What is the best size descriptor to use for pharmacokinetic studies in the obese?

The prevalence of obesity in the western world is dramatically rising, with many of these individuals requiring therapeutic intervention for a variety of disease states. Despite the growing prevalence of obesity there is a paucity of information describing how doses should be adjusted, or indeed whether they need to be adjusted, in the clinical setting. This review is aimed at identifying which descriptors of body size provide the most information about the relationship between dose and concentration in the obese. The size descriptors, weight, lean body weight, ideal body weight, body surface area, body mass index, fat-free mass, percent ideal body weight, adjusted body weight and predicted normal body weight were considered as potential size descriptors. We conducted an extensive review of the literature to identify studies that have assessed the quantitative relationship between the parameters clearance (CL) and volume of distribution (V) and these descriptors of body size. Surprisingly few studies have addressed the relationship between obesity and CL or V in a quantitative manner. Despite the lack of studies there were consistent findings: (i) most studies found total body weight to be the best descriptor of V. A further analysis of the studies that have addressed V found that total body weight or another descriptor that incorporated fat mass was the preferred descriptor for drugs that have high lipophilicity; (ii) in contrast, CL was best described by lean body mass and no apparent relationship between lipophilicity or clearance mechanism and preference for body size descriptor was found. In conclusion, no single descriptor described the influence of body size on both CL and V equally well. For drugs that are dosed chronically, and therefore CL is of primary concern, dosing for obese patients should not be based on their total weight. If a weight-based dose individualization is required then we would suggest that chronic drug dosing in the obese subject should be based on lean body weight, at least until a more robust size descriptor becomes available.

Quantitative justification for target concentration intervention - parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

CEDIA (R) sirolimus assay compared with HPLC-MS/MS and HPLC-UV in transplant recipient specimens

The role of the therapeutic drug monitoring laboratory in support of immunosuppressant drug therapy is well established, and the introduction of sirolimus (SRL) is a new direction in this field. The lack of an immunoassay for several years has restricted the availability of SRL assay services. The recent availability of a CEDIA (R) SRL assay has the potential to improve this situation. The present communication has compared the CEDIA (R) SRL method with 2 established chromatographic methods, HPLC-UV and HPLC-MS/MS. The CEDIA (R) method, run on a Hitachi 917 analyzer, showed acceptable validation criteria with within-assay precision of 9.1% and 3.3%, and bias of 17.1% and 5.8%, at SRL concentrations of 5.0 mu g/L and 20 mu g/L, respectively. The corresponding between-run precision values were 11.5% and 3.3% and bias of 7.1% and 2.9% at 5.0 mu g/L and 20 mu g/L, respectively, The lower limit of quantification was found to be 3.0 mu g/L. A series of 96 EDTA whole-blood samples predominantly from renal transplant recipients were assayed by the 3 methods for comparison. It was found that the CEDIA (R) method showed a Deming regression line of CEDIA = 1.20 X HPLC-MS/MS - 0.07 (r = 0.934, SEE = 1.47), with a mean bias of 20.4%. Serial blood samples from 8 patients included in this evaluation showed that the CEDIA (R) method reflected the clinical fluctuations in the chromatographic methods, albeit with the variable bias noted. The CEDIA (R) method on the H917 analyzer is therefore a useful adjunct to SRL dosage individualization in renal transplant recipients.

Comparison of the reintroduced MEIA (R) assay with HPLC-MS/MS for the determination of whole-blood sirolimus from transplant recipients

Therapeutic monitoring with dosage individualization of sirolimus drug therapy is standard clinical practice for organ transplant recipients. For several years sirolimus monitoring has been restricted as a result of lack of an immunoassay. The recent reintroduction of the microparticle enzyme immunoassay (MEIA (R)) for sirolimus on the IMx (R) analyser has the potential to address this situation. This Study, using patient samples, has compared the MEIA (R) sirolimus method with an established HPLC-tandem mass spectrometry method (HPLC-MS/MS). An established HPLC-UV assay was used for independent cross-validation. For quality control materials (5, 11, 22 mu g/L), the MEIA (R) showed acceptable validation criteria based on intra-and inter-run precision (CV) and accuracy (bias) of < 8% and < 13%, respectively. The lower limit of quantitation was found to be approximately 3 mu g/L. The performance of the immunoassay was compared with HPLC-MS/MS using EDTA whole-blood samples obtained from various types of organ transplant recipients (n = 116). The resultant Deming regression line was: MEIA = 1.3 x HPLC-MS/MS+ 1.3 (r = 0.967, s(y/x) = 1) with a mean bias of 49.2% +/- 23.1 % (range, -2.4% to 128%; P < 0.001). The reason for the large and variable bias was not explored in this study, but the sirolimus-metabolite cross-reactivity with the MEIA (R) antibody could be a substantive contributing factor. Whereas the MEIA (R) sirolimus method may be an adjunct to sirolimus dosage individualization in transplant recipients, users must consider the implications of the substantial and variable bias when interpreting results. In selected patients where difficult clinical issues arise, reference to a specific chromatographic method may be required.

Prediction of functional capacity in chronic heart failure patients using myocardial tissue Doppler

Background. Exercise therapy improves functional capacity in CHF, but selection and individualization of training would be helped by a simple non-invasive marker of peak VO2. Peak VO2 in these pts is difficult to predict without direct measurement, and LV ejection fraction is a poor predictor. Myocardial tissue velocities are less load-dependent, and may be predictive of the exercise response in CHF pts. We sought to use tissue velocity as a predictor of peak VO2 in CHF pts. Methods. Resting 2D-echocardiography and tissue Doppler imaging were performed in 182 CHF pts (159 male, age 62±10 years) before and after metabolic exercise testing. The majority of these patients (129, 71%) had an ischemic cardiomyopathy, with resting EF of 35±13% and a peak VO2 of 13.5±4.7 ml/kg/min. Results. Neither resting EF (r=0.15) nor peak EF (r=0.18, both p=NS) were correlated with peak VO2. However, peak VO2 correlated with peak systolic velocity in septal (Vss, r=0.31) and lateral walls (Vsl, r=0.26, both p=0.01). In a general linear model (r2 = 0.25), peak VO2 was calculated from the following equation: 9.6 + 0.68*Vss - 0.09*age + 0.06*maximum HR. This model proved to be a superior predictor of peak VO2 (r=0.51, p=0.01) than the standard prediction equations of Wasserman (r= -0.12, p=0.01). Conclusions. Resting tissue Doppler, age and maximum heart rate may be used to predict functional capacity in CHF patients. This may be of use in selecting and following the response to therapy, including for exercise training.