2019.1.DLLS_KS- dao tao lien tuc

"HIT HARD & HIT FAST“: nguyên tắc 4D 4D = chọn đúng kháng sinh theo phổ tác dụng, vị trí nhiễm khuẩn và nguy cơ nhiễm VK kháng thuốc, phối hợp kháng sinh hợp lý, liều dùng/chế độ liều ph

DƯỢC LÝ LÂM SÀNG TRONG

SỬ DỤNG KHÁNG SINH

Nguyễn Thùy Dương

BM Dược lý

Mục tiêu học tập

1 Giải thích được các bước tiếp cận hệ thống trong

lựa chọn kháng sinh

2 Thiết kế được chế độ liều trong sử dụng các kháng

sinh nhóm betalactam, aminoglycosid và

fluorquinolon dựa trên các dữ liệu dược động học

và dược lực học

Nguyên lý chung trong điều trị nhiễm khuẩn

Chẩn đoán nhiễm khuẩn

– Sốt > 37 o C

Chẩn đoán nhiễm khuẩn

Test đánh giá nhạy cảm

Bán định lượng

Test đánh giá nhạy cảm

Đĩa khuếch tán

Test đánh giá nhạy cảm

Nồng độ diệt khuẩn tối thiểu

Đánh giá định lượng: MIC, MBC

MIC < Ctrbình: sensible (S)

MIC > Cmax: resistant (R)

Ctrb < MIC < Cmax: intermediate (I)

Test đánh giá nhạy cảm

Xác định MIC trên đĩa 96 giếng

Test đánh giá nhạy cảm

Xác định MIC trên đĩa 96 giếng

Test đánh giá nhạy cảm

Epsilometer test (Etest)

Nguyên lý chung trong điều trị nhiễm khuẩn

"HIT HARD & HIT FAST“: nguyên tắc 4D

4D = chọn đúng kháng sinh theo phổ tác dụng, vị trí nhiễm khuẩn và nguy cơ nhiễm VK kháng thuốc, phối hợp kháng sinh hợp lý, liều dùng/chế độ liều phù hợp (PK/PD), xuống thang đúng cách

Denny KJ et al Expert Opin Drug Saf 2016; 15: 667-678.

Lựa chọn kháng sinh hợp lý

Vi khuẩn Kháng sinh

Người bệnh

Applied Pharmacokinetics and Pharmacodynamics, 4 th edition 2006.

Lựa chọn kháng sinh hợp lý

Lựa chọn kháng sinh hợp lý

– Dược động học: AUC, C peak , t 1/2

Lựa chọn kháng sinh theo vi sinh

Một số b-lactam

Glycopeptid

Fluoroquinolon Tetracyclin

Sulfonamid Một số b-lactam

Nguy cơ chọn lọc

đề kháng

!

Macrolid Aminoglycosid

Lựa chọn kháng sinh theo vi sinh

• Kìm khuẩn:

Kìm hãm sự phát triển vi khuẩn

• Diệt khuẩn:

Tiêu diệt vi khuẩn

Telithromycin vs S aureus Moxifloxacin vs S aureus

MIC

MIC Nồng độ

đỉnh

Nồng độ đỉnh

Seral et al, AAC (2003) 47:228 3-2292

Bệnh nhân suy giảm miễn dịch

!

Macrolid Tetracyclin

Fluoroquinolones Aminoglycosides b-lactams

Lựa chọn kháng sinh theo vi sinh

Lựa chọn kháng sinh dựa trên đặc điểm vi sinh

thuộc vi khuẩn nghi ngờ gây bệnh)

thấp nhất trên đa số vi khuẩn

Dược lực học: ảnh hưởng của thời gian

Tất cả các kháng sinh đều phụ thuộc thời gian

killing

Nhưng một số kháng sinh có tác dụng diệt khuẩn quá nhanh làm

cho thời gian không còn quan trọng

(tobramycin), hoặc

quinolon

(ciprofloxacin) tại nồng độ 4 X MIC, khả năng làm giảm

4 log số lượng vi khuẩn có thể đạt

sau 4-6h

killing

Dược lực học: ảnh hưởng của thời gian

Dược lực học: ảnh hưởng của thời gian

killing

Với kháng sinh lactam, chỉ đạt được giảm 2 log trong

beta-vòng 6 h,

… và tác dụng này không nhanh hơn khi nồng độ vượt quá 4xMIC

Dược lực học: tích hợp nồng độ và thời gian

Kháng sinh Tương quan Ảnh hưởng Hậu quả

liều- đáp ứng của thời gian lâm sàng

• Nồng độ cao không quan trọng

• Nồng độ đóng vai trò quyết định

• Thời gian không

là yếu tố ảnh hưởng

Lựa chọn kháng sinh theo PK-PD

Liều

dùng

Hiệu quả

Độc tính

Dược lực học (Pharmacodynamics)

Liều dùng Nồng độ KS trong máu biến thiên theo

thời gian

Nồng độ KS tại vị trí nhiễm khuẩn

Nồng độ KS tại các cơ quan khác

Hiệu quả điều trị

Tác dụng phụ/độc tính

Dược động học

Tối ưu hóa theo PK/PD

Kháng sinh phụ thuộc thời gian,

không có PAE

Kháng sinh phụ thuộc nồng độ,

PAE kéo dài

Nguồn: Rybak MJ Am J Med, 2006; 119 (6A): S37-44

Phân loại kháng sinh theo PK/PD

Ứng dụng của PK/PD

• Tối ưu hóa chế độ liều

Fundamentals of Antimicrobial Pharmacokinetics and Pharmacodynamics

ISBN 978-0-387-75612-7 ISBN 978-0-387-75613-4 (eBook) DOI 10.1007/978-0-387-75613-4

Springer New York Heidelberg Dordrecht London

Library of Congress Control Number: 2013953328 © Springer Science+Business Media New York 2014 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifi cally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer

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Printed on acid-free paper Springer is part of Springer Science+Business Media ( www.springer.com )

Editors

Alexander A Vinks Division of Clinical Pharmacology Cincinnati Children’s Hospital Medical Center and Department of Pediatrics

University of Cincinnati College of Medicine Cincinnati , OH , USA

Johan W Mouton Department of Medical Microbiology Radboudumc, Radboud University Nijmegen Nijmegen, The Netherlands

Hartmut Derendorf Department of Pharmaceutics University of Florida

Gainesville College of Pharmacy Gainesville , FL , USA

58

the agent will be most used For example, patients in intensive care units (ICU) generally have different pharmacokinetics with a higher volume of distribution and lower clearance than most other patients The use of different pharmacokinetic parameters in the simulations will obviously result in different conclusions with respect to the breakpoints, as was shown in case studies for ceftazidime (Mouton

et al 2005 ) and for other agents (Roberts et al 2009 ) MCS was performed using pharmacokinetic parameters from three different populations, human volunteers, patients with cystic fi brosis, and patients from the ICU In each population the derived breakpoints would have been different On the other hand, Muller et al recently showed that the results of MCS based on volunteer data obtained from phase 1 studies matched actual target attainments in phase 3 studies (Muller et al

2013 ) for ceftobiprole

The entire process as described in this chapter can be summarized in a fl ow gram as depicted in Fig 3.4 The diagram represents the different elements as recently described by the EUCAST and includes both the steps as required for new agents as well as those for established drugs (Mouton et al 2012 ) It should be borne

dia-in mdia-ind that breakpodia-ints are not set dia-in stone and that they are dependent on multiple factors Should one of these factors change, then the breakpoint should be reconsid- ered and possibly be changed if necessary The iterative process of optimizing dos- ing regimens and setting breakpoints continues after the breakpoint has been established.

Correlation Exposure -Effect

Preclinical PK/PD studies Clinical PK/PD studies

Correlation Exposure -Effect

PD target

Qualitative relationship (pk/pd index)

Quantitative relationship (value pk/pd index)

PD target

Clinical Dosing Regimen

Monte Carlo Simulations

Initial PK/PD breakpoint

PK/PD breakpoint

MIC distributions

MCS robustness Target population Dose adjustments

Fig 3.4 Summary of the process of setting PK/PD breakpoints by EUCAST (Mouton et al 2012 )

J.W Mouton

58

the agent will be most used For example, patients in intensive care units (ICU) generally have different pharmacokinetics with a higher volume of distribution and lower clearance than most other patients The use of different pharmacokinetic parameters in the simulations will obviously result in different conclusions with respect to the breakpoints, as was shown in case studies for ceftazidime (Mouton

et al 2005 ) and for other agents (Roberts et al 2009 ) MCS was performed using pharmacokinetic parameters from three different populations, human volunteers, patients with cystic fi brosis, and patients from the ICU In each population the derived breakpoints would have been different On the other hand, Muller et al recently showed that the results of MCS based on volunteer data obtained from phase 1 studies matched actual target attainments in phase 3 studies (Muller et al

Preclinical PK/PD studies Clinical PK/PD studies

Correlation Exposure -Effect

PD target

Qualitative relationship (pk/pd index)

Quantitative relationship (value pk/pd index)

PD target

Clinical Dosing Regimen

Monte Carlo Simulations

Initial PK/PD breakpoint

PK/PD breakpoint

MIC distributions

MCS robustness Target population Dose adjustments

Fig 3.4 Summary of the process of setting PK/PD breakpoints by EUCAST (Mouton et al 2012 )

J.W Mouton

58

the agent will be most used For example, patients in intensive care units (ICU)

generally have different pharmacokinetics with a higher volume of distribution and

lower clearance than most other patients The use of different pharmacokinetic

parameters in the simulations will obviously result in different conclusions with

respect to the breakpoints, as was shown in case studies for ceftazidime (Mouton

pharmacokinetic parameters from three different populations, human volunteers,

patients with cystic fi brosis, and patients from the ICU In each population the

derived breakpoints would have been different On the other hand, Muller et al

recently showed that the results of MCS based on volunteer data obtained from

phase 1 studies matched actual target attainments in phase 3 studies (Muller et al

The entire process as described in this chapter can be summarized in a fl ow

recently described by the EUCAST and includes both the steps as required for new

in mind that breakpoints are not set in stone and that they are dependent on multiple

factors Should one of these factors change, then the breakpoint should be

reconsid-ered and possibly be changed if necessary The iterative process of optimizing

dos-ing regimens and settdos-ing breakpoints continues after the breakpoint has been

established.

Correlation Exposure -Effect

Correlation Exposure -Effect

PD target

Qualitative relationship (pk/pd index)

Quantitative relationship (value pk/pd index)

PD target

Clinical Dosing Regimen

Monte Carlo Simulations

Initial PK/PD breakpoint

PK/PD breakpoint

MIC distributions

MCS robustness Target population

Dose adjustments

J.W Mouton

Tối ưu hóa chế độ liều theo PK/PD

• 3 nhóm KS theo chỉ số PK/PD

(i.e skin infection), it is primarily the unbound fraction of drugs that crosses the membrane to the infected tissues such as the subcutaneous adipose tissues, skin, or skeletal muscles An advanced methodology to overcome such problem is to utilize microdialysis as a technique to determine the free fraction of drug exposure at the site of infection An example of implementing this methodology in the clinical setting is shown in Fig 4.1 , where the profiles of unbound ceftobiprole concentra-

tions in different tissues are presented (Barbour et al 2009b ) Note that due to different unbound drug concentrations observed in plasma versus infected sites, an unoptimized dosing scheme could be proposed based on the total plasma drug profile alone, instead of the ideal scenario which is designed based on the free drug concentration.

Thirdly, the MIC-based PKPD modeling also rely on limited PD information

The single time point of MIC is empirical and assumes that it is stationary The MIC value is laboratory dependent; dilution factors, laboratory condition, and techni- cian’s interpretation of what constitutes no growth can contribute to the inter- laboratory variability The rate of bactericidal or bacteriostatic effect with changing drug concentrations is also unknown from such simplified approach Multiple killing patterns can converge to the same MIC when only one time point is measured

Table 4.1 Pattern of MIC-based PKPD index Ambrose et al., Clin Inf Dis 44:79 (2007 ) Antimicrobial agent Bactericidal pattern of in vitro activity PK–PD measure(s) Aminoglycosides Concentration dependent AUC0–24:MIC, Cmax:MIC

β-lactams

Penicillins Time dependent T > MIC Cephalosporins Time dependent T > MIC Carbapenems Time dependent T > MIC Monobactams Time dependent T > MIC

Glycopeptides/lipopeptides Daptomycin Concentration dependent AUC0–24:MIC, Cmax:MIC Oritavancin Concentration dependent T > MIC, Cmax:MIC

Macrolides and clindamycin Azithromycin Time dependent AUC 0–24 :MIC Clarithromycin Time dependent AUC0–24:MIC Teilithromycin Concentration dependent AUC0–24:MIC Metronidazole Concentration dependent AUC0–24:MIC, Cmax:MIC Oxazolidinones

Quinolones Concentration dependent AUC0–24:MIC, Cmax:MIC Tetracyclines

Note: AUC 0–24 :MIC, the ratio of the area under the concentration–time curve at 24 h to the MIC;

Cmax:MIC, the ratio of the maximal drug concentration to the MIC; T > MIC, duration of time a

drug concentration remains above the MIC

4 Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling 65

(i.e skin infection), it is primarily the unbound fraction of drugs that crosses the membrane to the infected tissues such as the subcutaneous adipose tissues, skin, or skeletal muscles An advanced methodology to overcome such problem is to utilize microdialysis as a technique to determine the free fraction of drug exposure at the site of infection An example of implementing this methodology in the clinical setting is shown in Fig 4.1 , where the profiles of unbound ceftobiprole concentra-

tions in different tissues are presented (Barbour et al 2009b ) Note that due to different unbound drug concentrations observed in plasma versus infected sites, an unoptimized dosing scheme could be proposed based on the total plasma drug profile alone, instead of the ideal scenario which is designed based on the free drug concentration.

Thirdly, the MIC-based PKPD modeling also rely on limited PD information The single time point of MIC is empirical and assumes that it is stationary The MIC value is laboratory dependent; dilution factors, laboratory condition, and techni- cian’s interpretation of what constitutes no growth can contribute to the inter- laboratory variability The rate of bactericidal or bacteriostatic effect with changing drug concentrations is also unknown from such simplified approach Multiple killing patterns can converge to the same MIC when only one time point is measured

Antimicrobial agent Bactericidal pattern of in vitro activity PK–PD measure(s)

Aminoglycosides Concentration dependent AUC0–24:MIC, Cmax:MIC

β-lactams

Penicillins Time dependent T > MIC

Cephalosporins Time dependent T > MIC

Carbapenems Time dependent T > MIC

Monobactams Time dependent T > MIC

Clindamycin Time dependent AUC0–24:MIC

Glycopeptides/lipopeptides

Daptomycin Concentration dependent AUC0–24:MIC, Cmax:MICOritavancin Concentration dependent T > MIC, Cmax:MIC

Vancomycin Time dependent AUC0–24:MIC

Macrolides and clindamycin

Azithromycin Time dependent AUC0–24:MIC

Clarithromycin Time dependent AUC0–24:MIC

Teilithromycin Concentration dependent AUC0–24:MIC

Metronidazole Concentration dependent AUC0–24:MIC, Cmax:MICOxazolidinones

Linezolid Time dependent AUC0–24:MIC

Quinolones Concentration dependent AUC0–24:MIC, Cmax:MICTetracyclines

Doxycyeline Time dependent AUC0–24:MIC

Tigecycline Time dependent AUC0–24:MIC

Note: AUC0–24:MIC, the ratio of the area under the concentration–time curve at 24 h to the MIC;

Cmax:MIC, the ratio of the maximal drug concentration to the MIC; T > MIC, duration of time a

drug concentration remains above the MIC

4 Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling

Tối ưu hóa chế độ liều theo PK/PD

hoặc ngắn

nhiễm với thuốc

Penicilin tự nhiên

viêm phổi, NK tiết niệu

hợp AG

Cephalosporin- phổ tác dụng