Translational Pharmacometric Evaluation of Typical Antibiotic Broad-Spectrum Combination Therapies Against Staphylococcus Aureus Exploiting In Vitro Information

Model-based translation of results from in vitro to in vivo experiments for afabicin activity against Staphylococcus aureus

BACKGROUND

Translation of experimental data on antibiotic activity typically relies on pharmacokinetic/pharmacodynamic (PK/PD) indices. Model-based approaches, considering the full antibiotic killing time course, could be an alternative.

OBJECTIVES

To develop a mechanism-based modelling framework to assess the in vitro and in vivo activity of the FabI inhibitor antibiotic afabicin, and explore the ability of a model built on in vitro data to predict in vivo outcome.

METHODS

A PK/PD model was built to describe bacterial counts from 162 static in vitro time-kill curves evaluating the effect of afabicin desphosphono, the active moiety of the prodrug afabicin, against 21 Staphylococcus aureus strains. Combined with a mouse PK model, outcomes of afabicin doses of 0.011-190 mg/kg q6h against nine S. aureus strains in a murine thigh infection model were predicted, and thereafter refined by estimating PD parameters.

RESULTS

A sigmoid Emax model, with EC50 scaled by the MIC described the afabicin desphosphono killing in vitro. This model predicted, without parameter re-estimation, the in vivo bacterial counts at 24 h within a ±1 log margin for most dosing groups. When parameters were allowed to be estimated, EC50 was 38%-45% lower in vivo, compared with in vitro, within the studied MIC range.

CONCLUSIONS

The developed PK/PD model described the time course of afabicin activity across experimental conditions and bacterial strains. This model showed translational capacity as parameters estimated on in vitro time-kill data could well predict the in vivo outcome for a wide variety of doses in a mouse thigh infection model.

PK/PD modelling and simulation of longitudinal meropenem in vivo effects against Escherichia coli and Klebsiella pneumoniae strains with high MICs

BACKGROUND

Carbapenem-resistant bacteria pose a threat to public health. Characterising the pharmacokinetics-pharmacodynamics (PKPD) of meropenem longitudinally in vivo against resistant bacteria could provide valuable information for development and translation of carbapenem-based therapies.

OBJECTIVES

To assess the time course of meropenem effects in vivo against strains with high MIC to predict PK/PD indices and expected efficacy in patients using a modelling approach.

METHODS

A PKPD model was built on longitudinal bacterial count data to describe meropenem effects against six Escherichia coli and Klebsiella pneumoniae strains (MIC values 32-128 mg/L) in a 24 h mouse thigh infection model. The model was used to derive PK/PD indices from simulated studies in mice and to predict the efficacy of different infusion durations with high-dose meropenem (2 g q8 h/q12 h for normal/reduced kidney function) in patients.

RESULTS

Data from 592 mice were available for model development. The estimated meropenem concentration-dependent killing rate was not associated with differences in MIC. The fraction of time that unbound concentrations exceeded EC50 (fT>EC50, EC50 = 1.01 mg/L) showed higher correlations than fT>MIC. For all investigated strains, bacteriostasis at 24 h was predicted for prolonged infusions of high-dose meropenem monotherapy in >90% of patients.

CONCLUSIONS

The developed PKPD model successfully described bacterial growth and meropenem killing over time in the thigh infection model. For the investigated strains, the MIC, determined in vitro, or MIC-based PK/PD indices, did not predict in vivo response. Simulations suggested prolonged infusions of high-dose meropenem to be efficacious in patients infected by the studied strains.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Towards clinically relevant dose ratios for Cabamiquine and Pyronaridine combination using P. falciparum field isolate data

The selection and combination of dose regimens for antimalarials involve complex considerations including pharmacokinetic and pharmacodynamic interactions. In this study, we use immediate ex vivo P. falciparum field isolates to evaluate the effect of cabamiquine and pyronaridine as standalone treatments and in combination therapy. We feed the data into a pharmacometrics model to generate an interaction map and simulate meaningful clinical dose ratios. We demonstrate that the pharmacometrics model of parasite growth and killing provides a detailed description of parasite kinetics against cabamiquine-susceptible and resistant parasites. Pyronaridine monotherapy provides suboptimal killing rates at doses as high as 720 mg. In contrast, the combination of a single dose of 330 mg cabamiquine and 360 mg pyronaridine provides over 90% parasite killing in most of the simulated patients. The described methodology that combines a rapid, 3R-compliant in vitro method and modelling to set meaningful doses for new antimalarials could contribute to clinical drug development.

Model-based dose optimization framework for bedaquiline, pretomanid and linezolid for the treatment of drug-resistant tuberculosis

AIMS

Bedaquiline, pretomanid and linezolid (BPaL) combination treatment against Mycobacterium tuberculosis is promising, yet safety and adherence concerns exist that motivate exploration of alternative dosing regimens. We developed a mechanistic modelling framework to compare the efficacy of the current and alternative BPaL treatment strategies.

METHODS

Pharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time-kill data. These models were combined with pharmacokinetic models, incorporating body weight, lesion volume, site-of-action distribution, bacterial susceptibility and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400 mg once daily [QD] for 14 days followed by 200 mg three times a week, linezolid 1200 mg QD) and alternative dosing regimens (bedaquiline 200 mg QD, linezolid 600 mg QD).

RESULTS

The framework adequately described the observed antibacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming unit (CFU)-clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non-replicating-clearance state was predicted to be 15 days from the CFU-clearance state.

CONCLUSIONS

The model-based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug-resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating- and non-replicating bacteria from lung and lesions.

Ceftolozane-Tazobactam against Pseudomonas aeruginosa Cystic Fibrosis Clinical Isolates in the Hollow-Fiber Infection Model: Challenges Imposed by Hypermutability and Heteroresistance

Pseudomonas aeruginosa remains a challenge in chronic respiratory infections in cystic fibrosis (CF). Ceftolozane-tazobactam has not yet been evaluated against multidrug-resistant hypermutable P. aeruginosa isolates in the hollow-fiber infection model (HFIM). Isolates CW41, CW35, and CW44 (ceftolozane-tazobactam MICs of 4, 4, and 2 mg/L, respectively) from adults with CF were exposed to simulated representative epithelial lining fluid pharmacokinetics of ceftolozane-tazobactam in the HFIM. Regimens were continuous infusion (CI; 4.5 g/day to 9 g/day, all isolates) and 1-h infusions (1.5 g every 8 hours and 3 g every 8 hours, CW41). Whole-genome sequencing and mechanism-based modeling were performed for CW41. CW41 (in four of five biological replicates) and CW44 harbored preexisting resistant subpopulations; CW35 did not. For replicates 1 to 4 of CW41 and CW44, 9 g/day CI decreased bacterial counts to <3 log10 CFU/mL for 24 to 48 h, followed by regrowth and resistance amplification. Replicate 5 of CW41 had no preexisting subpopulations and was suppressed below ~3 log10 CFU/mL for 120 h by 9 g/day CI, followed by resistant regrowth. Both CI regimens reduced CW35 bacterial counts to <1 log10 CFU/mL by 120 h without regrowth. These results corresponded with the presence or absence of preexisting resistant subpopulations and resistance-associated mutations at baseline. Mutations in ampC, algO, and mexY were identified following CW41 exposure to ceftolozane-tazobactam at 167 to 215 h. Mechanism-based modeling well described total and resistant bacterial counts. The findings highlight the impact of heteroresistance and baseline mutations on the effect of ceftolozane-tazobactam and limitations of MIC to predict bacterial outcomes. The resistance amplification in two of three isolates supports current guidelines that ceftolozane-tazobactam should be utilized together with another antibiotic against P. aeruginosa in CF.

PK/PD modeling and simulation of the in vitro activity of the combinations of isavuconazole with echinocandins against Candida auris

In vitro combination of echinocandins and isavuconazole against the emerging species Candida auris is mainly synergistic. However, this combination has not been evaluated in clinical settings. A pharmacokinetic/pharmacodynamic modeling and simulation approach based on in vitro data may be helpful to further study the therapeutic potential of these combinations. Therefore, the aims of this study were to characterize the time course of growth and killing of C. auris in response to the combination of the three approved echinocandins and isavuconazole using a semimechanistic model and to perform model-based simulations in order to predict the in vivo response to combination therapy. In vitro static time-kill curve data for isavuconazole and echinocandins combinations against six blood isolates of C. auris were best modeled considering the total killing of the fungal population as dependent on the additive effects of both drugs. Once assessed, the predictive performance of the model using simulations of different dosing and fungal susceptibility scenarios were conducted. Model-based simulations revealed that none of the combinations at standard or higher dosages would be effective against the studied isolates of C. auris and it was predicted that the combinations of isavuconazole with anidulafungin or caspofungin would be effective for minimum inhibitory concentrations up to 0.03 and 0.06 mg/L respectively, whereas the combination with micafungin would lead to treatment failure. The current approach highlights the importance of bridging the in vitro results to the clinic.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

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.

Efficient simulation of clinical target response surfaces

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

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.

A Model-Based Pharmacokinetic/Pharmacodynamic Analysis of the Combination of Amoxicillin and Monophosphoryl Lipid A Against S. pneumoniae in Mice

Combining amoxicillin with the immunostimulatory toll-like receptor 4 agonist monophosphoryl lipid A (MPLA) represents an innovative approach for enhancing antibacterial treatment success. Exploiting pharmacokinetic and pharmacodynamic data from an infection model of Streptococcus pneumoniae infected mice, we aimed to evaluate the preclinical exposure-response relationship of amoxicillin with MPLA coadministration and establish a link to survival. Antibiotic serum concentrations, bacterial numbers in lung and spleen and survival data of mice being untreated or treated with amoxicillin (four dose levels), MPLA, or their combination were analyzed by nonlinear mixed-effects modelling and time-to-event analysis using NONMEM® to characterize these treatment regimens. On top of a pharmacokinetic interaction, regarding the pharmacodynamic effects the combined treatment was superior to both monotherapies: The amoxicillin efficacy at highest dose was increased by a bacterial reduction of 1.74 log10 CFU/lung after 36 h and survival was increased 1.35-fold to 90.3% after 14 days both compared to amoxicillin alone. The developed pharmacometric pharmacokinetic/pharmacodynamic disease-treatment-survival models provided quantitative insights into a novel treatment option against pneumonia revealing a pharmacokinetic interaction and enhanced activity of amoxicillin and the immune system stimulator MPLA in combination. Further development of this drug combination flanked with pharmacometrics towards the clinical setting seems promising.

Decoding (patho-)physiology of the lung by advanced in vitro models for developing novel anti-infectives therapies

Advanced lung cell culture models provide physiologically-relevant and complex data for mathematical models to exploit host-pathogen responses during anti-infective drug testing.

Prospective observational study on the pharmacokinetic properties of the Irrua ribavirin regimen used in routine clinical practice in patients with Lassa fever in Nigeria

INTRODUCTION

Lassa fever (LF) is a severe and often fatal systemic disease in humans and affects a large number of countries in West Africa. Treatment options are limited to supportive care and the broad-spectrum antiviral agent ribavirin. However, evidence for ribavirin efficacy in patients with LF is poor and pharmacokinetic (PK) data are not available.Irrua Specialist Teaching Hospital (ISTH) developed an intravenous ribavirin regimen different to the WHO recommendation. Apart from a lower total daily dose the drug is usually administered once per day which reduces the exposure of personnel to patients with LF. The aim of this study is to characterise the PK of the Irrua ribavirin regimen.

METHODS AND ANALYSIS

This prospective, observational clinical study will assess PK properties of the Irrua ribavirin regimen on routinely ribavirin-treated patients with LF at ISTH, a referral hospital serving 19 local governmental areas in a LF endemic zone in Nigeria. Participants will be adults with PCR-confirmed LF. The primary objective is to describe classical PK parameters for ribavirin (maximum plasma drug concentration, time to maximum plasma drug concentration, area under the plasma drug concentration vs time curve, half-life time T1/2, volume of distribution). Blood samples will be collected at 0.5, 1, 3, 5, 8, 12 and 24 hours after doses on day 1, day 4 and day 10 of ribavirin treatment. Ribavirin plasma concentrations will be determined using liquid chromatography coupled to tandem mass spectrometry.

ETHICS AND DISSEMINATION

The study will be conducted in compliance with the protocol, the Declaration of Helsinki, Good Clinical Practice (GCP) and the Nigerian National Code for Health Research Ethics. The protocol has received approval by the Health Research Ethics Committee of ISTH. Results will be made available to LF survivors, their caregivers, the funders, LF research society and other researchers.

REGISTRATION DETAILS

ISRCTN11104750.

Development of a dosing algorithm for meropenem in critically ill patients based on a population pharmacokinetic/pharmacodynamic analysis

Effective antibiotic dosing is vital for therapeutic success in critically ill patients. This work aimed to develop an algorithm to identify appropriate meropenem dosing in critically ill patients. Population pharmacokinetic (PK) modelling was performed in NONMEM®7.3 based on densely sampled meropenem serum samples (n_patients = 48; n_samples = 1376) and included a systematic analysis of 27 pre-selected covariates to identify factors influencing meropenem exposure. Using Monte Carlo simulations newly considering the uncertainty of PK parameter estimates, standard meropenem dosing was evaluated with respect to attainment of the pharmacokinetic/pharmacodynamic (PK/PD) target and was compared with alternative infusion regimens (short-term, prolonged, continuous; daily dose, 2000-6000 mg). Subsequently, a dosing algorithm was developed to identify appropriate dosing regimens. The two-compartment population PK model included three factors influencing meropenem pharmacokinetics: the Cockcroft-Gault creatinine clearance (CLCR_CG) on meropenem clearance; and body weight and albumin on the central and peripheral volume of distribution, respectively; of these, only CLCR_CG was identified as a vital influencing factor on PK/PD target attainment. A three-level dosing algorithm was developed (considering PK parameter uncertainty), suggesting dosing regimens depending on renal function and the level (L) of knowledge about the infecting pathogen (L1, pathogen unknown; L2, pathogen known; L3_(-MIC), pathogen and susceptibility known; L3_(+MIC), MIC known). Whereas patients with higher CLCR_CG and lower pathogen susceptibility required mainly intensified dosing regimens, lower than standard doses appeared sufficient for highly susceptible pathogens. In conclusion, a versatile meropenem dosing algorithm for critically ill patients is proposed, indicating appropriate dosing regimens based on patient- and pathogen-specific information.

A rapid, simple and sensitive liquid chromatography tandem mass spectrometry assay to determine amoxicillin concentrations in biological matrix of little volume

To assess pharmacokinetics of amoxicillin (AMX) in mice, limitations such as a small sampling volume and low drug concentrations have to be addressed. Similar challenges are faced in a clinical framework, e.g. for therapeutic drug monitoring in neonates or small-scale in vitro investigations. An assay enabling quantification of small sample volumes but still at very low concentrations covering a broad concentration range is thus needed. A simple, rapid and highly sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed and successfully validated for quantification of AMX in mouse serum according to European Medicines Agency guidelines. Sample preparation enabled the use of only 10 μL of serum, which is 5-fold less than comparable assays and allows to reduce the number of mice used in pharmacokinetic studies. After protein precipitation with 40 μL chilled methanol and dilution of the supernatant with water, the sample was injected into the LC system on a Poroshell 120 Phenyl Hexyl column (2.1 × 100 mm, 2.7 μm). Chromatographic separation was achieved using a gradient method consisting of acetonitrile and ultra-pure water, both with 0.1% (V/V) formic acid. Positive electrospray ionisation in multiple reaction monitoring mode was used for detection and quantification of AMX. Application to murine study samples demonstrated the reliability of the developed method being accurate and precise with a quantification range from 0.01 to 10 μg/mL. The assay is easily transferable due to a simple sample preparation and confirmed stability of AMX under various applied conditions.

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.