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1.  Limited-Sampling Strategy Models for Itraconazole and Hydroxy-Itraconazole Based on Data from a Bioequivalence Study 
The extensive interindividual variability in oral bioavailability of itraconazole prompted an assessment of the bioequivalence of two formulations marketed in Brazil, namely, Sporanox (reference) and Traconal (test). Eighteen healthy volunteers received single 200-mg oral doses of each formulation at 2-week intervals in a randomized, crossover protocol. The concentrations of itraconazole and hydroxy-itraconazole in plasma were measured by high-performance liquid chromatography, and the datum points (n = 396) were subsequently used to develop limited-sampling strategy models for estimation of the areas under the curve (AUCs) for both compounds. The 90% confidence intervals for individual percent ratios (test/reference formulations) of the maximum concentration of drug in serum, the AUC from 0 to 48 h and the AUC from time zero to infinity (AUC0–∞) for itraconazole and hydoxy-itraconazole were below the range of 80 to 125%, suggesting that these formulations are not bioequivalent. Linear regression analysis of the AUC0–∞ against time and a “jackknife” validation procedure revealed that models based on three sampling times accurately predict (R2, >0.98; bias, <3%; precision, 3 to 7%) the AUC0–∞ for each of the four formulation-compound pairs tested. Increasing the number of sampling points to more than three adds little to the accuracy of the estimates of AUC0–∞. The three-point models developed for the reference formulation were validated retrospectively and were found to predict within 2% the AUC0–∞ reported in previous studies performed under similar protocols. In conclusion, the data in this study indicate (i) that the tested formulations are not bioequivalent when single doses are compared and (ii) that limited-sampling strategy models based on three points predict accurately the AUC0–∞s for itraconazole and hydroxy-itraconazole and could be a valuable tool in pharmacokinetic and bioequivalence studies of single oral doses of itraconazole.
PMCID: PMC89033  PMID: 9869578
2.  Underestimation of the Calculated Area Under the Concentration-Time Curve Based on Serum Creatinine for Vancomycin Dosing 
Infection & Chemotherapy  2014;46(1):21-29.
The ratio of the steady-state 24-hour area under the concentration-time curve (ssAUC24) to the MIC (AUC24/MIC) for vancomycin has been recommended as the preferred pharmacodynamic index. The aim of this study was to assess whether the calculated AUC24 (cAUC24) using the creatinine clearance (CLcr) differs from the ssAUC24 based on the individual pharmacokinetic data estimated by a commercial software.
Materials and Methods
The cAUC24 was compared with the ssAUC24 with respect to age, body mass index, and trough concentration of vancomycin and the results were expressed as median and interquartile ranges. A correlation between the cAUC24 and ssAUC24 and the trough concentration of vancomycin was evaluated. The probability of reaching an AUC24/MIC of 400 or higher was compared between the cAUC24 and ssAUC24 for different MICs of vancomycin and different daily doses by simulation in a subgroup with a trough concentration of 10 mg/L and higher.
The cAUC24 was significantly lower than the ssAUC24 (392.38 vs. 418.32 mg·hr/L, P < 0.0001) and correlated weakly with the trough concentration (r = 0.649 vs. r = 0.964). Assuming a MIC of 1.0 mg/L, the probability of reaching the value of 400 or higher was 77.5% for the cAUC24/MIC and 100% for the ssAUC24/MIC in patients with a trough concentration of 10 mg/L and higher. If the MIC increased to 2.0 mg/L, the probability was 57.7% for the cAUC24/MIC and 71.8% for the ssAUC24/MIC at a daily vancomycin dose of 4,000 mg.
The cAUC24 using the calculated CLcr is usually underestimated compared with the ssAUC24 based on individual pharmacokinetic data. Therefore, to obtain a more accurate AUC24, therapeutic monitoring of vancomycin rather than a simple calculation based on the CLcr should be performed, and a more accurate biomarker for renal function is needed.
PMCID: PMC3970305  PMID: 24693466
Vancomycin; Pharmacodynamics; Area under curve; Drug monitoring, Therapeutic
3.  Bacterial Strain-to-Strain Variation in Pharmacodynamic Index Magnitude, a Hitherto Unconsidered Factor in Establishing Antibiotic Clinical Breakpoints ▿  
Antimicrobial Agents and Chemotherapy  2009;53(12):5181-5184.
Antibiotic pharmacodynamic modeling allows variations in pathogen susceptibility and human pharmacokinetics to be accounted for when considering antibiotic doses, potential bacterial pathogen targets for therapy, and clinical susceptibility breakpoints. Variation in the pharmacodynamic index (area-under-the-concentration curve to 24 h [AUC24]/MIC; maximum serum concentration of drug in the serum/MIC; time the serum concentration remains higher than the MIC [T > MIC]) is not usually considered. In an in vitro pharmacokinetic model of infection using a dose-ranging design, we established the relationship between AUC24/MIC and the antibacterial effect for moxifloxacin against 10 strains of Staphylococcus aureus. The distributions of AUC24/MIC targets for 24-h bacteriostatic effect and 1-log, 2-log, and 3-log drops in bacterial counts were used to calculate potential clinical breakpoint values, and these were compared with those obtained by the more conventional approach of taking a single AUC24/MIC target. Consideration of the AUC24/MIC as a distribution rather than a single value resulted in a lower clinical breakpoint.
PMCID: PMC2786373  PMID: 19805569
4.  Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies 
Standardized uptake values (SUV) are commonly used for quantification of whole-body [18F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) studies. Changes in SUV following therapy, however, only provide a proper measure of response in case of homogeneous FDG uptake in the tumour. The purpose of this study was therefore to implement and characterize a method that enables quantification of heterogeneity in tumour FDG uptake.
Cumulative SUV-volume histograms (CSH), describing % of total tumour volume above % threshold of maximum SUV (SUVmax), were calculated. The area under a CSH curve (AUC) is a quantitative index of tumour uptake heterogeneity, with lower AUC corresponding to higher degrees of heterogeneity. Simulations of homogeneous and heterogeneous responses were performed to assess the value of AUC-CSH for measuring uptake and/or response heterogeneity. In addition, partial volume correction and image denoising was applied prior to calculating AUC-CSH. Finally, the method was applied to a number of human FDG scans.
Partial volume correction and noise reduction improved CSH curves. Both simulations and clinical examples showed that AUC-CSH values corresponded with level of tumour heterogeneity and/or heterogeneity in response. In contrast, this correspondence was not seen with SUVmax alone. The results indicate that the main advantage of AUC-CSH above other measures, such as 1/COV (coefficient of variation), is the possibility to measure or normalize AUC-CSH in different ways.
AUC-CSH might be used as a quantitative index of heterogeneity in tracer uptake. In response monitoring studies it can be used to address heterogeneity in response.
PMCID: PMC3151405  PMID: 21617975
Positron emission tomography (PET); Standardized uptake value (SUV); Intratumoural heterogeneity; Cumulative SUV-volume histogram (CSH); Intensity-volume histograms (IVH)
5.  AUC-based biomarker ensemble with an application on gene scores predicting low bone mineral density 
Bioinformatics  2011;27(21):3050-3055.
Motivation: The area under the receiver operating characteristic (ROC) curve (AUC), long regarded as a ‘golden’ measure for the predictiveness of a continuous score, has propelled the need to develop AUC-based predictors. However, the AUC-based ensemble methods are rather scant, largely due to the fact that the associated objective function is neither continuous nor concave. Indeed, there is no reliable numerical algorithm identifying optimal combination of a set of biomarkers to maximize the AUC, especially when the number of biomarkers is large.
Results: We have proposed a novel AUC-based statistical ensemble methods for combining multiple biomarkers to differentiate a binary response of interest. Specifically, we propose to replace the non-continuous and non-convex AUC objective function by a convex surrogate loss function, whose minimizer can be efficiently identified. With the established framework, the lasso and other regularization techniques enable feature selections. Extensive simulations have demonstrated the superiority of the new methods to the existing methods. The proposal has been applied to a gene expression dataset to construct gene expression scores to differentiate elderly women with low bone mineral density (BMD) and those with normal BMD. The AUCs of the resulting scores in the independent test dataset has been satisfactory.
Conclusion: Aiming for directly maximizing AUC, the proposed AUC-based ensemble method provides an efficient means of generating a stable combination of multiple biomarkers, which is especially useful under the high-dimensional settings.
Supplementary Information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3198577  PMID: 21908541
6.  Understanding Increments in Model Performance Metrics 
Lifetime data analysis  2012;19(2):10.1007/s10985-012-9238-0.
The area under the receiver operating characteristic curve (AUC) is the most commonly reported measure of discrimination for prediction models with binary outcomes. However, recently it has been criticized for its inability to increase when important risk factors are added to a baseline model with good discrimination. This has led to the claim that the reliance on the AUC as a measure of discrimination may miss important improvements in clinical performance of risk prediction rules derived from a baseline model. In this paper we investigate this claim by relating the AUC to measures of clinical performance based on sensitivity and specificity under the assumption of multivariate normality. The behavior of the AUC is contrasted with that of discrimination slope. We show that unless rules with very good specificity are desired, the change in the AUC does an adequate job as a predictor of the change in measures of clinical performance. However, stronger or more numerous predictors are needed to achieve the same increment in the AUC for baseline models with good versus poor discrimination. When excellent specificity is desired, our results suggest that the discrimination slope might be a better measure of model improvement than AUC. The theoretical results are illustrated using a Framingham Heart Study example of a model for predicting the 10-year incidence of atrial fibrillation.
PMCID: PMC3656609  PMID: 23242535
risk prediction; discrimination; AUC; IDI; Youden index; relative utility
7.  Relative bioavailability and pharmacokinetic comparison of two different enteric formulations of omeprazole*  
In order to comply with the requirements for a drug listed in China, the study was developed to compare the pharmacokinetics and relative bioavailability of two different enteric formulations of omeprazole (OPZ) in healthy Chinese subjects. A total of 32 volunteers participated in the study. Plasma concentrations were analyzed by nonstereospecific liquid chromatography/tandem mass spectrometric (LC-MS/MS) method. After administration of a single 40-mg dose of the two OPZ formulations, the comparative bioavailability was assessed by calculating individual AUC0‒t (the area under the concentration-time curve from time zero to the last measurable concentration), AUC0‒∞ (the area under the concentration-time curve extrapolated to infinity), C max (the maximum observed concentration), and T peak (the time to C max) values of OPZ, 5-hydroxyomeprazole (OH-OPZ), and omeprazole sulfone (OPZ-SFN), respectively. The 90% confidence intervals (CIs) of AUC0‒t, AUC0‒∞, and C max were 85.4%‒99.0%/88.8%‒98.6%/87.6%‒99.4%, 85.5%‒99.2%/89.0%‒98.6%/88.5%‒101.3%, and 72.3%‒87.6%/79.6%‒91.1%/88.4%‒99.1% for OPZ/OH-OPZ/OPZ-SFN, respectively, and T peak values did not differ significantly. In this study, the test formulation of OPZ in fasting healthy Chinese male volunteers met the Chinese bioequivalance standard to the reference formulation based on AUC, C max, and T peak.
PMCID: PMC3348225  PMID: 22556172
Omeprazole; 5-Hydroxyomeprazole; Omeprazole sulfone; Bioavailability; Pharmacokinetics; Liquid chromatography/tandem mass spectrometry (LC-MS/MS)
8.  Bioequivalence evaluation of epinephrine autoinjectors with attention to rapid delivery 
Timely and proper injection of epinephrine is critical to prevent serious consequences relating to anaphylaxis. In a recent bioavailability study comparing epinephrine delivery from the Auvi-Q™ and EpiPen® epinephrine autoinjectors, the Auvi-Q failed to meet the bioequivalence threshold when using partial area under the curve (AUC) analyses based on zero to Tmax recommended for highly variable drugs such as epinephrine. Peak plasma epinephrine concentrations for the EpiPen occurred 10 minutes (median Tmax) after dosing, while peak concentrations for the Auvi-Q occurred 20 minutes after dosing. Though bioequivalence may be concluded for Cmax, AUCinf, and AUC0–t, for fast-acting therapeutics used to treat life-threatening conditions, such as epinephrine, additional pharmacokinetic parameters such as AUC zero to Tmax may be important to evaluate when assessing bioequivalence.
PMCID: PMC3629870  PMID: 23610523
anaphylaxis; therapy; pharmacokinetics; bioavailability; EpiPen; Tmax
9.  Influence of pharmacogenetics on indinavir disposition and short-term response in HIV patients initiating HAART 
To assess the relationship between genetic polymorphisms and indinavir pharmacokinetic variability and to study the link between concentrations and short-term response or metabolic safety.
Forty protease inhibitor naive-patients initiating indinavir/ritonavir containing HAART and enrolled in the COPHAR 2 - ANRS 111 trial were studied. At week 2, 4 blood samples were taken before and up to 6 hours following drug intake. A population pharmacokinetic analysis was performed using the Stochastic Approximation Expectation Maximization (SAEM) algorithm implemented in the MONOLIX software. Indinavir area under the concentration-time curve (AUC), maximal (Cmax) and trough concentrations (Ctrough) were derived from the population model and tested for correlation with short-term viral response and safety measurements, while ritonavir AUC, Cmax and Ctrough were tested for correlation with short-term biochemical safety.
A one-compartment model with first-order absorption and elimination best described both indinavir and ritonavir concentrations. For indinavir, the estimated clearance and volume of distribution were 22.2 L/h and 97.3 L respectively. The eight patients *1B/*1B for CYP3A4 gene had an absorption decreased by 70% compared to *1A/*1B or *1A/*1A genotypes (0.5 versus 2.1, P=0.04, likelihood ratio test by permutation). Indinavir AUC and Ctrough were positively correlated with the HIV RNA decrease between week 0 and week 2 (r=−0.4, P = 0.03 and r=−0.4, P = 0.03, respectively). Patients with the *1B/*1B genotype had significantly lower indinavir Cmax (median 3.6 [range 2.1 – 5.2] ng/mL versus 4.4 [2.2 – 8.3] ng/mL, P=0.04) and a lower triglycerides increase during the first 4 weeks of treatment (0.1 [−0.7 – 1.4] versus 0.6 [−0.5 – 1.7] mmol/L, P = 0.02). For ritonavir, the estimated clearance and volume of distribution were 8.3 L/h and 60.7 L respectively and concentrations were not found to be correlated to biochemical safety. Indinavir and ritonavir absorption rate constants were found to be correlated, as well as their apparent volumes of distribution and clearances indicating correlated bioavailability of the two drugs.
CYP3A4*1B polymorphism was found to influence the pharmacokinetics of indinavir and in some extent the biochemical safety of indinavir.
PMCID: PMC2908290  PMID: 19440701
Adult; Anti-HIV Agents; adverse effects; pharmacokinetics; Antiretroviral Therapy, Highly Active; Area Under Curve; Clinical Trials as Topic; Cohort Studies; Cytochrome P-450 CYP3A; drug effects; Female; Genotype; HIV Infections; drug therapy; HIV Protease Inhibitors; adverse effects; pharmacokinetics; HIV-1; Humans; Indinavir; adverse effects; pharmacokinetics; Male; Metabolic Clearance Rate; Middle Aged; Models, Statistical; Multicenter Studies as Topic; Pharmacogenetics; Pilot Projects; Polymorphism, Genetic; Prospective Studies; Treatment Outcome; Pharmacokinetics; Nonlinear Mixed Effects Modeling; Protease inhibitors; CYP3A4; Safety; Efficacy
10.  Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling 
The area under the ROC curve (AUC) and partial area under the ROC curve (pAUC) are summary measures used to assess the accuracy of a biomarker in discriminating true disease status. The standard sampling approach used in biomarker validation studies is often inefficient and costly, especially when ascertaining the true disease status is costly and invasive. To improve efficiency and reduce the cost of biomarker validation studies, we consider a test-result-dependent sampling (TDS) scheme, in which subject selection for determining the disease state is dependent on the result of a biomarker assay. We first estimate the test-result distribution using data arising from the TDS design. With the estimated empirical test-result distribution, we propose consistent nonparametric estimators for AUC and pAUC and establish the asymptotic properties of the proposed estimators. Simulation studies show that the proposed estimators have good finite sample properties and that the TDS design yields more efficient AUC and pAUC estimates than a simple random sampling (SRS) design. A data example based on an ongoing cancer clinical trial is provided to illustrate the TDS design and the proposed estimators. This work can find broad applications in design and analysis of biomarker validation studies.
PMCID: PMC3564679  PMID: 23393612
Area under ROC curve (AUC); Empirical likelihood; Nonparametric; Partial area under ROC curve (pAUC); Simple random sampling; Test-result-dependent sampling
11.  Limited-Sampling Strategies for Anti-Infective Agents: Systematic Review 
Area under the concentration–time curve (AUC) is a pharmacokinetic parameter that represents overall exposure to a drug. For selected anti-infective agents, pharmacokinetic–pharmacodynamic parameters, such as AUC/MIC (where MIC is the minimal inhibitory concentration), have been correlated with outcome in a few studies. A limited-sampling strategy may be used to estimate pharmacokinetic parameters such as AUC, without the frequent, costly, and inconvenient blood sampling that would be required to directly calculate the AUC.
To discuss, by means of a systematic review, the strengths, limitations, and clinical implications of published studies involving a limited-sampling strategy for anti-infective agents and to propose improvements in methodology for future studies.
The PubMed and EMBASE databases were searched using the terms “anti-infective agents”, “limited sampling”, “optimal sampling”, “sparse sampling”, “AUC monitoring”, “abbreviated AUC”, “abbreviated sampling”, and “Bayesian”. The reference lists of retrieved articles were searched manually. Included studies were classified according to modified criteria from the US Preventive Services Task Force.
Twenty studies met the inclusion criteria. Six of the studies (involving didanosine, zidovudine, nevirapine, ciprofloxacin, efavirenz, and nelfinavir) were classified as providing level I evidence, 4 studies (involving vancomycin, didanosine, lamivudine, and lopinavir–ritonavir) provided level II-1 evidence, 2 studies (involving saquinavir and ceftazidime) provided level II-2 evidence, and 8 studies (involving ciprofloxacin, nelfinavir, vancomycin, ceftazidime, ganciclovir, pyrazinamide, meropenem, and alpha interferon) provided level III evidence. All of the studies providing level I evidence used prospectively collected data and proper validation procedures with separate, randomly selected index and validation groups. However, most of the included studies did not provide an adequate description of the methods or the characteristics of included patients, which limited their generalizability.
Many limited-sampling strategies have been developed for anti-infective agents that do not have a clearly established link between AUC and clinical outcomes in humans. Future studies should first determine if there is an association between AUC monitoring and clinical outcomes. Thereafter, it may be worthwhile to prospectively develop and validate a limited-sampling strategy for the particular anti-infective agent in a similar population.
PMCID: PMC2827000  PMID: 22478922
limited-sampling strategy; anti-infectives; pharmacokinetics; therapeutic drug monitoring; stratégies de prélèvements limités; agents anti-infectieux; pharmacocinétique; surveillance thérapeutique pharmacologique
12.  Identification of responders/non-responders to 5-fluorouracil based on individual 50% inhibitory area under the concentration curve of 5-fluorouracil obtained with collagen gel droplet-embedded culture-drug sensitivity test in colorectal cancer 
Oncology Letters  2011;2(2):309-313.
We previously reported the 5-fluorouracil (5-FU) sensitivity of cancer cells obtained from colorectal cancer (CRC) patients using the collagen gel droplet-embedded culture-drug sensitivity test (CD-DST). Multiple drug concentrations and contact durations, and the area under the concentration curve (AUC) and growth inhibition rate (IR) were combined, resulting in the AUC-IR curve, which was approximated to the logarithmic curve. Moreover, the individualized AUCIR50, the AUC value which gives 50% growth inhibition, was calculated using the AUC-IR curve. This study aimed to identify responders/non-responders to 5-FU based on the individual AUCIR50 obtained with CD-DST in order to establish individualized chemotherapy for CRC patients. The individual AUCIR50 was calculated from each AUC-inhibition rate regression curve in all patients using the CD-DST. The cumulative distribution of the individual AUCIR50 in CRC patients was evaluated. The cumulative distribution of the individual AUCIR50 was regressed over the sigmoid curve (logarithmic scale). The approximate expression was almost exactly y=ab^exp(−cx) (a=0.9739, b=1.7096E-21, c=0.8990, the sum of square residuals, 0.0279). In the 80 cases examined, no notable change was observed in the regression curve when the number of patients increased. A standard curve was obtained describing responders to 5-FU among all CRC patients. From this standard curve, we ascertained that non-responders accounted for approximately 5% of all patients. Moreover, we were able to classify responders into good or intermediate responders to 5-FU. The standard curve describing response to 5-FU in CRC patients offers a useful tool in the establishment of individualized chemotherapy.
PMCID: PMC3410600  PMID: 22866082
individualized chemotherapy; colorectal cancer; 5-fluorouracil; collagen gel droplet-embedded culture-drug sensitivity test; individual AUCIR50
13.  Satiety, but not total PYY, is increased with continuous and intermittent exercise 
Obesity (Silver Spring, Md.)  2013;21(10):2014-2020.
This study determined the hormonal and subjective appetite responses to exercise (1-h continuous v. intermittent exercise throughout the day) in obese individuals.
Design and Methods
Eleven obese subjects (>30 kg/m2) underwent 3, 12-hour study days: control condition (sedentary behavior-SED), continuous exercise condition ((EX) 1-h exercise), and intermittent exercise condition ((INT) 12 hourly, 5-minute bouts). Blood samples (every 10 min) were measured for serum insulin and total peptide YY (PYY) concentrations, with ratings of appetite (visual analog scale-VAS: every 20 minutes). Both total area under the curve (AUC), 2-h AUC and AUC above baseline, and subjective appetite ratings were calculated.
No differences were observed in total PYY AUC between conditions, but hunger was reduced with INT (INTEX and SED>EX; P<0.05). A correlation existed between the change in total PYY and insulin levels (r=−0.81; P<0.05), and total PYY and satiety (r=0.80; P<0.05) with the EX condition, not the SED and INT conditions.
The total PYY response to meals is not altered over the course of a 12-h day with either intermittent or continuous exercise; however, intermittent exercise increased satiety and reduced hunger to a greater extent than continuous exercise in obese individuals.
PMCID: PMC3661741  PMID: 23418154
gut hormones; exercise; visual analog scale; obesity
14.  Heart rate variability as a predictor of hypotension after spinal anesthesia in hypertensive patients 
Korean Journal of Anesthesiology  2013;65(4):317-321.
Hypotension is a common phenomenon after spinal anesthesia in hypertensive patients. We investigated whether heart rate variability could predict the occurrence of hypotension after spinal anesthesia in hypertensive patients.
Forty-one patients undergoing spinal anesthesia were included. Heart rate variability was measured at five different time points such as before fluid loading (baseline), after fluid loading as well as 5 min, 15 min and 30 min after spinal anesthesia. Fluid loading was performed using 5 ml/kg of a crystalloid solution. Baseline total power and low to high frequency ratio (LF/HF) in predicting hypotension after spinal anesthesia were analyzed by calculating the area under the receiver operating characteristic curves (AUC).
Moderate hypotension, defined as a decrease of mean arterial pressure to below 20-30% of the baseline, occurred in 13 patients and severe hypotension, defined as a decrease of mean arterial pressure greater than 30% below the baseline, occurred in 7 patients. LF/HF ratiosand total powers did not significantly change after spinal anesthesia. AUCs of LF/HF ratio for predicting moderate hypotension was 0.685 (P = 0.074), severe hypotension was 0.579 (P = 0.560) and moderate or severe hypotension was 0.652 (P = 0.101), respectively. AUCs of total power for predicting moderate hypotension was 0.571 (P = 0.490), severe hypotension was 0.672 (P = 0.351) and moderate or severe hypotension was 0.509 (P = 0.924), respectively.
Heart rate variability is not a reliable predictor of hypotension after spinal block in hypertensive patients whose sympathetic activity is already depressed.
PMCID: PMC3822023  PMID: 24228144
Hypertension; Hypotension; Parasympathetic nervous system; Spinal anesthesia; Sympathetic nervous system
15.  Comparison of Bobath based and movement science based treatment for stroke: a randomised controlled trial 
Objectives: Bobath based (BB) and movement science based (MSB) physiotherapy interventions are widely used for patients after stroke. There is little evidence to suggest which is most effective. This single-blind randomised controlled trial evaluated the effect of these treatments on movement abilities and functional independence.
Methods: A total of 120 patients admitted to a stroke rehabilitation ward were randomised into two treatment groups to receive either BB or MSB treatment. Primary outcome measures were the Rivermead Motor Assessment and the Motor Assessment Scale. Secondary measures assessed functional independence, walking speed, arm function, muscle tone, and sensation. Measures were performed by a blinded assessor at baseline, and then at 1, 3, and 6 months after baseline. Analysis of serial measurements was performed to compare outcomes between the groups by calculating the area under the curve (AUC) and inserting AUC values into Mann-Whitney U tests.
Results: Comparison between groups showed no significant difference for any outcome measures. Significance values for the Rivermead Motor Assessment ranged from p = 0.23 to p = 0.97 and for the Motor Assessment Scale from p = 0.29 to p = 0.87.
Conclusions: There were no significant differences in movement abilities or functional independence between patients receiving a BB or an MSB intervention. Therefore the study did not show that one approach was more effective than the other in the treatment of stroke patients.
PMCID: PMC1739598  PMID: 15774435
16.  Relevance of Soft-Tissue Penetration by Levofloxacin for Target Site Bacterial Killing in Patients with Sepsis 
Antimicrobial Agents and Chemotherapy  2003;47(11):3548-3553.
Antimicrobial therapy of soft tissue infections in patients with sepsis sometimes lacks efficiency, despite the documented susceptibility of the causative pathogen to the administered antibiotic. In this context, impaired equilibration between the antibiotic concentrations in plasma and those in tissues in critically ill patients has been discussed. To characterize the impact of tissue penetration of anti-infective agents on antimicrobial killing, we used microdialysis to measure the concentration-versus-time profiles of levofloxacin in the interstitial space fluid of skeletal muscle in patients with sepsis. Subsequently, we applied an established dynamic in vivo pharmacokinetic-in vitro pharmacodynamic approach to simulate bacterial killing at the site of infection. The population mean areas under the concentration-time curves (AUCs) for levofloxacin showed that levofloxacin excellently penetrates soft tissues, as indicated by the ratio of the AUC from time zero to 8 h (AUC0-8) for muscle tissue (AUC0-8 muscle) to the AUC0-8 for free drug in plasma (AUC0-8 plasma free) (AUC0-8 muscle/AUC0-8 plasma free ratio) of 0.85. The individual values of tissue penetration and maximum concentration (Cmax) in muscle tissue were highly variable. No difference in bacterial killing of a select Staphylococcus aureus strain for which the MIC was 0.5 μg/ml was found between individuals after exposure to dynamically changing concentrations of levofloxacin in plasma and tissue in vitro. In contrast, the decrease in the bacterial counts of Pseudomonas aeruginosa (MIC = 2 μg/ml) varied extensively when the bacteria were exposed to levofloxacin at the concentrations determined from the individual concentration-versus-time profiles obtained in skeletal muscle. The extent of bacterial killing could be predicted by calculating individual Cmax/MIC and AUC0-8 muscle/AUC0-8 plasma free ratios (R = 0.96 and 0.93, respectively). We have therefore shown in the present study that individual differences in the tissue penetration of levofloxacin may markedly affect target site killing of bacteria for which MICs are close to 2 μg/ml.
PMCID: PMC253769  PMID: 14576116
17.  Bioavailability of iron in oral ferrous sulfate preparations in healthy volunteers. 
The bioavailability of iron in five ferrous sulfate preparations was studied in 10 healthy male volunteers. The preparations were an oral solution, two types of film-coated tablets and two types of enteric-coated tablets. Blood samples were drawn hourly from 8 am to 6 pm on the day before each study day to assess baseline serum iron concentrations and on the study day. Spectrophotometry was used to measure the serum iron concentrations. The area under the curve (AUC), the maximum concentration and the time to achieve the maximum concentration were compared by analysis of variance. The enteric-coated preparations resulted in AUCs less than 30% of the AUC for the oral solution. The two film-coated products produced AUCs essentially equivalent to that of the oral solution. We conclude that the bioavailability of iron in the enteric-coated preparations was low, relative to that of the film-coated products and the oral solution, and that these products should not be considered interchangeable.
PMCID: PMC1451327  PMID: 2776093
18.  Interpreting Incremental Value of Markers Added to Risk Prediction Models 
American Journal of Epidemiology  2012;176(6):473-481.
The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.
PMCID: PMC3530349  PMID: 22875755
area under curve; biomarkers; discrimination; risk assessment; risk factors
19.  Incremental value of the CT coronary calcium score for the prediction of coronary artery disease 
European Radiology  2010;20(10):2331-2340.
To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS).
We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance.
Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model.
Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up.
PMCID: PMC2940023  PMID: 20559838
Coronary artery disease; X-ray computed tomography; Coronary calcium scoring; Logistic models; Diagnosis
20.  Change in mRNA Expression after Atenolol, a Beta-adrenergic Receptor Antagonist and Association with Pharmacological Response 
Archives of Drug Information  2009;2(3):41-50.
Genetic determinants of variability in response to β-blockers are poorly characterized. We defined changes in mRNA expression after a β-blocker to identify novel genes that could affect response and correlated these with inhibition of exercise-induced tachycardia, a measure of β-blocker sensitivity.
Nine subjects exercised before and after a single oral dose of 25mg atenolol and mRNA gene expression was measured using an Affymetrix GeneChip Human Gene 1.0 ST Array. The area under the heart rate-exercise intensity curve (AUC) was calculated for each subject; the difference between post- and pre-atenolol AUCs (Δ AUC), a measure of β-blocker response, was correlated with the fold-change in mRNA expression of the genes that changed more than 1.3-fold.
Fifty genes showed more than 1.3-fold increase in expression; 9 of these reached statistical significance (P < 0.05). Thirty-six genes had more than 1.3-fold decrease in expression after atenolol; 6 of these reached statistical significance (P < 0.05). Change in mRNA expression of FGFBP2 and Probeset ID 8118979 was significantly correlated with atenolol response (P = 0.03 and 0.02, respectively).
The expression of several genes not previously identified as part of the adrenergic signaling pathway changed in response to a single oral dose of atenolol. Variation in these genes could contribute to unexplained differences in response to β-blockers.
PMCID: PMC2773526  PMID: 19915711
Atenolol; mRNA expression; Microarray
21.  The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling 
PLoS Genetics  2010;6(2):e1000864.
Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator.
Author Summary
Genome-wide association studies in human populations have facilitated the creation of genomic profiles that combine the effects of many associated genetic variants to predict risk of disease. However, genomic profiles are inherently constrained in their ability to classify diseased from non-diseased individuals dictated by the genetic epidemiology of the disease. In this paper, we use a genetic interpretation to provide insight into the constraints on genomic profiles for risk prediction. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability available as an online calculator.
PMCID: PMC2829056  PMID: 20195508
22.  Mycophenolic acid area under the curve recovery time following rifampicin withdrawal 
Indian Journal of Nephrology  2010;20(1):51-53.
Renal transplant patients prescribed mycophenolate mofetil (MMF) may require treatment for tuberculosis with a regimen including the tuberculocidal drug rifampicin. MMF is an ester prodrug which is rapidly hydrolysed to the active compound, mycophenolic acid (MPA). Therapeutic drug monitoring of mycophenolate involves the measurement of MPA area under the curve (MPA-AUC0-12). Rifampicin is known to increase the metabolism and decrease enterohepatic recirculation of mycophenolic acid, (MPA). When MPA is monitored after the discontinuation of rifampicin, an important factor is the time required for the MPA area under the curve to return to the pre-rifampicin value. At present this is not known. This report describes one such renal allograft patient, on long term MMF and prescribed rifampicin by a local physician. As expected there was a clinically significant decrease in MPA-AUC0-12 Three weeks after rifampicin was discontinued the MPA-AUC0-12 was still only 65% of the pre-rifampicin value and only 55% of the steady state MPA-AUC0-12 measured six months later.
PMCID: PMC2878413  PMID: 20535273
Mycophenolate; rifampicin; interaction; renal transplantation
23.  Changes in Prandial Glucagon Levels After a 2-Year Treatment With Vildagliptin or Glimepiride in Patients With Type 2 Diabetes Inadequately Controlled With Metformin Monotherapy 
Diabetes Care  2010;33(4):730-732.
To determine if the dipeptidyl peptidase-4 inhibitor vildagliptin more effectively inhibits glucagon levels than the sulfonylurea glimepiride during a meal.
Glucagon responses to a standard meal were measured at baseline and study end point (mean 1.8 years) in a trial evaluating add-on therapy to metformin with 50 mg vildagliptin b.i.d. compared with glimepiride up to 6 mg q.d. in type 2 diabetes (baseline A1C 7.3 ± 0.6%).
A1C and prandial glucose area under the curve (AUC)0–2 h were reduced similarly in both groups, whereas prandial insulin AUC0–2 h increased to a greater extent by glimepiride. Prandial glucagon AUC0–2 h (baseline 66.6 ± 2.3 pmol · h−1 · l−1) decreased by 3.4 ± 1.6 pmol · h−1 · l−1 by vildagliptin (n = 137) and increased by 3.8 ± 1.7 pmol · h−1 · l−1 by glimepiride (n = 121). The between-group difference was 7.3 ± 2.1 pmol · h−1 · l−1 (P < 0.001).
Vildagliptin therapy but not glimepiride improves postprandial α-cell function, which persists for at least 2 years.
PMCID: PMC2845014  PMID: 20067974
24.  Detection of Occludable Angles with the Pentacam and the Anterior Segment Optical Coherence Tomography 
Yonsei Medical Journal  2009;50(4):525-528.
To assess efficacy of the Pentacam (PTC) and the anterior segment optical coherence tomography (AOCT) for detection of occludable angles.
Materials and Methods
Fourty-one eyes with gonioscopically diagnosed occludable angles and 32 normal open-angle eyes were included. Anterior chamber angle (ACA) and anterior chamber depth (ACD) were measured with PTC and AOCT. Receiver operating characteristic (ROC) curve was constructed for each parameter and the area under the ROC curve (AUC) was calculated.
Values of ACA and ACD measured by PTC and AOCT were similar not only in normal open angle eyes but also in occludable angle eyes. For detection of occludable angle, the AUCs of PTC with ACA and ACD were 0.935 and 0.969, respectively. The AUCs of AOCT with ACA and ACD were 0.904 and 0.947, respectively.
Both PTC and AOCT allow accurate discrimination between open and occludable angle eyes, so that they may aid to screening the occludable angles.
PMCID: PMC2730615  PMID: 19718401
Pentacam; anterior segment optical coherence tomography; occludable angle; anterior chamber angle; anterior chamber depth
25.  Meta-analysis of inter-patient pharmacokinetic variability of liposomal and non-liposomal anticancer agents 
Nanomedicine : nanotechnology, biology, and medicine  2013;10(1):10.1016/j.nano.2013.07.005.
A meta-analysis was conducted to evaluate the inter-patient pharmacokinetic (PK) variability of liposomal and small molecule (SM) anticancer agents.
Inter-patient PK variability of 9 liposomal and SM formulations of the same drug were evaluated. PK variability was measured as coefficient of variance (CV%) of area under the plasma concentration versus time curve (AUC) and the fold-difference between AUCmax and AUCmin (AUC range).
CV% of AUC and AUC ranges were 2.7-fold (P<0.001) and 16.7-fold (P=0.13) greater, respectively, for liposomal compared with SM drugs. There was an inverse linear relationship between the clearance (CL) of liposomal agents and PK variability with a lower CL associated with greater PK variability (R2 = 0.39). PK variability of liposomal agents was greater when evaluated from 0–336 h compared with 0–24 h.
PK variability of liposomes is significantly greater than SM. The factors associated with the PK variability of liposomal agents needs to be evaluated.
PMCID: PMC3877184  PMID: 23891988
CKD-602; S-CKD602; pharmacokinetic; variability; sampling schema; liposomes

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