marc H. Scheetz, Michael Noel Neely, Brandon Smith et al.
Open Forum Infectious Diseases
Antibiotics Pharmacokinetics and Efficacy
article

211. Defining Optimal Sampling Times for Cefepime Therapeutic Drug Monitoring in Clinical Practice

Patrick J. Kiel, Marc H. Scheetz, Michael Noel Neely, Brandon Smith, Jason A. Roberts, Samantha Pan, Fekade B. Sime, Maria‐Stephanie A. Hughes, Nathaniel J. Rhodes, Shannon Galvin, Erin Weslander, Ryan K. Shields, Jasmine Hughes
article en

Abstract

Abstract Background Clinicians performing beta-lactam therapeutic drug monitoring (TDM) lack evidence on when levels should ideally be drawn after a dose. Herein, we define the optimal timing (i.e., optimal sampling) for cefepime using real-world TDM data to validate our approach.Figure 1.External validation of the Bayesian prior non-parametric population PK model Methods De-identified data from two centers performing routine cefepime TDM were extracted by InsightRX and served as an external validation cohort. Plasma cefepime was quantified using validated LC-MS/MS assays for TDM and dosing was protocolized at each site. CRRT and ECMO patients were included but other dialysis patients were not. Bias (MPE) and precision (RMSE) of a non-parametric prior were assessed. Multiple-model optimal (MM-opt) sampling strategies were estimated for the first 24 hours of treatment. To mirror clinical practice, one- and two-sample designs were evaluated. Dose and covariate values informed optimal sampling times. Bayesian PK exposures were compared using all samples, trough-only sampling, or using a single optimally timed sample. AUCs were calculated from the posteriors. For fT >MIC analysis, the MIC was fixed at 8 mg/L. We used Pmetrics 2.1.1 for R.Figure 2.Distribution of MM-optimal sampling times in external validation data using n=1 sample Results 116 patients (42% female; median age, CRCL, and weight: 62 years, 76 mL/min, and 80 kg, respectively) contributed 235 levels. The PK model demonstrated acceptable bias and precision (-6% MPE, 30.9 RMSE) as a prior for estimating exposures from the TDM data (Fig1). For a one-sample approach, the most common MM-opt sampling times varied (Fig2) but were often a mid-point or trough. In the two-sample approach, sample one was often a mid-point and sample two was often a trough (Fig3). First 24-hr AUC and fT>MIC did not significantly differ using all available samples for analysis vs. limiting sampling to a single optimized time point vs. limiting sampling to a trough-only approach (P >0.05 for all comparisons; Fig4).Figure 3.Distribution of MM-optimal sampling times in external validation data using n=2 samples Conclusion Optimal cefepime sampling times depended on dosing regimen, and renal disposition. When limited to a single sample, optimal sampling times for cefepime TDM were often midpoint/trough levels, but when two samples were obtained the optimal sampling times were often a mid-point followed by a trough. Estimation of PK and PK/PD exposures was not significantly worse when using a validated Bayesian prior and a trough-only sampling approach.Figure 4.First 24 hours PK and PK/PD exposure estimates from Bayesian analysis of all external validation data and Bayesian analysis of n=1 optimally timed samples from the same population Disclosures Nathaniel J. Rhodes, PharmD MS, Apothecademy, LLC: Advisor/Consultant Brandon Smith, MD, PharmD, Melinta Therapeutics: Advisor/Consultant|Shionogi, INC: Advisor/Consultant Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support Jasmine Hughes, PhD, InsightRX: Employee|InsightRX: Stocks/Bonds (Private Company) Maria-Stephanie Hughes, PharmD, InsightRX: Employee of company|InsightRX: Stocks/Bonds (Private Company) Fekade B. Sime, PhD, S.Aust., Gilead: Grant/Research Support|Pfizer: Grant/Research Support Patrick J. Kiel, PharmD, Amgen: Employee|Amgen: Stocks/Bonds (Public Company) Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|Basilea: Advisor/Consultant|Cidara: Advisor/Consultant|DoseMe: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Lykos: Advisor/Consultant|Roche: Advisor/Consultant|Third Pole Therapeutics: Advisor/Consultant|Xelia: Advisor/Consultant

Open Forum Infectious DiseasesVol. 12(Supplement_1)
Northwestern University (US), University of Southern California (US), Midwestern University (US), Translational Research Institute (AU), Children's Hospital of Los Angeles (US), University of Pittsburgh (US), Northwestern Memorial Hospital (US), University of Pittsburgh Medical Center (US), Indiana University School of Medicine, Indiana University – Purdue University Indianapolis (US)
Openalex Percentile: Top 98%
Antibiotics Pharmacokinetics and Efficacy
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