Understanding the suitability of established antibiotics for oral inhalation from a pharmacokinetic perspective: an integrated model-based investigation based on rifampicin, ciprofloxacin and tigecycline in vivo data

BACKGROUND

Treating pulmonary infections by administering drugs via oral inhalation represents an attractive alternative to usual routes of administration. However, the local concentrations after inhalation are typically not known and the presumed benefits are derived from experiences with drugs specifically optimized for inhaled administration.

OBJECTIVES

A physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was developed to elucidate the pulmonary PK for ciprofloxacin, rifampicin and tigecycline and link it to bacterial PK/PD models. An exemplary sensitivity analysis was performed to potentially guide drug optimization regarding local efficacy for inhaled antibiotics.

METHODS

Detailed pulmonary tissue, endothelial lining fluid and systemic in vivo drug concentration-time profiles were simultaneously measured for all drugs in rats after intravenous infusion. Using this data, a PBPK/PD model was developed, translated to humans and adapted for inhalation. Simulations were performed comparing potential benefits of oral inhalation for treating bronchial infections, covering intracellular pathogens and bacteria residing in the bronchial epithelial lining fluid.

RESULTS

The PBPK/PD model was able to describe pulmonary PK in rats. Often applied optimization parameters for orally inhaled drugs (e.g. high systemic clearance and low oral bioavailability) showed little influence on efficacy and instead mainly increased pulmonary selectivity. Instead, low permeability, a high epithelial efflux ratio and a pronounced post-antibiotic effect represented the most impactful parameters to suggest a benefit of inhalation over systemic administration for locally acting antibiotics.

CONCLUSIONS

The present work might help to develop antibiotics for oral inhalation providing high pulmonary concentrations and fast onset of exposure coupled with lower systemic drug concentrations.

A pharmacokinetic-pharmacodynamic (PKPD) model-based analysis of tedizolid against enterococci using the hollow-fibre infection model

BACKGROUND

Tedizolid is a novel oxazolidinone antibiotic. Considering the higher antibacterial effect in immunocompetent compared with immunosuppressed animals, it is not recommended in immunocompromised patients.

OBJECTIVES

In this study, we assessed the 'pure' pharmacokinetic-pharmacodynamic (PKPD) relationship for tedizolid against Enterococcus in the hollow-fibre infection model (HFIM).

METHODS

Unbound plasma concentration time profiles (200-5000 mg/day IV) were simulated in the HFIM over 120 h against an Enterococcus faecalis strain and two clinical isolates of Enterococcus faecium (VRE-vanB and VRE-vanA). Next, a PKPD model describing tedizolid efficacy against bacterial isolates was developed. A population PK model was linked to the developed PKPD model and utilized to predict the bacterial kinetics in plasma and in target tissues [adipose, muscle, epithelial lining fluid (ELF) and sputum] over 120 h of therapy.

RESULTS

The PKPD model adequately described the bacterial kill kinetics for all bacterial populations. At the human recommended dose of 200 mg/day, bacterial growth was predicted in plasma and all tissues, except for ELF. Bacteriostasis was observed only at a higher dose of 1200 mg/day over 120 h. An fAUC/MIC of 80 related to stasis over 120 h. Subpopulations resistant to 3 × MIC were amplified in plasma and target tissues, except for ELF, at doses of 200-800 mg/day.

CONCLUSIONS

The human dose of 200 mg/day was insufficient to suppress bacterial growth in the HFIM, indicating that further components contribute to the clinical effect of tedizolid. This study supports the warning/precaution for tedizolid to limit its use in immunocompromised patients.

Population Pharmacokinetics of Busulfan and Its Metabolite Sulfolane in Patients with Myelofibrosis Undergoing Hematopoietic Stem Cell Transplantation

For patients with myelofibrosis, allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains the only curative treatment to date. Busulfan-based conditioning regimens are commonly used, although high inter-individual variability (IIV) in busulfan drug exposure makes individual dose selection challenging. Since data regarding the IIV in patients with myelofibrosis are sparse, this study aimed to develop a population pharmacokinetic (PopPK) model of busulfan and its metabolite sulfolane in patients with myelofibrosis. The influence of patient-specific covariates on the pharmacokinetics of drug and metabolite was assessed using non-linear mixed effects modeling in NONMEM^®. We obtained 523 plasma concentrations of busulfan and its metabolite sulfolane from 37 patients with myelofibrosis. The final model showed a population clearance (CL) and volume of distribution (V_d) of 0.217 L/h/kg and 0.82 L/kg for busulfan and 0.021 L/h/kg and 0.65 L/kg for its metabolite. Total body weight (TBW) and a single-nucleotide polymorphism of glutathione-S-transferase A1 (GSTA1 SNP) displayed a significant impact on volume of distribution and metabolite clearance, respectively. This is the first PopPK-model developed to describe busulfan’s pharmacokinetics in patients with myelofibrosis. Incorporating its metabolite sulfolane into the model not only allowed the characterization of the covariate relationship between GSTA1 and the clearance of the metabolite but also improved the understanding of busulfan’s metabolic pathway.

Inferring pulmonary exposure based on clinical PK data: accuracy and precision of model-based deconvolution methods

Determining and understanding the target-site exposure in clinical studies remains challenging. This is especially true for oral drug inhalation for local treatment, where the target-site is identical to the site of drug absorption, i.e., the lungs. Modeling and simulation based on clinical pharmacokinetic (PK) data may be a valid approach to infer the pulmonary fate of orally inhaled drugs, even without local measurements. In this work, a simulation-estimation study was systematically applied to investigate five published model structures for pulmonary drug absorption. First, these models were compared for structural identifiability and how choosing an inadequate model impacts the inference on pulmonary exposure. Second, in the context of the population approach both sequential and simultaneous parameter estimation methods after intravenous administration and oral inhalation were evaluated with typically applied models. With an adequate model structure and a well-characterized systemic PK after intravenous dosing, the error in inferring pulmonary exposure and retention times was less than twofold in the majority of evaluations. Whether a sequential or simultaneous parameter estimation was applied did not affect the inferred pulmonary PK to a relevant degree. One scenario in the population PK analysis demonstrated biased pulmonary exposure metrics caused by inadequate estimation of systemic PK parameters. Overall, it was demonstrated that empirical modeling of intravenous and inhalation PK datasets provided robust estimates regarding accuracy and bias for the pulmonary exposure and pulmonary retention, even in presence of the high variability after drug inhalation.

In vivo / in vitro Correlation of Pharmacokinetics of Gentamicin, Vancomycin, Teicoplanin and Doripenem in a Bovine Blood Hemodialysis Model

Background: Elimination of a drug during renal replacement therapy is not only dependent on flow rates, molecular size and protein binding, but is often influenced by difficult to predict drug membrane interactions. In vitro models allow for extensive profiling of drug clearance using a wide array of hemofilters and flow rates. We present a bovine blood based in vitro pharmacokinetic model for intermittent renal replacement therapy. Methods: Four different drugs were analyzed: gentamicin, doripenem, vancomicin and teicoplanin. The investigated drug was added to a bovine blood reservoir connected to a hemodialysis circuit. In total seven hemofilter models were analyzed using commonly employed flow rates. Pre-filter, post-filter and dialysate samples were drawn, plasmaseparated and analyzed using turbidimetric assays or HPLC. Protein binding of doripenem and vancomycin was measured in bovine plasma and compared to previously published values for human plasma. Results: Clearance values were heavily impacted by choice of membrane material and surface as well as by dialysis parameters such as blood flow rate. Gentamicin clearance ranged from a minimum of 90.12 ml/min in a Baxter CAHP-170 diacetate hemofilter up to a maximum of 187.90 ml/min in a Fresenius medical company Fx80 polysulfone model (blood flow rate 400 ml/min, dialysate flow rate 800 ml/min). Clearance of Gentamicin vs Vancomicin over the F80s hemofilter model using the same flow rates was 137.62 mL vs 103.25 ml/min. Doripenem clearance with the Fx80 was 141.25 ml/min. Conclusion: Clearance values corresponded very well to previously published data from clinical pharmacokinetic trials. In conjunction with in silico pharmacometric models. This model will allow precise dosing recommendations without the need of large scale clinical trials.

Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients

OBJECTIVES

Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance.

METHODS

Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression.

RESULTS

Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p = 0.007) significantly influenced the performance of the Goti model.

CONCLUSIONS

The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

Towards a Quantitative Mechanistic Understanding of Localized Pulmonary Tissue Retention-A Combined In Vivo/In Silico Approach Based on Four Model Drugs

Increasing affinity to lung tissue is an important strategy to achieve pulmonary retention and to prolong the duration of effect in the lung. As the lung is a very heterogeneous organ, differences in structure and blood flow may influence local pulmonary disposition. Here, a novel lung preparation technique was employed to investigate regional lung distribution of four drugs (salmeterol, fluticasone propionate, linezolid, and indomethacin) after intravenous administration in rats. A semi-mechanistic model was used to describe the observed drug concentrations in the trachea, bronchi, and the alveolar parenchyma based on tissue specific affinities (Kp) and blood flows. The model-based analysis was able to explain the pulmonary pharmacokinetics (PK) of the two neutral and one basic model drugs, suggesting up to six-fold differences in Kp between trachea and alveolar parenchyma for salmeterol. Applying the same principles, it was not possible to predict the pulmonary PK of indomethacin, indicating that acidic drugs might show different pulmonary PK characteristics. The separate estimates for local Kp, tracheal and bronchial blood flow were reported for the first time. This work highlights the importance of lung physiology- and drug-specific parameters for regional pulmonary tissue retention. Its understanding is key to optimize inhaled drugs for lung diseases.

How to design a study to evaluate therapeutic drug monitoring in infectious diseases?

BACKGROUND

Therapeutic drug monitoring (TDM) is a tool to personalize and optimize dosing by measuring the drug concentration and subsequently adjusting the dose to reach a target concentration or exposure. The evidence to support TDM is however often ranked as expert opinion. Limitations in study design and sample size have hampered definitive conclusions of the potential added value of TDM.

OBJECTIVES

We aim to give expert opinion and discuss the main points and limitations of available data from antibiotic TDM trials and emphasize key elements for consideration in design of future clinical studies to quantify the benefits of TDM.

SOURCES

The sources were peer-reviewed publications, guidelines and expert opinions from the field of TDM.

CONTENT

This review focuses on key aspects of antimicrobial TDM study design: describing the rationale for a TDM study, assessing the exposure of a drug, assessing susceptibility of pathogens and selecting appropriate clinical endpoints. Moreover we provide guidance on appropriate study design.

IMPLICATIONS

This is an overview of different aspects relevant for the conduct of a TDM study. We believe that this paper will help researchers and clinicians to design and conduct high-quality TDM studies.

Impact of Inaccurate Documentation of Sampling and Infusion Time in Model-Informed Precision Dosing

Background

Routine clinical TDM data is often used to develop population pharmacokinetic (PK) models, which are applied in turn for model-informed precision dosing. The impact of uncertainty in documented sampling and infusion times in population PK modeling and model-informed precision dosing have not yet been systematically evaluated. The aim of this study was to investigate uncertain documentation of (i) sampling times and (ii) infusion rate exemplified with two anti-infectives.

Methods

A stochastic simulation and estimation study was performed in NONMEM^® using previously published population PK models of meropenem and caspofungin. Uncertainties, i.e. deviation between accurate and planned sampling and infusion times (standard deviation (SD) ± 5 min to ± 30 min) were added randomly in R before carrying out the simulation step. The estimation step was then performed with the accurate or planned times (replacing real time points by scheduled study values). Relative bias (rBias) and root mean squared error (rRMSE) were calculated to determine accuracy and precision of the primary and secondary PK parameters on the population and individual level. The accurate and the misspecified (using planned sampling times) model were used for Bayesian forecasting of meropenem to assess the impact on PK/PD target calculations relevant to dosing decisions.

Results

On the population level, the estimates of the proportional residual error (prop.-err.) and the interindividual variability (IIV) on the central volume of distribution (V1) were most affected by erroneous records in the sampling and infusion time (e.g. rBias of prop.-err.: 75.5% vs. 183% (meropenem) and 10.1% vs. 109% (caspofungin) for ± 5 vs. ± 30 min, respectively). On the individual level, the rBias of the planned scenario for the typical values V1, Q and V2 increased with increasing uncertainty in time, while CL, AUC and elimination half-life were least affected. Meropenem as a short half-life drug (~1 h) was more affected than caspofungin (~ 9-11 h). The misspecified model provided biased PK/PD target information (e.g. falsely overestimated time above MIC (T > MIC) when true T > MIC was <0.4 and thus patients at risk of undertreatment), while the accurate model gave precise estimates of the indices across all simulated patients.

Conclusions

Even 5-minute-uncertainties caused bias and significant imprecision of primary population and individual PK parameters. Thus, our results underline the importance of accurate documentation of time.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting

OBJECTIVES

Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated.

METHOD

Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting.

RESULTS

A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions.

CONCLUSION

There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.

Tigecycline in critically ill patients on continuous renal replacement therapy: a population pharmacokinetic study

Background

Tigecycline is a vital antibiotic treatment option for infections caused by multiresistant bacteria in the intensive care unit (ICU). Acute kidney injury (AKI) is a common complication in the ICU requiring continuous renal replacement therapy (CRRT), but pharmacokinetic data for tigecycline in patients receiving CRRT are lacking.

Methods

Eleven patients mainly with intra-abdominal infections receiving either continuous veno-venous hemodialysis (CVVHD, n = 8) or hemodiafiltration (CVVHDF, n = 3) were enrolled, and plasma as well as effluent samples were collected according to a rich sampling schedule. Total and free tigecycline was determined by ultrafiltration and high-performance liquid chromatography (HPLC)-UV. Population pharmacokinetic modeling using NONMEM® 7.4 was used to determine the pharmacokinetic parameters as well as the clearance of CVVHD and CVVHDF. Pharmacokinetic/pharmacodynamic target attainment analyses were performed to explore the potential need for dose adjustments of tigecycline in CRRT.

Results

A two-compartment population pharmacokinetic (PK) model was suitable to simultaneously describe the plasma PK and effluent measurements of tigecycline. Tigecycline dialysability was high, as indicated by the high mean saturation coefficients of 0.79 and 0.90 for CVVHD and CVVHDF, respectively, and in range of the concentration-dependent unbound fraction of tigecycline (45–94%). However, the contribution of CRRT to tigecycline clearance (CL) was only moderate (CL_CVVHD: 1.69 L/h, CL_CVVHDF: 2.71 L/h) in comparison with CL_body (physiological part of the total clearance) of 18.3 L/h. Bilirubin was identified as a covariate on CL_body in our collective, reducing the observed interindividual variability on CL_body from 58.6% to 43.6%. The probability of target attainment under CRRT for abdominal infections was ≥ 0.88 for minimal inhibitory concentration (MIC) values ≤ 0.5 mg/L and similar to patients without AKI.

Conclusions

Despite high dialysability, dialysis clearance displayed only a minor contribution to tigecycline elimination, being in the range of renal elimination in patients without AKI. No dose adjustment of tigecycline seems necessary in CRRT.

Trial registration

EudraCT, 2012–005617-39. Registered on 7 August 2013.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2278-4) contains supplementary material, which is available to authorized users.

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

Broad‐spectrum antibiotic combination therapy is frequently applied due to increasing resistance development of infective pathogens. The objective of the present study was to evaluate two common empiric broad‐spectrum combination therapies consisting of either linezolid (LZD) or vancomycin (VAN) combined with meropenem (MER) against Staphylococcus aureus (S. aureus) as the most frequent causative pathogen of severe infections. A semimechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model mimicking a simplified bacterial life‐cycle of S. aureus was developed upon time‐kill curve data to describe the effects of LZD, VAN, and MER alone and in dual combinations. The PK‐PD model was successfully (i) evaluated with external data from two clinical S. aureus isolates and further drug combinations and (ii) challenged to predict common clinical PK‐PD indices and breakpoints. Finally, clinical trial simulations were performed that revealed that the combination of VAN‐MER might be favorable over LZD‐MER due to an unfavorable antagonistic interaction between LZD and MER.