This was a prospective, open-label PPK study conducted in the ICU department of Peking University First Hospital. Patients aged ≥18 years who were diagnosed with sepsis according to Sepsis 3.0 criteria (Singer et al., 2016), confirmed or suspected with gram-positive coccal infection, and expected to be treated with teicoplanin for ≥4 days, were enrolled. Pregnant and lactating patients, patients with special sites (endocardium, bone, joints, etc.) infections, or those who received any special treatment that may affect drug elimination, such as renal replacement therapy, were excluded.

For each patient, demographic information [sex, age, height, weight, body mass index (BMI)], disease information (diagnosis, combined disease), laboratory inspection information [alanine aminotransferase (ALT), aspartate transaminase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), total protein (TP), albumin (ALB), white blood count (WBC), serum creatinine (Scr), glomerular filtration rate (GFR), urea nitrogen (BUN)], medication information [dosage, formulation, administration time, dosing interval, comedication] and teicoplanin blood concentrations were accurately recorded.

According to the standard dosage regimen, teicoplanin was intravenously infused at 400 mg every 12 h (q12h) for 3 doses, followed by 400 mg once daily, and subsequent adjustments were conducted according to patients’ response to the treatment. The infusion duration was 30–60 min. A 2 mL teicoplanin blood sample was collected at 4 time points (immediately before the 3rd, 4th, 5th, and 6th teicoplanin doses) from each patient. Additionally, in 1/3 patients, 2 mL more blood sample was collected immediately after the 5th infusion; in the other 1/3 patients, 2 mL more blood sample was collected 1 h after the 5th dose, and in the remaining 1/3 patients, 2 mL more concentration sample was taken 1 h before the 6th dose. All blood samples were collected in blood collection tubes anticoagulated by ethylene diamine tetra acetic acid (EDTA) for concentration determination.

Teicoplanin concentrations were detected by high-performance liquid chromatography (HPLC) with the Agilent 1,100 Series HPLC System (Agilent Technologies, Santa Clara, CA, USA). Blood samples were centrifuged within 12 h to obtain plasma. Then, 0.4 mL plasma samples were added with 0.05 mL internal standard solution (piperacillin sodium 0.15 mg/mL). Consequently, 0.6 mL acetonitrile was added, and the sample was vigorously shaken for 1 min and centrifuged at 12,000 r/min for 5 min. Then, 0.9 mL of the supernatant was transferred to a centrifuge tube, and 0.4 mL of dichloromethane was added. The solution was vigorously shaken for 1 min and centrifuged at 12,000 r/min for 5 min. The supernatant was then transferred into a vial for detection. HPLC detection conditions were as follows: Waters Symmetry C18 column (250 × 4.6 mm, 5 μm); mobile phase: 0.01 mol/L NaH₂PO₄ (pH = 3.3)-acetonitrile (75:25), isocratic elution; detection wavelength: 240 nm; flow rate: 1.0 mL/min; injection volume: 0.020 mL; column temperature: 35 °C (Dong et al., 2011). The limit of quantification (LOQ) of teicoplanin was 3.125 μg/mL. The above analytical method has passed the methodological verification. The concentrations of teicoplanin presented good linearity in the range of 3.125–100 μg/mL (correlation coefficient r = 0.9994). In terms of sensitivity, the signal-to-noise ratio (S/N) of the LOQ was >10. The relative standard deviations (RSDs) of the intra-day and inter-day precision of simulated plasma samples with high, medium and low concentrations were all less than 10%, and the intra-day and inter-day accuracies were 91.01%–101.13% and 92.98%–98.99%, respectively. The extraction recovery of teicoplanin was above 90%. The stability of simulated plasma samples with high, medium and low concentrations was good after being placed at room temperature for 4 h, frozen and thawed 3 times and stored at −80°C for 60 days, respectively, with the RSDs of tested samples all less than 10%.

Teicoplanin PPK modeling was conducted using the non-linear mixed-effect modeling approach by NONMEM software (version 7.4, ICON Development Solutions).

The model was established in a stepwise manner. In the basic model screening process, one-, two- and three-compartment models with additive, proportional and mixed error were tested respectively, and the basic model was selected by the objective function value (OFV) and visual diagnostic plots (Gao et al., 2020). The model parameters were assumed to follow a log-normal distribution, and the inter-individual variability for each structural parameter was modeled using the following equation: P i = P T V × Exp   η i (1) where P _i represents parameters for the i _th individual, P _TV are the typical values of the parameters, and η _i are random variables with zero mean and variance of ω².

Demographic and clinical characteristics that were considered plausible for affecting teicoplanin PK were tested as covariates, including sex, age, height, weight, BMI, ALT, AST, TBIL, DBIL, TP, ALB, WBC, Scr, GFR, and BUN. Individual PK estimates obtained from the selected structural model were firstly plotted against covariate values to assess relationships (Byrne et al., 2017). When a relationship between the covariate and the PK parameter was observed, the covariate was tested for inclusion into the PPK model. Continuous covariates were incorporated into the model by the following formula (Cazaubon et al., 2017): C L i = C L p o p × C O V i / C O V m e d i a n β × e η C L , i (2) where CL _i represents the CL of the i_th subject, CL_pop is the population parameter estimate, COV _i is the covariate value of subject i, COV_median is the median value of the covariate in the study population, β is the covariate effect to be estimated, and η_CL,i represents the individual random effect.

For binary covariates, the formula was: C L i = C L p o p × e β · C O V i × e η C L , i (3) where the value of COV _i is 0 or 1.

If the inclusion of the covariates resulted in a decrease of >3.84 in the OFV (P = 0.05, χ² distribution with one degree of freedom), it was supported for inclusion in the full regression model (Roberts et al., 2015). After that, the covariates were removed one by one from the full regression model, and covariates resulting in an increase of >10.828 in the OFV (P = 0.001, χ² distribution with one degree of freedom) were retained in the final model.

The performance of the final model was evaluated by internal validations, including goodness-of-fit (GOF), bootstrap and prediction corrected visual predictive check (pc-VPC). GOF evaluation was performed by plotting the corresponding individual (IPRED) and population predictive values (PRED) against the observed values as well as the PRED and time against conditional weighted residual errors (CWRES). A bootstrap resampling technique was used for model validation. One thousand bootstrap-resampled data sets were generated from the original model group data set, and each was individually fitted to the final model. All parameters were estimated, and the median and 95% confidence intervals (CIs) (2.5th percentile and 97.5th percentile) were compared with the final parameter estimates. Pc-VPC was used to graphically assess the appropriateness of the compartment model based on 1,000 replicates of the dataset (Bergstrand et al., 2011).

To provide recommendations for teicoplanin dosage selection, Monte Carlo simulations were performed using the final PPK model with GFR covariate to generate PK profiles for candidate dosage regimens. Trough concentrations on Day 4 (C_{min, 72h}) and at steady state 7 days after the initial TDM (Day 11, C_{min, 240h}) (Hanai et al., 2022) and the AUC_0-24 were obtained from each condition. IV teicoplanin loading doses ranging from 10–15 mg/kg, administered every 12 h for either 3 or 5 doses with maintenance doses ranging from 3–15 mg/kg, administered per 24–72 h to a typical 65 kg patient were studied. Five GFR levels of 15, 30, 60, 90 and 120 mL/min/1.73 m² were simulated. At each GFR level, 1,000 simulations were performed for each candidate regimen. The concentration-time profile was simulated between 0 and 264 h.

AUC_0-24 values estimated by the linear trapezoidal method from the concentration-time profiles were divided by putative MIC values to obtain AUC_0-24/MIC ratios. According to the European Committee on Antimicrobial Susceptibility Testing (EUCAST, https://mic.eucast.org/Eucast2/) data, MIC values of 0.25, 0.5, 1, 2, and 4 were selected. PTAs for achieving an AUC_0-24/MIC of ≥610 (Ramos-Martín et al., 2017; Abdul-Aziz et al., 2020) were calculated. Based on the MIC distributions for MRSA reported by the EUCAST, CFR was calculated with the following formula (Wei et al., 2021) to define the optimal dosage regimens attaining the AUC_0-24/MIC target of 610: C F R = ∑ i = 1 n P T A M I C i · p M I C i (4) where PTA(MICi) is the PTA value at the i_th MIC category of MRSA, and p (MICi) is the fraction of isolates of MRSA at this MIC category.

A dosing regimen resulting in an AUC_0-24/MIC value of ≥610, a PTA value of ≥90%, a C_min value of 15–30 mg/L in non-complicated MRSA infectious patients was considered to be the optimal regimen. The regimen that achieved ≥90% CFR was considered the optimal empirical therapy, while a CFR between 80% and 90% was associated with a moderate probability of success (Bradley et al., 2003).

In this prospective study, 249 plasma samples were collected from 59 subjects (22 females and 37 males). The median (range) age and weight were 72.0 (28.0–92.0) years and 65.0 (35.0–90.0) kg, respectively. Among the included patients, 23.7% had normal kidney function (GFR ≥90 mL/min/1.73 m²), 33.9% had mildly impaired renal function (GFR between 60 and 90 mL/min/1.73 m²), 23.7% had moderately impaired renal function (GFR between 30 and 60 mL/min/1.73 m²), 8.5% had severely impaired renal function (GFR between 15 and 30 mL/min/1.73 m²), and 10.2% of the patients had kidney failure (GFR <15 mL/min/1.73 m²). A summary of the characteristics of the included patients is provided in Table 1.

During the modeling process, one-, two- and three-compartment models with additive, proportional and mixed residual variability were evaluated to describe teicoplanin’s PK characteristics, and a two-compartment model with proportional residual best described the data. The first-order conditional estimation method with the η-ε interaction option was adopted, and the ADVAN3, TRANS4 subroutines were selected in NONMEM software. The proportional residual variability was expressed as Equation 5. C i j = C p r e d , i j × 1 + ε p r o p , i j (5) where C _ij is the j _th observation for the i _th subject, C _pred,ij is the j _th predicted value for the i _th subject, and ε _prop,ij is the proportional residual of the measured concentrations, with means of 0 and variances of σ_prop ².

Covariate relationship plots and the impact of covariates on the Bayesian posthoc PK parameters from the final PPK basic model are provided in Supplementary Figure S1 and Supplementary Table S1 (available as Supplementary Data). After covariate screening, GFR was found to significantly influence teicoplanin CL, resulting in a decrease of 17.95 in OFV and a reduction of 9.8% in ω_CL, and was the only covariate included in the final model. The final model parameter estimates for CL, V1, Q, and V2 were 1.03 L/h, 20.1 L, 3.12 L/h, and 101 L, respectively (Table 2). The final model equation was as follows: C L   L / h = 1.03 × G F R 71.88 0.437 × e 0.29 (6) V 1   L = 20.1 × e 0.37 (7) Q   L / h = 3.12 × e 0.29 (8) V 2   L = 101 × e 0.10 (9)

The GOF plots for the final model illustrated that PRED and IPRED were in good accordance with the observed concentrations (Figure 1). The CWRES showed good scattering, with all points between ±4. The final model demonstrated strong stability, with 923 out of 1,000 bootstrap runs fitting successfully, and the parameter estimates were similar to the bootstrap median estimates meanwhile were within the 95% percentile interval (PI) of the bootstrap simulation results (Table 2). The pc-VPC results confirmed a sufficient predictive power of the final teicoplanin model, with the observed data included in the range of 95% CI and the median and 95% CI lines of the observations located near the middle of the 1,000 simulation results (Figure 2).

Table 3 demonstrates the median and variability of the C_min and AUC_0-24/MIC simulation results achieved with the various dosage regimens. Only the AUC_0-24/MIC and recommended regimens when MIC = 1 are presented in Table 3 since the results are applicable to most cases with MIC ≤1, and AUC_0-24/MIC at other MIC levels can be calculated accordingly. As expected, teicoplanin C_min and exposure generally increased with increasing dosage and decreasing renal function. As seen in the table, the results of loading dosage regimens were mainly reflected in C_{min, 72h}, while the results of the maintenance dosage regimens were mainly reflected in C_{min, 240h} and AUC_0-24/MIC. Taking the simulated patients with a GFR of 120 mL/min/1.73 m² as an example, though the simulated C_{min, 72h} under the second dosage regimen was higher than that of the first due to the higher frequency of the loading doses (15 mg/kg q12h*5 vs. 15 mg/kg q12h*3), the C_{min, 240h} and AUC_0-24/MIC were less than that due to the lower maintenance doses [7.5 vs. 15 mg/kg every 24 h (q24h)]. Besides, for patients with renal insufficiency (simulated GFR of 60, 30, 15 mL/min/1.73 m²), extending the dosing interval seemed more likely to achieve target PK/PD results than reducing the unit dosage. Based on the above simulations, for patients with a GFR of 90–120 mL/min/1.73 m², the recommended dosing regimen was 15 mg/kg q12h for 5 times, followed by 15 mg/kg q24h. For patients with a GFR of 30–60 mL/min/1.73 m², a dosage regimen of 15 mg/kg q12h for 3 times followed by 15 mg/kg on Day 3 and 15 mg/kg q48h from Day 4 was able to make the teicoplanin C_min and exposure achieve the targets. For patients with a GFR of 15 mL/min/1.73 m², a regimen of 12 mg/kg q12h for 3 times followed by 12 mg/kg on Day 3 and 12 mg/kg q72h from Day 4 was enough to make the above parameters meet the standard (Table 3).

Figure 3 shows the median C_{min, 72h} achieved with different loading dose regimens in subgroups stratified by GFR. As seen from the figure, for the same unit dose, increasing the number of loading doses may significantly elevate teicoplanin trough concentrations at 72 h for a faster attainment of the target (Figures 3A, B). Generally, lower loading doses were required in patients with lower GFR. For patients with GFR 90–120 mL/min/1.73 m², a loading dose of 15 mg/kg for 5 times made it possible to achieve a C_{min, 72h} of ≥15 mg/L; for patients with GFR 30–60 mL/min/1.73 m², a loading dosage regimen of 15 mg/kg for 3 times was enough, while for patients with GFR 15 mL/min/1.73 m², the loading dosage may be reduced to 12 mg/kg for 3 times in patients with non-complicated infections.

Figure 4 displays the PTA against MRSA associated with the PK/PD target of efficacy, AUC_0-24/MIC ≥610. According to the EUCAST data, most MRSA had an MIC distribution for teicoplanin of 0.5–1 mg/L. When considering a target of PTA ≥90%, we found that the conventional dosing regimen of 400 mg (6 mg/kg) or 600 mg (10 mg/kg) q24h was not adequate for the simulated patients (not listed in Figure 4). For patients with GFR 90–120 mL/min/1.73 m², two regimens with a maintenance dose of 15 mg/kg q24h resulted in similar PTAs, which were higher than the maintenance dose of 12 mg/kg q24h. Consistent with the trend in Table 3, for patients with GFR 30–60 mL/min/1.73 m², the PTAs of the maintenance dose 15 mg/kg q48h regimen was higher than that of the maintenance dose 7.5 mg/kg q24h; the latter was similar to the maintenance dose of 12 mg/kg q48h. For patients with GFR 15 mL/min/1.73 m², the PTA values obtained by the three maintenance schemes of 15 mg/kg q72h, 12 mg/kg q72h, and 5 mg/kg q24h gradually decreased, which also confirmed that extending the dosing interval might be easier to achieve the PTA target than reducing the unit dose for patients with renal insufficiency. Generally, for patients with lower GFR, lower maintenance doses were required to achieve PTA targets. For MRSA isolates, the vast majority of the listed regimens achieved PTA targets when MIC = 0.5 mg/L; however, no dosage achieved 90% of PTA when MIC = 1 mg/L (Figure 4).

As shown in Figure 5, the simulated dosing regimens of 15 mg/kg q24h in patients with a GFR of 90 mL/min/1.73 m², 15 mg/kg q48h in patients with a GFR of 30 mL/min/1.73 m², and 15 mg/kg q72h in patients with a GFR of 15 mL/min/1.73 m² almost achieved CFR values of ≥80% for the target of AUC_0-24/MIC ≥610. However, none of the simulated dosing regimens could make CFR values exceed 90%. For the same simulated regimen, the lower GFR level of patients might result in higher CFR values.