Decreased protein binding of moxifloxacin in patients with sepsis?

Host Biomarkers and Antibiotic Tissue Penetration in Sepsis: Insights from Moxifloxacin

BACKGROUND AND OBJECTIVE

Sepsis-induced pathophysiological changes may lead to pharmacokinetic variability which alters antibiotic concentrations at the host infection site. This poses a challenge in clinical practice, as sufficient antibiotic concentrations in tissue are necessary to effectively eradicate bacterial pathogens. In this exploratory study, we aimed to evaluate the potential of routinely used laboratory biomarkers to predict subcutaneous and muscle tissue penetration of moxifloxacin in septic patients.

METHODS

We retrospectively analyzed data from 10 septic patients included in a pharmacokinetic study, in which moxifloxacin concentrations in subcutaneous adipose and muscle tissues were measured with microdialysis. We correlated the tissue-to-plasma ratio and protein binding with various clinical biomarkers.

RESULTS

Our results revealed significant correlations for CRP, LDH, BUN, GPT, and total protein with moxifloxacin subcutaneous penetration, and BUN, GOT, and GPT with muscle penetration. Notably, all biomarkers except CRP correlated negatively with tissue penetration. Moreover, we found a positive correlation between moxifloxacin protein binding and total plasma proteins and albumin.

CONCLUSION

Biomarker tissue correlations suggest that the penetration of moxifloxacin into tissues is a complex process influenced by factors like inflammation, tissue integrity, liver function, protein levels, and renal function. Understanding these interactions might help optimize antibiotic dosing strategies.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs

BACKGROUND

Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum CFU over 14 days, as the primary end-point for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from USD 7 million to USD 19.6 million on average, while >30% of drugs fail to progress to phase 3. Better utilising pre-clinical data to predict and prioritise the most likely drugs to succeed will thus help to accelerate drug development and reduce costs. We aim to predict clinical EBA using pre-clinical in vivo pharmacokinetic (PK)-pharmacodynamic (PD) data and a model-based translational pharmacology approach.

METHODS AND FINDINGS

First, mouse PK, PD and clinical PK models were compiled. Second, mouse PK-PD models were built to derive an exposure-response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PK-PD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations.

CONCLUSION

This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Determination of free and total meropenem levels in human plasma and its application for the consistency evaluation of generic drugs

RATIONALE

The consistency evaluation of generic drugs is important for the overall reformation of drug registration in China. In this study, we used meropenem as a model drug to explore the key techniques for clinical consistency evaluation by studying the plasma protein binding (PPB) ratio of different preparations. Because the free portion of drug is the effective part in vivo, it is essential to measure the free drug concentration in the circulatory system. Therefore, in this study, a fast and accurate high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) method was developed to determine the total and free concentrations of meropenem in human plasma.

METHODS

Simple protein precipitation procedures were used for the sample processing assay, and ultrafiltration was implemented for the separation of free drugs. Liquid chromatography separation was performed using a hydrophilic interaction liquid chromatography (HILIC) silica column (2.1 × 50 mm, 3 μm). The mobile phase and sample preparation procedures were optimized. Factors affecting the measurement of free drug concentration were also determined. Nonspecific binding of the ultrafiltration membrane was negligible because the recovery rate for post-ultrafiltration was greater than 96%.

RESULTS

Under optimal conditions, the drug concentrations were linear from 0.5 to 50 μg/ml for both total and free drug concentrations. The PPB ratio was calculated based on the free and total drug concentrations. The PPB of meropenem varied from 1.4% to 24.2% in different subjects. The validated method was applied to evaluate PPB of four preparations, and the results varied from 6.57 ± 3.19% to 10.40 ± 8.31%. One-way analysis of variance (ANOVA) showed no significant differences between the four preparations.

CONCLUSIONS

We established a rapid, robust, and reliable method for the determination of total and free meropenem concentrations using LC-MS/MS with ultrafiltration techniques. The method provided a new perspective for the clinical consistency evaluation of generic drugs.

Biomarkers Predicting Tissue Pharmacokinetics of Antimicrobials in Sepsis: A Review

The pathophysiology of sepsis alters drug pharmacokinetics, resulting in inadequate drug exposure and target-site concentration. Suboptimal exposure leads to treatment failure and the development of antimicrobial resistance. Therefore, we seek to optimize antimicrobial therapy in sepsis by selecting the right drug and the correct dosage. A prerequisite for achieving this goal is characterization and understanding of the mechanisms of pharmacokinetic alterations. However, most infections take place not in blood but in different body compartments. Since tissue pharmacokinetic assessment is not feasible in daily practice, we need to tailor antibiotic treatment according to the specific patient’s pathophysiological processes. The complex pathophysiology of sepsis and the ineffectiveness of current targeted therapies suggest that treatments guided by biomarkers predicting target-site concentration could provide a new therapeutic strategy. Inflammation, endothelial and coagulation activation markers, and blood flow parameters might be indicators of impaired tissue distribution. Moreover, hepatic and renal dysfunction biomarkers can predict not only drug metabolism and clearance but also drug distribution. Identification of the right biomarkers can direct drug dosing and provide timely feedback on its effectiveness. Therefore, this might decrease antibiotic resistance and the mortality of critically ill patients. This article fills the literature gap by characterizing patient biomarkers that might be used to predict unbound plasma-to-tissue drug distribution in critically ill patients. Although all biomarkers must be clinically evaluated with the ultimate goal of combining them in a clinically feasible scoring system, we support the concept that the appropriate biomarkers could be used to direct targeted antibiotic dosing.