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BACKGROUND AND AIM Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments. METHODS Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use. RESULTS Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations. CONCLUSION All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of KConcomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing.A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

Purpose To characterize pertuzumab pharmacokinetics (PK) in FeDeriCa (NCT03493854: fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] versus intravenous pertuzumab plus trastuzumab); derive individual pertuzumab exposures in the PH FDC SC arm for subsequent pertuzumab exposure–response (ER) analyses; compare observed trastuzumab PK with predicted exposures from a previous SC trastuzumab model; assess whether pertuzumab affects trastuzumab PK; evaluate pertuzumab exposure–efficacy and –safety relationships and support the approved SC dosing regimen. Methods Population pharmacokinetic modeling and simulations were used to describe the data. Standard goodness-of-fit diagnostics and prediction-corrected visual predictive checks were used for model performance assessment. Covariates were included from previously reported models. ER analysis was conducted using logistic regression. Results SC pertuzumab PK was described adequately by a two-compartment model with first-order absorption; significant covariates included in the final model were albumin, lean body weight, and Asian region; however, these appeared not to be clinically relevant. Trastuzumab concentrations were described adequately by the previous model; there was no evidence of a pertuzumab effect on trastuzumab PK as part of PH FDC SC and higher model-predicted pertuzumab exposure was not associated with differences in pathologic complete response rate or an increased probability of selected grade ≥ 3 adverse events of interest. Conclusion The approved PH FDC SC dose [loading: 1200/600 mg pertuzumab/trastuzumab (15 mL); maintenance: 600 mg pertuzumab/trastuzumab (10 mL) and 2000 U/mL recombinant human hyaluronidase every 3 weeks] provides a positive benefit–risk profile with comparable efficacy and safety to intravenous pertuzumab plus trastuzumab.

To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment. Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies. Thirty-two paediatric patients (0.2–16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1–14.6% using InsightRX and from 3.4–7.8% using NextDose. Precision ranged from median RMSE of 0.19–0.32 mg·h·L−1 for InsightRX and 0.08–0.1 mg·h·L−1 for NextDose. A median reduction in busulfan clearance from day 1 to day 4 was observed in the clinical data (−10.9%), when using InsightRX (−18.6%) and with NextDose (−14.7%). Bayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses.

We aimed to review the pharmacokinetics (PK) of intravenous busulfan in paediatric patients, identify covariate factors influencing exposure, investigate evidence of changes in PK behaviour over time, and correlate exposure with efficacy and toxicity outcomes. A literature review was undertaken of original research published between 2007 and 2019, investigating the PK and pharmacodynamics (PD) of intravenous busulfan in patients ≤ 18 years of age. The review identified 41 publications characterising the PK, and 45 publications describing the PD, of busulfan. Median typical clearance (CL) was 0.22 L/h/kg and median typical volume of distribution was 0.69 L/kg. Patient weight, age, glutathione-S-transferase A1 (GSTA1) genotype and busulfan dosing day/time were the most commonly identified factors affecting CL. Of nine studies investigating changes in CL, seven reported reduced CL over the 4-day course of treatment. Exposure monitoring methods and therapeutic targets were heterogeneous across studies. Relationships between busulfan exposure and patient outcomes were observed in five studies. One study observed a cumulative area under the concentration-time curve over all days of treatment of between 78 and 101 mg/L·h, and two studies observed an average concentration at first dose of 1.88 ng/mL. Patient weight, age and GSTA1 genotype are important covariates to consider when individualising busulfan therapy. Reduced busulfan CL over time may need to be accounted for, particularly in patients not receiving phenytoin co-therapy. Standardised monitoring of busulfan exposure over the entire course of treatment and further investigation of the role of busulfan metabolites and pharmacogenomics is warranted.

The number of publications applying nonlinear mixed-effect (NLME) modeling has increased yearly since its first appearance in 1979. Here, we evaluated articles that have used NLME modeling, were published in journals that attract a broader audience, and we discussed the standard of presentation of these to stimulate target audience-specific improvements for increased impact in the future.

Contains fulltext : 249069.pdf (Publisher’s version ) (Open Access) AIMS: Abacavir is part of WHO-recommended regimens to treat HIV in children under 15 years of age. In a pooled analysis across four studies, we describe abacavir population pharmacokinetics to investigate the influence of age, concomitant medications, malnutrition and formulation. METHODS: A total of 230 HIV-infected African children were included, with median (range) age of 2.1 (0.1-12.8) years and weight of 9.8 (2.5-30.0) kg. The population pharmacokinetics of abacavir was described using nonlinear mixed-effects modelling. RESULTS: Abacavir pharmacokinetics was best described by a two-compartment model with first-order elimination, and absorption described by transit compartments. Clearance was predicted around 54% of its mature value at birth and 90% at 10 months. The estimated typical clearance at steady state was 10.7 L/h in a child weighing 9.8 kg co-treated with lopinavir/ritonavir, and was 12% higher in children receiving efavirenz. During coadministration of rifampicin-based antituberculosis treatment and super-boosted lopinavir in a 1:1 ratio with ritonavir, abacavir exposure decreased by 29.4%. Malnourished children living with HIV had higher abacavir exposure initially, but this effect waned with nutritional rehabilitation. An additional 18.4% reduction in clearance after the first abacavir dose was described, suggesting induction of clearance with time on lopinavir/ritonavir-based therapy. Finally, absorption of the fixed dose combination tablet was 24% slower than the abacavir liquid formulation. CONCLUSION: In this pooled analysis we found that children on lopinavir/ritonavir or efavirenz had similar abacavir exposures, while concomitant TB treatment and super-boosted lopinavir gave significantly reduced abacavir concentrations.

To characterize pertuzumab pharmacokinetics (PK) in FeDeriCa (NCT03493854: fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] versus intravenous pertuzumab plus trastuzumab); derive individual pertuzumab exposures in the PH FDC SC arm for subsequent pertuzumab exposure-response (ER) analyses; compare observed trastuzumab PK with predicted exposures from a previous SC trastuzumab model; assess whether pertuzumab affects trastuzumab PK; evaluate pertuzumab exposure-efficacy and -safety relationships and support the approved SC dosing regimen.Population pharmacokinetic modeling and simulations were used to describe the data. Standard goodness-of-fit diagnostics and prediction-corrected visual predictive checks were used for model performance assessment. Covariates were included from previously reported models. ER analysis was conducted using logistic regression.SC pertuzumab PK was described adequately by a two-compartment model with first-order absorption; significant covariates included in the final model were albumin, lean body weight, and Asian region; however, these appeared not to be clinically relevant. Trastuzumab concentrations were described adequately by the previous model; there was no evidence of a pertuzumab effect on trastuzumab PK as part of PH FDC SC and higher model-predicted pertuzumab exposure was not associated with differences in pathologic complete response rate or an increased probability of selected grade ≥ 3 adverse events of interest.The approved PH FDC SC dose [loading: 1200/600 mg pertuzumab/trastuzumab (15 mL); maintenance: 600 mg pertuzumab/trastuzumab (10 mL) and 2000 U/mL recombinant human hyaluronidase every 3 weeks] provides a positive benefit-risk profile with comparable efficacy and safety to intravenous pertuzumab plus trastuzumab.

Aims Describe the pharmacokinetics (PK) of the antiretroviral drugs abacavir and lamivudine in malnourished paediatric patients and relate to viral load outcomes after 12 and 48 weeks of treatment. Methods Severely malnourished human immunodeficiency virus-infected children were randomized to early (within 14 days) or delayed (after nutritional recovery) initiation of antiretroviral treatment (ART) using World Health Organization weight-band dosages. Abacavir and lamivudine concentrations were measured as a secondary objective on day 1 and day 14 and patients were followed-up to week 48. Population PK of abacavir and lamivudine were described using NONMEM. Results In total, 623 abacavir and 627 lamivudine concentrations were collected from 75 paediatric patients aged 0.1-10.8 (median 1.4) years. Abacavir PK was described by a 2-compartment model, patients randomized to early ART showed increased bioavailability of 31%. Apparent clearance (CL/F, L/h/7 kg) of abacavir increased from day 1 to day 14 from 3.33 (95% confidence interval 2.71-4.12) to 5.86 (95% confidence interval 4.78-7.3). A 1-compartment model described lamivudine PK, variability on CL/F was explained by maturation with age, with age at half-matured CL/F being 4 months. For both drugs allometrically scaled total body weight was related to CL/F and apparent volume of distribution. PK exposure did not correlate with virological outcomes or death at 12 or 48 weeks. Conclusion Increases in Abacavir's CL/F between day 1 to day 14, bioavailability and PK variability with early start of ART was found in this cohort of severely malnourished children; however, these changes did not influence virological outcomes. The study supports the use of weight-band dosage tables.

Background and purpose: Students find clinical pharmacokinetics and pharmacodynamics courses challenging, partly due to their mathematical nature. This study aimed to retrospectively evaluate the impact of “feedback quizzes” on the experiences and academic performance of undergraduate pharmacy students at an Australian university. Educational activity and setting: Formative paper-based quizzes were introduced into tutorials in an intermediate third-year pharmacokinetics course, and summative online quizzes were introduced into a subsequent advanced fourth-year course that included a pharmacokinetics component. Experience data were drawn from institutional student evaluation surveys, and academic performance was obtained from exam results. Student experiences and academic performance were compared pre- and post-intervention using the Test of Equal Proportions and Mann–Whitney–Wilcoxon, respectively. Findings: A greater proportion of students in both quiz cohorts were satisfied with the overall course experience compared to the pre-curricular change cohort (intermediate: 87% vs. 78%; advanced: 63% vs. 50%). Students who received quizzes in both years performed better in the clinical pharmacokinetics component of the advanced course compared to a prior cohort who had no quizzes (85.7% vs. 77.8%). Feedback quizzes, whether formative or summative, administered in-class or outside class, can enhance learning and performance and lead to improvements in student satisfaction with clinical pharmacokinetics courses. Scaffolding of feedback quizzes across year levels can provide students with added confidence when attempting assessment. Collaboration between research-focused and teaching-focused staff can lead to increased scholarship of teaching and learning activities.

BackgroundWhile Aspergillus detection rates in adults, adolescents and older children with cystic fibrosis (CF) have increased, the risk of acquiring this fungal pathogen in young children is unknown.AimTo determine the risk and explanatory factors of acquiring Aspergillus in children with CF by age 5 years.MethodsCross-sectional analysis of clinical, bronchoalveolar lavage and treatment data from the Australasian Cystic Fibrosis Bronchoalveolar Lavage study was used to identify predictive factors for detecting Aspergillus at age 5 years. A parametric repeated time-to-event model quantitatively described the risk and factors associated with acquiring Aspergillus and Pseudomonas aeruginosa from birth until age 5 years.ResultsCross-sectional analysis found that the number of P. aeruginosa eradication courses increased the odds of detecting Aspergillus at age 5 years (OR 1.61, 95% CI 1.23 to 2.12). The median (IQR) age for the first P. aeruginosa positive culture was 2.38 (1.32–3.79) years and 3.69 (1.68–4.74) years for the first Aspergillus positive culture. The risk of P. aeruginosa and Aspergillus events changes with time after the first year of study entry. It also decreases for P. aeruginosa after completing P. aeruginosa eradication (HR 0.15, 95% CI 0.00 to 0.79), but increases for Aspergillus events (HR 2.75, 95% CI 1.45 to 5.41). The risk of acquiring both types of events increases after having had a previous event.ConclusionIn young children with CF, completing P. aeruginosa eradication therapy and previous Aspergillus events are associated with increased risk of acquiring Aspergillus.

To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment.Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies.Thirty-two paediatric patients (0.2-16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1-14.6% using InsightRX and from 3.4-7.8% using NextDose. Precision ranged from median RMSE of 0.19-0.32 mg·h·LBayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses.

We aimed to review the pharmacokinetics (PK) of intravenous busulfan in paediatric patients, identify covariate factors influencing exposure, investigate evidence of changes in PK behaviour over time, and correlate exposure with efficacy and toxicity outcomes. A literature review was undertaken of original research published between 2007 and 2019, investigating the PK and pharmacodynamics (PD) of intravenous busulfan in patients ≤ 18 years of age. The review identified 41 publications characterising the PK, and 45 publications describing the PD, of busulfan. Median typical clearance (CL) was 0.22 L/h/kg and median typical volume of distribution was 0.69 L/kg. Patient weight, age, glutathione-S-transferase A1 (GSTA1) genotype and busulfan dosing day/time were the most commonly identified factors affecting CL. Of nine studies investigating changes in CL, seven reported reduced CL over the 4-day course of treatment. Exposure monitoring methods and therapeutic targets were heterogeneous across studies. Relationships between busulfan exposure and patient outcomes were observed in five studies. One study observed a cumulative area under the concentration-time curve over all days of treatment of between 78 and 101 mg/L·h, and two studies observed an average concentration at first dose of 600 ng/mL improved overall survival, transplant-related mortality, or relapse. One study observed increased sinusoidal obstructive syndrome with maximum busulfan concentration 1.88 ng/mL. Patient weight, age and GSTA1 genotype are important covariates to consider when individualising busulfan therapy. Reduced busulfan CL over time may need to be accounted for, particularly in patients not receiving phenytoin co-therapy. Standardised monitoring of busulfan exposure over the entire course of treatment and further investigation of the role of busulfan metabolites and pharmacogenomics is warranted.

Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions. The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020. Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug–drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies). The effect of clinically significant drug–drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

The objective of this article is to investigate the influence of blood sampling times on tobramycin exposure estimation and clinical decisions and to determine the best sampling times for two estimation methods used for therapeutic drug monitoring. Adult patients with cystic fibrosis, treated with once-daily intravenous tobramycin, were intensively sampled over one 24-h dosing interval to determine true exposure (AUC0–24). The AUC0–24s were then estimated using both log-linear regression and Bayesian forecasting methods for 21 different sampling time combinations. These were compared to true exposure using relative prediction errors. The differences in subsequent dose recommendations were calculated. Twelve patients, with a median (range) age of 25 years (18–36) and weight of 66.5 kg (50.6–76.4) contributed 96 tobramycin concentrations. Five hundred and eighty-eight estimated AUC0–24s were compared to 12 measured true AUC0–24 values. Median relative prediction errors ranged from − 34.7 to 45.5% for the log-linear regression method and from − 14.46 to 11.23% for the Bayesian forecasting method across the 21 sampling combinations. The most unbiased exposure estimation was provided from concentrations sampled at 100/640 min after the start of the infusion using log-linear regression and at 70/160 min using Bayesian forecasting. Subsequent dosing recommendations varied greatly depending on the estimation method and the sampling times used. Sampling times markedly influence bias in AUC0–24 estimation, leading to greatly varied dose adjustments. The impact of blood sampling times on dosing decisions is reduced when using Bayesian forecasting.

BACKGROUND: In developing countries, malnutrition remains a common clinical syndrome at antiretroviral treatment (ART) initiation. Physiological changes due to malnutrition and during nutritional recovery could affect the pharmacokinetics of antiretroviral drugs. METHODS: HIV-infected children admitted with severe acute malnutrition were randomised to early or delayed initiation of lopinavir/ritonavir, abacavir and lamivudine using WHO weight-band dosage charts. Lopinavir concentrations were measured on day 1 and day 14. Thereafter patients were followed-up to week 48. The population pharmacokinetics of lopinavir was described using NONMEM v7.3. Covariates were screened to assess their influence on the pharmacokinetics of lopinavir and the relationship between pharmacokinetic variability and treatment outcomes were assessed. RESULTS: 502 lopinavir concentrations were collected from 62 pediatric patients aged 0.1-3.9 years (median: 0.9 years). Rifampin-based ant-ituberculosis treatment and “super-boosted” lopinavir/ritonavir was prescribed in 20 patients. Lopinavir disposition was well described by a one-compartment model with first order elimination. Neither randomization to early or delayed ART, tuberculosis co-medications nor anthropometrical measurements explained the pharmcokinetic variability. Allometrically scaled fat-free-mass (FFM) influenced apparent clearance (CL/F) and volume of distribution (V(d)/F). Pharmacokinetic exposure did not correlate with virologic outcomes or death at 12 or 48 weeks. CONCLUSIONS: Lopinavir pharmacokinetics was influenced by FFM and not by timing of ART initiation or tuberculosis co-medication in severely malnourished HIV-infected children. Lopinavir pharmacokinetics was found to be highly variable and bioavailability greatly reduced, resulting in a high CL estimate in this population. The role of lopinavir dose adjustment should be further evaluated in severely malnourished children initiating ART.

Aims Glomerular filtration rate (GFR) is estimated daily in paediatric oncology patients; however, few equations, particularly ones that do not include serum creatinine, have been evaluated in this population. We aimed to compare the predictive performance of different equations available to estimate GFR in paediatric oncology patients. Methods GFR was measured (mGFR) in paediatric oncology patients based on a chromium 51-labeled ethylene diamine tetraacetic acid excretion test. GFR was estimated (eGFR) in these same patients using equations identified from the literature. mGFR and eGFR values were compared, and the predictive performance of various eGFR equations was assessed in terms of their bias, precision and accuracy. Results In total, 124 mGFR values ranging from 7 to 146 mL/min were available for analysis from 73 children. Twenty-two equations were identified from the literature. The Flanders metadata equation displayed the lowest absolute bias (mean error of 0.9 mL/min) and the greatest precision (root mean square error of 13.1 mL/min). The univariate Schwartz equation predicted the highest percentage (81.5%) of eGFR values within 30% of mGFR values, and the Rhodin fat-free mass equation predicted the highest percentage (37.1%) of eGFR values within 10% of mGFR values. Conclusions A number of equations were identified that could be used to estimate renal function in paediatric oncology patients; however, none was found to be highly accurate. The Flanders metadata equation and univariate Schwartz performed the best in this study, and we would suggest that these two equations may be used cautiously in paediatric oncology patients for clinical decision making, understanding their limitations.

Dosing gentamicin in pediatric patients can be difficult due to its narrow therapeutic index. A significantly higher percentage of fat mass has been observed in children receiving oncology treatment than in those who are not. Differences in the pharmacokinetics of gentamicin between oncology and nononcology pediatric patients and individual dosage requirements were evaluated in this study, using normal fat mass (NFM) as a body size descriptor. Data from 423 oncology and 115 nononcology patients were analyzed. Differences in drug disposition were observed between the oncology and nononcology patients, with oncology patients having a 15% lower central volume of distribution and 32% lower intercompartmental clearance. Simulations based on the population pharmacokinetic model demonstrated low exposure target attainment in all individuals at the current clinical recommended starting dose of 7.5 mg/kg of body weight once daily, with 57.4% of oncology and 35.7% of nononcology subjects achieving a peak concentration (C(max)) of ≥25 mg/liter and 64.3% of oncology and 65.6% of nononcology subjects achieving an area under the concentration-time curve at 24 h postdose (AUC(24)) of ≥70 mg · h/liter after the first dose. Based on simulations, the extent of the impact of differences in drug disposition between the two cohorts appeared to be dependent on the exposure target under examination. Greater differences in achieving a C(max) target of >25 mg/liter than an AUC(24) target of ≥70 mg · h/liter between the cohorts was observed. Further investigation into whether differences in the pharmacokinetics of gentamicin between oncology and nononcology patients are a consequence of changes in body composition is required.

Aims: To evaluate three Bayesian Forecasting (BF) programs: TDMx, InsighRx® and DoseMe® on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists. Methods: Clinical pharmacists across three Australian hospitals, that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialed. Participants were given four case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a chi-square Test of Equal or Given Proportions to identify significant differences in response. Results: Twenty-seven pharmacists, from hospitals participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (p=0.02) of participants recording a Likert score of '4' or '5' for DoseMe®, TDMx and InsightRx®, respectively. Participant's found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision making with >50% of participants scoring a '4' or '5' across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display. Conclusion: Although differences exist between programs, all three programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

The pharmacokinetics of gentamicin in pediatric patients with febrile neutropenia is described, and the adequacy of initial dosing of once-daily gentamicin assessed at Queensland's largest Children's Hospital.Data were retrospectively collected from all pediatrics with febrile neutropenia admitted over a 2-year period who had at least 2 gentamicin concentration-time measurements (a paired set within 1 dosing interval). Gentamicin clearance, volume of distribution, area under the concentration-time curve from 0 to 24 hours postdose (AUC0-24), and maximum concentration values were estimated with log-linear regression using each paired set. The percentage of paired sets associated with gentamicin exposure within predefined hospital targets was calculated, and exposure was examined in relation to the bacterial culture status.Data were collected from 69 patients [median (interquartile range) age 3.7 years (2.2-8.9)] and comprised 121 paired concentration sets characterizing 80 separate admissions. Median (interquartile range) gentamicin clearance and volume of distribution were 8.1 L·h·70 kg (5.8-12.4) and 21.8 L/70 kg (16.9-29.5), respectively. Predefined hospital exposure targets were achieved for both AUC0-24 and maximum concentration for 10% of paired sets; one or the other of these targets were met for 36% of paired sets, and neither target was achieved for 54% of paired sets. Achievement of targets improved with repeated monitoring during the same admission. Median AUC0-24 achieved was significantly higher in patients with a confirmed Gram-negative infection compared with those without 71 (50-91) mg·h·L versus 55 (40.8-67.5) mg·h·L, respectively (P = 0.003). Over the study period, a median gentamicin dose of 10.8 and 6.4 mg/kg was estimated to be necessary to achieve an AUC target of 80 mg·h·L in children ≤10 years and10 years of age.Based on a log-linear method of analysis, current dosing seems to be consistently producing gentamicin exposure below predefined pharmacokinetic targets, suggesting that an increase in the recommended starting dose of gentamicin may be required.

Aim: To characterise usage and monitoring of intravenous tobramycin in cystic fibrosis (CF) patients in Australia and the UK. Methods: An anonymous, online survey of healthcare professionals caring for CF patients was conducted. Survey questions were designed to obtain information on tobramycin dosing, therapeutic drug monitoring and toxicity monitoring. Results: The survey was sent to pharmacists and clinicians in 73 CF centres. Responses were received from 32 and 40 healthcare professionals, from Australia and the UK, respectively. Once-daily dosing of tobramycin was the preferred administration regimen for 93.8 and 67.5% of participants in Australia and the UK, respectively. Among them 68.8% of Australian and 55% of UK participants initiated tobramycin therapy at a dose of 10 mg/kg/day or greater. Australian participants most commonly adjusted tobramycin dosage using log-linear regression analysis (40.6%) or trough measurements (28.1%). UK participants most commonly adjusted tobramycin dosage using trough (55%) or peak and trough measurements (37.5%). In 90.6% of Australian and 95% of UK participants practices, serum creatinine was routinely monitored during admission. Standard pure tone audiometry was performed by 15.6% of Australian and 17.5% of UK participants and high-frequency pure tone audiometry was performed by 15.6% of Australian and 10% of UK participants, once or twice a year. Conclusions: Many discrepancies exist between Australia and the UK and within each country with respect to monitoring of intravenous tobramycin in CF patients. Greater ototoxicity monitoring is likely necessary in both countries. Further education of health professionals about the existence of national guidelines and guidance on how they can be applied in practice is likely required.

In February 2019, the Population Approach Group of Australia and New Zealand (PAGANZ) celebrated 20 years of providing education, mentoring, and support to the Australasian pharmacometrics community. This article provides insight into the history of this group and its operations to the present date. Refereed/Peer-reviewed

To (i) describe tacrolimus (TAC) pre-dose concentrations (C0), (ii) calculate apparent oral TAC clearance (CL/FHCT) adjusted for measured haematocrit (HCTi) and standardised to a HCT of 45%, across three observation time points and (iii) explore if low TAC C0 or high mean CL/FHCT are associated with an increased risk of rejection episodes early after lung transplantation. TAC whole blood concentration-time profiles and transbronchial biopsies were performed prospectively at weeks 3, 6 and 12 after lung transplantation. The TAC pre-dose concentration (C0) was measured, and CL/FHCT was determined using non-compartmental analysis. The associations between TAC C0 and CL/FHCT and rejection status were explored using repeated measures logistic regression. Eighteen patients provided 377 TAC whole blood concentrations. Considerable variability around the median (IQR) CL/FHCT 6.8 (4.2–15.9) L h−1, and the median C0 12.7 (9.9–16.6) μg L−1 was noted. Despite adjustment for haematocrit, a significant decrease was observed in CL/FHCT in all patients over time: CL/FHCT 14 (5.4–23) at week 3, CL/FHCT 7.7 (4.5–12) at week 6 and CL/FHCT 3.9 (2.4–11) L h−1 at week 12 (p

Bayesian forecasting (BF) methods for tobramycin dose individualisation has not seen widespread clinical adoption, despite being endorsed by clinical practice guidelines. Several freeware and commercial programmes using BF methods are available to support personalised dosing. This study evaluated exposure estimates, dose recommendations, and predictive performance compared with current clinical practice. Data from 105 patients (50 adults and 55 children) with cystic fibrosis who received intravenous tobramycin treatment and had paired concentration–time measurements were analysed using (1) log-linear regression analysis, and (2) three BF programmes: TDMx, InsightRX, and DoseMe. Exposure estimates and dose recommendations were compared using the Wilcoxon signed-rank test and Bland–Altman analysis. Predictive performance of BF programmes was compared based on bias and imprecision. Median estimated tobramycin exposure with current clinical practice was significantly lower (87.8 vs. 92.5, 94.0 and 90.3 mg h l−1; p ≤ 0.01), hence median subsequent dose recommendations were significantly higher (10.1 vs. 9.4, 9.4 and 9.2 mg kg−1; p ≤ 0.01) compared with BF programmes. Furthermore, median relative dose-adjustment differences were higher in adults (> 10%) compared with children (4.4–7.8%), and differences in individual dose recommendations were > 20% on 19.1–27.4% of occasions. BF programmes showed low bias (

This study aimed to determine the optimal starting dose of gentamicin in paediatric oncology patients. A population pharmacokinetic model describing drug exposure, a semi-mechanistic model describing bacterial killing and an Emax model describing renal cortex accumulation were linked in a utility function using NONMEM®. The optimal gentamicin starting dose was estimated in patients aged from 0.1 to 18.2 years, by balancing the probability of efficacy on day 1 against relative renal function reduction on day 7 with continued dosing. Using achievement of a gentamicin area under the concentration time curve to bacterial minimum inhibitor concentration (MIC) ratio of ≥ 100 and maximum concentration to MIC ratio of ≥ 10 as the efficacy endpoints, a starting dose of 7.1, 9.5, 10.8 and 14.6 mg/kg/q24h was optimal at a MIC of 0.5, 1, 2 and 4 mg/L respectively, with ≥ 75% probability of obtainment. Using achievement of a 2-log10 bacterial count reduction at 24-h post-dose as the efficacy endpoint, a starting dose of 12.8 mg/kg/q24h was optimal, with 85.6% probability of obtainment. Under these different dosing scenarios, relative reduction in renal function ranged on average from 6.9 to 14.5% on day 7. The current recommended starting dose of gentamicin of 7.5 mg/kg/q24h may not be sufficient to achieve efficacy on day 1 if bacterial MIC is > 0.5 mg/L. A higher initial dose (up to 14.6 mg/kg/q24h), in less sensitive microorganisms, would likely cause only a relatively small reduction in renal function at day 7. Close monitoring is crucial if high doses are given, especially for longer than 7 days.

To ensure the safe and effective dosing of gentamicin in children, therapeutic drug monitoring (TDM) is recommended. TDM utilizing Bayesian forecasting software is recommended but is unavailable, as no population model that describes the pharmacokinetics of gentamicin in pediatric oncology patients exists. This study aimed to develop and externally evaluate a population pharmacokinetic model of gentamicin to support personalized dosing in pediatric oncology patients. A nonlinear mixed-effect population pharmacokinetic model was developed from retrospective data. Data were collected from 423 patients for model building and a further 52 patients for external evaluation. A two-compartment model with first-order elimination best described the gentamicin disposition. The final model included renal function (described by fat-free mass and postmenstrual age) and the serum creatinine concentration as covariates influencing gentamicin clearance (CL). Final parameter estimates were as follow CL, 5.77 liters/h/70 kg; central volume of distribution, 21.6 liters/70 kg; peripheral volume of distribution, 13.8 liters/70 kg; and intercompartmental clearance, 0.62 liter/h/70 kg. External evaluation suggested that current models developed in other pediatric cohorts may not be suitable for use in pediatric oncology patients, as they showed a tendency to overpredict the observations in this population. The final model developed in this study displayed good predictive performance during external evaluation (root mean square error, 46.0%; mean relative prediction error, −3.40%) and may therefore be useful for the personalization of gentamicin dosing in this cohort. Further investigations should focus on evaluating the clinical application of this model.

Glomerular filtration rate (GFR) is estimated daily in paediatric oncology patients; however, few equations, particularly ones that do not include serum creatinine, have been evaluated in this population. We aimed to compare the predictive performance of different equations available to estimate GFR in paediatric oncology patients.GFR was measured (mGFR) in paediatric oncology patients based on a chromium 51-labeled ethylene diamine tetraacetic acid excretion test. GFR was estimated (eGFR) in these same patients using equations identified from the literature. mGFR and eGFR values were compared, and the predictive performance of various eGFR equations was assessed in terms of their bias, precision and accuracy.In total, 124 mGFR values ranging from 7 to 146 mL/min were available for analysis from 73 children. Twenty-two equations were identified from the literature. The Flanders metadata equation displayed the lowest absolute bias (mean error of 0.9 mL/min) and the greatest precision (root mean square error of 13.1 mL/min). The univariate Schwartz equation predicted the highest percentage (81.5%) of eGFR values within 30% of mGFR values, and the Rhodin fat-free mass equation predicted the highest percentage (37.1%) of eGFR values within 10% of mGFR values.A number of equations were identified that could be used to estimate renal function in paediatric oncology patients; however, none was found to be highly accurate. The Flanders metadata equation and univariate Schwartz performed the best in this study, and we would suggest that these two equations may be used cautiously in paediatric oncology patients for clinical decision making, understanding their limitations.

Purpose Methadone is a potent analgesic used to treat refractory cancer pain. It is administered as a racemic mixture, with the l-enantiomer being primarily a μ-receptor agonist, whereas the d-enantiomer is an N -methyl- d -aspartate antagonist and inhibits serotonin and norepinephrine reuptake. Dose requirements vary greatly among patients to achieve optimal pain control and to avoid the risk of adverse effects. The relationship between plasma and saliva methadone enantiomer concentrations was investigated to determine if saliva could be a substitute for plasma in pharmacodynamic and pharmacokinetic studies for clinical monitoring and dose optimization of methadone in patients with advanced cancer. Methods Patients with advanced cancer who were prescribed varying doses of oral methadone for pain management were recruited to obtain paired plasma and saliva samples. Pain scores were recorded at the time of sampling. The total and unbound plasma and saliva concentrations of the l- and d-enantiomers of methadone were quantified by using an HPLC-MS/MS method. The relationship between plasma (total and unbound) and saliva concentrations were compared. The saliva-to-plasma concentration ratio was compared versus the dose administered and the time after dosing for both enantiomers. The association of methadone concentrations with reported pain scores was compared by using a Mann-Whitney U test for significance. Findings Fifty patients receiving a mean dose of 11mg/d of methadone provided 151 paired plasma and saliva samples. The median age of the population was 61 years with an interquartile range of 53-71 years with total body weight ranging from 59-88 kg. Median (interquartile) total plasma concentrations for l- and d-methadone were 50.78 ng/mL (30.6–113.0 ng/mL) and 62.0 ng/mL (28.7–116.0 ng/mL), respectively. Median (interquartile range) saliva concentrations for l- and d-methadone were 81.5 ng/mL (28.0–203.2 ng/mL) and 44.2 (16.2–149.7 ng/mL). No relationship could be established between plasma and saliva concentrations for l- and d-methadone ( r 2 = 0.35 and 0.25). The saliva-to-plasma concentration analyzed with the methadone dose showed higher saliva concentrations at lower doses. Dose-normalized saliva concentrations followed a similar pattern over time compared with plasma concentrations. No correlation was found between l-methadone plasma, d-methadone plasma, l-methadone saliva, d-methadone saliva concentrations, and pain score. Implications Saliva concentration was not a better predictor of pain control than plasma concentration for dose optimization and monitoring studies of methadone in patients with cancer. Although the saliva-to-plasma ratio of the concentration of methadone enantiomers was stable across the dosing range, due to the variability in individual saliva-to-plasma ratios, saliva sampling may not be a valid substitute in pharmacokinetic studies of methadone in cancer.

Background Use of inhaled tobramycin therapy for treatment of Pseudomonas aeruginosa infections in young children with cystic fibrosis (CF) is increasing. Safety data for pre-school children are sparse. Methods The aim of this study was to assess the safety of tobramycin solution for inhalation (TOBI®-TSI) administered twice daily for 2months/course concurrently to intravenous (IV) tobramycin during P. aeruginosa eradication therapy in children (0–5years). Audiological assessment and estimation of glomerular filtration rate (GFR) was measured prior to any exposure and end of the study. Results Data were available from 142 patients who were either never exposed to aminoglycosides (n=39), exposed to IV aminoglycosides only (n=36) or exposed to IV+TSI (n=67). Median exposure to TSI was 113days [59, 119]. Comparison of effects on audiometry results and GFR, showed no detectable difference between the groups. Conclusions Use of TSI and IV tobramycin in pre-school children with CF was not associated with detectable renal toxicity or ototoxicity.

When performing a population pharmacokinetic modelling analysis covariates are often added to the model. Such additions are often justified by improved goodness of fit and/or decreased in unexplained (random) parameter variability. Increased goodness of fit is most commonly measured by the decrease in the objective function value. Parameter variability can be defined as the sum of unexplained (random) and explained (predictable) variability. Increase in magnitude of explained parameter variability could be another possible criterion for judging improvement in the model. The agreement between these three criteria in diagnosing covariate-parameter relationships of different strengths and nature using stochastic simulations and estimations as well as assessing covariate-parameter relationships in four previously published real data examples were explored. Total estimated parameter variability was found to vary with the number of covariates introduced on the parameter. In the simulated examples and two real examples, the parameter variability increased with increasing number of included covariates. For the other real examples parameter variability decreased or did not change systematically with the addition of covariates. The three criteria were highly correlated, with the decrease in unexplained variability being more closely associated with changes in objective function values than increases in explained parameter variability were. The often used assumption that inclusion of covariates in models only shifts unexplained parameter variability to explained parameter variability appears not to be true, which may have implications for modelling decisions.

Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.

While several studies have examined the pharmacokinetics of tobramycin in patients with cystic fibrosis (CF), there is no consensus on whether they differ in patients with and without CF. The objectives of this study were to identify covariates which explain pharmacokinetic variability and to examine whether having the disease CF in itself alters these relationships and drug dose requirements. To investigate this issue, a population pharmacokinetic meta-analysis of data from eight centres was undertaken. NONMEM® 7.2 was used to analyse the data, which comprised 4,514 concentration–time measurements from 465 adults and children with CF and 1,095 concentration–time measurements from 267 adults and children without CF. Tobramycin disposition was well described by a two-compartment model with first-order elimination. Patient age, fat-free mass, serum creatinine concentration and sex were identified as significant covariates in the final model. Fat-free mass was superior to total bodyweight as a descriptor of clearance, volume of distribution of the central and peripheral compartments and inter-compartmental clearance. CF as an independent disease-specific factor had no significant influence on the pharmacokinetics of tobramycin at any stage during covariate model building. An optimal dose of 11 mg/kg every 24 h was defined for CF patients using a utility function approach. The pharmacokinetics of tobramycin do not differ significantly in CF patients compared with patients without CF when subject age, fat-free mass, sex and renal function are taken into consideration. Variations in tobramycin dosing between CF and non-CF patients should therefore reflect target concentrations or exposures based on differences in expected pathogen sensitivity and not the presence of CF.

Population pharmacokinetic modelling, Monte Carlo simulation and semi-mechanistic pharmacodynamic modelling are all tools that can be applied to personalize gentamicin therapy. This review summarizes and evaluates literature knowledge on the population pharmacokinetics and pharmacodynamics of gentamicin and identifies areas where further research is required to successfully individualize gentamicin therapy using modelling and simulation techniques. Thirty-five studies have developed a population pharmacokinetic model of gentamicin and 15 studies have made dosing recommendations based on Monte Carlo simulation. Variability in gentamicin clearance was most commonly related to renal function in adults and body weight and age in paediatrics. Nine studies have related aminoglycoside exposure indices to clinical outcomes. Most commonly, efficacy has been linked to a Cmax/MIC ≥7-10 and a AUC24/MIC ≥70-100. No study to date has shown a relationship between predicted achievement of exposure targets and actual clinical success. Five studies have developed a semi-mechanistic pharmacokinetic/pharmacodynamic model to predict bacteria killing and regrowth following gentamicin exposure and one study has developed a deterministic model of aminoglycoside nephrotoxicity. More complex semi-mechanistic models are required that consider the immune response, use of multiple antibiotics, the severity of illness, and both efficacy and toxicity. As our understanding grows, dosing of gentamicin based on sound pharmacokinetic/pharmacodynamic principles should be applied more commonly in clinical practice.

Fixed tobramycin (mg/kg) dosing is often inappropriate in patients with cystic fibrosis (CF), as pharmacokinetics are highly variable. The area under the concentration-time curve (AUC) is an exposure metric suited to monitoring in this population. Bayesian strategies to estimate AUC have been available for over 20 years but are not standard practice in the clinical setting. To assess their suitability for use in clinical practice, three AUC estimation methods using limited sampling were compared to measured true exposure by using intensive sampling tobramycin data. Adults prescribed once daily intravenous tobramycin had eight concentrations taken over 24 h. An estimate of true exposure within one dosing interval was calculated using the trapezoidal method and compared to three alternate estimates determined using (i) a two-sample log-linear regression (LLR) method (local hospital practice); (ii) a Bayesian estimate using one concentration (AUC 1 ); and (iii) a Bayesian estimate using two concentrations (AUC 2 ). Each method was evaluated against the true measured exposure by a Bland-Altman analysis. Twelve patients with a median (range) age and weight of 25 (18 to 36) years and 66.5 (51 to 76) kg, respectively, were recruited. There was good agreement between the true exposure and the three alternate estimates of AUC, with a mean AUC bias of 1 ), and 1.0 (AUC 2 ). Bayesian analysis-based and LLR estimation methods of tobramycin AUC are equivalent to true exposure estimation. All three methods may be suitable for use in the clinical setting; however, a one-sample Bayesian method may be most useful in ambulatory patients for which coordinating blood samples is difficult. Suitably powered, randomized clinical trials are required to assess patient outcomes.