Population pharmacokinetics of daptomycin in patients with bone and joint infection: minimal effect of rifampicin co-administration and confirmation of a sex difference

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Revolutionizing Daptomycin Dosing: A Single 7-11 Hour Sample for Pragmatic Application

Precision daptomycin dosing faces clinical implementation barriers despite known exposure-safety concerns with the use of twice the regulatory-approved doses. We propose achieving a single 7-11 hour post-dose plasma target concentration of 30 mg/L to 43 mg/L to be a practical starting point to facilitate precision daptomycin dosing.

Daptomycin Exposure as a Risk Factor for Daptomycin-Induced Eosinophilic Pneumonia and Muscular Toxicity

BACKGROUND

High-dose daptomycin is increasingly used in patients with bone and joint infection (BJI). This raises concerns about a higher risk of adverse events (AEs), including daptomycin-induced eosinophilic pneumonia (DIEP) and myotoxicity. We aimed to examine pharmacokinetic and other potential determinants of DIEP and myotoxicity in patients with BJI receiving daptomycin.

METHODS

All patients receiving daptomycin for BJI were identified in a prospective cohort study. Cases were matched at a 1:3 ratio, with controls randomly selected from the same cohort. Bayesian estimation of the daptomycin daily area under the concentration-time curve over 24 hours (AUC24h) was performed with the Monolix software based on therapeutic drug monitoring (TDM) data. Demographic and biological data were also collected. Risk factors of AEs were analyzed using Cox proportional hazards model.

RESULTS

From 1130 patients followed over 7 years, 9 with DIEP, 26 with myotoxicity, and 106 controls were included in the final analysis. Daptomycin AUC24h, C-reactive protein, and serum protein levels were associated with the risk of AEs. The adjusted hazard ratio of DIEP or myotoxicity was 3.1 (95% confidence interval [CI], 1.48-6.5; P < .001) for daptomycin AUC24h > 939 mg/h/L, 9.8 (95% CI, 3.94-24.5; P < .001) for C-reactive protein > 21.6 mg/L, and 2.4 (95% CI, 1.02-5.65; P = .04) for serum protein <72 g/L.

CONCLUSIONS

We identified common determinants of DIEP and myotoxicity in patients with BJI. Because the risk of AEs was associated with daptomycin exposure, daptomycin TDM and model-informed precision dosing may help optimize the efficacy and safety of daptomycin treatment in this setting. A target AUC24h range of 666 to 939 mg/h/L is suggested.

Physiologically based pharmacokinetic modelling to inform combination dosing regimens of ceftaroline and daptomycin in special populations

AIMS

The combination of daptomycin and ceftaroline used as salvage therapy is associated with higher survival and decreased clinical failure in complicated methicillin-resistant Staphylococcus aureus (MRSA) infections that are resistant to standard MRSA treatment. This study aimed to evaluate dosing regimens for coadministration of daptomycin and ceftaroline in special populations including paediatrics, renally impaired (RI), obese and geriatrics that generate sufficient coverage against daptomycin-resistant MRSA.

METHODS

Physiologically based pharmacokinetic models were developed from pharmacokinetic studies of healthy adults, geriatric, paediatric, obese and RI patients. The predicted profiles were used to evaluate joint probability of target attainment (PTA), as well as tissue-to-plasma ratios.

RESULTS

The adult dosing regimens of 6 mg/kg every (q)24h or q48h daptomycin and 300-600 mg q12h ceftaroline fosamil by RI categories achieved ≥90% joint PTA when the minimum inhibitory concentrations in the combination are at or below 1 and 4 μg/mL against MRSA. In paediatrics, wherein there is no recommended daptomycin dosing regimen for S. aureus bacteraemia, ≥90% joint PTA is achieved when the minimum inhibitory concentrations in the combination are up to 0.5 and 2 μg/mL for standard paediatric dosing regimens of 7 mg/kg q24h daptomycin and 12 mg/kg q8h ceftaroline fosamil. Model predicted tissue-to-plasma ratios of 0.3 and 0.7 in the skin and lung, respectively, for ceftaroline and 0.8 in the skin for daptomycin.

CONCLUSION

Our work illustrates how physiologically based pharmacokinetic modelling can inform appropriate dosing of adult and paediatric patients and thereby enable prediction of target attainment in the patients during multitherapies.

Pharmacovigilance Study on Eosinophilic Pneumonia Induced by Anti-MRSA Agents: Analysis Based on the FDA Adverse Event Reporting System

Background

Eosinophilic pneumonia (EP) is a rare adverse event caused by several types of drugs, such as antibiotics; however, its characteristics remain poorly described. This study aimed to analyze the disproportionality between the occurrence of EP and anti-methicillin-resistant Staphylococcus aureus (anti-MRSA) agents and to characterize anti-MRSA agent-induced EP events using the Food and Drug Administration Adverse Event Reporting System (FAERS).

Method

Disproportionality linking EP and anti-MRSA agents was analyzed through bayesian confidence propagation neural networks of information components and reporting odds ratio methodologies. The FAERS data set for the fourth quarter of 2012 to the fourth quarter of 2022 was used. We also analyzed the characteristics of EP induced by anti-MRSA agents.

Results

A total of 14 805 795 reports were obtained from FAERS. Disproportionality analysis revealed that the EP signal was detected only in cases with the administration of daptomycin (DAP). This disproportionality signal was consistently detected in the sensitivity analysis. When compared with other reports of DAP-related adverse events, the reports of DAP-related EP were characterized by male sex (odds ratio [OR], 1.94; 95% CI, 1.12-3.37), older age (>70 years; OR, 2.70; 95% CI, 1.68-4.33), and longer duration of treatment (>21 days; OR, 5.08; 95% CI, 3.21-8.05).

Conclusions

This study revealed that among the anti-MRSA agents, disproportionality in the occurrence of EP was observed only with DAP. Our results suggest that sex, age, and treatment duration may affect the occurrence of DAP-induced EP. Clinicians should exercise caution regarding EP during DAP administration.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Seventeen Cases of Daptomycin-Induced Eosinophilic Pneumonia in a Cohort of Patients Treated for Bone and Joint Infections: Proposal for a New Algorithm

BACKGROUND

Daptomycin is increasingly used in the treatment of bone and joint infections (BJIs) and may be responsible for daptomycin-induced eosinophilic pneumonia (DIEP), a potentially severe adverse drug reaction. The aim of this study was to describe DIEP in patients treated at a referral center for the management of BJI, and to revisit current definitions of this disease.

METHODS

Patients treated from 1 January 2012 to 31 March 2021 were included in a prospective cohort (NCT02817711), in which all potential serious adverse events are prospectively recorded. Patients diagnosed with DIEP were retrospectively analyzed using different definitions.

RESULTS

In a total of 4664 patients included in the cohort during the study period, 1021 patients (21.9%) received daptomycin, of whom 17 (1.7%) were diagnosed with DIEP. Most patients were male (n = 11 [64.7%]), and periprosthetic joint infection was the commonest BJI (n = 12 [70.6%]). Only 1 patient had bronchoalveolar lavage (BAL) eosinophil count ≥25%, while most patients had peripheral blood eosinophilia (n = 15 [88.2%]). Chest computed tomography (CT) was compatible with eosinophilic pneumonia in 13 of 14 cases (92.9%). All patients recovered upon discontinuation of daptomycin. Using the different definitions available, only a minority of cases fulfilled existing criteria for DIEP. We propose a new algorithm that includes specific CT scan signs, and systemic instead of BAL eosinophilia.

CONCLUSIONS

DIEP is a rare event that requires prompt discontinuation of the causative antibiotic. Current criteria to diagnose definite DIEP are too restrictive and not easily applicable in clinical practice. A new algorithm is proposed here (Lyon algorithm) to facilitate the early identification of DIEP.

Daptomycin Physiology-Based Pharmacokinetic Modeling to Predict Drug Exposure and Pharmacodynamics in Skin and Bone Tissues

BACKGROUND AND OBJECTIVE

Daptomycin has been recommended in the treatment of bone and joint infection. Previous work showed that the approved dosage of daptomycin may be insufficient to achieve optimal exposure in patients with bone and joint infection. However, those studies assumed that bone exposure was similar to steady-state daptomycin-free plasma concentrations. We sought to establish a physiologically based pharmacokinetic (PBPK) model of daptomycin to describe the dynamics of daptomycin disposition in bone and skin tissue.

METHODS

A PBPK model of daptomycin was built using PK-Sim^®. Daptomycin concentrations in plasma and bone were obtained from three previously published studies. Physicochemical drug characteristics, mass balance, anthropometrics, and experimental data were used to build and refine the PBPK model. Internal validation of the PBPK model was performed using the usual diagnostic plots. The final PBPK model was then used to run simulations with doses of 6, 8, 10, and 12 mg/kg/24 h. Pharmacokinetic profiles were simulated in 1000 subjects and the probabilities of target attainment for the area under the concentration-time curve over the bacterial minimum inhibitory concentration were computed in blood, skin, and bone compartments.

RESULTS

The final model showed a good fit of all datasets with an absolute average fold error between 0.5 and 2 for all pharmacokinetic quantities in blood, skin and bone tissues. Results of dosing simulations showed that doses ≥10 mg/kg should be used in the case of bacteremia caused by Staphylococcus aureus with a minimum inhibitory concentration >0.5 mg/L or Enterococcus faecalis with a minimum inhibitory concentration >1 mg/L, while doses ≥12 mg/kg should be used in the case of bone and joint infection or complicated skin infection. When considering a lower minimum inhibitory concentration, doses of 6-8 mg/kg would likely achieve a sufficient success rate. However, in the case of infections caused by E. faecalis with a minimum inhibitory concentration >2 mg/L, a higher dosage and combination therapy would be necessary to maximize efficacy.

CONCLUSIONS

We developed the first daptomycin PBPK/pharmacodynamic model for bone and joint infection, which confirmed that a higher daptomycin dosage is needed to optimize exposure in bone tissue. However, such higher dosages raise safety concerns. In this setting, therapeutic drug monitoring and model-informed precision dosing appear necessary to ensure the right exposure on an individual basis.

Model-Informed Precision Dosing of Antibiotics in Osteoarticular Infections

As a heterogeneous and wide inflammation, osteoarticular infection (OAI) shows an increasing incidence in recent years. Staphylococcus aureus is the most important pathogen causing OAI. The antibiotic treatment will affect the outcomes of OAI patients, and the drug selection and dosage regimen highly rely on patients’ variability, pathogen susceptibility, and drug property like bone permeability. Model-informed precision dosing (MIPD) provides options to describe and quantify the pharmacokinetic (PK) variability of the OAI population using different models, such as the population pharmacokinetic (PPK) model and physiological-based pharmacokinetic (PB/PK) model. In the present review, we highlighted that the MIPD of antibiotics played a critical role in OAI and listed the dose regimen recommended by the model. Collectively, our current study provided a valuable reference for the treatment of patients and improved the safety and efficiency of drug use.