Population pharmacokinetics and pharmacodynamics of teicoplanin in neonates: making better use of C-reactive protein to deliver individualized therapy

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection

OBJECTIVES

To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery.

METHODS

We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method.

RESULTS

In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2.

CONCLUSIONS

CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

Prediction of C-reactive protein dynamics during meropenem treatment in neonates and infants

AIMS

C-reactive protein (CRP) is used to determine the effect of antibiotic treatment on sepsis in neonates/infants. We aimed to develop pharmacokinetic-pharmacodynamic (PKPD) model of meropenem and CRP in neonates/infants and evaluate its predictive performance of CRP dynamics.

METHODS

Data from neonates/infants treated with meropenem in 3 previous studies were analysed. To the previously developed meropenem PK models, the addition of turnover, transit or effect compartment, delay differential equation PD models of CRP as a function of meropenem concentration or its cumulative area under the curve (AUC) were evaluated. The percentage of neonates/infants (P0.1 , P0.2 ) in whom the ratio of the fifth day CRP to its peak value was predicted with an error of <0.1 (<0.2) was calculated.

RESULTS

A total of 60 meropenem treatment episodes (median [range] gestational age 27.6 [22.6-40.9] weeks, postnatal age 13 [2-89] days) with a total of 351 CRP concentrations (maximum value 65.5 [13-358.4] mg/L) were included. Turnover model of CRP as a function of meropenem cumulative AUC provided the best fit and included CRP at the start of treatment, use of prior antibiotics, study and causative agent Staphylococcus aureus or enterococci as covariates. Using meropenem population predictions and data available at 0, 24, 48, 72 h after the start of treatment, P0.1 (P0.2 ) was 36.4, 36.4, 60.6 and 66.7% (70.0, 66.7, 72.7 and 78.7%), respectively.

CONCLUSION

The developed PKPD model of meropenem and CRP as a function of meropenem cumulative AUC incorporating several patient characteristics predicts CRP dynamics with an error of <0.2 in most neonates/infants.

Exploring the Impact of Model-Informed Precision Dosing on Procalcitonin Concentrations in Critically Ill Patients: A Secondary Analysis of the DOLPHIN Trial

Model-informed precision dosing (MIPD) might be used to optimize antibiotic treatment. Procalcitonin (PCT) is a biomarker for severity of infection and response to antibiotic treatment. The aim of this study was to assess the impact of MIPD on the course of PCT and to investigate the association of PCT with pharmacodynamic target (PDT) attainment in critically ill patients. This is a secondary analysis of the DOLPHIN trial, a multicentre, open-label, randomised controlled trial. Patients with a PCT value available at day 1 (T1), day 3 (T3), or day 5 (T5) after randomisation were included. The primary outcome was the absolute difference in PCT concentration at T1, T3, and T5 between the MIPD and the standard dosing group. In total, 662 PCT concentrations from 351 critically ill patients were analysed. There was no statistically significant difference in PCT concentration between the trial arms at T1, T3, or T5. The median PCT concentration was highest in patients who exceeded 10× PDT at T1 [13.15 ng/mL (IQR 5.43-22.75)]. In 28-day non-survivors and in patients that exceeded PDT at T1, PCT decreased significantly between T1 and T3, but plateaued between T3 and T5. PCT concentrations were not significantly different between patients receiving antibiotic treatment with or without MIPD guidance. The potential of PCT to guide antibiotic dosing merits further investigation.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Therapeutic Drug Monitoring and Population Pharmacokinetic Analysis of Teicoplanin among Chinese Patients with Gram-Positive Infections in a Tertiary Hospital

Background. To explore the use of teicoplanin among Chinese patients with Gram-positive infections in a tertiary hospital. Methods. The medical records of patients, who were monitored for teicoplanin plasma concentration (TPC) from December 2017 to February 2019, were collected. By combining the therapeutic drug monitoring (TDM) and nonlinear mixed-effects model, a population pharmacokinetic (PPK) model of teicoplanin was established. Results. The proportions of TPCs lower and higher than 10 mg/L were nearly the same (102 vs. 108 cases). A two-compartment model of teicoplanin PPK in Chinese patients was established. Compared with 400 mg, the 600 mg regimen was more able to reach the target concentration (10 mg/L), especially for high-weight patients. Conclusions. The standard regimen of teicoplanin, 400 mg, failed to reach the target value in the present population. Moreover, the 600 mg regimen was feasible for high-weight patients based on TDM and individualized pharmacokinetic dosing adjustment.

Management of Bacterial and Fungal Infections in the ICU: Diagnosis, Treatment, and Prevention Recommendations

Bacterial and fungal infections are common issues for patients in the intensive care unit (ICU). Large, multinational point prevalence surveys have identified that up to 50% of ICU patients have a diagnosis of bacterial or fungal infection at any one time. Infection in the ICU is associated with its own challenges. Causative organisms often harbour intrinsic and acquired mechanisms of drug-resistance, making empiric and targeted antimicrobial selection challenging. Infection in the ICU is associated with worse clinical outcomes for patients. We review the epidemiology of bacterial and fungal infection in the ICU. We discuss risk factors for acquisition, approaches to diagnosis and management, and common strategies for the prevention of infection.

Current Status of Pharmacokinetic Research in Children: A Systematic Review of Clinical Trial Records

BACKGROUND

Many medications have different pharmacokinetics in children than in adults. Knowledge about the safety and efficacy of medications in children requires research into the pharmacokinetic profiles of children's medicines. By analysing registered clinical trial records, this study determined how frequently pharmacokinetic data is gathered in paediatric drug trials.

METHODS

We searched for the pharmacokinetic data from clinical trial records for preterm infants and children up to the age of 16 from January 2011 to April 2022. The records of trials involving one or more drugs in preterm infants and children up to the age of 16 were examined for evidence that pharmacokinetic data would be collected.

RESULTS

In a total of 1483 records of interventional clinical trials, 136 (9.17%) pharmacokinetic data involved adults. Of those 136 records, 60 (44.1%) records were pharmacokinetics trials involving one or more medicines in children up to the age of 16. 20 (33.3 %) in America, followed by 19 (31.6 %) in Europe. Most trials researched medicines in the field of infection or parasitic diseases 20 (33.3%). 27 (48.2%) and 26 (46.4%) trials investigated medicines that were indicated as essential medicine.

CONCLUSION

The pharmacokinetic characteristics of children's drugs need to be better understood. The current state of pharmacokinetic research appears to address the knowledge gap in this area adequately. Despite slow progress, paediatric clinical trials have experienced a renaissance as the significance of paediatric trials has gained international attention. The outcome of paediatric trials will have an impact on children's health in the future. In recent years, the need for greater availability and access to safe child-size pharmaceuticals has received a lot of attention.

Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay differential equations

Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor α (TNF-α), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h^-1 and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-α, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans.

Use of Antibiotics in Preterm Newborns

Due to complex maturational and physiological changes that characterize neonates and affect their response to pharmacological treatments, neonatal pharmacology is different from children and adults and deserves particular attention. Although preterms are usually considered part of the neonatal population, they have physiological and pharmacological hallmarks different from full-terms and, therefore, need specific considerations. Antibiotics are widely used among preterms. In fact, during their stay in neonatal intensive care units (NICUs), invasive procedures, including central catheters for parental nutrition and ventilators for respiratory support, are often sources of microbes and require antimicrobial treatments. Unfortunately, the majority of drugs administered to neonates are off-label due to the lack of clinical studies conducted on this special population. In fact, physiological and ethical concerns represent a huge limit in performing pharmacokinetic (PK) studies on these subjects, since they limit the number and volume of blood sampling. Therapeutic drug monitoring (TDM) is a useful tool that allows dose adjustments aiming to fit plasma concentrations within the therapeutic range and to reach specific drug target attainment. In this review of the last ten years’ literature, we performed Pubmed research aiming to summarize the PK aspects for the most used antibiotics in preterms.

Optimizing antimicrobial use: challenges, advances and opportunities

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.

Biomarker-Guided Individualization of Antibiotic Therapy

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

From Therapeutic Drug Monitoring to Model-Informed Precision Dosing for Antibiotics

Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended.

Therapeutic Drug Monitoring and Nephrotoxicity of Teicoplanin Therapy in Chinese Children: A Retrospective Study

Purpose

This study aims to 1) describe the distribution characteristics of teicoplanin trough concentration (C_min) and explore the related influencing factors and 2) evaluate the nephrotoxicity of teicoplanin in children.

Patients and Methods

A cohort of children who were treated with teicoplanin intravenously were included in this retrospective study. Regression analysis was performed to explore the factors associated with the fluctuations of teicoplanin C_min and the development of nephrotoxicity. Classification and regression tree analysis was used to identify the population at high risk for teicoplanin nephrotoxicity.

Results

A total of 269 plasma samples from 186 children were collected. Underexposure (C_min < 10 mg/L) was documented in 52.7% of cases. The C_min/dose after administering the loading dose was strongly associated with age (P = 0.008), weight (P = 0.039), and serum creatinine (P = 0.022). The C_min/dose after administering the maintenance dose was strongly associated with gender (P = 0.014) and serum creatinine (P = 0.006). C_min (P = 0.012) and the concomitant treatment with amphotericin B (P = 0.001) were the independent risk factors for teicoplanin-related nephrotoxicity. Children who were concomitantly treated by amphotericin B with teicoplanin C_min > 9.81 mg/L or patients with teicoplanin C_min > 21.94 mg/L were at high risk for nephrotoxicity.

Conclusion

The fluctuations of teicoplanin C_min could be affected by age, weight, gender, and serum creatinine. C_min and concomitant treatment with amphotericin B were the independent risk factors for nephrotoxicity. We suggested that the therapeutic drug monitoring of teicoplanin should be performed in children.

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

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

Population Pharmacokinetics of Teicoplanin in Preterm and Term Neonates: Is It Time for a New Dosing Regimen?

Our objective was to develop a population pharmacokinetic (PK) model in order to evaluate the currently recommended dosing regimen in term and preterm neonates. By using an optimal design approach, a prospective PK study was designed and implemented in 60 neonates with postmenstrual ages (PMA) of 26 to 43 weeks. A loading dose of 16 mg/kg was administered at day 1, followed by a maintenance dose of 8 mg/kg daily. Plasma concentrations were quantified by high-pressure liquid chromatography-mass spectrometry. Population PK (popPK) analysis was performed using NONMEM software. Monte-Carlo (MC) simulations were performed to evaluate currently recommended dosing based on a pharmacodynamic index of area under the concentration-time curve (AUC)/MIC ratio of ≥400. A two-compartment model with linear elimination best described the data by the following equations: clearance (CL) = 0.0227 × (weight [wt]/1,765)^0.75 × (estimated creatinine clearance [eCRCL]/22)^0.672, central compartment volume of distribution (V1) = 0.283 (wt/1,765), intercompartmental clearance (Q) = 0.151 (wt/1,765)^0.75, and peripheral compartment volume (V2) = 0.541 (wt/1,765). The interindividual variability estimates for CL, V1, and V2 were 36.5%, 45.7%, and 51.4%, respectively. Current weight (wt) and estimated creatinine clearance (eCRCL) significantly explained the observed variability. MC simulation demonstrated that, with the current dosing regimen, an AUC/MIC ratio of ≥400 was reached by only 68.5% of neonates with wt of <1 kg when the MIC was equal to 1 mg/kg, versus 82.2%, 89.7%, and 92.7% of neonates with wt of 1 to <2, 2 to <3, or ≥3 kg, respectively. Augmentation of a maintenance dose up to 10 or 11 mg/kg for preterm neonates with wt of 1 to <2 or <1 kg, respectively, increases the probability of reaching the therapeutic target; the recommended doses seem to be adequate for neonates with wt of ≥2 kg. Teicoplanin PK are variable in neonates, with wt and eCRCL having the most significant impact. Neonates with wt of <2 kg need higher doses, especially for Staphylococcus spp. with an MIC value of ≥1 mg/liter.

Population Pharmacokinetics and Pharmacodynamics of Teicoplanin and C-Reactive Protein in Hospitalized Patients With Gram-Positive Infections

Teicoplanin is an antibiotic agent used for the treatment of Gram-positive infections. The clinical benefit of teicoplanin is associated with its blood concentrations, but the optimal dosing regimen is not yet known. To explore the optimal individual dosing regimen, we performed a population pharmacokinetic (PK) and pharmacodynamic (PD) analysis targeting a large-scale population, including patients with a wide range of ages, body weights, and renal functions. The PK of teicoplanin was described with a 2-compartment model, and the PD of C-reactive protein (CRP) concentrations was described with a turnover maximum inhibition model. The elimination half-life of teicoplanin calculated from the final estimated parameters was 169 hours, and renal function was a significant covariate of teicoplanin clearance. The teicoplanin concentration producing 50% of the maximum inhibition of CRP production was estimated to be 2.66 mg/L. The minimum concentration of teicoplanin in patients with higher loading doses (15 mg/kg) reached the target range (15-30 mg/L) with a probability of >50% in the dosing simulation. We described the influence of body size, body composition, and renal function on the PK of teicoplanin. The population PKPD model of teicoplanin and CRP in this study should provide useful information for development of a dosing strategy including the sequential clinical benefit of teicoplanin.

Exploring the Use of C-Reactive Protein to Estimate the Pharmacodynamics of Vancomycin

BACKGROUND

C-reactive protein (CRP) pharmacodynamic (PD) models have the potential to provide adjunctive methods for predicting the individual exposure response to antimicrobial therapy. We investigated CRP PD linked to a vancomycin pharmacokinetic (PK) model using routinely collected data from noncritical care adults in secondary care.

METHODS

Patients receiving intermittent intravenous vancomycin therapy in secondary care were identified. A 2-compartment vancomycin PK model was linked to a previously described PD model describing CRP response. PK and PD parameters were estimated using a Non-Parametric Adaptive Grid technique. Exposure-response relationships were explored with vancomycin area-under-the-concentration-time-curve (AUC) and EC50 (concentration of drug that causes a half maximal effect) using the index, AUC:EC50, fitted to CRP data using a sigmoidal Emax model.

RESULTS

Twenty-nine individuals were included. Median age was 62 (21-97) years. Fifteen (52%) patients were microbiology confirmed. PK and PD models were adequately fitted (r 0.83 and 0.82, respectively). There was a wide variation observed in individual Bayesian posterior EC50 estimates (6.95-48.55 mg/L), with mean (SD) AUC:EC50 of 31.46 (29.22). AUC:EC50 was fitted to terminal CRP with AUC:EC50 >19 associated with lower CRP value at 96-120 hours of therapy (100 mg/L versus 44 mg/L; P < 0.01).

CONCLUSIONS

The use of AUC:EC50 has the potential to provide in vivo organism and host response data as an adjunct for in vitro minimum inhibitory concentration data, which is currently used as the gold standard PD index for vancomycin therapy. This index can be estimated using routinely collected clinical data. Future work must investigate the role of AUC:EC50 in a prospective cohort and explore linkage with direct patient outcomes.

Delivering precision antimicrobial therapy through closed-loop control systems

Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.

Population pharmacokinetics of teicoplanin in hospitalized elderly patients using cystatin C as an indicator of renal function

OBJECTIVE

Serum cystatin C (CysC) has recently been proposed as an alternative marker to serum creatinine (SCR) for estimating renal clearance. In the present study, we performed a population pharmacokinetic analysis of teicoplanin (TEIC), which is mainly eliminated through the kidneys, using CysC as a predictor for renal clearance.

METHODS

Thirty-six patients with MRSA infections who were administrated to the National Hospital Organization Beppu Medical Center between January 2012 and December 2013 were enrolled and gave 123 sets of blood TEIC concentration data. Renal clearance was estimated by the Hoek equation using CysC, by creatinine clearance predicted by the Cockcroft-Gault equation using SCR, or directly by CysC. One compartment open model with inter-individual variabilities for renal clearance and the volume of distribution as well as an additional residual error model was used to estimate population pharmacokinetic parameters for TEIC.

RESULTS

The model with the best predictability was that with CysC as a predictor for renal clearance; it showed better significance than the models using estimated the glomerular filtration rate by the Hoek equation or CLcr. The final model was as follows: CL (L/hr) = 0.510 × (CysC/1.4)^-0.68 × Total body weight/60^0.81, omega (CL) = 19.8% CV, VC (L) = 78.1, omega (V) = 42.7% CV.

CONCLUSION

The present results show the usefulness of CysC to more accurately predict the pharmacokinetics of drugs mainly eliminated through the kidneys, such as TEIC. However, since the sample size in this study was relatively small, further investigations on renal clearance predictability using CysC are needed.

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.)

The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections

Critically ill patients with severe infections are at high risk of suboptimal antimicrobial dosing. The pharmacokinetics (PK) and pharmacodynamics (PD) of antimicrobials in these patients differ significantly from the patient groups from whose data the conventional dosing regimens were developed. Use of such regimens often results in inadequate antimicrobial concentrations at the site of infection and is associated with poor patient outcomes. In this article, we describe the potential of in vitro and in vivo infection models, clinical pharmacokinetic data and pharmacokinetic/pharmacodynamic models to guide the design of more effective antimicrobial dosing regimens. Individualised dosing, based on population PK models and patient factors (e.g. renal function and weight) known to influence antimicrobial PK, increases the probability of achieving therapeutic drug exposures while at the same time avoiding toxic concentrations. When therapeutic drug monitoring (TDM) is applied, early dose adaptation to the needs of the individual patient is possible. TDM is likely to be of particular importance for infected critically ill patients, where profound PK changes are present and prompt appropriate antibiotic therapy is crucial. In the light of the continued high mortality rates in critically ill patients with severe infections, a paradigm shift to refined dosing strategies for antimicrobials is warranted to enhance the probability of achieving drug concentrations that increase the likelihood of clinical success.