Bulik Catharine C, Bader Justin C, Zhang Li, Van Wart Scott A, Rubino Christopher M, Bhavnani Sujata M, Sweeney Kim L, Ambrose Paul G
Institute for Clinical Pharmacodynamics (ICPD), 242 Broadway, Schenectady, NY, 12305, USA.
Sanofi, Bridgewater, New Jersey, USA.
J Pharmacokinet Pharmacodyn. 2017 Apr;44(2):161-177. doi: 10.1007/s10928-017-9518-0. Epub 2017 Mar 28.
Antimicrobial stewardship programs face many challenges, one of which is a lack of guidance regarding antimicrobial dose, interval, and duration. There is no tool that considers patient demographic, pathogen susceptibility, and pharmacokinetic-pharmacodynamic (PK-PD) targets for efficacy in order to evaluate appropriate antimicrobial dosing regimens. The PK-PD Compass, an educational mobile application, was developed to address this unmet need. The application consists of a Monte Carlo simulation algorithm which integrates pharmacokinetic (PK) and PK-PD data, patient-specific characteristics, and pathogen susceptibility data. Through the integration of these data, the application allows practitioners to assess the percent probability of PK-PD target attainment for 35 intravenous antimicrobial agents across 29 infection categories. Population PK models for each drug were identified, evaluated, and refined as needed. Susceptibility breakpoints were based upon FDA and CLSI criteria. By incorporating these data into one interface, clinicians can select the infection, pathogen, and antimicrobial agents of interest and obtain the percent probability of PK-PD target attainment for each regimen based upon patient-specific characteristics. The antimicrobial dosing regimens provided include those recommended by standard guidelines and reference texts. However, unlike these references, potential choices are prioritized based on percent probabilities of PK-PD target attainment. Such data will educate clinicians on selecting optimized antibiotic regimens through the lens of PK-PD.
抗菌药物管理计划面临诸多挑战，其中之一是缺乏关于抗菌药物剂量、给药间隔和疗程的指导。目前尚无一种工具能够综合考虑患者人口统计学特征、病原体敏感性以及药效学-药代动力学（PK-PD）靶点以评估合适的抗菌药物给药方案。为满足这一未被满足的需求，开发了一款教育性移动应用程序——PK-PD指南针。该应用程序包含一个蒙特卡洛模拟算法，该算法整合了药代动力学（PK）和PK-PD数据、患者特异性特征以及病原体敏感性数据。通过整合这些数据，该应用程序使从业者能够评估29种感染类型中35种静脉用抗菌药物达到PK-PD靶点的概率百分比。根据需要识别、评估并完善了每种药物的群体PK模型。敏感性断点基于美国食品药品监督管理局（FDA）和美国临床和实验室标准协会（CLSI）的标准。通过将这些数据整合到一个界面中，临床医生可以选择感兴趣的感染、病原体和抗菌药物，并根据患者特异性特征获得每种给药方案达到PK-PD靶点的概率百分比。所提供的抗菌药物给药方案包括标准指南和参考文献中推荐的方案。然而，与这些参考文献不同的是，潜在选择是根据达到PK-PD靶点的概率百分比进行排序的。这些数据将指导临床医生从PK-PD的角度选择优化的抗生素方案。
