Prediction of steady state bioequivalence relationships using single dose data II-nonlinear kinetics

Two nonlinear pharmacokinetic models were simulated to investigate the relationship between single and multiple dose bioequivalency parameters for drugs such as phenytoin and propranolol which exhibit either saturable elimination kinetics or a capacity limited first pass effect. Mean Tmax, Cmax and area under the plasma-concentration time curve values from 0 to infinity (AUC 0-infinity) were compared after a single and multiple dose(s) of a test or reference drug. The aim was to determine if there were systematic changes in the limits of the single dose confidence interval at steady state that would limit the usefulness of confidence intervals following a single dose in accurately predicting bioavailability following multiple dosing. The 90 per cent confidence interval expressed as a percentage of the reference mean for Tmax, Cmax, and AUC 0-infinity showed model dependent changes from single to multiple dosing in response to the level of data error and changes in absorption. Changes in clearance also seemed to have a marked effect on the observed limits of the single and multiple dose confidence intervals especially for Cmax which showed a characteristic change in the intervals as a function of the clearance ratio. The model used to describe phenytoin had confidence intervals for Cmax and AUC 0-infinity from single to multiple dosing that were similar to that seen for the experimental data. However, the model predictions for Tmax confidence intervals following single and multiple dosing was at variance with the experimental data for formulations A and B.     

Automatic discrimination of myoelectric signals via parallel cascade identification

It has recently been shown that it is possible to discriminate accurately among myoelectric signals underlying different muscle contraction types, specifically elbow flexion and extension and forearm pronation and supination. It was reported that once a number of distinctive features had been extracted from the myoelectric signals, a neural network could be trained to distinguish the contraction types with an impressively high accuracy. In the present paper, we show that a technique known as parallel cascade identification can be used to construct classifiers that can also accurately, differentiate the contraction types. The use of parallel cascades has the benefit of dispensing with the need for feature extraction, so that raw myoelectric signal data can be used directly. In addition, very little data are required to train the parallel cascades to distinguish accurately novel incoming myoelectric signals. Results of using parallel cascades to distinguish foream pronation, supination, and elbow flexion are presented.     

一种新的正交参数选优法及其在非线性回归分析中的应用

非线性回归分析是工程中经常采用的一种用来估计数学模型参数的方法，该方法能否顺利运用与参数初始值的选择有极大关系。本研究提出一种新的正交参数选优法——阻尼正交表法，它不仅可以保证非线性回归分析算法的顺利收敛，而且能够显著提高后者的收敛速度，进而极大改善非线性回归分析算法的应用性能。本研究的数值试验及心肌造影超声心动图定量分析应用实例表明，作为对传统正交参数选优法的一种改进，阻尼正交表法在科学与工程计算或信号与信息处理领域有着很好的应用前景。     

Performance in population models for count data,part II: A new SAEM algorithm

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 []). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7% for all explored scenarios. The longest CPU time was 95 s for parameter estimation and 56 s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009).     

Identification of the three-element windkessel model incorporating a pressure-dependent compliance

A new one-step computational procedure is presented for estimating the parameters of the nonlinear three-element windkessel model of the arterial system incorporating a pressure-dependent compliance. The data required are pulsatile aortic pressure and flow. The basic assumptions are a steadystate periodic regime and a purely elastic compliant element. By stating two conditions, zero mean flow and zero mean power in the compliant element, peripheral and characteristic resistances are determined through simple closed form formulas as functions of mean values of the square of aortic pressure, the square of aortic flow, and the product of aortic pressure with aortic flow. The pressure across as well as the flow through the compliant element can be then obtained so allowing the calculation of volume variation and compliance as functions of pressure. The feasibility of this method is studied by applying it to both simulated and experimental data relative to different circulatory conditions and comparing the results with those obtained by an iterative parameter optimization algorithm and with the actual values when available. The conclusion is that the proposed method appears to be effective in identifying the three-element windkessel even in the case of nonlinear compliance.     

Non-invasive evaluation of abdominal aortic properties: lumped circuit model and estimation of its parameters

To non-invasively determine abdominal aortic properties, a five-element lumped circuit model was adopted. The model consists of resistance due to blood viscosity (R1), inertia of blood flow, compliances of the vessel (C1, C2), resistance of the peripheral arteries (R2) and the impedance of the femoral arteries (termination). Patterns of the central velocity of the upper abdominal aorta and the femoral artery are measured by pulsed Doppler echocardiography, and confours of flow volume rates are calculated. The pressure pattern of the lower limb is recorded by a pulse wave rransducer and corrected according to sphygmomanometer values. Contours are transformed into respective Fourier transform components. The current transfer function is described theoretically and calculated from the acquired Fourier components. Values of every element are evaluated by the nonlinear least squares method. In 94 subjects (17–92 years), the values of each element are estimated. R2 values are greater in the elderly group, than in the young group and r₁ (R1/cm) increased with age. This model demonstrates that vessel compliance (c₁+c₂ (C1+C2/cm)) decreases with age, and it is suggested that this may be a useful marker of arteriosclerosis.     

The Topography of Non-Linear Cortical Dynamics at Rest, in Mental Calculation and Moving Shape Perception

Differential cortical activation by cognitive processing was studied using dimensional complexity, a measure derived from nonlinear dynamics that indicates the degrees of freedom (complexity) of a dynamic system. We examined the EEG of 32 healthy subjects at rest, during a visually presented calculation task, and during a moving shape perception task. As a nonlinear measure of connectivity, the mutual dimension of selected electrode pairs was used. The first Lyapunov coefficient was also calculated. Data were tested for non-linearity using a surrogate data method and compared to spectral EEG measures (power, coherence). Surrogate data testing confirmed the presence of nonlinear structure in the data. Cognitive activation led to a highly significant rise in dimensional complexity. While both tasks activated central, parietal and temporal areas, mental arithmetic showed frontal activation and an activity maximum at T3, while the moving shape task led to occipital activation and a right parietal activity maximum. Analysis of mutual dimension showed activation of a bilateral temporal-right frontal network in calculation. The Lyapunov coefficent showed clear topographic variation, but was not significantly changed by mental tasks (p<.09). While dimensional complexity was almost unrelated to power values, nonlinear (mutual dimension) and linear (coherence) measures of connectivity shared up to 37% of variance. Data are interpreted in terms of increased cortical complexity as a result of recruitment of asynchronously active, distributed neuronal assemblies in cognition. The topography of nonlinear dynamics are related to neuropsychological and neuroimaging findings on mental calculation and moving shape perception.     

A Dynamic Morphometric Model of the Normal Lung for Studying Expiratory Flow Limitation in Mechanical Ventilation

A nonlinear dynamic morphometric model of breathing mechanics during artificial ventilation is described. On the basis of the Weibel symmetrical representation of the tracheobronchial tree, the model accurately accounts for the geometrical and mechanical characteristics of the conductive zone and packs the respiratory zone into a viscoelastic Voigt body. The model also accounts for the main mechanisms limiting expiratory flow (wave speed limitation and viscous flow limitation), in order to reproduce satisfactorily, under dynamic conditions, the expiratory flow limitation phenomenon occurring in normal subjects when the difference between alveolar pressure and tracheal pressure (driving pressure) is high. Several expirations characterized by different levels of driving pressure are simulated and expiratory flow limitation is detected by plotting the isovolume pressure–flow curves. The model is used to study the time course of resistance and total cross-sectional area as well as the ratio of fluid velocity to wave speed (speed index), in conductive airway generations. The results highlight that the coupling between dissipative pressure losses and airway compliance leads to onset of expiratory flow limitation in normal lungs when driving pressure is increased significantly by applying a subatmospheric pressure to the outlet of the ventilator expiratory channel; wave speed limitation becomes predominant at still higher driving pressures.     

Characteristics of\dot V_A /\dot Q distributions recovered from inert gas elimination datadistributions recovered from inert gas elimination data

The resolving powers of the enforced smoothing and log-normal parametric estimation techniques in recovering ventilation/perfusion ratio distributions were evaluated using noisy inert gas elimination data simulated from hypothetical distribution functions representing various degrees of heterogeneity. The resolving powers were assessed in terms of the statistical recoverabilities of the shunt, dead space, modality, and modal moments characterizing the perfusion distribution. For all distributions tested, both modal mean and shunt were estimated by either technique with sufficient accuracies. Modal dispersions (σ) were consistently overestimated by up to 0.15 decade for narrow distributions, but the mean errors became negligible for σ greater than 0.2 decade. As compared with the shunt, the dead space estimates were more variable and biased, probably due to their indirect estimation from the perfusion distribution, which was imperfectly recovered. Both broad unimodal and widely separated bimodal or trimodal distributions (σ>0.6 decade) were recovered as bimodal distributions of similar forms, so that detection of modality was difficult. The recoveries by both techniques were comparable in most cases studied, except that parametric estimation generally tended to be more sensitive to measurement errors and was computationally less efficient. These results provide a useful basis for the interpretation of distributions obtained from empirical inert gas data.