Population pharmacokinetics, exposure-safety, and immunogenicity of atezolizumab in pediatric and young adult patients with cancer.

BACKGROUND: The iMATRIX-atezolizumab study was a phase I/II, multicenter, open-label study designed to assess the safety and pharmacokinetics of atezolizumab in pediatric and young adult patients. We describe the pharmacokinetics (PK), exposure-safety, and immunogenicity of atezolizumab in pediatric and young adults with metastatic solid tumors or hematologic malignancies enrolled in this study. METHODS: Patients aged < 18 years (n = 69) received a weight-adjusted dose of atezolizumab (15 mg/kg every 3 weeks [q3w]; maximum 1200 mg); those aged ≥ 18 years (n = 18) received a flat dose (1200 mg q3w). A prior two-compartment intravenous infusion input adult population-PK (popPK) model of atezolizumab was used as a basis to model pediatric data. RESULTS: A total of 431 atezolizumab serum concentrations from 87 relapse-refractory pediatric and young adult patients enrolled in the iMATRIX-atezolizumab study were used for the popPK analysis. The dataset comprised predominantly patients aged < 18 years, including two infants aged < 2 years, with a wide body weight and age range. The clearance and volume of distribution estimates of atezolizumab were 0.217 L/day and 3.01 L, respectively. Atezolizumab geometric mean trough exposures were ~ 20% lower in pediatric patients versus young adults; this was not clinically meaningful as both groups achieved the target concentration (6 µg/mL). Safety was similar between pediatric and young adult patients with no exposure-safety relationship observed. Limited responses (4/87) precluded an exposure-response assessment on outcomes. A comparable rate (13% vs 11%) of atezolizumab anti-drug antibodies was seen in pediatric and young adult patients. CONCLUSIONS: These findings demonstrate a similar exposure-safety profile of atezolizumab in pediatric and young adult patients, supportive of weight-based dosing in pediatric patients. TRIAL REGISTRATION: NCT02541604.

A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models.

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.

Demographic, clinical and lifestyle factors associated with high-intensity statin therapy in Australia: the AusDiab study.

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.

Prevalence and Variability in Medications Contributing to Polypharmacy in Long-Term Care Facilities.

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.

Do statin users adhere to a healthy diet and lifestyle? The Australian Diabetes, Obesity and Lifestyle Study.

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.

Drug Burden Index and change in cognition over time in community-dwelling older men: the CHAMP study.

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.

Quantifying Uncertainty in the Ratio of Two Measured Variables: A Recap and Example.

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.

Comparative Plasma and Cerebrospinal Fluid Pharmacokinetics of Paracetamol After Intravenous and Oral Administration.

BACKGROUND: We compared plasma and cerebrospinal fluid (CSF) pharmacokinetics of paracetamol after intravenous (IV) and oral administration to determine dosing regimens that optimize CSF concentrations. METHODS: Twenty-one adult patients were assigned randomly to 1 g IV, 1 g oral or 1.5 g oral paracetamol. An IV cannula and lumbar intrathecal catheter were used to sample venous blood and CSF, respectively, over 6 hours. The plasma and CSF maximum concentrations (Cmax), times to maximum concentrations (Tmax), and area under the plasma and CSF concentration-time curves (AUCs) were calculated using noncompartmental techniques. Significance was defined by P < .0167 (Bonferroni correction for 3 comparisons for each parameter). Probability (X < Y) (p″) with Bonferroni corrected 95% confidence intervals (CIs) were calculated (CIs including 0.5 meet the null hypothesis). Results are presented as median (range) or p″ (CI). P values are listed as 1 g IV vs 1 g orally, 1 g IV vs 1.5 g orally and 1 g orally vs 1.5 g orally, respectively. RESULTS: Wide variation in measured paracetamol concentrations was observed, especially in the oral groups. The median plasma Cmax in the 1 g IV group was significantly greater than the oral groups. In contrast, the median CSF Cmax was not different between groups. The median plasma Tmax in the 1 g IV group was 105 and 75 minutes earlier than in the 1 and 1.5 g oral groups. The median CSF Tmax was not significantly different between groups. The median plasma AUC (total) was not significantly different between groups; however, in the first hour, the median plasma AUC was significantly greater in the IV group than in the oral groups. In the second hour, there was no difference between groups. The median CSF AUC (total) did not significantly differ between groups; however, in the first hour, the median CSF AUC was significantly greater in the IV compared with the orally groups. In the second hour, there was no difference between groups. Our analysis indicated that the median Cmax, Tmax, and AUC values lacked precision because of small sample sizes. CONCLUSIONS: Peak plasma concentrations were greater and reached earlier after IV than oral dosing. Evidence for differences in CSF Cmax and Tmax was lacking because of the small size of this study.

Polypharmacy and medication regimen complexity as factors associated with staff informant rated quality of life in residents of aged care facilities: a cross-sectional study.

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.

Effects of Changes in Number of Medications and Drug Burden Index Exposure on Transitions Between Frailty States and Death: The Concord Health and Ageing in Men Project Cohort Study.

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.

Polypharmacy cut-points in older people with cancer: how many medications are too many?

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.

A systematic review of the statistical methods in prospective cohort studies investigating the effect of medications on cognition in older people.

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.

Analgesic Use and Pain in Robust, Pre-Frail and Frail Older Outpatients with Cancer.

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.

Maximising students' use of purpose-built shade in secondary schools: quantitative and qualitative results of a built-environment intervention.

A trial installing shade sails at secondary schools found increased students' use of newly shaded areas, but relatively low use overall. We examined site features and weather related to use of these shaded areas. Tables with seats and temperatures ≥27°C increased student use of shaded areas, presence of grass decreased use. Focus groups at eight schools suggest students were unaware of changes to their habitual use of favoured locations. Results infer careful selection of locations for built-shade and provision of tables with seats will assist in maximising student use and investments in shade sails.

Reporting a population pharmacokinetic-pharmacodynamic study: a journal's perspective.

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.

Assessing population-wide behaviour change: concordance of 10-year trends in self-reported and observed sun protection.

OBJECTIVES: Increases in socially desirable responses in self-reports might occur in the context of ongoing public education. We examine concordance of trends in two long-term studies monitoring population impact for SunSmart. METHODS: One study employed telephone interviews of Melbourne residents; the other entailed observations at public recreation venues across Melbourne. The studies assessed people's sun protection on identical weekend dates (Nw = 33 dates). Data from five summers between 1992 and 2001 (n ~ 23,000 individuals) were analysed. A body cover index score was calculated for participants on each date. Outcomes were aggregated separately for Saturdays and Sundays by date and year. Regression analyses tested whether these trends differed by survey method. RESULTS: The pattern of change in body cover over time was similar for both surveys. Self-reported body cover was consistently higher than observed body cover, suggesting that social desirability bias may be present. Regression analyses showed no divergence between self-reported and observed trends in mean body cover, suggesting no evidence of significant increased social desirability bias in self-reporting over time. CONCLUSION: Findings suggest that self-report offers a valid means of assessing change in a population's sun protection compliance over time, at least when self-reports are precisely focussed for time and activity context.

A robust design for identification of the Parasite Clearance Estimator.

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.

Nonlinear mixed-effects modelling of in vitro drug susceptibility and molecular correlates of multidrug resistant Plasmodium falciparum.

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.

Assessing the utility of an anti-malarial pharmacokinetic-pharmacodynamic model for aiding drug clinical development.

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.