Prediction of Tissue Exposures of Meropenem, Colistin, and Sulbactam in Pediatrics Using Physiologically Based Pharmacokinetic Modeling

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.

Metabolomics revealed mechanism for the synergistic effect of sulbactam, polymyxin-B and amikacin combination against Acinetobacter baumannii

Introduction

The emergence of multidrug-resistant (MDR) Acinetobacter baumannii prompts clinicians to consider treating these infections with polymyxin combination.

Methods

Metabolomic analysis was applied to investigate the synergistic effects of polymyxin-B, amikacin and sulbactam combination therapy against MDR A. baumannii harboring OXA-23 and other drug resistant genes. The drug concentrations tested were based on their clinical breakpoints: polymyxin-B (2 mg/L), amikacin (16 mg/L), polymyxin-B/amikacin (2/16 mg/L), and polymyxin-B/amikacin/sulbactam (2/16/4 mg/L).

Results

The triple antibiotic combination significantly disrupted levels of metabolites involved in cell outer membrane structure including fatty acids, glycerophospholipids, nucleotides, amino acids and peptides as early as 15 min after administration. Amikacin and polymyxin-B alone perturbed a large number of metabolites at 15 min and 1 h, respectively, but the changes in metabolites were short-lived lasting for less than 4 h. In contrast, the combination treatment disrupted a large amount of metabolites beyond 4 h. Compared to the double-combination, the addition of sulbactam to polymyxin-B/amikacin combination produce a greater disorder in A. baumannii metabolome that further confer susceptibility of bacteria to the antibiotics.

Conclusion

The metabolomic analysis identified mechanisms responsible for the synergistic activities of polymyxin-B/amikacin/sulbactam against MDR A. baumannii.

Physiologically-based pharmacokinetic modeling to inform dosing regimens and routes of administration of rifampicin and colistin combination against Acinetobacter baumannii

BACKGROUND

Carbapenem-resistant Acinetobacter baumannii (CRAB) is resistant to major antibiotics such as penicillin, cephalosporin, fluoroquinolone and aminoglycoside, and has become a significant nosocomial pathogen. The efficacy of rifampicin and colistin combination against CRAB could be dependent on the administration routes and drug concentrations at the site of infection.

OBJECTIVE

The objective is to predict drug disposition in biological tissues. Treatment efficacy is extrapolated by assessing respective pharmacodynamic (PD) indices, as well as parameters associated with the emergence of resistance.

METHODS

Physiologically-based pharmacokinetic models of rifampicin and colistin were utilized to predict tissue exposures. Dosing regimens and administration routes for combination therapy were evaluated in terms of in vitro antimicrobial susceptibility of A. baumannii associated with targeted PD indices and resistance parameters.

RESULTS

Simulated exposures in blood, heart, lung, skin and brain were consistent with reported penetration rates. The results demonstrated that a combination of colistin and rifampicin using conventional intravenous (i.v.) doses could achieve effective exposures in the blood and skin. However, for lung infections, colistin by inhalation would be required due to low lung penetration from intravenous route. Inhaled colistin alone provided good PD coverage but this practice could encourage the emergence of additional resistance which may be overcome by a combination regimen that includes inhaled colistin.

CONCLUSION

This in silico extrapolation provides valuable information on dosing regimens and routes of administration against CRAB infections in specific tissues. The PBPK modeling approach could be a non-invasive way to inform therapeutic benefits of combination antimicrobial therapy.

Predictive Performance of Physiologically Based Pharmacokinetic Modelling of Beta-Lactam Antibiotic Concentrations in Adipose, Bone, and Muscle Tissues

Physiologically based pharmacokinetic (PBPK) models consist of compartments representing different tissues. As most models are only verified based on plasma concentrations, it is unclear how reliable associated tissue profiles are. This study aimed to assess the accuracy of PBPK-predicted beta-lactam antibiotic concentrations in different tissues and assess the impact of using effect site concentrations for evaluation of target attainment. Adipose, bone, and muscle concentrations of five beta-lactams (piperacillin, cefazolin, cefuroxime, ceftazidime, and meropenem) in healthy adults were collected from literature and compared with PBPK predictions. Model performance was evaluated with average fold errors (AFEs) and absolute AFEs (AAFEs) between predicted and observed concentrations. In total, 26 studies were included, 14 of which reported total tissue concentrations and 12 unbound interstitial fluid (uISF) concentrations. Concurrent plasma concentrations, used as baseline verification of the models, were fairly accurate (AFE: 1.14, AAFE: 1.50). Predicted total tissue concentrations were less accurate (AFE: 0.68, AAFE: 1.89). A slight trend for underprediction was observed but none of the studies had AFE or AAFE values outside threefold. Similarly, predictions of microdialysis-derived uISF concentrations were less accurate than plasma concentration predictions (AFE: 1.52, AAFE: 2.32). uISF concentrations tended to be overpredicted and two studies had AFEs and AAFEs outside threefold. Pharmacodynamic simulations in our case showed only a limited impact of using uISF concentrations instead of unbound plasma concentrations on target attainment rates. The results of this study illustrate the limitations of current PBPK models to predict tissue concentrations and the associated need for more accurate models. SIGNIFICANCE STATEMENT: Clinical inaccessibility of local effect site concentrations precipitates a need for predictive methods for the estimation of tissue concentrations. This is the first study in which the accuracy of PBPK-predicted tissue concentrations of beta-lactam antibiotics in humans were assessed. Predicted tissue concentrations were found to be less accurate than concurrent predicted plasma concentrations. When using PBPK models to predict tissue concentrations, this potential relative loss of accuracy should be acknowledged when clinical tissue concentrations are unavailable to verify predictions.

The combination effect of meropenem/sulbactam/polymyxin-B on the pharmacodynamic parameters for mutant selection windows against carbapenem-resistant Acinetobacter baumannii

The objective of this study was to evaluate whether combinations of sulbactam, meropenem, and polymyxin-B could reduce or close the gap of mutant selection window (MSW) of individual antibiotics against Acinetobacter baumannii harboring OXA-23. MICs of three antimicrobials used alone and in combination (meropenem/polymyxin-B or meropenem/polymyxin-B/sulbactam) were obtained in 11 clinical isolates and mutant prevention concentrations were determined in 4 of the 11 isolates. All isolates were resistant to meropenem or polymyxin-B. Combining meropenem and polymyxin-B with or without sulbactam resulted in synergistic bactericidal activities. Pharmacokinetic (PK) simulations of drug concentrations in the blood and epithelial lining fluid coupled with pharmacodynamic (PD) evaluations revealed that the fractions of time over the 24-h in terms of free drug concentration within the MSW (fT_MSW) and above the MPC (fT_>MPC) were optimized by combination therapy. The resultant clinical regimens of meropenem, polymyxin-B, and sulbactam evaluated in the PK-PD analysis were 2 g q8h, 2.5 mg/kg loading dose followed by 1.5 mg/kg q12h, and 3 g q8h, respectively, in patients with normal renal function. Subsequent corresponding equivalent exposure regimens would depend on the extent of renal failure. The overall results indicate that combination antibiotics consisting of sulbactam/meropenem/polymyxin-B can confer potential efficacy against A. baumannii harboring OXA-23, and reduce the opportunity for bacteria to develop further resistance. This study provides a framework for pharmacodynamic evaluation of drug-resistant mutant suppression in an antimicrobial co-administration setting. The results thereby lay the groundwork for additional studies and future clinical confirmation is warranted.

Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically-based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P-PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P-PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients.

Metabolomic profiling of polymyxin-B in combination with meropenem and sulbactam against multi-drug resistant Acinetobacter baumannii

Empirical therapies using polymyxins combined with other antibiotics are recommended in the treatment of Acinetobacter baumannii infections. In the present study, the synergistic activities of polymyxin-B, meropenem, and sulbactam as combination therapy were investigated using metabolomic analysis. The metabolome of A. baumannii was investigated after treatment with polymyxin-B alone (2 mg/l), meropenem (2 mg/l) alone, combination of polymyxin-B/meropenem at their clinical breakpoints, and triple-antibiotic combination of polymyxin-B/meropenem and 4 mg/l sulbactam. The triple-antibiotic combination significantly changed the metabolite levels involved in cell outer membrane and cell wall biosynthesis, including fatty acid, glycerophospholipid, lipopolysaccharide, peptidoglycan, and nucleotide within 15 min of administration. In contrast, significant changes in metabolome were observed after 1 h in sample treated with either meropenem or polymyxin-B alone. After 1 h of administration, the double and triple combination therapies significantly disrupted nucleotide and amino acid biosynthesis pathways as well as the central carbon metabolism, including pentose phosphate and glycolysis/gluconeogenesis pathways, and tricarboxylic acid cycle. The addition of sulbactam to polymyxin-B and meropenem combination appeared to be an early disruptor of A. baumannii metabolome, which paves the way for further antibiotic penetration into bacteria cells. Combination antibiotics consisting of sulbactam/meropenem/polymyxin-B can effectively confer susceptibility to A. baumannii harboring OXA-23 and other drug resistant genes. Metabolomic profiling reveals underlying mechanisms of synergistic effects of polymyxin-B combined with meropenem and sulbactam against multi-drug resistant A. baumannii.