Physical enhancement of transdermal drug application: is delivery technology keeping up with pharmaceutical development?

Advances in molecular biology have given us a wide range of protein and peptide-based drugs that are unsuitable for oral delivery because of their high degree of first-pass metabolism. Though parenteral delivery is the obvious answer, for the successful development of commercial chronic and self-administration usage formulations it is not the ideal choice. Transdermal delivery is emerging as the biggest application target for these agents, however, the skin is extremely efficient at keeping out such large molecular weight compounds and therapeutic levels are never going to be realistically achieved by passive absorption. Physical enhancement mechanisms including: iontophoresis, electroporation, ultrasound, photomechanical waves, microneedles and jet-propelled particles are emerging as solutions to this topical delivery dilemma. Adding proteins and peptides to the list of other large molecular weight drugs with insufficient passive transdermal fluxes to be therapeutically useful, we have a collection of pharmacological agents waiting for efficient delivery methods to be introduced. This article reviews the current state of physical transdermal delivery technology, assesses the pros and cons of each technique and summarises the evidence-base of their drug delivery capabilities.

The measurement of nicotine in human plasma by high-performance liquid chromatography-electrospray-tandem mass spectrometry

The authors describe a reverse-phase high-performance liquid chromatography-electrospray-tandem mass spectrometry method for the measurement of nicotine in human plasma. Samples (500 muL) with added deuterium-labeled d(3)-nicotine as an internal standard (IS) were treated with a 2-step process of ether extraction (6 mL) followed by back-extraction into 0.1% formic acid (50 muL). Chromatography was performed on a phenyl Novapak column with a mobile phase consisting of 50% 10 mM ammonium fortriate (pH 3.3) and acetonitrile (50:50, vol/vol). A flow rate of 0.2 mL/min resulted in a total analysis time of 5 minutes per sample. Mass spectrometric detection was by selected reactant monitoring (nicotine m/z 163.2 --> 130.2; IS m/z 166.2 --> 87.2). The assay was linear from 0.5 to 100 mug/L (r > 0.993, n = 9). The accuracy and imprecision of the method for quality control sampleswere 87.5% to 113% and < 10.2%, respectively. Interday accuracy and imprecision at the limit of quantification (0.5 mug/L) was 113% and 7.2% (n = 4). The process efficiency for nicotine in plasma was > 75%. The method described has good process efficiency, stabilized nicotine, avoided concentration steps, and most importantly minimized potential contamination. Further, we have established that water-based standards and controls are interchangeable with plasma-based samples. This method was used successfully to measure the pharmacokinetic profiles of subjects involved in the development of an aerosol inhalation drug delivery system.

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

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.

A standard weight descriptor for dose adjustment in the obese patient

Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data 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/m(2)] 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 (30 kg/m(2)).

Free mycophenolic acid should be monitored in renal transplant recipients with hypoalbuminemia

The current approach for therapeutic drug monitoring in renal transplant recipients receiving mycophenolate mofetil (MMF) is measurement of total mycophenolic acid (MPA) concentration. Because MPA is highly bound, during hypoalbuminemia the total concentration no longer reflects the free (pharmacologically active) concentration. The authors investigated what degree of hypoalbuminemia causes a significant change in protein binding and thus percentage free MPA. Forty-two renal transplant recipients were recruited for the study. Free and total concentrations of MPA (predose, and 1, 3, and 6 hours post-MMF dose samples) and plasma albumin concentrations were determined on day 5 posttransplantation. Six-hour area under the concentration-time curve (AUC(0-6)) values were calculated for free and total MPA, and percentage free MPA was determined for each patient. The authors found a significant relationship between low albumin concentrations and increased percentage free MPA (Spearman correlation = -0.54, P < 0.0001). Receiver operating characteristic (ROC) curve analysis was performed on the albumin versus percentage free MPA data. The cutoff value of albumin determined from the ROC analysis that differentiated normal from elevated percentage free MPA (defined as greater than or equal to3%) in this patient population was 31 g/L. At this cutoff value albumin was found to be a good predictor of altered free MPA percentage, with a sensitivity and specificity of 0.75 and 0.80, respectively, and an area under the ROC curve of 0.79. To rationalize MMF dosing regimens in hypoalbuminemic patients (plasma albumin less than or equal to 31 g/L), clinicians should consider monitoring the free MPA concentration.

Pilot study to determine the ability of health-care professionals to undertake drug dose calculations

Background: It is essential for health-care professionals to calculate drug doses accurately. Previous studies have demonstrated that many hospital doctors were unable to accurately convert dilutions (e.g. 1:1000) or percentages (e.g. percentage w/v) of drug concentrations into mass concentrations (e.g. mg/mL). Aims: The aims of the present study were to evaluate the ability of health-care professionals to perform drug dose calculations accurately and to determine their preferred concentration convention when calculating drug doses. Methods: A selection of nurses, medical students, house surgeons, registrars and pharmacists undertook a written survey to assess their ability to perform five drug dose calculations. Participants were also asked which concentration convention they preferred when calculating drug doses. The surveys were marked then analysed for health-care professionals as a whole and then by subgroup analysis to assess the performance of each health-care-professional group. Results: Overall, less than 14% of the surveyed health-care professionals could answer all five questions correctly. Subgroup analysis revealed that health-care pro-fessionals' ability to calculate drug doses were ranked in the following order: registrars approximate to pharmacists > house surgeons > medical students >> nurses. Ninety per cent of health-care professionals preferred to calculate drug doses using the mass concentration convention. Conclusions: Overall, drug dose calculations were performed poorly. Mass concentration was clearly indicated as the preferred convention for calculating drug doses.

Do risk factors for lactic acidosis influence dosing of metformin?

Background: Metformin is commonly prescribed to treat type 2 diabetes mellitus, however it is associated with the potentially lethal condition of lactic acidosis. Prescribing guidelines have been developed to minimize the risk of lactic acidosis development, although some suggest they are inappropriate and have created confusion amongst prescribers. The aim of this study was to investigate whether metformin dose was influenced by the presence of risk factors for lactic acidosis. Methods: The study was prospective, and retrieved information from patients admitted to hospital who were prescribed metformin at their time of admission. Results: Eighty-three patients were included in the study, 60 of whom had a least one risk factor for lactic acidosis. Of those 60 patients, 78.3% had a dose adjustment, with renal impairment, hepatic impairment, surgery and use of radiological contrast media - the risk factors most likely to result in a dose adjustment. When dose adjustments did occur, metformin was withheld on 88.7% of occasions. Conclusion: Metformin dose was influenced by the presence of risk factors for lactic acidosis, although it was dependent upon the number and particular risk factor/s present.