Purpose: Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P450 3A4 to an active metabolite (SU12662). The objective of this analysis was to assess sunitinib and SU12662 pharmacokinetics and to identify covariates that might explain variability in exposure following oral administration. Experimental Design: Data from 590 subjects (73 volunteers and 517 patients) in 14 studies were analyzed. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling to estimate population pharmacokinetic parameters, as well as relationships between these parameters and gender, race, age, weight, creatinine clearance, Eastern Cooperative Oncology Group score, and tumor type. Simulations were done to determine the predicted effect of these covariates on exposure.Results: Separate models were developed for sunitinib and SU12662 (each a two-compartment model with first-order absorption and elimination). Sunitinib parameters were estimated as CL/F, 51.8 L/h andVd/F central , 2,030 liters. SU12662 parameters were estimated as CL/F, 29.6 L/h and Vd/F central , 3,080 liters. Tumor type (except acute myeloid leukemia), Asian race, gender, body weight, and elevated Eastern Cooperative Oncology Group score described a portion of the variability in CL/F for sunitinib and metabolite; gender and body weight explained some of the variability in Vd/F central for sunitinib and metabolite. Among patients, the predicted changes in sunitinib and metabolite AUC and C max as a result of the individual covariates ranged up to 17%. Conclusion: The magnitude of the predicted changes in exposure with the covariates studied minimizes the necessity for dose adjustment in any of these subpopulations.

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Dapoxetine is being developed as a treatment for premature ejaculation and has demonstrated rapid absorption and elimination in previous pharmacokinetic studies. Two open-label studies were conducted in healthy men: a parallel-group pharmacokinetic and safety study in young and elderly men and a randomized crossover food-effect study. Maximal plasma dapoxetine concentrations (C(max)) were similar in young and elderly men (338 and 310 ng/mL, respectively), as were the corresponding area under the plasma concentration versus time curve (AUC) values (2040 and 2280 ng x h/mL, respectively). When coadministered with food, C(max) was reduced by 11% (398 vs 443 ng/mL in the fed and fasted states, respectively), and the peak was delayed by approximately 30 minutes, indicating that food slowed the rate of absorption; however, systemic exposure to dapoxetine (ie, AUC) was not affected by food consumption. Thus, age or consumption of a high-fat meal has only a modest impact on dapoxetine pharmacokinetics in healthy men.

Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

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Tremelimumab, a fully human monoclonal antibody specific for human cytotoxic T-lymphocyte-associated antigen 4, has been studied in clinical trials. We have reported the results of population pharmacokinetics for tremelimumab in 654 metastatic melanoma patients. Population estimates (inter-individual variability [IIV]) for pharmacokinetic parameters in a final model were clearance (CL), 0.26 L/day (31.8%) and central volume of distribution, 3.97 L (20.4%). CL was faster in males, patients with higher values of creatinine clearance and endogenous immunoglobulin, and patients with relatively poor baseline prognostic factors. No dose adjustment was needed based on the magnitude of the change of CL (<30%). The association of CL and overall survival (OS) was investigated. In a Phase 3 trial evaluating tremelimumab as first-line-treatment, median OS for the 147 patients in the fast-CL group (≥ median CL value) was 9.6 months versus 15.8 months for the 146 patients in the slow-CL group (

Abstract. Computer-aided segmentation of cardiac images obtained by various modalities plays an important role and is a prerequisite for a wide range of cardiac applications by facilitating the delineation of anatomical regions of interest. Numerous computerized methods have been developed to tackle this problem. Recent studies employ sophisticated techniques using available cues from cardiac anatomy such as geometry, visual appearance, and prior knowledge. In addition, new minimization and computational methods have been adopted with improved computational speed and robustness. We provide an overview of cardiac segmentation techniques, with a goal of providing useful advice and references. In addition, we describe important clinical applications, imaging modalities, and validation methods used for cardiac segmentation.

We describe an accurate, yet simple and fast sample size computation method for hypothesis testing in population PK/PD studies. We use a first order approximation to the nonlinear mixed effects model and chi-square distributed Wald statistic to compute the minimum sample size to achieve given degree of power in rejecting a null hypothesis in population PK/PD studies. The method is an extension of Rochon's sample size computation method for repeated measurement experiments. We compute sample sizes for PK and PK/PD models with different conditions, and use Monte Carlo simulation to show that the computed sample size retrieves the required power. We also show the effect of different sampling strategies, such as minimal, i.e., as many observations per individual as parameters in the model, and intensive on sample size. The proposed sample size computation method can produce estimates of minimum sample size to achieve the desired power in hypothesis testing in a greatly reduced time than currently available simulation-based methods. The method is rapid and efficient for sample size computation in population PK/PD study using nonlinear mixed effect models. The method is general and can accommodate any type of hierarchical models. Simulation results suggest that intensive sampling allows the reduction of the number of patients enrolled in a clinical study.