Yong Kyun Kim (1,*), Kyeong Min Jo (2,*), Jae Ha Lee (3), Ji Hoon Jang (3), Eun Jun Choe (3), Gaeun Kang (4), Dae Young Zang (5), Dong-Hwan Lee (6,†)
Hallym University Sacred Heart Hospital
Objectives: Teicoplanin, along with vancomycin, a glycopeptide antibiotic, plays a pivotal role in the treatment of gram-positive bacterial infections, including those caused by methicillin-resistant Staphylococcus aureus (MRSA) [1,2]. Due to the complexity of calculating the AUC through multiple blood samplings, using Ctrough as a surrogate marker for AUC has been considered customary in clinical practice. Developing a robust PK model for teicoplanin and creating Bayesian software would greatly assist in the precision dosing of teicoplanin. When developing new drugs, the evaluation of tolerance and PK in healthy individuals, who are not affected by the confounding factors of disease, is conducted to explore the standard characteristics of the drug. Similarly, calculating the standard PK parameters of a drug in population PK studies is not only anticipated to aid in under-standing the population PK in patients through subsequent patient-focused research but also in creating models with superior predictive power. The purpose of this study is to develop a population PK model for healthy adults and to use this model to predict the optimal dosage regimen in patients with normal renal function.
Methods: The recruitment target for the clinical study was healthy adults aged 19 to 55. In the study, a 200 mg dose of teicoplanin in 100 mL normal saline was adminis-tered intravenously to participants over 30 minutes. Venous blood samples (6 mL each) were planned to be collected at 33, 36, 45, 90 minutes, and 4, 8, 48-120, 168-240 hours after starting infusion. Plasma levels of teicoplanin were quantified employing a tandem mass spectrom-etry assay coupled with high-performance liquid chromatography (HPLC-LC-MS/MS). The analysis of teicoplanin’s PK was conducted through a nonlinear mixed-effects modeling technique, utilizing the NONMEM software (version 7.5, ICON Clinical Re-search LLC, North Wales, PA, USA). To develop dosage recommendations for teicoplanin in patients with normal renal function, we utilized Monte Carlo simulations based on the final PK model. Building upon this model, we generated pharmacokinetic parameters for 5,000 virtual patients to assess the therapeutic targets for teicoplanin. We calculated the (probability of target attainment) PTA where the Ctrough was at least 20 mg/L and, concurrently, the the ratio of the AUC from 0 to 24 hours to the pathogen’s minimum inhibitory concentration was at least 800. For all simulation scenarios conducted in this study, encompassing various combinations, the proportion of instances where Ctrough exceeds 60 mg/L, considered as a marker of nephrotoxicity
Results: A total of 12 healthy adults (6 females, 6 males) participated in the study. A total of 96 plasma samples were used for this analysis. The time course of teicoplanin concentrations was best described by a three-compartment PK model (Table 1). In the final PK model, which had an OFV of -209.055, the GFR, as estimated by the CKD-EPI equation using creatinine levels, was a significant factor affecting total clearance (CL). The results of the Monte Carlo Simulation are as follows. When the therapeutic targets were set to a Ctrough >20 mg/dL and AUC/MIC ≥800, the PTA for various LDs and MDs administered at 12-hour and 24-hour intervals were presented in Figure 1. The PTA with administrations at 12-hour intervals for both LDs and MDs were depicted in Figure 2. In patients with normal renal function who are infected with a pathogen with an MIC of 0.25 mg/L, administering a LD of 16 mg/kg at 12-hour intervals for four doses followed by a MD of over 10 mg/kg at 24-hour intervals resulted in a PTA of ≥90% for the therapeutic target from day 3 to day 7 after the start of dosing (Figure 1). For the same patients infected with a pathogen with an MIC of 1 mg/L, administering a LD of 16 mg/kg at 12-hour intervals for four doses followed by a MD of over 12 mg/kg at 24-hour intervals achieved a PTA of ≥90% for the therapeutic target from day 3 to day 7 (Figure 1). In patients with normal renal function infected with a pathogen with an MIC of 0.5 mg/L, administering a LD of 12 mg/kg at 12-hour intervals for four doses followed by a MD of over 8 mg/kg at 12-hour intervals resulted in a PTA of ≥90% for the therapeutic target from day 3 to day 7 (Figure 2). For the same patients infected with a pathogen with an MIC of 2 mg/L, even when a LD of 16 mg/kg and a MD of 16 mg/kg were administered at the same intervals, the PTA remained below 90% on days 3 and 4, with a PTA of ≥90% achieved starting from day 5 (Figure 2). When the MD was administered at 24-hour intervals, the proportion of instances with Ctrough >60 mg/L was mostly 0 or near 0 (not shown). However, at 12-hour intervals, as either the LD or MD increased, the proportion of instances with Ctrough >60 mg/L also increased, and these values are displayed at the top of each panel in Figure 4, according to the pathogen’s MIC.
Conclusions: Our study assesses the PK properties of teicoplanin in healthy subjects by applying a population approach. The concentration–time profile of teicoplanin is well explained by a three-compartment model. Results from Monte-Carlo simulations sug-gest that, in patients with normal renal function, an increase in both LDs and MDs, or a decrease in the interval of MDs, should be considered. Specifically, for pathogens with an MIC of 1 mg/L, we recommend administering a LD of 14 mg/kg every 12 hours for four doses, followed by a MD of 16 mg/kg every 24 hours. However, due to teicoplanin’s long half-life, in cases requiring long-term administration, it is necessary to perform TDM at appropriate times to prevent nephrotoxicity due to high Ctrough.
References:
[1] Rowland, M. Clinical pharmacokinetics of teicoplanin. Clin Pharmacokinet 1990, 18, 184-209, doi:10.2165/00003088-199018030-00002.
[2] Wilson, A.P. Clinical pharmacokinetics of teicoplanin. Clin Pharmacokinet 2000, 39, 167-183, doi:10.2165/00003088-200039030-00001.
Reference: PAGE 32 (2024) Abstr 11273 [www.page-meeting.org/?abstract=11273]
Poster: Drug/Disease Modelling - Infection