A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

PK/PD-Guided Strategies for Appropriate Antibiotic Use in the Era of Antimicrobial Resistance

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating the optimal use of existing antibiotics. Pharmacokinetic/pharmacodynamic (PK/PD) principles provide a scientific framework for optimizing antimicrobial therapy, particularly to respond to evolving resistance patterns. This review examines PK/PD strategies for antimicrobial dosing optimization, focusing on three key aspects. First, we discuss the importance of drug concentration management for enhancing efficacy while preventing toxicity, considering various patient populations, including pediatric and elderly patients with their unique physiological characteristics. Second, we analyze different PK modeling approaches: the classic top-down approach exemplified by population PK analysis, the bottom-up approach represented by physiologically based PK modeling, and hybrid models combining both approaches for enhanced predictive performance. Third, we explore clinical applications, including nomogram-based dosing strategies, Bayesian estimation, and emerging artificial intelligence applications, for real-time dose optimization. Critical challenges in implementing PK/PD simulation are addressed, particularly the selection of appropriate PK models, the optimization of PK/PD indices, and considerations concerning antimicrobial concentrations at infection sites. Understanding these principles and challenges is crucial for optimizing antimicrobial therapy and combating AMR through improved dosing strategies.

Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams

Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host's immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.

Meropenem pharmacokinetics in cerebrospinal fluid: comparing intermittent and continuous infusion strategies in critically ill patients-a prospective cohort study

Meropenem penetration into the cerebrospinal fluid (CSF) is subject to high interindividual variability resulting in uncertain target attainment in CSF. Recently, several authors recommended administering meropenem as a continuous infusion (CI) to optimize CSF exposure. This study aimed to compare the concentrations and pharmacokinetics of meropenem in CSF after intermittent infusion (II) and CI. This prospective, observational study (NCT04426383) included critically ill patients with external ventricular drains who received either II or CI of meropenem. Meropenem pharmacokinetics in plasma and CSF were characterized using population pharmacokinetic modeling (NONMEM 7.5). The developed model was used to compare the concentration-time profile and probability of target attainment (PTA) between II and CI. A total of 16 patients (8 CI, 8 II; samples: nplasma = 243, nCSF = 263) were recruited, with nine patients (5 CI, 4 II) suffering from cerebral and seven patients from extracerebral infections. A one-compartment model described the plasma concentrations adequately. Meropenem penetration into the CSF (partition coefficient (KP), cCSF/cplasma) was generally low (6.0%), exhibiting substantial between-subject variability (coefficient of variation: 84.0%). There was no correlation between the infusion mode and KP, but interleukin (IL)-6 measured in CSF showed a strong positive correlation with KP (P < 0.001). Dosing simulations revealed no relevant differences in CSF concentrations and PTA in CSF between CI and II. Our study did not demonstrate increased penetration rates or higher concentrations of meropenem in the CSF with CI compared with II.

CLINICAL TRIALS

This study is registered with ClinicalTrials.gov as NCT04426383.

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023-1 mg/L and MICCST 0.5-0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against Streptococcus suis

Introduction: Streptococcus suis (S. suis) is a zoonotic pathogen threatening public health. Aditoprim (ADP), a novel veterinary medicine, exhibits an antibacterial effect against S. suis. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was used to determine the dosage regimens of ADP against S. suis and withdrawal intervals. Methods: The PBPK model of ADP injection can predict drug concentrations in plasma, liver, kidney, muscle, and fat. A semi-mechanistic pharmacodynamic (PD) model, including susceptible subpopulation and resistant subpopulation, is successfully developed by a nonlinear mixed-effect model to evaluate antibacterial effects. An integrated PBPK/PD model is conducted to predict the time-course of bacterial count change and resistance development under different ADP dosages. Results: ADP injection, administrated at 20 mg/kg with 12 intervals for 3 consecutive days, can exert an excellent antibacterial effect while avoiding resistance emergence. The withdrawal interval at the recommended dosage regimen is determined as 18 days to ensure food safety. Discussion: This study suggests that the PBPK/PD model can be applied as an effective tool for the antibacterial effect and safety evaluation of novel veterinary drugs.

Pharmacokinetic model of human exposure to ciprofloxacin through consumption of fish

Fluoroquinolones are broad-spectrum antibiotics that accumulate in the environment. To assess human exposure through the food chain, we developed a pharmacokinetic model of fluoroquinolone accumulation in fish and a human pharmacokinetic model to predict gastrointestinal concentrations of ciprofloxacin, a common fluoroquinolone, following consumption of fish. At 70 ng/L ciprofloxacin, the average in North American surface waters, the fish steady-state concentration was calculated to be 7.5 × 10-6 µg/g. Upon human consumption of the FDA-recommended portion of 113 g of fish containing this ciprofloxacin level, the predicted human intestinal concentration was 2 × 10-6 µg/mL. At 4 × 106 ng/L (4 µg/mL) ciprofloxacin, the highest recorded environmental measurement, these numbers were 0.42 µg/g in fish and 0.1 µg/mL in the human intestine. Thus, based on the ciprofloxacin MIC for E. coli of 0.13 µg/mL, background environmental ciprofloxacin levels are unlikely to be problematic, but environmental pollution can result in high intestinal levels that may cause gut dysbiosis and antibiotic resistance.

Occupational exposure to veterinary antibiotics: Pharmacokinetics of enrofloxacin in humans after dermal, inhalation and oral uptake - A clinical study

Occupational exposure to veterinary antibiotics in hen houses at poultry feeding farms was demonstrated by biomonitoring campaigns in the past. The objective of this study was to investigate pharmacokinetics of three uptake routes: dermal, oral and inhaled. In an open-label cross-over study, six healthy volunteers were exposed to single occupational relevant doses of enrofloxacin. Plasma and urine samples were analysed for enrofloxacin and ciprofloxacin. Physiologically based pharmacokinetic (PBPK) modelling based on bioanalysis data showed underestimation for the elimination rate in comparison to experimental data pointing towards a lack of sufficient ADME information and limitations of available physico-chemical properties of the parent drug. The data obtained in this study indicate that oral uptake with its various sources, e.g. airborne enrofloxacin, direct hand-mouth contact, is the major source for occupational exposure to enrofloxacin in hen houses. Dermal exposure was considered negligible.

All Roads Lead to Rome: Enhancing the Probability of Target Attainment with Different Pharmacokinetic/Pharmacodynamic Modelling Approaches

In light of rising antimicrobial resistance and a decreasing number of antibiotics with novel modes of action, it is of utmost importance to accelerate development of novel treatment options. One aspect of acceleration is to understand pharmacokinetics (PK) and pharmacodynamics (PD) of drugs and to assess the probability of target attainment (PTA). Several in vitro and in vivo methods are deployed to determine these parameters, such as time-kill-curves, hollow-fiber infection models or animal models. However, to date the use of in silico methods to predict PK/PD and PTA is increasing. Since there is not just one way to perform the in silico analysis, we embarked on reviewing for which indications and how PK and PK/PD models as well as PTA analysis has been used to contribute to the understanding of the PK and PD of a drug. Therefore, we examined four recent examples in more detail, namely ceftazidime-avibactam, omadacycline, gepotidacin and zoliflodacin as well as cefiderocol. Whereas the first two compound classes mainly relied on the ‘classical’ development path and PK/PD was only deployed after approval, cefiderocol highly profited from in silico techniques that led to its approval. Finally, this review shall highlight current developments and possibilities to accelerate drug development, especially for anti-infectives.

PBPK Model To Predict Marbofloxacin Distribution in Edible Tissues and Intestinal Exposure in Pigs

Marbofloxacin (MAR) is a fluoroquinolone antibiotic used in food-producing animals in European Union, especially in pigs. In this study, MAR concentrations in plasma, comestible tissues, and intestinal segments were determined in pigs injected with MAR. Based on these data and the literature, a flow-limited PBPK model was developed to predict the tissue distribution of MAR and estimate the withdrawal period after label-use in Europe. A submodel describing the different segments of the intestinal lumen was also developed to assess the intestinal exposure of MAR for the commensal bacteria. During model calibration, only four parameters were estimated. Then, Monte Carlo simulations were performed to generate a virtual population of pigs. The simulation results were compared with the observations from an independent data set during the validation step. A global sensitivity analysis was also carried out to identify the most influential parameters. Overall, the PBPK model was able to adequately predict the MAR kinetics in plasma and edible tissues, as well as in small intestines. However, the simulated concentrations in the large intestine were mostly underestimated, highlighting the need for improvements in the field of PBPK modeling to assess the intestinal exposure of antimicrobials in food animals.

Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Pharmacokinetic-pharmacodynamic (PKPD) models have emerged as valuable tools for the characterisation and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics like MIC and PKPD indices, PKPD models enable to describe the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. We conducted a literature review to identify PKPD models based on (i) antibiotic compounds, (ii) Gram-positive or Gram-negative pathogens, and (iii) in vitro or in vivo longitudinal colony forming unit data. We identified 132 publications released between 1963 and 2021, including models based on exposure with single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g., static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarises the details of each model, i.e. variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects, and interactions. Furthermore, the review highlights main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models shall assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time and ultimately facilitate model-informed antibiotic translation, dosing and drug development.

Key Factors in Effective Patient-Tailored Dosing of Fluoroquinolones in Urological Infections: Interindividual Pharmacokinetic and Pharmacodynamic Variability

Fluoroquinolones (FQs) are a critical group of antimicrobials prescribed in urological infections as they have a broad antimicrobial spectrum of activity and a favorable tissue penetration at the site of infection. However, their clinical practice is not problem-free of treatment failure, risk of emergence of resistance, and rare but important adverse effects. Due to their critical role in clinical improvement, understanding the dose-response relation is necessary to optimize the effectiveness of FQs therapy, as it is essential to select the right antibiotic at the right dose for the right duration in urological infections. The aim of this study was to review the published literature about inter-individual variability in pharmacological processes that can be responsible for the clinical response after empiric dose for the most commonly prescribed urological FQs: ciprofloxacin, levofloxacin, and moxifloxacin. Interindividual pharmacokinetic (PK) variability, particularly in elimination, may contribute to treatment failure. Clearance related to creatinine clearance should be specifically considered for ciprofloxacin and levofloxacin. Likewise, today, undesired interregional variability in FQs antimicrobial activity against certain microorganisms exists. FQs pharmacology, patient-specific characteristics, and the identity of the local infecting organism are key factors in determining clinical outcomes in FQs use.

Optimization and Validation of Dosage Regimen for Ceftiofur against Pasteurella multocida in Swine by Physiological Based Pharmacokinetic-Pharmacodynamic Model

Model informed drug development is a valuable tool for drug development and clinical application due to its ability to integrate variability and uncertainty of data. This study aimed to determine an optimal dosage of ceftiofur against P. multocida by ex vivo pharmacokinetic/pharmacodynamic (PK/PD) model and validate the dosage regimens by Physiological based Pharmacokinetic-Pharmacodynamic (PBPK/PD) model. The pharmacokinetic profiles of ceftiofur both in plasma and bronchoalveolar lavage fluid (BALF) are determined. PD performance of ceftiofur against P. multocida was investigated. By establishing PK/PD model, PK/PD parameters and doses were determined. PBPK model and PBPK/PD model were developed to validate the dosage efficacy. The PK/PD parameters, AUC_{0–24 h}/MIC, for bacteriostatic action, bactericidal action and elimination were determined as 44.02, 89.40, and 119.90 h and the corresponding dosages were determined as 0.22, 0.46, and 0.64 mg/kg, respectively. AUC_{24 h}/MIC and AUC _{72 h}/MIC are simulated by PBPK model, compared with the PK/PD parameters, the therapeutic effect can reach probability of target attainment (PTA) of 90%. The time-courses of bacterial growth were predicted by the PBPK/PD model, which indicated the dosage of 0.46 mg/kg body weight could inhibit the bacterial growth and perform good bactericidal effect.

A dosing nomograph for cerebrospinal fluid penetration of meropenem applied by continuous infusion in patients with nosocomial ventriculitis

OBJECTIVES

In difficult to treat infections such as nosocomial ventriculitis, meropenem exposure in the infected compartment is often uncertain but crucial for antibacterial effects. The aim of this study was to investigate the cerebrospinal fluid (CSF) penetration of meropenem in patients with nosocomial ventriculitis and to derive a nomograph to predict effective meropenem doses as a function of clinical parameters.

METHODS

Retrospective patient data including meropenem serum and CSF levels, as well as CSF inflammation markers were analysed using NONMEM® to assess the general pharmacokinetics and CSF penetration. Monte Carlo simulations (MCS) were used to evaluate different meropenem dosing regimens. Probability of target attainment (PTA) in CSF was assessed and a nomograph to achieve a target concentration of 4 mg/L was developed.

RESULTS

A one-compartment model with meropenem clearance dependent on the estimated glomerular filtration rate (CKD-EPI eGFR, p< 5 e-10) best described meropenem serum pharmacokinetics of 51 critically ill patients. CSF penetration ratio was correlated with the amount of protein in CSF (p< 1 e-8), with higher CSF protein levels accounting for higher penetration ratios. Preserved renal function (CKD-EPI GFR> 50 ml/min/1.73 m²) as well as low CSF protein levels (<500 mg/L) resulted in 80 % PTA (100 %fT>_2xMIC) for a meropenem dose of 6 g/24 h.

CONCLUSIONS

High interindividual variability in meropenem CSF concentration was observed in patients with nosocomial ventriculitis. A nomograph to predict the daily meropenem dose required for target attainment for a given eGFR and CSF protein count was developed.

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development.

Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

Prediction of Human Pharmacokinetic Profiles of the Antituberculosis Drug Delamanid from Nonclinical Data: Potential Therapeutic Value against Extrapulmonary Tuberculosis

Delamanid has been studied extensively and approved for the treatment of pulmonary multidrug-resistant tuberculosis; however, its potential in the treatment of extrapulmonary tuberculosis remains unknown. We previously reported that, in rats, delamanid was broadly distributed to various tissues in addition to the lungs. In this study, we simulated human plasma concentration-time courses (pharmacokinetic profile) of delamanid, which has a unique property of metabolism by albumin, using two different approaches (steady-state concentration of plasma-mean residence time [C_ss-MRT] and physiologically based pharmacokinetic [PBPK] modeling). In C_ss-MRT, allometric scaling predicted the distribution volume at steady state based on data from mice, rats, and dogs. Total clearance was predicted by in vitro-in vivo extrapolation using a scaled albumin amount. A simulated human pharmacokinetic profile using a combination of human-predicted C_ss and MRT was almost identical to the observed profile after single oral administration, which suggests that the pharmacokinetic profile of delamanid could be predicted by allometric scaling from these animals and metabolic capacity in vitro. The PBPK model was constructed on the assumption that delamanid was metabolized by albumin in circulating plasma and tissues, to which the simulated pharmacokinetic profile was consistent. Moreover, the PBPK modeling approach demonstrated that the simulated concentrations of delamanid at steady state in the lung, brain, liver, and heart were higher than the in vivo effective concentration for Mycobacterium tuberculosis. These results indicate that delamanid may achieve similar concentrations in various organs to that of the lung and may have the potential to treat extrapulmonary tuberculosis.

Development and Application of a Water Temperature Related Physiologically Based Pharmacokinetic Model for Enrofloxacin and Its Metabolite Ciprofloxacin in Rainbow Trout

Enrofloxacin (ENR) has been approved for the treatment of infections in aquaculture, but it may cause tissue residue. This research aimed to develop and validate a water temperature related PBPK model, including both ENR and ciprofloxacin (CIP), in rainbow trout, and to predict further their residue concentrations and the withdrawal periods for ENR at different water temperatures. With the published concentrations data, a flow-limited PBPK model including both ENR and CIP sub-models was developed to predict ENR and CIP concentrations in tissues and plasma/serum after intravenous, oral, or immersion administration. A Monte Carlo simulation including 500 iterations was further incorporated into this model. Based on the model and Monte Carlo analysis, the withdrawal intervals were estimated for different dosage regimens and at different water temperatures, ranging from 80 to 272 degree-days. All of these values were shorter than the labeled withdrawal period (500 degree-days) in fish. This model provided a useful tool for predicting the tissue residues of ENR and CIP in rainbow trout under different dosage regimens and at different water temperatures.

Pharmacodynamics of immune response biomarkers of interest for evaluation of treatment effects in bacterial infections

This mini-review discusses the pharmacodynamics of immune-related biomarkers in the area of bacterial infectious diseases that could be of interest from a pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) perspective in the evaluation of treatment effects. The host response to an infection is often poorly defined both in preclinical assessments and in clinical practice when it comes to characterisation of PK and PK/PD relationships. Through population modelling, the time courses and variability of immune response variables can be quantified. Incorporation of such biomarker information into PK and PK/PD models may guide the evaluation of individual response to treatment (right antibiotic, more antibiotic, less antibiotic) and when to stop treatment. Furthermore, translation of results from preclinical systems to clinical scenarios may be improved with the incorporation of biomarker information. Potential biomarkers for these purposes are discussed and a few modelling examples are provided.

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine

Abstract

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10928-020-09695-z) contains supplementary material, which is available to authorized users.

Physiologically Based Population Pharmacokinetic Modeling Approach for Ciprofloxacin in Bone of Patients Undergoing Orthopedic Surgery

Ciprofloxacin is highly active against bacteria that commonly cause bone infections. However, the time-course of ciprofloxacin in bone has not been characterized using population pharmacokinetic modeling. Thirty-nine patients received a 1-h infusion of 400 mg of ciprofloxacin before orthopedic surgery. Blood and bone samples were collected at 0.5 to 20 h following the start of the infusion. Bone samples were separated into cortical and cancellous bone and pulverized under liquid nitrogen using a cryogenic mill. Ciprofloxacin in plasma, and cortical and cancellous bone was quantified by liquid chromatography-tandem mass spectrometry. A physiologically based pharmacokinetic modeling approach was utilized to describe the concentration-time profiles in plasma and bone. Ciprofloxacin concentrations ranged from 0.176 to 5.98 mg/L (median, 1.67; density, 1.99 g/cm³) in cortical, and 0.224 to 14.6 mg/L (median, 1.22; 1.92 g/cm³) in cancellous bone. The average observed cortical bone/plasma concentration ratio was 0.67 at 0.5 to 2 h (n = 7) and 5.1 at 13 to 20 h (n = 9). For cancellous bone the respective average ratios were 0.77 and 4.4. The population PK model included a central (blood) compartment, two peripheral tissue compartments, and compartments for the organic and inorganic (hydroxyapatite) matrix in cortical and cancellous bone. The population mean ciprofloxacin clearance was 20.7 L/h. The estimated partition coefficients of the organic bone matrix were 3.39 for cortical and 5.11 for cancellous bone. Ciprofloxacin achieved higher concentrations in bone than plasma. Slow redistribution from bone to plasma may have been due to binding to the inorganic bone matrix. The developed model presents a step toward optimized antibiotic dosing in osteomyelitis.

Combination of polymyxin B and minocycline against multidrug-resistant Klebsiella pneumoniae: interaction quantified by pharmacokinetic/pharmacodynamic modelling from in vitro data

Lack of effective treatment for multidrug-resistant Klebsiella pneumoniae (MDR-Kp) necessitates finding and optimising combination therapies of old antibiotics. The aims of this study were to quantify the combined effect of polymyxin B and minocycline by building an in silico semi-mechanistic pharmacokinetic/pharmacodynamic (PKPD) model and to predict bacterial kinetics when exposed to the drugs alone and in combination at clinically achievable unbound drug concentration-time profiles. A clinical K. pneumoniae strain resistant to polymyxin B [minimum inhibitory concentration (MIC) = 16 mg/L] and minocycline (MIC = 16 mg/L) was selected for extensive in vitro static time-kill experiments. The strain was exposed to concentrations of 0.0625-48 × MIC, with seven samples taken per experiment for viable counts during 0-28 h. These observations allowed the development of the PKPD model. The final PKPD model included drug-induced adaptive resistance for both drugs. Both the minocycline-induced bacterial killing and resistance onset rate constants were increased when polymyxin B was co-administered, whereas polymyxin B parameters were unaffected. Predictions at clinically used dosages from the developed PKPD model showed no or limited antibacterial effect with monotherapy, whilst combination therapy kept bacteria below the starting inoculum for >20 h at high dosages [polymyxin B 2.5 mg/kg + 1.5 mg/kg every 12 h (q12h); minocycline 400 mg + 200 mg q12h, loading + maintenance doses]. This study suggests that polymyxin B and minocycline in combination may be of clinical benefit in the treatment of infections by MDR-Kp and for isolates that are non-susceptible to either drug alone.

In vitro - in vivo relations for the parenteral liposomal formulation of Amphotericin B: A clinically relevant approach with PBPK modeling

In vitro release testing is a useful tool for the quality control of controlled release parenteral formulations, but in vitro release test conditions that reflect or are able to predict the in vivo performance are advantageous. Therefore, it is important to investigate the factors that could affect drug release from formulations and relate them to in vivo performance. In this study the effect of media composition including albumin presence, type of buffer and hydrodynamics on drug release were evaluated on a liposomal Amphotericin B formulation (Ambisome®). A physiologically based pharmacokinetic (PBPK) model was developed using plasma concentration profiles from healthy subjects, in order to investigate the impact of each variable from the in vitro release tests on the prediction of the in vivo performance. It was found that albumin presence was the most important factor for the release of Amphotericin B from Ambisome®; both hydrodynamics setups, coupled with the PBPK model, had comparable predictive ability for simulating in vivo plasma concentration profiles. The PBPK model was extrapolated to a hypothetical hypoalbuminaemic population and the Amphotericin B plasma concentration and its activity against fungal cells were simulated. Selected in vitro release tests for these controlled release parenteral formulations were able to predict the in vivo AmB exposure, and this PBPK driven approach to release test development could benefit development of such formulations.

A Physiologically-Based Pharmacokinetic Model to Describe Ciprofloxacin Pharmacokinetics Over the Entire Span of Life

Background

Physiologically-based pharmacokinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics by continuous knowledge integration.

Objective

The objective of this study was to build a ciprofloxacin PBPK model for intravenous and oral dosing based on a comprehensive literature review, and evaluate the predictive performance towards pediatric and geriatric patients.

Methods

The aim of this report was to establish confidence in simulations of the ciprofloxacin PBPK model along the development process to facilitate reliable predictions outside of the tested adult age range towards the extremes of ages. Therefore, mean data of 69 published clinical trials were identified and integrated into the model building, simulation and verification process. The predictive performance on both ends of the age scale was assessed using individual data of 258 subjects observed in own clinical trials.

Results

Ciprofloxacin model verification demonstrated no concentration-related bias and accurate simulations for the adult age range, with only 4.8% of the mean observed data points for intravenous administration and 12.1% for oral administration being outside the simulated twofold range. Predictions towards the extremes of ages for the area under the plasma concentration–time curve (AUC) and the maximum plasma concentration (C_max) over the entire span of life revealed a reliable estimation, with only two pediatric AUC observations outside the 90% prediction interval.

Conclusion

Overall, this ciprofloxacin PBPK modeling approach demonstrated the predictive power of a thoroughly informed middle-out approach towards age groups of interest to potentially support the decision-making process.

Electronic supplementary material

The online version of this article (10.1007/s40262-018-0661-6) contains supplementary material, which is available to authorized users.

Towards a Generic Tool for Prediction of Meropenem Systemic and Infection-Site Exposure: A Physiologically Based Pharmacokinetic Model for Adult Patients with Pneumonia

OBJECTIVE

The objective of this study was to develop a physiologically based pharmacokinetic model for meropenem using a retrograde approach, which could serve as a basis for prediction of the systemic and infection-site drug exposures in different populations and indications. We intended this model to be a useful tool to inform (local) pharmacokinetic-based optimal dosing of meropenem in different settings.

METHODS

We developed a reduced physiologically based pharmacokinetic model with NONMEM software using a top-down approach. We used historical (previously published) data for model development and qualification. We used steady-state systemic and infection-site concentrations from 60 adult patients diagnosed with severe lung infection for model development and internal evaluation. The data included rich plasma and sparse epithelial lining fluid samples. We based the internal validation of the model on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness-of-fit plot with no indication of bias, and acceptable performance of visual predictive checks. We performed external validation by fitting the model to independent data from five previously published studies: four studies in patients with pneumonia, with different grades of renal impairment, and one study in morbidly obese patients.

RESULTS

We successfully fitted a reduced physiologically based pharmacokinetic model with six compartments (arterial and venous pools, infection site [lungs], liver, kidneys and rest of the body) to the data and adequately estimated model parameters. We successfully qualified the model (internally and externally) using established methods. Estimated values for tissue-to-plasma partition coefficients were 0.2629 and 0.1946 for lungs and non-fat tissues (kidneys and liver), respectively. Estimated total clearance was 8.174 L/h for a typical patient with a glomerular filtration rate of 65 mL/min. Consistent with the known mechanism of meropenem elimination and previously published models, renal clearance accounted for 70% of total clearance. The model had good predictive performances on data from five different sources including populations with different characteristics with regard to body size, renal function and morbidity.

CONCLUSIONS

We successfully developed a physiologically based pharmacokinetic model for meropenem in adult patients to be used as a basis for prediction of concentrations in different groups of patients, and eventually for effective dose individualisation in different subgroups of the population.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.

Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection

In a recent multicenter population pharmacokinetic study of ciprofloxacin administered to children suffering from complicated urinary tract infection (cUTI), the apparent volume of distribution (V) and total plasma clearance (CL) were decreased by 83.6% and 41.5% respectively, compared to healthy children. To understand these differences, a physiologically-based pharmacokinetic model (PBPK) for ciprofloxacin was developed for cUTI children. First, a PBPK model in adults was developed, modified incorporating age-dependent functions and evaluated with paediatric data generated from a published model in healthy children. Then, the model was then adapted to a cUTI paediatric population according to the degree of renal impairment (KF) affecting renal clearance (CL_Renal,) and CYP1A2 clearance (CL_CYP1A2). Serum and urine samples obtained from 22 cUTI children were used for model evaluation. Lastly, a parameter sensitivity analysis identified the most influential parameters on V and CL. The PBPK model predicted the ciprofloxacin exposure in adults and children, capturing age-related pharmacokinetic changes. Plasma concentrations and fraction excreted unchanged in urine (f_e) predictions improved in paediatric cUTI patients once CL_renal and CL_CYP1A2 were corrected by KF. The presented PBPK model for ciprofloxacin demonstrates its adequacy to simulate different dosing scenarios to obtain PK predictions in a healthy population from 3 months old onwards. Model adaptation of CL_Renal and CL_CYP1A2 according to KF explained partially the differences seen in the plasma drug concentrations and f_e vs time profiles between healthy and cUTI children. Nevertheless, it is necessary to further investigate the disease-related changes in cUTI to improve model predictions.

The use of PBPK modeling across the pediatric age range using propofol as a case

The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp^®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration-time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration-time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.

A Whole-Body Physiologically Based Pharmacokinetic Model for Colistin and Colistin Methanesulfonate in Rat

Colistin is a polymyxin antibiotic used to treat patients infected with multidrug-resistant Gram-negative bacteria (MDR-GNB). The objective of this work was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model to predict tissue distribution of colistin in rat. The distribution of a drug in a tissue is commonly characterized by its tissue-to-plasma partition coefficient, K_p . Colistin and its prodrug, colistin methanesulfonate (CMS) K_p priors, were measured experimentally from rat tissue homogenates or predicted in silico. The PK parameters of both compounds were estimated fitting in vivo their plasma concentration-time profiles from six rats receiving an i.v. bolus of CMS. The variability in the data was quantified by applying a nonlinear mixed effect (NLME) modelling approach. A WB-PBPK model was developed assuming a well-stirred and perfusion-limited distribution in tissue compartments. Prior information on tissue distribution of colistin and CMS was investigated following three scenarios: K_p was estimated using in silico K_p priors (I) or K_p was estimated using experimental K_p priors (II) or K_p was fixed to the experimental values (III). The WB-PBPK model best described colistin and CMS plasma concentration-time profiles in scenario II. Colistin-predicted concentrations in kidneys in scenario II were higher than in other tissues, which was consistent with its large experimental K_p prior. This might be explained by a high affinity of colistin for renal parenchyma and active reabsorption into the proximal tubular cells. In contrast, renal accumulation of colistin was not predicted in scenario I. Colistin and CMS clearance estimates were in agreement with published values. The developed model suggests using experimental priors over in silico K_p priors for kidneys to provide a better prediction of colistin renal distribution. Such models might serve in drug development for interspecies scaling and investigate the impact of disease state on colistin disposition.

Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations

BACKGROUND

Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens.

AIMS

To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies.

SOURCES

PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations.

CONTENT

Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens.

IMPLICATIONS

Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.