Software for dosage individualization of voriconazole for immunocompromised patients

Evaluation of the predictive performance of an online voriconazole dose calculator in children

BACKGROUND

The dosing of voriconazole is challenging in pediatrics. One approach to improve the dosing is through the use of Bayesian concentration-guided dosing software. Our study assessed the predictive performance of a freely available online voriconazole dose calculator in pediatric patients "NextDose" ( https://www.nextdose.org/ ).

METHODS

Per each dose calculator, we predicted voriconazole concentrations. We did both a priori and a posteriori Bayesian predictions.

RESULTS

A total of 51 patients were included in this study. For a priori predictions, bias was + 26% while imprecision was 70%. For a posteriori predictions, bias and imprecision were 0.01% and 46%.

DISCUSSION

In conclusion, the available online dose calculator was overpredicting the concentrations before voriconazole observations were available. However, with just one measured concentration, the predictions improved with minimal bias and an acceptable level of imprecision. There is a need for more prospective studies evaluating the use of voriconazole dosing calculators in the pediatric population to assess if they can improve the achievement of therapeutic target concentrations compared to standard of care.

External Evaluation of Population Pharmacokinetic Models for High-Dose Methotrexate in Adult Patients with Hematological Tumors

Currently, numerous population pharmacokinetic (popPK) models for methotrexate (MTX) have been published for estimating PK parameters and variability. However, it is unclear whether the accuracy of these models is sufficient for clinical application. The aim of this study is to evaluate published models and assess their predictive performance according to the standards of scientific research. A total of 237 samples from 74 adult patients who underwent high-dose MTX (HDMTX) treatment at Shanghai Changzheng Hospital were collected. The software package NONMEM was used to perform an external evaluation for each model, including prediction-based diagnosis, simulation-based diagnosis, and Bayesian forecasting. The simulation-based diagnosis includes normalized prediction distribution error (NPDE) and visual predictive check (VPC). Following screening, 7 candidate models suitable for external validation were identified for comparison. However, none of these models exhibited excellent predictive performance. Bayesian simulation results indicated that the prediction precision and accuracy of all models significantly improved when incorporating prior concentration information. The published popPK models for MTX exhibit significant differences in their predictive performance, and none of the models were able to accurately predict MTX concentrations in our data set. Therefore, before adopting any model in clinical practice, extensive evaluation should be conducted.

Therapeutic Drug Monitoring of Voriconazole in Critically Ill Pediatric Patients: A Single-Center Retrospective Study

BACKGROUND AND OBJECTIVE

Voriconazole pharmacokinetics are highly variable in pediatric patients, and the optimal dosage has yet to be determined. The purpose of this study was to describe voriconazole pharmacokinetic and pharmacodynamic targets achieved and evaluate the efficacy and safety of voriconazole for critically ill pediatrics.

METHODS

This is a single-center retrospective study conducted at a pediatric intensive care unit at a tertiary/quaternary hospital. Pediatrics admitted to the pediatric intensive care unit and who received voriconazole for a proven or suspected fungal infection with at least one measured trough concentration were included. The primary outcomes included the percentage of pediatric patients who achieved the pharmacokinetic and pharmacodynamic targets. Secondary outcomes included assessing the correlation between voriconazole trough concentrations and clinical/microbiological outcomes. All statistical analyses were performed using the R statistical software and Microsoft Excel. Multiple logistic regression was used to assess the predictors of both clinical and microbiologic cures. Multiple linear regression was used to determine significant factors associated with trough concentrations.

RESULTS

A total of 129 voriconazole trough concentrations were measured from 71 participants at steady state after at least three doses of voriconazole. The mean (± standard deviation) of the first and second trough concentrations were 2.9 (4.2) and 2.3 (3.3) mg/L, respectively. Among the first trough concentrations, only 33.8% were within the therapeutic range (1-5 mg/L), 46.5% were below the therapeutic range, and 19.7% were above the therapeutic range. A clinical cure occurred in 78% of patients, while a microbiologic cure occurred in 80% of patients.

CONCLUSIONS

Voriconazole trough concentrations vary widely in critically ill pediatric patients and only a third of the patients achieved therapeutic concentrations with initial doses.

The Current Status and Future Perspectives of Beta-Lactam Therapeutic Drug Monitoring in Critically Ill Patients

Beta-lactams (BL) are the first line agents for the antibiotic management of critically ill patients with sepsis or septic shock. BL are hydrophilic antibiotics particularly subject to unpredictable concentrations in the context of critical illness because of pharmacokinetic (PK) and pharmacodynamics (PD) alterations. Thus, during the last decade, the literature focusing on the interest of BL therapeutic drug monitoring (TDM) in the intensive care unit (ICU) setting has been exponential. Moreover, recent guidelines strongly encourage to optimize BL therapy using a PK/PD approach with TDM. Unfortunately, several barriers exist regarding TDM access and interpretation. Consequently, adherence to routine TDM in ICU remains quite low. Lastly, recent clinical studies failed to demonstrate any improvement in mortality with the use of TDM in ICU patients. This review will first aim at explaining the value and complexity of the TDM process when translating it to critically ill patient bedside management, interpretating the results of clinical studies and discussion of the points which need to be addressed before conducting further TDM studies on clinical outcomes. In a second time, this review will focus on the future aspects of TDM integrating toxicodynamics, model informed precision dosing (MIPD) and “at risk” ICU populations that deserve further investigations to demonstrate positive clinical outcomes.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing.

Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed.

Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F₂₀ of 57.27% and F₃₀ of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration.

Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.

Precision Therapy for Invasive Fungal Diseases

Invasive fungal infections (IFI) are a common infection-related cause of death in immunocompromised patients. Approximately 10 million people are at risk of developing invasive aspergillosis annually. Detailed study of the pharmacokinetics (PK) and pharmacodynamics (PD) of antifungal drugs has resulted in a better understanding of optimal regimens for populations, drug exposure targets for therapeutic drug monitoring, and establishing in vitro susceptibility breakpoints. Importantly, however, each is an example of a “one size fits all strategy”, where complex systems are reduced to a singularity that ensures antifungal therapy is administered safely and effectively at the level of a population. Clearly, such a notion serves most patients adequately but is completely counter to the covenant at the centre of the clinician–patient relationship, where each patient should know whether they are well-positioned to maximally benefit from an antifungal drug. This review discusses the current therapy of fungal infections and areas of future research to maximise the effectiveness of antifungal therapy at an individual level.

Managing uncertainty in antifungal dosing: antibiograms, therapeutic drug monitoring and drug-drug interactions

PURPOSE OF REVIEW

A number of pharmacokinetic and pharmacodynamic factors in critically ill or severely immunosuppressed patients influence the effectiveness of antifungal therapy making dosing less certain. Recent position papers from infectious diseases societies and working groups have proposed methods for dosage individualization of antibiotics in critically ill patients using a combination of population pharmacokinetic models, Monte-Carlo simulation and therapeutic drug monitoring (TDM) to guide dosing. In this review, we examine the current limitations and practical issues of adapting a pharmacometrics-guided dosing approaches to dosing of antifungals in critically ill or severely immunosuppressed populations.

RECENT FINDINGS

We review the current status of antifungal susceptibility testing and challenges in incorporating TDM into Bayesian dose prediction models. We also discuss issues facing pharmacometrics dosage adjustment of newer targeted chemotherapies that exhibit severe pharmacokinetic drug-drug interactions with triazole antifungals.

SUMMARY

Although knowledge of antifungal pharmacokinetic/pharmacodynamic is maturing, the practical application of these concepts towards point-of-care dosage individualization is still limited. User-friendly pharmacometric models are needed to improve the utility of TDM and management of a growing number of severe pharmacokinetic antifungal drug-drug interactions with targeted chemotherapies.

Dosage Individualization of Linezolid: Precision Dosing of Linezolid To Optimize Efficacy and Minimize Toxicity

The high interindividual variability in the pharmacokinetics (PK) of linezolid has been described, which results in an unacceptably high proportion of patients with either suboptimal or potentially toxic concentrations following the administration of a fixed regimen. The aim of this study was to develop a population pharmacokinetic model of linezolid and use this to build and validate alogorithms for individualized dosing. A retrospective pharmacokinetic analysis was performed using data from 338 hospitalized patients (65.4% male, 65.5 [±14.6] years) who underwent routine therapeutic drug monitoring for linezolid. Linezolid concentrations were analyzed by using high-performance liquid chromatography. Population pharmacokinetic modeling was performed using a nonparametric methodology with Pmetrics, and Monte Carlo simulations were employed to calculate the 100% time >MIC after the administration of a fixed regimen of 600 mg administered every 12 h (q12h) intravenously (i.v.). The dose of linezolid needed to achieve a PTA ≥ 90% for all susceptible isolates classified according to EUCAST was estimated to be as high as 2,400 mg q12h, which is 4 times higher than the maximum licensed linezolid dose. The final PK model was then used to construct software for dosage individualization, and the performance of the software was assessed using 10 new patients not used to construct the original population PK model. A three-compartment model with an absorptive compartment with zero-order i.v. input and first-order clearance from the central compartment best described the data. The dose optimization software tracked patients with a high degree of accuracy. The software may be a clinically useful tool to adjust linezolid dosages in real time to achieve prespecified drug exposure targets. A further prospective study is needed to examine the potential clinical utility of individualized therapy.

Bacteroides Fragilis Polysaccharide A Ameliorates Abnormal Voriconazole Metabolism Accompanied With the Inhibition of TLR4/NF-κB Pathway

The antifungal agent voriconazole (VRC) exhibits extreme inter-individual and intra-individual variation in terms of its clinical efficacy and toxicity. Inflammation, as reflected by C-reactive protein (CRP) concentrations, significantly affects the metabolic ratio and trough concentrations of voriconazole. Bacteroides fragilis (B. fragilis) is an important component of the human intestinal microbiota. Clinical data have shown that B. fragilis abundance is comparatively higher in patients not presenting with adverse drug reactions, and inflammatory cytokine (IL-1β) levels are negatively correlated with B. fragilis abundance. B. fragilis natural product capsular polysaccharide A (PSA) prevents various inflammatory disorders. We tested the hypothesis that PSA ameliorates abnormal voriconazole metabolism by inhibiting inflammation. Germ-free animals were administered PSA intragastrically for 5 days after lipopolysaccharide (LPS) stimulation. Their blood and liver tissues were collected to measure VRC concentrations. PSA administration dramatically improved the resolution phase of LPS-induced hepatic VRC metabolism and inflammatory factor secretion. It reversed inflammatory lesions and alleviated hepatic pro-inflammatory factor secretion. Both in vitro and in vivo data demonstrate that PSA reversed LPS-induced IL-1β secretion, downregulated the TLR4/NF-κB signaling pathway and upregulated CYP2C19 and P-gp. To the best of our knowledge, this study is the first to show that PSA from the probiotic B. fragilis ameliorates abnormal voriconazole metabolism by inhibiting TLR4-mediated NF-κB transcription and regulating drug metabolizing enzyme and transporter expression. Thus, PSA could serve as a clinical adjunct therapy.

Therapeutic drug monitoring of commonly used anti-infective agents: A nationwide cross-sectional survey of Australian hospital practices

When performed according to best-practice principles, therapeutic drug monitoring (TDM) can optimise anti-infective treatment and directly benefit clinical outcomes. We evaluated TDM performance and clinical decision-making for established anti-infective agents amongst Australian hospitals. A nationwide cross-sectional survey was conducted between August and September 2019. The survey consisted of multiple-choice questions regarding TDM of anti-infective agents in general as well as clinical vignettes specific to vancomycin, gentamicin and voriconazole. We sought to survey all Australian hospitals operating both in the public and private health sectors. Responses were captured from 85 unique institutions, from all Australian states and territories. Regarding guidelines, 26% of hospitals did not have endorsed guidelines to advise on the ordering, sampling and interpretation of TDM for any anti-infective agent. Admitting teams were predominantly responsible for ordering TDM (85%) and interpreting results (76%). Only 51% of hospitals had access to dose prediction software, with access generally better amongst principal referral (69%) (P = 0.01) and children's hospitals (100%) (P = 0.04). Whenever a laboratory-derived minimum inhibitory concentration (MIC) was not available to guide dosing decisions, a surrogate target MIC was assumed in 77% of hospitals. This was based on a 'worst-case' scenario infection in 11% of hospitals. The rates of clinical practice consistent with current guideline recommendations across all aspects of TDM were demonstrated to be 0% for vancomycin, 4% for gentamicin and 35% for voriconazole. At present, there is significant institutional variability in the clinical practice of TDM for anti-infective agents in Australia for established TDM drugs.

Development of a methodology to make individual estimates of the precision of liquid chromatography-tandem mass spectrometry drug assay results for use in population pharmacokinetic modeling and the optimization of dosage regimens

Background

The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE’s for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes.

Methods

Following method validation, three independent experiments were conducted to develop AEE’s using various least squares linear or nonlinear, and median-based linear regression techniques. SD’s were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 (“small” sample sets) or 20 (“large” sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE’s were developed by combining the SD’s of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated.

Results

Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil’s regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD’s, including SD’s below the lower limit of quantification.

Conclusions

In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.

Precision dosing to avoid adverse drug reactions

Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

Software for Dosage Individualization of Voriconazole: a Prospective Clinical Study

Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care.

Voriconazole is a first-line antifungal agent. Therapeutic drug monitoring is a standard of care. The best way to adjust dosages to achieve desired drug exposure endpoints is unclear due to nonlinear and variable pharmacokinetics. Previously described software was used to prospectively adjust voriconazole dosages. The CYP2C19, CYP3A4, and CYP3A5 genotypes were determined. The primary endpoint was the proportion of patients with a C_min at 120 h in the range 1 to 3 mg/liter using software to adjust voriconazole dosages. A total of 19 patients were enrolled, and 14 were evaluable. Of these, 12/14 (85.7%; 95% confidence interval = 57.2 to 98.2%) had a C_min at 120 h posttreatment initiation of 1 to 3 mg/liter, which was higher than the a priori expected proportion of 33%. There was no association of CYP genotype-derived metabolizer phenotype with voriconazole AUC. Software can be used to adjust the dosages of voriconazole to achieve drug exposures that are safe and effective. (The clinical trial discussed in this paper has been registered in the European Clinical Trials Database under EudraCT no. 2013-0025878-34 and in the ISRCTN registry under no. ISRCTN83902726.)

Integrating anatomo-physiological changes and pharmacogenomics in anti-infective therapy management: is it a major concern?

Success of anti-infective therapy is a major challenge in some patients given anatomo-physiological changes and genetic variations. In this case anecdote, we report the management strategy of a patient suffering from chronic pulmonary aspergillosis in a context of anorexia nervosa and genetic polymorphism.

Impact of Infection Status and Cyclosporine on Voriconazole Pharmacokinetics in an Experimental Model of Cerebral Scedosporiosis

Cerebral Scedosporium infections usually occur in lung transplant recipients as well as in immunocompetent patients in the context of near drowning. Voriconazole is the first-line treatment. The diffusion of voriconazole through the blood-brain barrier in the context of cerebral infection and cyclosporine administration is crucial and remains a matter of debate. To address this issue, the pharmacokinetics of voriconazole was assessed in the plasma, cerebrospinal fluid (CSF), and brain in an experimental model of cerebral scedosporiosis in rats receiving or not receiving cyclosporine. A single dose of voriconazole (30 mg/kg, i.v.) was administered to six groups of rats randomized according to the infection status and the cyclosporine dosing regimen (no cyclosporine, a single dose, or three doses; 15 mg/kg each). Voriconazole concentrations in plasma, CSF, and brain samples were quantified using ultra-performance liquid chromatography-tandem mass spectrometry and high-performance liquid chromatography UV methods and were documented up to 48 hours after administration. Pharmacokinetic parameters were estimated using a noncompartmental approach. Voriconazole pharmacokinetic profiles were similar for plasma, CSF, and brain in all groups studied. The voriconazole C_max and area under the curve (AUC) (AUC_{0 ≥ 48 hours}) values were significantly higher in plasma than in CSF [CSF/plasma ratio, median (range) = 0.5 (0.39-0.55) for AUC_{0 ≥ 48 hours} and 0.47 (0.35 and 0.75) for C_max]. Cyclosporine administration was significantly associated with an increase in voriconazole exposure in the plasma, CSF, and brain. In the plasma, but not in the brain, an interaction between the infection and cyclosporine administration reduced the positive impact of cyclosporine on voriconazole exposure. Together, these results emphasize the impact of cyclosporine on brain voriconazole exposure.

Pharmacokinetic Modeling of Voriconazole To Develop an Alternative Dosing Regimen in Children

The pharmacokinetic variability of voriconazole (VCZ) in immunocompromised children is high, and adequate exposure, particularly in the first days of therapy, is uncertain. A population pharmacokinetic model was developed to explore VCZ exposure in plasma after alternative dosing regimens. Concentration data were obtained from a pediatric phase II study. Nonlinear mixed effects modeling was used to develop the model. Monte Carlo simulations were performed to test an array of three-times-daily (TID) intravenous dosing regimens in children 2 to 12 years of age. A two-compartment model with first-order absorption, nonlinear Michaelis-Menten elimination, and allometric scaling best described the data (maximal kinetic velocity for nonlinear Michaelis-Menten clearance [V_max] = 51.5 mg/h/70 kg, central volume of distribution [V₁] = 228 liters/70 kg, intercompartmental clearance [Q] = 21.9 liters/h/70 kg, peripheral volume of distribution [V₂] = 1,430 liters/70 kg, bioavailability [F] = 59.4%, K_m = fixed value of 1.15 mg/liter, absorption rate constant = fixed value of 1.19 h^-1). Interindividual variabilities for V_max, V₁, Q, and F were 63.6%, 45.4%, 67%, and 1.34% on a logit scale, respectively, and residual variability was 37.8% (proportional error) and 0.0049 mg/liter (additive error). Monte Carlo simulations of a regimen of 9 mg/kg of body weight TID simulated for 24, 48, and 72 h followed by 8 mg/kg two times daily (BID) resulted in improved early target attainment relative to that with the currently recommended BID dosing regimen but no increased rate of accumulation thereafter. Pharmacokinetic modeling suggests that intravenous TID dosing at 9 mg/kg per dose for up to 3 days may result in a substantially higher percentage of children 2 to 12 years of age with adequate exposure to VCZ early during treatment. Before implementation of this regimen in patients, however, validation of exposure, safety, and tolerability in a carefully designed clinical trial would be needed.

Voriconazole Therapeutic Drug Monitoring Practices in Intensive Care

BACKGROUND

Routine therapeutic drug monitoring of voriconazole seems to be beneficial. This study investigated the therapeutic drug monitoring practices in intensive care to derive possible recommendations for improvement.

METHODS

A retrospective chart review was performed for patients aged ≥18 years who started treatment with voriconazole, which lasted for at least 3 days while being admitted to an intensive care unit to assess possible differences between the patients with and without voriconazole trough concentrations measured.

RESULTS

In 64 (76%) of the 84 patients, voriconazole trough concentrations were measured. The groups differed significantly with respect to the duration of voriconazole treatment and intensive care unit admission. Time of sampling was very early and therefore inappropriate for 49% of the first measured voriconazole trough concentrations and in 48% of the subsequent measured concentrations. Of the 349 trough concentrations measured, 129 (37%) were outside the therapeutic window. In 11% of these cases, no recommendation was provided without identifiable reason. In addition, 27% of recommended dose adjustments were not implemented, probably because the advice was not suited for the specific clinical situation.

CONCLUSIONS

The performance of voriconazole therapeutic drug monitoring can still be improved although voriconazole concentrations were monitored in most patients. A multidisciplinary approach-for instance by means of antifungal stewardship-will probably be able to overcome problems encountered such as timing of sampling, incompleteness of data in clinical context, and lack of implementation of recommendations.

Pharmacodynamic studies of voriconazole: informing the clinical management of invasive fungal infections

INTRODUCTION

Voriconazole is a broad-spectrum antifungal agent commonly used to treat invasive fungal infections (IFI), including aspergillosis, candidiasis, Scedosporium infection, and Fusarium infection. IFI often occur in immunocompromised patients, leading to increased morbidity and mortality.

AREAS COVERED

The objective of this review is to summarize the pharmacodynamic properties of voriconazole and to provide considerations for potential optimal dosing strategies. Studies have demonstrated superior clinical response when an AUC/MIC >25 or Cmin/MIC >1 is attained in adult patients, correlating to a trough concentration range as narrow as 2-4.5 mg/L; however, these targets are poorly established in the pediatric population. Topics in this discussion include voriconazole use in multiple age groups, predisposing patient factors for IFI, and considerations for clinicians managing IFI. Expert commentary: The relationship between voriconazole dosing and exposure is not well defined due to the large inter- and intra-subject variability. Development of comprehensive decision support tools for individualizing dosing, particularly in children who require higher dosing, will help to increase the probability of achieving therapeutic efficacy and decrease sub-therapeutic dosing and adverse events.

Tools for the Individualized Therapy of Teicoplanin for Neonates and Children

The aim of this study was to develop a population pharmacokinetic (PK) model for teicoplanin across childhood age ranges to be used as Bayesian prior information in the software constructed for individualized therapy. We developed a nonparametric population model fitted to PK data from neonates, infants, and older children. We then implemented this model in the BestDose multiple-model Bayesian adaptive control algorithm to show its clinical utility. It was used to predict the dosages required to achieve optimal teicoplanin predose targets (15 mg/liter) from day 3 of therapy. We performed individual simulations for an infant and a child from the original population, who provided early first dosing interval concentration-time data. An allometric model that used weight as a measure of size and that also incorporated renal function using the estimated glomerular filtration rate (eGFR), or the ratio of postnatal age (PNA) to serum creatinine concentration (SCr) for infants <3 months old, best described the data. The median population PK parameters were as follows: elimination rate constant (Ke) = 0.03 · (wt/70)^−0.25 · Renal (h⁻¹); V = 19.5 · (wt/70) (liters); Renal = eGFR^0.07 (ml/min/1.73 m²), or Renal = PNA/SCr (μmol/liter). Increased teicoplanin dosages and alternative administration techniques (extended infusions and fractionated multiple dosing) were required in order to achieve the targets safely by day 3 in simulated cases. The software was able to predict individual measured concentrations and the dosages and administration techniques required to achieve the desired target concentrations early in therapy. Prospective evaluation is now needed in order to ensure that this individualized teicoplanin therapy approach is applicable in the clinical setting. (This study has been registered in the European Union Clinical Trials Register under EudraCT no. 2012-005738-12.)

Linezolid in liver failure: exploring the value of the maximal liver function capacity (LiMAx) test in a pharmacokinetic pilot study

Patients in the intensive care unit frequently require antibiotic treatment. Liver impairment poses substantial challenges for dose selection in these patients. The aim of the present pilot study was to assess the novel maximal liver function capacity (LiMAx test) in comparison with conventional liver function markers as covariates of drug clearance in liver failure using linezolid as a model drug. A total of 28 patients with different degrees of liver failure were recruited. LiMAx test as well as plasma, dialysate and urine sampling were performed under linezolid steady-state therapy (600 mg twice daily). NONMEM^® was used for a pharmacometric analysis in which the different clearance routes of linezolid were elucidated. Linezolid pharmacokinetics was highly variable in patients with liver failure. The LiMAx score displayed the strongest association with non-renal clearance (CL_non-renal) [ = 4.46∙(body weight/57.9) ^0.75∙(LiMAx/221.5)^0.388 L/h], which reduced interindividual variability in CL_non-renal from 46.6% to 33.6%, thereby being superior to other common markers of liver function (international normalised ratio, gamma-glutaryl transferase, bilirubin, thrombocytes, alanine aminotransferase, aspartate aminotransferase). For LiMAx < 100 µg/kg/h, 64% of linezolid trough concentrations were above the recommended trough concentration of 8 mg/L, indicating the necessity of therapeutic drug monitoring in these patients. This is the first pilot application of the LiMAx test in a pharmacokinetic (PK) study demonstrating its potential to explain PK variability in linezolid clearance. Further studies with a larger patient collective and further drugs are highly warranted to guide dosing in patients with severe liver impairment.

Right Dose, Right Now: Customized Drug Dosing in the Critically Ill

Drugs are key weapons that clinicians have to battle against the profound pathologies encountered in critically ill patients. Antibiotics in particular are commonly used and can improve patient outcomes dramatically. Despite this, there are strong opportunities for further reducing the persisting poor outcomes for infected critically ill patients. However, taking these next steps for improving patient care requires a new approach to antibiotic therapy. Giving the right dose is highly likely to increase the probability of clinical cure from infection and suppress the emergence of resistant pathogens. Furthermore, in some patients with higher levels of sickness severity, reduced mortality from an optimized approach to antibiotic use could also occur. To enable optimized dosing, the use of customized dosing regimens through either evidence-based dosing nomograms or preferably through the use of dosing software supplemented by therapeutic drug monitoring data should be embedded into daily practice. These customized dosing regimens should also be given as soon as practicable as reduced time to initiation of therapy has been shown to improve patient survival, particularly in the presence of septic shock. However, robust data supporting these logical approaches to therapy, which may deliver the next step change improvement for treatment of infections in critically ill patients, are lacking. Large prospective studies of patient survival and health system costs are now required to determine the value of customized antibiotic dosing, that is, giving the right dose at the right time.

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software

BACKGROUND

Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden.

METHODS

The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building.

RESULTS

Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08).

CONCLUSIONS

BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

Pharmacodynamics of Voriconazole in Children: Further Steps along the Path to True Individualized Therapy

Voriconazole is the agent of choice for the treatment of invasive aspergillosis in children at least 2 years of age. The galactomannan index is a routinely used diagnostic marker for invasive aspergillosis and can be useful for following the clinical response to antifungal treatment. The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PK-PD) mathematical model that links the pharmacokinetics of voriconazole with the galactomannan readout in children. Twelve children receiving voriconazole for treatment of proven, probable, and possible invasive fungal infections were studied. A previously published population PK model was used as the Bayesian prior. The PK-PD model was used to estimate the average area under the concentration-time curve (AUC) in each patient and the resultant galactomannan-time profile. The relationship between the ratio of the AUC to the concentration of voriconazole that induced half maximal killing (AUC/EC50) and the terminal galactomannan level was determined. The voriconazole concentration-time and galactomannan-time profiles were both highly variable. Despite this variability, the fit of the PK-PD model was good, enabling both the pharmacokinetics and pharmacodynamics to be described in individual children. (AUC/EC50)/15.4 predicted terminal galactomannan (P= 0.003), and a ratio of >6 suggested a lower terminal galactomannan level (P= 0.07). The construction of linked PK-PD models is the first step in developing control software that enables not only individualized voriconazole dosages but also individualized concentration targets to achieve suppression of galactomannan levels in a timely and optimally precise manner. Controlling galactomannan levels is a first critical step to maximizing clinical response and survival.

Clinical Decision Support Tools: The Evolution of a Revolution

Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug monitoring. In the treatment of inflammatory bowel disease patients with infliximab, dashboards may reduce therapeutic failures and treatment costs. The history and future development of modern Bayesian dashboard systems is described.

Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

BACKGROUND

Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians.

METHODS

A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately.

RESULTS

Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04).

CONCLUSIONS

Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.

Optimizing azole antifungal therapy in the prophylaxis and treatment of fungal infections

PURPOSE OF REVIEW

Azole antifungals are widely used in the prophylaxis and treatment of fungal infections, but are associated with a range of pharmacokinetic challenges and safety issues that necessitate individualized therapy to achieve optimal clinical outcomes. Recent advances in our knowledge of azole exposure-response relationships, therapeutic drug monitoring and individualized dosing strategies are reviewed as follows.

RECENT FINDINGS

Recent studies have significantly improved the understanding of exposure-response relationships for efficacy and toxicity, increasing confidence in target exposure ranges for azole antifungal agents. Population pharmacokinetic modelling of voriconazole has led to studies demonstrating the feasibility of model-guided dose individualization strategies with the drug, which holds significant promise for optimizing therapy. The recent approval of a solid oral tablet formulation of posaconazole with improved bioavailability and once-daily dosing has significantly improved the clinical utility of this agent. Further clinical experience with the investigational azole isavuconazole is needed to determine the role of individualized therapy.

SUMMARY

The coordination of CYP2C19 pharmacogenomic testing with model-guided dose individualization holds significant promise for optimizing therapy with voriconazole. Pharmacokinetic challenges with itraconazole, voriconazole and posaconazole oral suspension continue to require therapeutic drug monitoring to individualize therapy and optimize treatment outcomes.

Achieving target voriconazole concentrations more accurately in children and adolescents

Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiple-model Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, -17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was -0.7% (interquartile range, -7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, -1.4 to 14.7%; P = 0.16 versus the full posterior parameter value) and the dose bias was -6.7% (interquartile range, -18.7 to 2.4%; P = 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, -13.1 to 18%; P = 0.32) and a dose bias of -3.5% (interquartile range, -18 to 14%; P = 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.).

Anti-infective drugs during continuous hemodialysis - using the bench to learn what to do at the bedside

PURPOSE

The main objective of this study was to investigate the clearance of 11 selected anti-infectives in an in vitro model of continuous veno-venous hemodialysis (CVVHD), in order to suggest rational dosing strategies for clinical practice.

METHODS

Ceftazidime, ciprofloxacin, flucloxacillin, gentamicin, linezolid, meropenem, metronidazole, piperacillin, rifampicin, vancomycin and voriconazole were studied in two different solvents (sodium chloride 0.9% and HSA 5%) using a multifiltrate dialysis device by Fresenius Medical Care (Bad Homburg, Germany). For each solution, prefilter, postfilter, and dialysate samples were drawn simultaneously during one hour of dialysis and were assayed.

RESULTS

The clearance of all drugs except rifampicin in sodium chloride 0.9% was comparable (mean 1.76 ± 0.11 l/h). The clearance of these agents in human serum albumin solution 5% was reduced by between 5.3% and 72.2%. The unbound drug fraction correlated with a lower clearance in HSA 5% (Pearson correlation coefficient r = 0.933; p = 0.00008). No correlation between clearance in HSA 5% and the drugs' molecular weight was found (Pearson correlation coefficient r = 0.388; p = 0.268). Rifampicin was detected to bind to the surface of the polysulfone filter used. Dialysis clearance of ceftazidime, gentamicin, linezolid, meropenem, metronidazole, piperacillin and vancomycin during CVVHD accounted for over 25% of the total body clearance of population pharmacokinetic data for renally impaired patients.

CONCLUSIONS

The results from this study highlight that dose adaptations are needed for most of the drugs under investigation for patients undergoing CVVHD. In combination with polysulfone filters, rifampicin should be used with care in this setting.

Population pharmacokinetics of teicoplanin in children

Teicoplanin is frequently administered to treat Gram-positive infections in pediatric patients. However, not enough is known about the pharmacokinetics (PK) of teicoplanin in children to justify the optimal dosing regimen. The aim of this study was to determine the population PK of teicoplanin in children and evaluate the current dosage regimens. A PK hospital-based study was conducted. Current dosage recommendations were used for children up to 16 years of age. Thirty-nine children were recruited. Serum samples were collected at the first dose interval (1, 3, 6, and 24 h) and at steady state. A standard 2-compartment PK model was developed, followed by structural models that incorporated weight. Weight was allowed to affect clearance (CL) using linear and allometric scaling terms. The linear model best accounted for the observed data and was subsequently chosen for Monte Carlo simulations. The PK parameter medians/means (standard deviation [SD]) were as follows: CL, [0.019/0.023 (0.01)] × weight liters/h/kg of body weight; volume, 2.282/4.138 liters (4.14 liters); first-order rate constant from the central to peripheral compartment (Kcp), 0.474/3.876 h(-1) (8.16 h(-1)); and first-order rate constant from peripheral to central compartment (Kpc), 0.292/3.994 h(-1) (8.93 h(-1)). The percentage of patients with a minimum concentration of drug in serum (Cmin) of <10 mg/liter was 53.85%. The median/mean (SD) total population area under the concentration-time curve (AUC) was 619/527.05 mg · h/liter (166.03 mg · h/liter). Based on Monte Carlo simulations, only 30.04% (median AUC, 507.04 mg · h/liter), 44.88% (494.1 mg · h/liter), and 60.54% (452.03 mg · h/liter) of patients weighing 50, 25, and 10 kg, respectively, attained trough concentrations of >10 mg/liter by day 4 of treatment. The teicoplanin population PK is highly variable in children, with a wider AUC distribution spread than for adults. Therapeutic drug monitoring should be a routine requirement to minimize suboptimal concentrations. (This trial has been registered in the European Clinical Trials Database Registry [EudraCT] under registration number 2012-005738-12.).

Voriconazole pharmacokinetics and exposure-response relationships: assessing the links between exposure, efficacy and toxicity

The triazole antifungal voriconazole (VCZ) exhibits broad-spectrum antifungal activity and is the first-line treatment for invasive aspergillosis. Highly variable, non-linear pharmacokinetics, metabolism via the polymorphic drug-metabolising enzyme CYP2C19, and a range of serious adverse events (AEs) including hepatotoxicity and neurotoxicity complicate the clinical utility of VCZ. As interest in optimising VCZ treatment has increased, a growing number of studies have examined the relationships between VCZ exposure and efficacy in the treatment and prevention of invasive fungal infections, as well as associations with VCZ-related AEs. This review provides a critical analysis of VCZ pharmacokinetics and exposure-response (E-R) relationships, assessing the links between VCZ exposure, efficacy and toxicity. Low VCZ exposure has frequently been associated with a higher incidence of treatment failure; fewer studies have addressed E-R relationships with prophylactic VCZ. VCZ-related neurotoxicity appears common at high VCZ concentrations and can be minimised by maintaining concentrations below the recommended upper concentration thresholds; hepatotoxicity appears to be associated with increased VCZ exposure but is also prevalent at low concentrations. Further research should aim to inform and optimise the narrow therapeutic range of VCZ as well as develop interventions to individualise VCZ dosing to achieve maximal efficacy with minimal toxicity.

Individualization of piperacillin dosing for critically ill patients: dosing software to optimize antimicrobial therapy

Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection. Piperacillin concentrations measured in the first dosing interval, from each of 8 additional individuals, combined with the population model were embedded into the dose optimization software. The impact of the number of observations was assessed. Precision was assessed by (i) the predicted piperacillin dosage and by (ii) linear regression of the observed-versus-predicted piperacillin concentrations from the second 24 h of treatment. We found that a linear clearance model with creatinine clearance and weight as covariates for drug clearance and volume of distribution, respectively, best described the observed data. When there were at least two observed piperacillin concentrations, the dose optimization software predicted a mean piperacillin dosage of 4.02 g in the 8 patients administered piperacillin doses of 4.00 g. Linear regression of the observed-versus-predicted piperacillin concentrations for 8 individuals after 24 h of piperacillin dosing demonstrated an r² of >0.89. In conclusion, for most critically ill patients, individualized piperacillin regimens delivering a target serum piperacillin concentration is achievable. Further validation of the dosage optimization software in a clinical trial is required.

How severe is antibiotic pharmacokinetic variability in critically ill patients and what can be done about it?

The pharmacokinetics (PK) of antimicrobial agents administered to critically ill patients exhibit marked variability. This variability results from pathophysiological changes that occur in critically ill patients. Changes in volume of distribution, clearance, and tissue penetration all affect the drug concentrations at the site of infection. PK-pharmacodynamic indices (fCmax:MIC; AUC0-24:MIC; fT>MIC; fCmin:MIC) for both antimicrobial effect and suppression of emergence of resistance are described for many antimicrobial drugs. Changing the regimen by which antimicrobial drugs are delivered can help overcome the PK variability and optimise target attainment. This will deliver optimised antimicrobial chemotherapy to individual critically ill patients. Delivery of β-lactams antimicrobial agents by infusions, rather than bolus dosing, is effective at increasing the duration of the dosing interval that the drug concentration is above the MIC. Therapeutic drug monitoring, utilising population PK mathematical models with Bayesian estimation, can also be used to optimise regimens following measurement of plasma drug concentrations. Clinical trials are required to establish if patient outcomes can be improved by implementing these techniques.

Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions

Infections in critically ill patients are associated with persistently poor clinical outcomes. These patients have severely altered and variable antibiotic pharmacokinetics and are infected by less susceptible pathogens. Antibiotic dosing that does not account for these features is likely to result in suboptimum outcomes. In this Review, we explore the challenges related to patients and pathogens that contribute to inadequate antibiotic dosing and discuss how to implement a process for individualised antibiotic therapy that increases the accuracy of dosing and optimises care for critically ill patients. To improve antibiotic dosing, any physiological changes in patients that could alter antibiotic concentrations should first be established; such changes include altered fluid status, changes in serum albumin concentrations and renal and hepatic function, and microvascular failure. Second, antibiotic susceptibility of pathogens should be confirmed with microbiological techniques. Data for bacterial susceptibility could then be combined with measured data for antibiotic concentrations (when available) in clinical dosing software, which uses pharmacokinetic/pharmacodynamic derived models from critically ill patients to predict accurately the dosing needs for individual patients. Individualisation of dosing could optimise antibiotic exposure and maximise effectiveness.

Opinion: the pharmacometrics of infectious disease

The application of pharmacometric principles to the treatment of infectious diseases must address important biological issues across the diversity of pathogenic organisms. Recent applications of pharmacometric tools in this therapeutic area have had important translational impact not only in drug development but on real-world clinical practice. The fruitful fusion of preclinical and population methodologies promises increasingly personalized and mechanistic approaches.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e70; doi:10.1038/psp.2013.46; published online 28 August 2013.