PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment

Detalhes bibliográficos
Autor(a) principal: Menezes, Bruna Kochhann
Data de Publicação: 2021
Outros Autores: Alves, Izabel Almeida, Staudt, Keli Jaqueline, Beltrame, Betina Montanari, Venz, Letícia, Michelin, Lessandra, Araújo, Bibiana Verlindo de, Tasso, Leandro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/249536
Resumo: Objectives The aim of this study was to investigate the effect of daptomycin against methicillin-resistant staphylococci (MRSA and MRSE) bacteremia using computer modeling. Methods A pharmacokinetic/pharmacodynamic (PK/PD) modeling strategy to explain the data from an in vitro dynamic model employing time-kill curves for MRSA and MRSE was proposed. Bacterial killing was followed over time by determining viable counts and the resulting time-kill data was analyzed. Monte Carlo simulations were performed using pharmacokinetic parameters and pharmacodynamic data to determine the probabilities of target attainment and cumulative fractions of response in terms of area under the concentration curve/minimum inhibition concentration (MIC) targets of daptomycin. Simulations were conducted to assess the reduction in the number of colony-forming units (CFU)/mL for 18 days of treatment with daptomycin at doses of 6, 8, and 10 mg/kg/24 h or 48 h with variations in creatinine clearance ( CLCR): 15–29 mL/ min/1.73 m2, 30–49 mL/min/1.73 m2, 50–100 mL/min/1.73 m2, as well as for defining the probability of reaching the target fAUC/MIC = 80 in the same dose and clearance range. A PK/PD model with saturation in the number of bacteria in vitro, growth delay, and bacterial death, as well as Hill’s factor, was used to describe the data for both MRSA and MRSE. Results Monte Carlo simulations showed that for MRSA there was a reduction > 2 log CFU/mL with doses ≥ 6 mg/kg/day in 75th percentile of the simulated population after 18 days of treatment with daptomycin, whereas for MRSE this reduction was observed in 95th percentile of the population. Conclusions The presented in vitro PK/PD model and associated modeling approach were able to characterize the timekill kinetics of MRSA and MRSE. Our study based on PTAs suggests that doses ≥ 6 mg/kg/day of daptomycin should be used to treat bacteremia caused by MRSA and MRSE in patients with CLCR of 15–29 mL/min/1.73 m2. For patients with CLCR ≥ 50 mL/min/1.73 m2, it would be necessary to employ a dose of 10 mg/kg/day to treat complicated bacteremias.
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spelling Menezes, Bruna KochhannAlves, Izabel AlmeidaStaudt, Keli JaquelineBeltrame, Betina MontanariVenz, LetíciaMichelin, LessandraAraújo, Bibiana Verlindo deTasso, Leandro2022-10-03T04:48:32Z20211517-8382http://hdl.handle.net/10183/249536001149163Objectives The aim of this study was to investigate the effect of daptomycin against methicillin-resistant staphylococci (MRSA and MRSE) bacteremia using computer modeling. Methods A pharmacokinetic/pharmacodynamic (PK/PD) modeling strategy to explain the data from an in vitro dynamic model employing time-kill curves for MRSA and MRSE was proposed. Bacterial killing was followed over time by determining viable counts and the resulting time-kill data was analyzed. Monte Carlo simulations were performed using pharmacokinetic parameters and pharmacodynamic data to determine the probabilities of target attainment and cumulative fractions of response in terms of area under the concentration curve/minimum inhibition concentration (MIC) targets of daptomycin. Simulations were conducted to assess the reduction in the number of colony-forming units (CFU)/mL for 18 days of treatment with daptomycin at doses of 6, 8, and 10 mg/kg/24 h or 48 h with variations in creatinine clearance ( CLCR): 15–29 mL/ min/1.73 m2, 30–49 mL/min/1.73 m2, 50–100 mL/min/1.73 m2, as well as for defining the probability of reaching the target fAUC/MIC = 80 in the same dose and clearance range. A PK/PD model with saturation in the number of bacteria in vitro, growth delay, and bacterial death, as well as Hill’s factor, was used to describe the data for both MRSA and MRSE. Results Monte Carlo simulations showed that for MRSA there was a reduction > 2 log CFU/mL with doses ≥ 6 mg/kg/day in 75th percentile of the simulated population after 18 days of treatment with daptomycin, whereas for MRSE this reduction was observed in 95th percentile of the population. Conclusions The presented in vitro PK/PD model and associated modeling approach were able to characterize the timekill kinetics of MRSA and MRSE. Our study based on PTAs suggests that doses ≥ 6 mg/kg/day of daptomycin should be used to treat bacteremia caused by MRSA and MRSE in patients with CLCR of 15–29 mL/min/1.73 m2. For patients with CLCR ≥ 50 mL/min/1.73 m2, it would be necessary to employ a dose of 10 mg/kg/day to treat complicated bacteremias.application/pdfengBrazilian journal of microbiology. São Paulo. Vol. 52 (2021), p. 1967–1979AntibacterianosDaptomicinaBacteriemiaBacteremiaDaptomycinPK/PD modelingMonte Carlo simulationPK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatmentinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001149163.pdf.txt001149163.pdf.txtExtracted Texttext/plain43736http://www.lume.ufrgs.br/bitstream/10183/249536/2/001149163.pdf.txt27811c11d6aee19703a343266c09af76MD52ORIGINAL001149163.pdfTexto completo (inglês)application/pdf2291897http://www.lume.ufrgs.br/bitstream/10183/249536/1/001149163.pdf982bea6688772aea68d84b31fb96e0d5MD5110183/2495362022-10-04 05:01:02.504oai:www.lume.ufrgs.br:10183/249536Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2022-10-04T08:01:02Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
title PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
spellingShingle PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
Menezes, Bruna Kochhann
Antibacterianos
Daptomicina
Bacteriemia
Bacteremia
Daptomycin
PK/PD modeling
Monte Carlo simulation
title_short PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
title_full PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
title_fullStr PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
title_full_unstemmed PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
title_sort PK/PD modeling of daptomycin against MRSA and MRSE and Monte Carlo simulation for bacteremia treatment
author Menezes, Bruna Kochhann
author_facet Menezes, Bruna Kochhann
Alves, Izabel Almeida
Staudt, Keli Jaqueline
Beltrame, Betina Montanari
Venz, Letícia
Michelin, Lessandra
Araújo, Bibiana Verlindo de
Tasso, Leandro
author_role author
author2 Alves, Izabel Almeida
Staudt, Keli Jaqueline
Beltrame, Betina Montanari
Venz, Letícia
Michelin, Lessandra
Araújo, Bibiana Verlindo de
Tasso, Leandro
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Menezes, Bruna Kochhann
Alves, Izabel Almeida
Staudt, Keli Jaqueline
Beltrame, Betina Montanari
Venz, Letícia
Michelin, Lessandra
Araújo, Bibiana Verlindo de
Tasso, Leandro
dc.subject.por.fl_str_mv Antibacterianos
Daptomicina
Bacteriemia
topic Antibacterianos
Daptomicina
Bacteriemia
Bacteremia
Daptomycin
PK/PD modeling
Monte Carlo simulation
dc.subject.eng.fl_str_mv Bacteremia
Daptomycin
PK/PD modeling
Monte Carlo simulation
description Objectives The aim of this study was to investigate the effect of daptomycin against methicillin-resistant staphylococci (MRSA and MRSE) bacteremia using computer modeling. Methods A pharmacokinetic/pharmacodynamic (PK/PD) modeling strategy to explain the data from an in vitro dynamic model employing time-kill curves for MRSA and MRSE was proposed. Bacterial killing was followed over time by determining viable counts and the resulting time-kill data was analyzed. Monte Carlo simulations were performed using pharmacokinetic parameters and pharmacodynamic data to determine the probabilities of target attainment and cumulative fractions of response in terms of area under the concentration curve/minimum inhibition concentration (MIC) targets of daptomycin. Simulations were conducted to assess the reduction in the number of colony-forming units (CFU)/mL for 18 days of treatment with daptomycin at doses of 6, 8, and 10 mg/kg/24 h or 48 h with variations in creatinine clearance ( CLCR): 15–29 mL/ min/1.73 m2, 30–49 mL/min/1.73 m2, 50–100 mL/min/1.73 m2, as well as for defining the probability of reaching the target fAUC/MIC = 80 in the same dose and clearance range. A PK/PD model with saturation in the number of bacteria in vitro, growth delay, and bacterial death, as well as Hill’s factor, was used to describe the data for both MRSA and MRSE. Results Monte Carlo simulations showed that for MRSA there was a reduction > 2 log CFU/mL with doses ≥ 6 mg/kg/day in 75th percentile of the simulated population after 18 days of treatment with daptomycin, whereas for MRSE this reduction was observed in 95th percentile of the population. Conclusions The presented in vitro PK/PD model and associated modeling approach were able to characterize the timekill kinetics of MRSA and MRSE. Our study based on PTAs suggests that doses ≥ 6 mg/kg/day of daptomycin should be used to treat bacteremia caused by MRSA and MRSE in patients with CLCR of 15–29 mL/min/1.73 m2. For patients with CLCR ≥ 50 mL/min/1.73 m2, it would be necessary to employ a dose of 10 mg/kg/day to treat complicated bacteremias.
publishDate 2021
dc.date.issued.fl_str_mv 2021
dc.date.accessioned.fl_str_mv 2022-10-03T04:48:32Z
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/249536
dc.identifier.issn.pt_BR.fl_str_mv 1517-8382
dc.identifier.nrb.pt_BR.fl_str_mv 001149163
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dc.language.iso.fl_str_mv eng
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dc.relation.ispartof.pt_BR.fl_str_mv Brazilian journal of microbiology. São Paulo. Vol. 52 (2021), p. 1967–1979
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