Bruce Green

Quantification of lean bodyweight

AbstractBackground: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA — a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18–82 years), bodyweight (40.7–216.5kg) and BMI values (17.1–69.9 kg/m2). Patients in population B had BMI values of 18.7–38.4 kg/m2. A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r2 = 0.78, mean error [ME] = 2.30 × 10−3, root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r2 = 0.93, ME = −0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r2 = 0.85, ME = −0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

Dosing in obesity: A simple solution to a big problem

The global epidemic of obesity has led to an increased prevalence of chronic diseases and need for pharmacological intervention. However, little is known about the influence of obesity on the drug exposure profile, resulting in few clear dosing guidelines for the obese. Here we present a semi‐mechanistic model for lean body weight (LBW) that we believe is sufficiently robust to quantify the influence of body composition on drug clearance, and is therefore an ideal metric for adjusting chronic dosing in the obese.

Pharmacokinetics and pharmacodynamics of monoclonal antibodies: concepts and lessons for drug development.

Monoclonal antibodies (mAbs) have complex pharmacology; pharmacokinetics and pharmacodynamics depend on mAb structure and target antigen. mAbs targeting soluble antigens often exhibit linear phar-macokinetic behavior, whereas mAbs targeting cell surface antigens frequently exhibit nonlinear behavior due to receptor-mediated clearance. Where nonlinear kinetics exist, clearance can change due to receptor loss following repeated dosing and/or disease severity. mAb pharmacodynamics are often indirect, with delayed clinically relevant outcomes. This behavior provides challenges during clinical development; studies must be carefully planned to account for complexities specific to each agent.Selection of a starting dose for human studies can be difficult. Species differences in pharmacology need to be considered. Various metrics are available for scaling from animals to humans. Optimal dose selection should ensure uniform mAb exposure across all individuals. Traditional approaches such as flat dosing and variable dosing based upon body surface area or weight should be supported by pharmacokinetic and pharmacodynamic behavior, including target antigen and concurrent disease states. The use of loading doses or dose adjustments to improve clinical response is also a consideration.The evaluation of drug interactions requires innovative designs. Due to the pharmacokinetic properties of mAbs, interacting drugs may need to be administered for protracted periods. Consequently, population pharmacokinetic and pharmacodynamic model-based approaches are often implemented to evaluate mAb drug interactions.

The relationship between drug clearance and body size: systematic review and meta-analysis of the literature published from 2000 to 2007

AbstractBackground: A variety of body size covariates have been used in population pharmacokinetic analyses to describe variability in drug clearance (CL), such as total body weight (TBW), body surface area (BSA), lean body weight (LBW) and allometric TBW. There is controversy, however, as to which body size covariate is most suitable for describing CL across the whole population. Given the increasing worldwide prevalence of obesity, it is essential to identify the best size descriptor so that dosing regimens can be developed that are suitable for patients of any size. Aim: The aim of this study was to explore the use of body size covariates in population pharmacokinetic analyses for describing CL. In particular, we sought to determine if any body size covariate was preferential to describe CL and quantify its relationship with CL, and also identify study design features that result in the identification of a nonlinear relationship between TBW and CL. Methods: Population pharmacokinetic articles were identified from MEDLINE using defined keywords. A database was developed to collect information about study designs, model building and covariate analysis strategies, and final reported models for CL. The success of inclusion for a variety of covariates was determined. A meta-analysis of studies was then performed to determine the average relationship reported between CL and TBW. For each study, CL was calculated across the range of TBW for the study population and normalized to allow comparison between studies. BSA, LBW, and allometric TBW and LBW relationships with exponents of 3/4, 2/3, and estimated values were evaluated to determine the relationship that best described the data overall. Additionally, joint distributions of TBW were compared between studies reporting a ‘nonlinear’ relationship between CL and TBW (i.e. LBW, BSA and allometric TBW-shaped relationships) and those reporting ‘other’ relationships (e.g. linear increase in CL with TBW, ideal body weight or height). Results: A total of 458 out of 2384 articles were included in the analysis, from which 484 pharmacokinetic studies were reviewed. Fifty-six percent of all models for CL included body size as a covariate, with 52% of models including a nonlinear relationship between CL and TBW. No single size descriptor was more successful than others for describing CL. LBW with a fixed exponent of 2/3, i.e. (LBW/50.45)2/3, or estimated exponent of 0.646, i.e. ∼2/3, was found to best describe the average reported relationship between CL and TBW. The success of identifying a nonlinear increase in CL with TBW was found to be higher for those studies that included a wider range of subject TBW. Conclusions: To the best of our knowledge, this is the first study to have performed a meta-analysis of covariate relationships between CL and body size. Although many studies reported a linear relationship between CL and TBW, the average relationship was found to be nonlinear. LBW with an allometric exponent of ∼2/3 may be most suitable for describing an increase in CL with body size as it accounts for both body composition and allometric scaling principles concerning differences in metabolic rates across size.

A standard weight descriptor for dose adjustment in the obese patient

ObjectiveTo develop a standard weight descriptor that can be used for estimation of patient size for obese patients.Patients and methodsData were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16–100 years), weight (25–165kg) and body mass index (BMI) [12–52 kg/m2] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (<25 kg/m2). In addition, the clinical utility of the predicted normal weight (PNWT) descriptor was assessed by (i) comparing body surface area and allometric scaling calculations based on actual weight of obese patients versus PNWT; and (ii) comparing the predictive performance of creatinine clearance using the Cockcroft and Gault equation when using actual weight of obese patients versus PNWT to predict gentamicin clearance.ResultsThe PNWT equations developed from the oncology data set predicted accurately the actual weight of normal weighted (BMI <25 kg/m2) general medical patients (R2 = 0.968 men, R2 = 0.946 women). Using actual weight when computing body surface area and when allometric scaling for obese patients results in significant overestimation of patient size, especially for female patients and those with BMIs >35 kg/m2. The use of PNWT in the Cockcroft and Gault equation provided better predictions of gentamicin clearance than when using actual weight.ConclusionsA standard weight descriptor has been developed that can be used in dosing algorithms for patients who are obese (BMI >30 kg/m2).

A Pharmacokinetic-Pharmacodynamic model for the mobilization of CD34+ hematopoietic progenitor cells by AMD3100

AMD3100 is a small‐molecule CXCR4 antagonist that has been shown to induce the mobilization of CD34+ hematopoietic progenitor cells from bone marrow to peripheral blood. AMD3100 has also been shown to augment the mobilization of CD34+ cells in cancer patients when administered in combination with granulocyte colony‐stimulating factor (G‐CSF) (filgrastim). The purpose of this study was to characterize the exposure‐response relationship of AMD3100 in mobilizing CD34+ cells when administered as a single agent in healthy volunteers.

Factors predictive of intravenous fluid administration errors in Australian surgical care wards

Background: Intravenous (IV) fluid administration is an integral component of clinical care. Errors in administration can cause detrimental patient outcomes and increase healthcare costs, although little is known about medication administration errors associated with continuous IV infusions. Objectives: (1) To ascertain the prevalence of medication administration errors for continuous IV infusions and identify the variables that caused them. (2) To quantify the probability of errors by fitting a logistic regression model to the data. Methods: A prospective study was conducted on three surgical wards at a teaching hospital in Australia. All study participants received continuous infusions of IV fluids. Parenteral nutrition and non-electrolyte containing intermittent drug infusions (such as antibiotics) were excluded. Medication administration errors and contributing variables were documented using a direct observational approach. Results: Six hundred and eighty seven observations were made, with 124 (18.0%) having at least one medication administration error. The most common error observed was wrong administration rate. The median deviation from the prescribed rate was −47 ml/h (interquartile range −75 to +33.8 ml/h). Errors were more likely to occur if an IV infusion control device was not used and as the duration of the infusion increased. Conclusions: Administration errors involving continuous IV infusions occur frequently. They could be reduced by more common use of IV infusion control devices and regular checking of administration rates.

Lean body mass normalizes the effect of obesity on renal function

Descriptors of body size and renal function are the most important covariates in pharmacokinetic studies. Several methods can be used to estimate glomerular filtration rate (GFR) [1], the most common being Cockcroft and Gault [2]. Studies of GFR in the obese population have shown both increases in GFR [3] and no change [4]. Methods based on creatinine (see for an overview [5]) may overestimate GFR due to its active secretion. In contrast, inulin is considered an accurate measure of GFR [6]. We hypothesize that GFR, when scaled by lean body weight, will not be different between obese and non-obese subjects. GFR data were obtained from a previous study [7] undertaken at Tel Aviv University Medical School, Israel by one of the co-authors (A.C.). The dataset comprised 17 patients (seven male and 10 female), ranging in age from 23 to 46 years, of whom nine (three male and six female) were normal weight [body mass index (BMI) 30 kg m−2). All patients had normal serum creatinine concentration. GFR was determined by inulin clearance, which was 145 ± 38.5 ml min−1 (mean ± SD) and 89.8 ± 15.3 ml min−1 in the obese and lean population, respectively. The obese patients underwent gastroplasty after the initial renal function tests. Measurements of GFR were repeated at least 12 months after surgery and were 109.9 ± 20.6 ml min−1. Only two individuals in the postsurgery obesity group achieved a BMI of <30 kg m−2; the remaining six individuals, although achieving significant reductions in body mass, remained above the BMI cut-off. This provided 11 observations in the lean group (nine originally lean and two obese who became lean) and 14 observations in the obese group (eight originally obese and repeated measures on six who remained obese). The non-normalized values of GFR were compared between obese and non-obese individuals, as were GFR values when normalized by total body weight and lean body mass (see Figure 1). Lean body mass was calculated using the method of Janmahasatian [8], which is a function of total body weight, height and sex. Statistical comparisons were performed using repeated measures analysis of variance. Figure 1 Box plots of glomerular filtration rate (GFR) for lean and obese subjects. In all figures there were 11 observations of GFR from lean subjects (nine from the original lean patients and two from patients who were obese and became lean after surgery) and ... The (non-normalized) GFR values were higher by 42% in the obese compared with non-obese patients (P = 0.003) (Figure 1a) and 36% lower (P = 0.002) in obese than normal weight individuals when normalized by total body weight. In contrast, when normalizing GFR by lean body mass, there was no apparent difference in the GFR between obese and control individuals (P = 0.27) (Figure 1c). Renal function, as defined by GFR, is increased in the obese population. Normalizing GFR to total body weight (to produce ml min−1 kg−1) results in overcompensation of the effects and the conclusion that obese patients have a lower GFR (per kg) than non-obese patients. This suggests that the increase in excess adipose tissue does not contribute entirely to an increase in renal function. In contrast, normalizing GFR by lean body mass appears to explain ‘apparent’ differences between obese and non-obese individuals. Indeed, further support for the benefit of normalizing by lean mass was gained when the index variable, BMI, was randomly reassigned (thereby eliminating the true influence of obesity) in the data and the analysis re-performed many times. If lean body mass was seen as an appropriate descriptor then no dataset generated under this method should show a statistically significant difference between the reclassified ‘lean’ and ‘obese’ patients. No statistical differences in GFR when normalised by lean body mass were evident in any of the randomised datasets. In conclusion, it appears that renal function is closely related to lean body mass, which should be used in preference to total body weight for estimating creatinine clearance.

Prospective Evaluation of a D-Optimal Designed Population Pharmacokinetic Study

Recently, methods for computing D-optimal designs for population pharmacokinetic studies have become available. However there are few publications that have prospectively evaluated the benefits of D-optimality in population or single-subject settings. This study compared a population optimal design with an empirical design for estimating the base pharmacokinetic model for enoxaparin in a stratified randomized setting. The population pharmacokinetic D-optimal design for enoxaparin was estimated using the PFIM function (MATLAB version 6.0.0.88). The optimal design was based on a one-compartment model with lognormal between subject variability and proportional residual variability and consisted of a single design with three sampling windows (0–30 min, 1.5–5 hr and 11–12 hr post-dose) for all patients. The empirical design consisted of three sample time windows per patient from a total of nine windows that collectively represented the entire dose interval. Each patient was assigned to have one blood sample taken from three different windows. Windows for blood sampling times were also provided for the optimal design. Ninety six patients were recruited into the study who were currently receiving enoxaparin therapy. Patients were randomly assigned to either the optimal or empirical sampling design, stratified for body mass index. The exact times of blood samples and doses were recorded. Analysis was undertaken using NONMEM (version 5). The empirical design supported a one compartment linear model with additive residual error, while the optimal design supported a two compartment linear model with additive residual error as did the model derived from the full data set. A posterior predictive check was performed where the models arising from the empirical and optimal designs were used to predict into the full data set. This revealed the “optimal” design derived model was superior to the empirical design model in terms of precision and was similar to the model developed from the full dataset. This study suggests optimal design techniques may be useful, even when the optimized design was based on a model that was misspecified in terms of the structural and statistical models and when the implementation of the optimal designed study deviated from the nominal design.

ANALYSIS OF POPULATION PHARMACOKINETIC DATA USING NONMEM AND WinBUGS

ABSTRACT The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration–time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance–covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance–covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.