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Background: The Australian state of Victoria has run a population-based skin cancer prevention program called SunSmart since 1988, incorporating substantial public education efforts and environmental change strategies. Trends over 15 years in behavioral risk factors for skin cancer were examined in a population exposed to the SunSmart program. Whether outcomes were associated with extent of SunSmart television advertising was then assessed. Methods: In nine cross-sectional surveys from 1987 to 2002, 11,589 adults were interviewed by telephone about their sun exposure and sun protection during outdoor activities on summer weekends. Analyses completed in 2007 adjusted for ambient temperature and ultraviolet radiation. Results: Sun protection and sunburn show substantial general improvement over time, but have stalled in recent years. Use of hats and sunscreens significantly increased over time and peaked during the mid to late 1990s, compared with the pre-SunSmart baseline. The mean proportion of unprotected skin was reduced and was lowest in the summer of 1997–1998. Summer sunburn incidence declined over time and was 9.1% in 2002, almost half baseline (OR=0.53; 95% CI=0.39–0.73). Higher exposure to SunSmart advertising in the 4 weeks before the interview increased: (1) preference for no tan, (2) hat and sunscreen use, and (3) proportion of body surface protected from the sun. Conclusions: The general improvement in sun-protective behaviors over time highlight that a population’s sun-protective behaviors are amenable to change. Population-based prevention programs incorporating substantial television advertising campaigns into the mix of strategies may be highly effective in improving a population’s sun-protective behaviors. [Copyright &y& Elsevier]

Purpose: Clinical guidelines specify who should receive high-intensity statins; however, it is unclear how high-intensity statins are used in Australia. Our objective was to determine the demographic, clinical, and lifestyle factors associated with high-intensity statin therapy in Australia., Methods: Data from the Australian Diabetes, Obesity and Lifestyle study collected in 2011-2012 were analyzed. High-, moderate-, and low-intensity statins were defined as use of statins at doses demonstrated to reduce low-density lipoprotein cholesterol levels by > 50, 30-50, and < 30%, respectively. Logistic regression was used to estimate adjusted odd ratios (ORs) and 95% confidence intervals (CIs) for factors associated with high- versus low-to-moderate-intensity statin therapy., Results: Overall, 1108 (24%) study participants used a statin. Data on statin intensity were available for 1072 participants. The proportions of high-, moderate-, and low-intensity statin therapy were 32 (n = 341), 65 (n = 696), and 3% (n = 35), respectively. Overall, 51% of people with prior cardiovascular disease (CVD) used a high-intensity statin. In addition to prior CVD (OR = 3.34, 95% CI = 1.95-5.73), no (OR = 1.84, 95%CI 1.02-3.31) or insufficient physical activity (OR = 1.51, 95% CI = 1.01-2.25), obesity (OR = 1.87, 95% CI = 1.13-3.10), and consuming > 2 alcoholic drinks daily (OR = 1.66, 95% CI = 1.08-2.55) were associated with high versus low-to-moderate-intensity statin therapy. Conversely, age 65-74 vs. < 65 years was inversely associated with high-intensity statin therapy (OR = 0.62, 95% CI = 0.41-0.94)., Conclusions: Prior CVD was the strongest factor associated with high-intensity statin therapy. Although the prevalence of CVD increases with age, older people were less likely to be treated with high-intensity statins.

A visual predictive check (VPC) is a common diagnostic procedure for population pharmacometric models. Typically, VPCs are generated by specifying intervals, or "bins", of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPCs. The objective of this work was to demonstrate how regression techniques can be used to derive VPCs and prediction-corrected VPCs (pcVPCs) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPCs and pcVPCs via regression techniques., (© 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Background: Research into which medications contribute to polypharmacy and the variability in these medications across long-term care facilities (LTCFs) has been minimal., Objective: Our objective was to investigate which medications were more prevalent among residents with polypharmacy and to determine the variability in prescribing of these medications across LTCFs., Methods: This was a cross-sectional study of 27 LTCFs in regional and rural Victoria, Australia. An audit of the medication charts and medical records of 754 residents was performed in May 2015. Polypharmacy was defined as nine or more regular medications. Logistic regression was performed to determine the association between medications and resident characteristics with polypharmacy. Analyses were adjusted for age, sex and Charlson's comorbidity index. Variability in the use of the ten most prevalent medication classes was explored using funnel plots. Characteristics of LTCFs with low (< 30%), moderate (30-49%) and high (≥ 50%) polypharmacy prevalence were compared., Results: Polypharmacy was observed in 272 (36%) residents. In adjusted analyses, each of the top ten most prevalent medication classes, with the exception of antipsychotics, were associated with polypharmacy. Between 7 and 23% of LTCFs fell outside the 95% control limits for each of the ten most prevalent medications. LTCFs with ≥ 50% polypharmacy prevalence were predominately smaller., Conclusion: Polypharmacy was associated with nine of the ten most prevalent medication classes. There was greater than fourfold variability in nine of the ten most prevalent medications across LTCFs. Further studies are needed to investigate the clinical appropriateness of the variability in polypharmacy.

Background Lifestyle and dietary advice typically precedes or accompanies the prescription of statin medications. However, evidence for adherence to this advice is sparse. The objective was to compare saturated fat intake, exercise, alcohol consumption and smoking between statin users and non-users in Australia. Methods Data were analysed for 4614 participants aged ≥37 years in the Australian Diabetes, Obesity and Lifestyle study in 2011-2012. Statin use, smoking status and physical activity were self-reported. Saturated fat and alcohol intake were measured via a food frequency questionnaire. Multinomial logistic regression was used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between statin use and the four lifestyle factors. All models were adjusted for age, sex, education, number of general practitioner visits, body mass index, hypertension, diabetes and prior cardiovascular diseases. Results In total 1108 (24%) participants used a statin. Statin users were 29% less likely to be within the highest quartile versus the lowest quartile of daily saturated fat intake compared to non-users (OR 0.71, 95% CI 0.54-0.94). There were no statistically significant associations between statin use and smoking, physical activity or alcohol consumption. Conclusions Smoking status, alcohol consumption and exercise level did not differ between users and non-users of statins. However, statin users were less likely to consume high levels of saturated fat than non-users. We found no evidence that people took statins to compensate for a poor diet or lifestyle.

Objective: Anticholinergic and sedative medications are associated with acute cognitive impairment, but the long-term impact on change in cognition is unclear. This study investigated the effect of anticholinergic and sedative medications, quantified using the Drug Burden Index (DBI), on change in cognition over time in community-dwelling older men., Methods: This was a prospective cohort study of men aged ≥70 years in Sydney, Australia. DBI was assessed at baseline, 2, and 5 years. Cognitive performance was assessed using the Mini-Mental State Exam (MMSE) at each wave. Logistic quantile mixed-effects modelling was used to assess the adjusted effect of DBI on the median MMSE-time profile. Analyses were restricted to men with English-speaking backgrounds (n = 1059, 862, and 611 at baseline, 2, and 5 years)., Results: Overall, 292 (27.7%), 258 (29.9%), and 189 (31.3%) men used anticholinergic or sedative medications at baseline, 2, and 5 years. There was a concave relationship between MMSE and time, where higher DBI corresponded to lower MMSE scores (coefficient: -0.161; 95% CI: -0.250 to -0.071) but not acceleration of declining MMSE over time., Conclusions: Exposure to anticholinergic and sedative medications is associated with a small impairment in cognitive performance but not decline in cognition over time. KEY MESSAGES Exposure to anticholinergic and sedative medications, quantified using the Drug Burden Index, is associated with small cross-sectional impairments in cognitive performance. There was no evidence that exposure to anticholinergic and sedative medications is associated with accelerating decline in cognitive performance over a 5-year follow-up. Older people taking anticholinergic and sedative medications may derive immediate but small benefits in cognitive performance from clinical medication reviews to minimize or cease prescribing of these medications.

Estimating uncertainty in the ratio of 2 measured variables can be achieved via 2 seemingly different approaches: by determining the variance of the first-order Taylor approximation to the ratio, or by the so-called "Propagation of Error" approach. This Lesson Learned shows that the 2 approaches are mathematically equivalent, and provides an example of the approach., (Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study is to investigate the association between polypharmacy with health-related quality of life (HRQoL) and medication regimen complexity with HRQoL in residential aged care facilities (RACFs)., Methods: A cross-sectional study of 383 residents from six Australian RACFs was conducted. The primary exposures were polypharmacy (≥9 regular medications) and the validated Medication Regimen Complexity Index (MRCI). The outcome measure was staff informant rated quality of life assessed using the Quality of Life Alzheimer's disease (QoL-AD) scale. Covariates included age, sex, Charlson's comorbidity index, activities of daily living, and dementia severity. Logistic quantile regression was used to characterize the association between polypharmacy and QoL-AD (model 1) and MRCI and QoL-AD (model 2)., Results: The median age of the 383 residents was 88 years and 297 (78 %) residents were female. In total, 63 % of residents were exposed to polypharmacy and the median MRCI score (range) was 43.5 (4-113). After adjusting for the covariates, polypharmacy was not associated with either higher or lower QoL-AD scores (estimate -0.02; 95 % confidence interval (CI) -0.165, 0.124; p = 0.78). Similarly, after adjusting for the covariates, MRCI was not associated with either higher or lower QoL-AD scores (estimate -0.0009, 95 % CI -0.005, 0.003; p = 0.63)., Conclusions: These findings suggest that polypharmacy and medication regimen complexity are not associated with staff informant rated HRQoL. Further research is needed to investigate how specific medication classes may impact change in quality of life over time.

Purpose: Polypharmacy is often defined as use of 'five-or-more-medications'. However, the optimal polypharmacy cut-point for predicting clinically important adverse events in older people with cancer is unclear. The aim was to determine the sensitivities and specificities of a range of polypharmacy cut-points in relation to a variety of adverse events in older people with cancer., Methods: Data on medication use, falls and frailty criteria were collected from 385 patients aged ≥70 years presenting to a medical oncology outpatient clinic. Receiver operating characteristic (ROC) curves were produced to examine sensitivities and specificities for varying definitions of polypharmacy in relation to exhaustion, falls, physical function, Karnofsky Performance Scale (KPS) and frailty. Sub-analyses were performed when stratifying by age, sex, comorbidity status and analgesic use., Results: Patients had a mean age of 76.7 years. Using Youden's index, the optimal polypharmacy cut-point was 6.5 medications for predicting frailty (specificity 67.0 %, sensitivity 70.0 %), physical function (80.2 %, 49.3 %) and KPS (69.8 %, 52.1 %), 5.5 for falls (59.2 %, 73.0 %) and 3.5 for exhaustion (43.4 %, 74.5 %). For polypharmacy defined as five-or-more-medications, the specificities and sensitivities were frailty (44.9 %, 77.5 %), physical function (58.0 %, 69.7 %), KPS (47.7 %, 69.4 %), falls (44.5 %, 75.7 %) and exhaustion (52.6 %, 64.1 %). The optimal polypharmacy cut-points were similar when the sample was stratified by age, sex, comorbidity status and analgesic use., Conclusions: Our results suggest that no single polypharmacy cut-point is optimal for predicting multiple adverse events in older people with cancer. In this population, the common definition of five-or-more-medications is reasonable for identifying 'at-risk' patients for medication review.

Objectives: To investigate the effects of number of medications and Drug Burden Index (DBI) on transitions between frailty stages and death in community-dwelling older men., Design: Cohort study., Setting: Sydney, Australia., Participants: Community-dwelling men aged 70 and older (N=1,705)., Measurements: Self-reported questionnaires and clinic visits were conducted at baseline and 2 and 5 years. Frailty was assessed at all three waves according to the modified Fried frailty phenotype. The total number of regular prescription medications and DBI (a measure of exposure to sedative and anticholinergic medications) were calculated over the three waves. Data on mortality over 9 years were obtained. Multistate modeling was used to characterize the transitions across three frailty states (robust, prefrail, frail) and death., Results: Each additional medication was associated with a 22% greater risk of transitioning from the robust state to death (adjusted 95% confidence interval (CI)=1.06-1.41). Every unit increase in DBI was associated with a 73% greater risk of transitioning from the robust state to the prefrail state (adjusted 95% CI=1.30-2.31) and a 2.75 times greater risk of transitioning from the robust state to death (adjusted 95% CI=1.60-4.75). There was no evidence of an adjusted association between total number of medications or DBI and the other transitions., Conclusion: Although the possibility of confounding by indication cannot be excluded, additional medications were associated with greater risk of mortality in robust community-dwelling older men. Greater DBI was also associated with greater risk of death and transitioning from the robust state to the prefrail state., (© 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.)

Background: There is increasing awareness that medications can contribute to cognitive decline. Prospective cohort studies are rich sources of clinical data. However, investigating the contribution of medications to cognitive decline is challenging because both medication exposure and cognitive impairment can be associated with attrition of study participants, and medication exposure status may change over time. The objective of this review was to investigate the statistical methods in prospective cohort studies assessing the effect of medications on cognition in older people., Methods: A systematic literature search was conducted to identify prospective cohort studies of at least 12 months duration that investigated the effect of common medications or medication classes (anticholinergics, antihistamines, hypnotics, sedatives, opioids, statins, estrogens, testosterone, antipsychotics, anticonvulsants, antidepressants, anxiolytics, antiparkinson agents and bronchodilators) on cognition in people aged 65 years and older. Data extraction was performed independently by two investigators. A descriptive analysis of the statistical methods was performed., Results: A total of 44 articles were included in the review. The most common statistical methods were logistic regression (24.6% of all reported methods), Cox proportional hazards regression (22.8%), linear mixed-effects models (21.1%) and multiple linear regression (14.0%). The use of advanced techniques, most notably linear mixed-effects models, increased over time. Only 6 articles (13.6%) reported methods for addressing missing data., Conclusions: A variety of statistical methods have been used for investigating the effect of medications on cognition in older people. While advanced techniques that are appropriate for the analysis of longitudinal data, most notably linear mixed-effects models, have increasingly been employed in recent years, there is an opportunity to implement alternative techniques in future studies that could address key research questions., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Pain management can be challenging in frail older people with cancer due to drug-drug interactions and heightened susceptibility to adverse drug events., Objective: To investigate the relationship between analgesic use and pain by frailty status in older outpatients with cancer., Methods: A total of 385 consecutive patients aged 70 years and over who presented to an outpatient oncology clinic between January 2009 and July 2010 completed structured assessments of analgesic use (opioids, paracetamol or non-steroidal anti-inflammatory drugs), pain (10-point visual analogue scale) and clinical factors. Frailty was derived using modified Fried's frailty phenotype. Logistic regression was used to compute adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) for the relationship between analgesic use and pain for each frailty group (robust, pre-frail or frail)., Results: For robust outpatients (n = 101), there was weak evidence for a 30 % relative increase in the adjusted odds of analgesic use between outpatients who differed by one unit of pain score (95 % CI 0.995-1.71, p = 0.0532). For pre-frail outpatients (n = 190), there was evidence for a negative quadratic relationship (adjusted OR for the quadratic coefficient: 0.952, 95 % CI 0.910-0.993, p = 0.0244). For frail outpatients (n = 94), there was an 8 % relative increase in the adjusted odds of analgesic use between outpatients who differed by one unit of pain score, but no statistical evidence for association (95 % CI 0.934-1.26; p = 0.298)., Conclusions: These findings can be considered for the ongoing development of safe, effective strategies for analgesic use in older outpatients with cancer.

The key purpose of performing pharmacometric research is to aid optimization of drug dosing strategies. The statistical techniques required for this research are advanced, which can make interpretation of results difficult to convey to the target audience if they are unfamiliar with pharmacometric concepts. This article provides a basic guide for authors who wish to publish pharmacometric analyses in peer-reviewed journals. This guide is intended to enhance the readability, reproducibility and understanding of the work for a general readership, which may include clinicians, pharmacists and pharmacometricians. Presentation techniques and examples are offered, as well as a checklist of suggested contents for the manuscript.

Background: Anti-malarial efficacy needs to be monitored continually to ensure optimal dosing in the face of emerging anti-malarial drug resistance. The efficacy of artemisinin based combination therapies (ACT) is assessed by repeated measurements of parasite density in the blood of patients following treatment. Parasite density is measured from a capillary or venous blood sample, but this can be logistically and ethically challenging if multiple samples are required within a short time period. The aim of this work was to apply optimal design theory to derive clinically feasible blood sampling schedules from which parasite clearance could be defined using the Parasite Clearance Estimator (PCE), a recently developed tool to identify and quantify artemisinin resistance., Methods: Robust T-optimal design methodology was applied to offer a sampling schedule that allows for discrimination across models that best describe an individual patient's parasite-time profile. The design was based on typical parasite-time profiles derived from the literature combined with key sampling constraints of no more than six samples per patient within 48 hours of initial treatment. The design was evaluated with a simulation-estimation procedure that implemented the PCE., Results: The optimal sampling times (sampling windows) were: 0 (0 to 1.1), 5.8 (4.0 to 6.0), 9.9 (8.4 to 11.5), 24.8 (24.0 to 24.9), 36.3 (34.8 to 37.2) and 48 (47.3, 48.0) hours post initial treatment. The simulation-estimation procedure showed that the design supported identification of the appropriate method by the PCE to determine an individual's parasite clearance rate constant (the main output calculation from the PCE)., Conclusions: The proposed sampling design requires six samples per patient within the first 48 hours. The derived design requires validation in a real world setting, but should be considered for future studies that intend to employ the PCE.

The analysis of in vitro anti-malarial drug susceptibility testing is vulnerable to the effects of different statistical approaches and selection biases. These confounding factors were assessed with respect to pfmdr1 gene mutation and amplification in 490 clinical isolates. Two statistical approaches for estimating the drug concentration associated with 50% effect (EC50 ) were compared: the commonly used standard two-stage (STS) method, and nonlinear mixed-effects modelling. The in vitro concentration-effect relationships for, chloroquine, mefloquine, lumefantrine and artesunate, were derived from clinical isolates obtained from patients on the western border of Thailand. All isolates were genotyped for polymorphisms in the pfmdr1 gene. The EC50 estimates were similar for the two statistical approaches but 15-28% of isolates in the STS method had a high coefficient of variation (>15%) for individual estimates of EC50 and these isolates had EC50 values that were 32 to 66% higher than isolates derived with more precision. In total 41% (202/490) of isolates had amplification of pfmdr1 and single nucleotide polymorphisms were found in 50 (10%). Pfmdr1 amplification was associated with an increase in EC50 for mefloquine (139% relative increase in EC50 for 2 copies, 188% for 3+ copies), lumefantrine (82% and 75% for 2 and 3+ copies respectively) and artesunate (63% and 127% for 2 and 3+ copies respectively). In contrast pfmdr1 mutation at codons 86 or 1042 were associated with an increase in chloroquine EC50 (44-48%). Sample size calculations showed that to demonstrate an EC50 shift of 50% or more with 80% power if the prevalence was 10% would require 430 isolates and 245 isolates if the prevalence was 20%. In conclusion, although nonlinear mixed-effects modelling did not demonstrate any major advantage for determining estimates of anti-malarial drug susceptibility, the method includes all isolates, thereby, potentially improving confirmation of candidate molecular markers of anti-malarial drug susceptibility.

Background: Mechanistic within-host models relating blood anti-malarial drug concentrations with the parasite-time profile help in assessing dosing schedules and partner drugs for new anti-malarial treatments. A comprehensive simulation study to assess the utility of a stage-specific pharmacokinetic-pharmacodynamic (PK-PD) model for predicting within-host parasite response was performed., Methods: Three anti-malarial combination therapies were selected: artesunate-mefloquine, dihydroartemisinin-piperaquine, and artemether-lumefantrine. The PK-PD model included parameters to represent the concentration-time profiles of both drugs, the initial parasite burden and distribution across the parasite life cycle, and the parasite multiplication factor due to asexual reproduction. The model also included the maximal killing rate of each drug, and the blood drug concentration associated with half of that killing effect (in vivo EC50), derived from the in vitro IC50, the extent of binding to 0.5% Albumax present in the in vitro testing media, and the drugs plasma protein binding and whole blood to plasma partitioning ratio. All stochastic simulations were performed using a Latin-Hypercube-Sampling approach., Results: The simulations demonstrated that the proportion of patients cured was highly sensitive to the in vivo EC50 and the maximal killing rate of the partner drug co-administered with the artemisinin derivative. The in vivo EC50 values that corresponded to on average 95% of patients cured were much higher than the adjusted values derived from the in vitro IC50. The proportion clinically cured was not strongly influenced by changes in the parameters defining the age distribution of the initial parasite burden (mean age of 4 to 16 hours) and the parasite multiplication factor every life cycle (ranging from 8 to 12 fold/cycle). The median parasite clearance times, however, lengthened as the standard deviation of the initial parasite burden increased (i.e. the infection became more asynchronous)., Conclusions: This simulation study demonstrates that the PD effect predicted from in vitro growth inhibition assays does not accord well with the PD effect of the anti-malarials observed within the patient. This simulation-based PK-PD modelling approach should not be considered as a replacement to conducting clinical trials but instead as a decision tool to improve the design of a clinical trial during drug development.

Background: Artemisinin-based combination therapy (ACT) is currently recommended as first-line treatment for uncomplicated malaria, but of concern, it has been observed that the effectiveness of the main artemisinin derivative, artesunate, has been diminished due to parasite resistance. This reduction in effect highlights the importance of the partner drugs in ACT and provides motivation to gain more knowledge of their pharmacokinetic (PK) properties via population PK studies. Optimal design methodology has been developed for population PK studies, which analytically determines a sampling schedule that is clinically feasible and yields precise estimation of model parameters. In this work, optimal design methodology was used to determine sampling designs for typical future population PK studies of the partner drugs (mefloquine, lumefantrine, piperaquine and amodiaquine) co-administered with artemisinin derivatives., Methods: The optimal designs were determined using freely available software and were based on structural PK models from the literature and the key specifications of 100 patients with five samples per patient, with one sample taken on the seventh day of treatment. The derived optimal designs were then evaluated via a simulation-estimation procedure., Results: For all partner drugs, designs consisting of two sampling schedules (50 patients per schedule) with five samples per patient resulted in acceptable precision of the model parameter estimates., Conclusions: The sampling schedules proposed in this paper should be considered in future population pharmacokinetic studies where intensive sampling over many days or weeks of follow-up is not possible due to either ethical, logistic or economical reasons.

Background: Currently, population pharmacokinetic (PK) studies of anti-malarial drugs are designed primarily by the logistical and ethical constraints of taking blood samples from patients, and the statistical models that are fitted to the data are not formally considered. This could lead to imprecise estimates of the target PK parameters, and/or designs insufficient to estimate all of the parameters. Optimal design methodology has been developed to determine blood sampling schedules that will yield precise parameter estimates within the practical constraints of sampling the study populations. In this work optimal design methods were used to determine sampling designs for typical future population PK studies of dihydroartemisinin, the principal biologically active metabolite of oral artesunate., Methods: Optimal designs were derived using freely available software and were based on appropriate structural PK models from an analysis of data or the literature and key sampling constraints identified in a questionnaire sent to active malaria researchers (3-4 samples per patient, at least 15 minutes between samples). The derived optimal designs were then evaluated via simulation-estimation., Results: The derived optimal sampling windows were 17 to 29 minutes, 30 to 57 minutes, 2.5 to 3.7 hours and 5.8 to 6.6 hours for non-pregnant adults; 16 to 29 minutes, 31 minutes to 1 hour, 2.0 to 3.4 hours and 5.5 to 6.6 hours for designs with non-pregnant adults and children and 35 to 59 minutes, 1.2 to 3.4 hours, 3.4 to 4.9 hours and 6.0 to 8.0 hours for pregnant women. The optimal designs resulted in acceptable precision of the PK parameters., Conclusions: The proposed sampling designs in this paper are robust and efficient and should be considered in future PK studies of oral artesunate where only three or four blood samples can be collected.

Background: Characterization of anti-malarial drug concentration profiles is necessary to optimize dosing, and thereby optimize cure rates and reduce both toxicity and the emergence of resistance. Population pharmacokinetic studies determine the drug concentration time profiles in the target patient populations, including children who have limited sampling options. Currently, population pharmacokinetic studies of anti-malarial drugs are designed based on logistical, financial and ethical constraints, and prior knowledge of the drug concentration time profile. Although these factors are important, the proposed design may be unable to determine the desired pharmacokinetic profile because there was no formal consideration of the complex statistical models used to analyse the drug concentration data., Methods: Optimal design methods incorporate prior knowledge of the pharmacokinetic profile of the drug, the statistical methods used to analyse data from population pharmacokinetic studies, and also the practical constraints of sampling the patient population. The methods determine the statistical efficiency of the design by evaluating the information of the candidate study design prior to the pharmacokinetic study being conducted., Results: In a hypothetical population pharmacokinetic study of intravenous artesunate, where the number of patients and blood samples to be assayed was constrained to be 50 and 200 respectively, an evaluation of varying elementary designs using optimal design methods found that the designs with more patients and less samples per patient improved the precision of the pharmacokinetic parameters and inter-patient variability, and the overall statistical efficiency by at least 50%., Conclusion: Optimal design methods ensure that the proposed study designs for population pharmacokinetic studies are robust and efficient. It is unethical to continue conducting population pharmacokinetic studies when the sampling schedule may be insufficient to estimate precisely the pharmacokinetic profile.

Controversy exists over whether the lower airway inflammation that characterizes cystic fibrosis (CF) is initiated primarily by the genetic defect. To determine if inflammation precedes infection, we examined bronchoalveolar lavage (BAL) fluid cytology, cytokines (interleukin (IL)-1beta, IL-4, IL-5, IL-6, IL-8, IL-10, and tumor necrosis factor-alpha), and free neutrophil elastase activity from 70 CF (aged 1.5-71 months) children detected by newborn screening and 19 (aged 2.0-48 months) controls with chronic stridor. CF subjects were selected and categorized as pristine (13 aged /= 10(5) colony-forming units/ml of pathogenic bacteria in BAL), and uninfected (15 aged > 6 months, asymptomatic, not taking antibiotics at bronchoscopy, and free of pathogens in their BAL). To further resolve if inflammation develops without infection, inflammatory mediators in paired annual BAL samples from 38 CF subjects were measured, and results were grouped according to whether BAL showed persistence (n = 6), acquisition (n = 8), clearance (n = 13), or absence (n = 11) of infection. While pristine, uninfected, and control subjects had similar BAL profiles, infected patients showed elevated inflammatory indices, including increased IL-10 (P < 0.001). Pristine subjects had the fewest signs of inflammation. Analysis of BAL pairs found differences between the four infection groups for changes in neutrophil percentages, IL-8 (P < 0.001), and free neutrophil elastase (P = 0.009). Infection was associated with elevated inflammatory mediators in BAL fluid. In contrast, minimal or reduced signs of inflammation accompanied absence of eradication of infection from BAL fluid. We conclude that in CF, infection initiates and sustains airway inflammation., ((c) 2005 Wiley-Liss, Inc.)

Background: Despite the widespread use of megestrol acetate (MA) among a growing number of pediatric oncology departments, there is only one published study on the use of MA in children with malignant disease. The objectives of the current study were to examine the effect of MA in improving the nutritional status of children with malignant disease and to describe and consider the implications of MA-associated adrenal suppression that was found consistently., Methods: Medical records of 19 children with malignant disease who were treated with MA were reviewed. During MA therapy, clinical assessments every 4 weeks included anthropometrics, caloric intake, quality-of-life scores, and appetite scores. Serum cortisol levels, lipid profiles (including cholesterol levels) random blood glucose levels, and coagulation screening were measured at 4-6-week intervals., Results: MA use was associated with significant increases in weight, weight z score, middle-upper arm circumference, triceps skin-fold thickness, appetite, and caloric intake. MA was extremely useful in aiding the efficient tapering of nasogastric feeds. However, a significant and potentially dangerous decrease in cortisol was seen in 10 of 11 patients tested, with 1 patient who manifested clinical hypoadrenalism with hemodynamic collapse, requiring inotropic support. This is the first report of MA-associated clinical adrenal suppression in a child with malignant disease., Conclusions: Although the results of this study support the ability of MA to improve nutritional status, its use was complicated by severe adrenal suppression in almost all patients tested, with a serious clinical adverse event occurring in one patient. Routine hydrocortisone supplementation throughout MA treatment should be considered as well as larger doses for patients with acute illness and patients who undergo surgery., (Copyright 2003 American Cancer Society.)