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PubMed2002

Mechanism-based pharmacokinetic-pharmacodynamic modeling of concentration-dependent hysteresis and biphasic electroencephalogram effects of alphaxalone in rats.

The neuroactive steroid alphaxalone reveals a complex biphasic concentration-effect relationship using the 11.5 to 30 Hz frequency band of the electroencephalogram (EEG) as biomarker. The purpose of the present investigation was to develop a mechanism-based pharmacokinetic-pharmacodynamic model to describe this observation. The proposed model is based on receptor theory and aims to separate the drug-receptor interaction from the transduction of the initial stimulus into the observed biphasic response. Individual concentration-time courses of alphaxalone were obtained in combination with continuous recording of the EEG parameter. Alphaxalone was administered intravenously in various dosages. The pharmacokinetics were described by a two-compartment model, and parameter estimates for clearance, intercompartmental clearance, volume of distribution 1 and 2 were 158 +/- 29 ml. min(-1). kg(-1), 143 +/- 31 ml. min(-1). kg(-1), 122 +/- 20 ml. kg(-1) and 606 +/- 48 ml. kg(-1), respectively. Concentration-effect relationships exhibited a biphasic pattern and delay in onset of effect. The hysteresis was described on the basis of an effect-compartment model with C(max) as covariate. The pharmacodynamic model consisted of a receptor model, featuring a monophasic saturable receptor activation model in combination with a biphasic stimulus-response model. The in vivo affinity (K(PD)) was estimated at 432 +/- 26 ng. ml(-1). Unique parameter estimates were obtained that were independent of the dose and the duration of the infusion. In conclusion, we have shown that this mechanism-based approach, which separates drug- and system-related properties in vivo, was successfully applied for the characterization of the biphasic effect versus time patterns of alphaxalone. The model should be of use in the characterization of other biphasic responses.

Algorithms↗

PubMed1992

DIMSUM: an expert system for multiexponential model discrimination.

DIMSUM is a highly automated, rule-based expert system designed to fit multiexponential models of increasing dimension to time series data, followed by selection of the best candidate model based on a user-modifiable and weighted decision tree of statistical criteria for model discrimination. The major features of DIMSUM are 1) an interactive and friendly user interface; 2) options for incorporating prior information about the parameters, the data, and/or the system from which the data were collected, in the form of equality and inequality constraints; 3) a built-in algorithm for automatically obtaining starting values for parameter estimation; 4) a robust weighted least-squares parameter estimation algorithm operating in an adaptive, user-adjustable search space; 5) comprehensive statistical results comparing different order candidate models fitted to the data; and 6) a novel, user-modifiable (learning) rule-based advisory subsystem providing an "expert's" interpretation of these statistical results and an explanation of all advice.

Algorithms↗

PubMed2000

Comparison of methods for analyzing kinetic data from mechanism-based enzyme inactivation: application to nitric oxide synthase.

The goals of this study were (1) to investigate the performance of 2 classical methods of kinetic analysis when applied to data from enzyme systems in which mechanism-based inactivation and enzyme degradation are present, and (2) to develop and validate a nonlinear method of kinetic data analysis that may perform better under these situations. A composite equation was derived to link various parameters that govern the kinetics of mechanism-based inactivation, viz., enzyme activity, inhibitor-binding affinity (K(I)), inactivation rate (k(inact)), and enzyme degradation (k(deg)). The relative accuracy and precision of parameter estimation by the Dixon and Kitz-Wilson methods and a new nonlinear method were evaluated by computer simulation. The behavior of these methods of analysis were validated experimentally, using the nitric oxide synthase enzyme, both in purified form and as expressed in murine macrophage cell cultures. We showed that the Dixon method, as expected, could not provide accurate estimates of K(I) in the presence of either enzyme inactivation or instability. The Kitz-Wilson method could provide accurate estimates of these parameters; however, the precisions of these estimates were poorer than those obtained using the nonlinear method of analysis. We conclude that the nonlinear approach is superior to classical methods of data analysis for enzyme inhibitor kinetics, based on better efficiency, accuracy, and precision.

Algorithms↗

PubMed1988

Development of individual growth curve standards for estimated fetal weight: I. Weight estimation procedure.

In this investigation the weight estimation procedure of Rossavik was reassessed with particular emphasis on parameter estimation and performance over a wide weight range. Using a cross-sectional data set (193 patients), a longitudinal data set (20 patients), and an iterative procedure, parameter estimates were obtained based solely on regression analysis. Comparison of weight estimates obtained using a function based on these parameter values with actual birth weights indicated virtually no systematic errors over a 250-g to 4750-g weight range and random errors (+/- 1 SD) of 10% to 13% below 200 g and 6% to 8% above 2000 g. The weights of small- and large-for-gestational age fetuses were systematically overestimated (4.1%) and underestimated (-3.0%), respectively, but systematic errors were not found in average-for-gestational age fetuses. No differences in random errors were seen in these three growth categories. Comparison with other weight estimation procedures indicated that the Rossavik procedure gives weight estimates that are at least as accurate as those obtained with other methods over a wide range of weight and growth categories. However, the Rossavik procedure can be used to generate individual growth curve standards for weight estimates, a characteristic not shared with other weight estimation procedures.

Birth Weight↗

PubMed2006

Using LinLog and FACETS to model item components in the LLTM.

The current study investigates the performance of two Rasch measurement programs and their parameter estimations on the linear logistic test model (LLTM; Fischer, 1973). These two programs, LinLog (Whitely & Nieh, 1981) and FACETS (Linacre, 2002), are used to investigate within-item complexity factors in a spatial memory measure tool. LinLog uses conditional maximum likelihood to estimate person and item parameters and is an LLTM specific program. FACETS is usually reserved for the many-facet Rasch model (MFRM; Linacre, 1989), however in the case of specifically designed within-item solution processes, a multifaceted approach makes good sense. It is possible to consider each dimension within the item as a separate facet, just as if there were multiple raters for each item. Simulations of 500 and 1000 persons expand the original data set (114 persons) to better examine each estimation technique. LinLog and FACETS analyses show strikingly similar results in both the simulation and original data conditions, indicating that the FACETS program produces accurate LLTM parameter estimates.

Humans↗

PubMed2005

The effects of ignored seabed variability in geoacoustic inversion.

Geoacoustic inversion using a matched-field inversion algorithm is a well-established technique for estimating the geoacoustic parameters of the seabed. This paper demonstrates how parameter estimation can be affected by unknown or wishfully ignored random range dependence of the true environment when the inversion model is--for practicality--assumed to be range independent. Simulations with controlled statistics were carried out using a simple shallow water model: an isospeed water column over a homogeneous elastic halfspace. The inversion parameters included water depth, compressional speed in the seabed, seabed density, and compressional wave attenuation. On average the environment is range independent: some parameters are constant while other parameters are random with range-independent means and variances. A Parabolic Equation underwater acoustic propagation model is used to calculate the simulated data fields for the range-dependent environment as well as to calculate the model fields for the range-independent inversion model. The Adaptive Simplex Simulated Annealing inversion algorithm is used to estimate the best-fit solution. It is found that ignoring the variability of even a single geoacoustic parameter leads to significant and correlated uncertainty (bias and variance) in the estimation of all inverted parameters. Results are presented for range variation of compressional sound speed and water depth.

Journal Article↗

PubMed1992

Physiologically based liver modeling and risk assessment.

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.

Animals↗

PubMed2005

Partitioned Bayesian analyses, partition choice, and the phylogenetic relationships of scincid lizards.

Partitioned Bayesian analyses of approximately 2.2 kb of nucleotide sequence data (mtDNA) were used to elucidate phylogenetic relationships among 30 scincid lizard genera. Few partitioned Bayesian analyses exist in the literature, resulting in a lack of methods to determine the appropriate number of and identity of partitions. Thus, a criterion, based on the Bayes factor, for selecting among competing partitioning strategies is proposed and tested. Improvements in both mean -lnL and estimated posterior probabilities were observed when specific models and parameter estimates were assumed for partitions of the total data set. This result is expected given that the 95% credible intervals of model parameter estimates for numerous partitions do not overlap and it reveals that different data partitions may evolve quite differently. We further demonstrate that how one partitions the data (by gene, codon position, etc.) is shown to be a greater concern than simply the overall number of partitions. Using the criterion of the 2 ln Bayes factor > 10, the phylogenetic analysis employing the largest number of partitions was decisively better than all other strategies. Strategies that partitioned the ND1 gene by codon position performed better than other partition strategies, regardless of the overall number of partitions. Scincidae, Acontinae, Lygosominae, east Asian and North American "Eumeces" + Neoseps; North African Eumeces, Scincus, and Scincopus, and a large group primarily from sub-Saharan Africa, Madagascar, and neighboring islands are monophyletic. Feylinia, a limbless group of previously uncertain relationships, is nested within a "scincine" clade from sub-Saharan Africa. We reject the hypothesis that the nearly limbless dibamids are derived from within the Scincidae, but cannot reject the hypothesis that they represent the sister taxon to skinks. Amphiglossus, Chalcides, the acontines Acontias and Typhlosaurus, and Scincinae are paraphyletic. The globally widespread "Eumeces" is polyphyletic and we make necessary taxonomic changes.

Animals↗

PubMed1996

Prediction of daily protein accretion rates of pigs from estimates of fat-free lean gain between 20 and 120 kilograms live weight.

The objective of this study was to evaluate a method for predicting daily protein accretion rates of various genotypes of pigs reared in different environmental conditions using easily obtained mean daily fat-free growth rates. Data were obtained for seven genotype-environment groups of gilts and nine groups of barrows. Daily empty body protein accretion rates were estimated at 1.0-kg intervals between 20 and 120 kg live weight. The estimates were fitted to a generalized exponential function, PA = A e(B x WT + C/WT + D x WT2), where WT is kilograms of live weight and A, B, C, and D are estimated parameters for each sex. Nonlinear least squares methods were used to estimate the intercept and regression coefficients expressing each parameter estimate (A, B, C, and D) as a linear function of the mean fat-free lean gain for each sex-genotype-environment group. The mean percentage absolute errors were 3.5% for gilts and 6.1% for barrows. The largest errors occurred between 110 and 120 kg live weight. From 20 to 110 kg, mean percentage errors averaged 2.7% for gilts vs 4.8% for barrows. These results offer encouraging evidence that a generalized equation can be used to predict daily protein accretion rates from mean fat-free lean growth data. Further research, with additional genotype-environment populations, is needed to increase accuracy of the generalized growth functions.

Animal Husbandry↗

PubMed1993

Use of sibling data to estimate family mortality effects in Guatemala.

This paper examines the potential bias in estimates of child mortality determinants produced by the questionable assumption that sibling data are independent, and estimates the unmeasured familial effects shared among siblings. The parameter estimates yielded by the multivariate hazard model are very similar to those yielded by the standard hazard model. The standard errors of the parameter estimates, however, tend to be underestimated in conventional analyses. The contribution to child mortality from the familial factors seems modest net of household socioeconomic status, at least in this Guatemalan data set.

Birth Order↗

PubMed1989

Selecting the smoothing parameter for estimation of slowly changing evoked potential signals.

Brain evoked potential (EP) data consist of a true response ("signal") and random background activity ("noise"), which are observed over repeated stimulus presentations ("trials"). A signal that changes slowly from trial to trial can be estimated by smoothing across trials and over time within trials. We present a method for selecting the smoothing parameter by minimizing an estimate of the mean average squared error (MASE). We evaluate the performance of this method using simulated EP data, and apply the method to an example set of real flash evoked potentials.

Analysis of Variance↗

PubMed1997

Sensitivity of segregation analysis to data structure and transformation: a case study of trypanotolerance in mice.

Sensitivity of segregation analysis for data structure and data transformation was studied using data from two trials in which mice were challenged at three months of age with a cloned isolate of Trypanosoma congolense and survival time was recorded. Data included records from three inbred strains (C57BL/6 (tolerant), A/J, and BALB/c (both susceptible)) and their crosses. Data were standardized and normalized using a modified power transformation. Segregation analysis was applied to both untransformed and transformed data to determine the genetic inheritance of trypanotolerance in these mice. Data from the two trials were analysed separately and combined. Four genetic models were compared; a one locus model, a polygenic model, a mixed model with common variance, and a mixed model with different variances for each major genotype. Even though the separate data sets and the combined data set all supported the hypothesis of a major gene (or a tightly linked cluster of genes) with different variances within each genotype, parameter estimates were highly sensitive to data transformation and several sets of parameter estimates gave similar likelihood values because of high dependency between parameters. Based on the results segregation analysis can be very sensitive to data structure in a crossbreeding design and to data transformation. Interpretation of the results can be misleading if the entire parameter space is not studied carefully.

Animals↗

PubMed1988

Estimating ultrasonic parameters of biological tissue by adaptive filtering technique.

Diagnostic ultrasonic imaging is widely used as a non-invasive technique for examining the body. However, the resolution of images still does not meet the order of the wavelength, and only a part of the information on tissue characteristics contained in reflected signals is used. In this paper we propose a method for estimating two tissue parameters; i.e., the reflection coefficient and attenuation coefficient from the reflected ultrasound for the purpose of improving the resolution of ultrasonic images and obtaining information on tissue characterization. The reflection coefficient is estimated by a deconvolution technique using a Kalman filter, taking into account the distortion of the propagating pulse due to frequency-dependent attenuation. The attenuation is estimated by adaptive processing based upon the criterion function calculated using estimates of the reflection coefficient. Simulated signals are used to investigate the ability of this method. Additionally, experimental results for reflected signals from a human liver show that the method is applicable to clinical cases.

Filtration↗

PubMed1984

Sensitivity functions in the estimation of parameters of cellular exchange.

The analysis of multiple-indicator dilution curves to estimate the rates of transport of ions and substrates across the sarcolemma of myocardial cells requires the formulation of models for the blood-interstitial fluid-cell exchanges. The fitting of models to the sets of experimental data is dependent on acquiring a large enough data set, in one physiological state, that there is at least as much information in the data as there are unknown model parameters to be determined. Inasmuch as data are necessarily noisy, redundancy of data and overdetermination of the unknowns are highly desirable. Sensitivity functions are useful in demonstrating which portions of the data relate to which unknown parameters. They are also useful in adjusting model parameters to fit the model to the data, and therefore in parameter evaluation.

Biological Transport↗

PubMed2002

A generalized discriminant rule when training population and test population differ on their descriptive parameters.

Standard discriminant analysis methods make the assumption that both the labeled sample used to estimate the discriminant rule and the nonlabeled sample on which this rule is applied arise from the same population. In this work, we consider the case where the two populations are slightly different. In the multinormal context, we establish that both populations are linked through linear mapping. Estimation of the nonlabeled sample discriminant rule is then obtained by estimating parameters of this linear relationship. Several models describing this relationship are proposed and associated estimated parameters are given. An experimental illustration is also provided in which sex of birds that differ morphometrically over their geographical range is to be deterrmined and a comparison with the standard allocation rule is performed. Extension to a partially labeled sample is also discussed.

Animals↗

PubMed1994

Analysis of population genetic structure with RAPD markers.

Recent advances in the application of the polymerase chain reaction make it possible to score individuals at a large number of loci. The RAPD (random amplified polymorphic DNA) method is one such technique that has attracted widespread interest. The analysis of population structure with RAPD data is hampered by the lack of complete genotypic information resulting from dominance, since this enhances the sampling variance associated with single loci as well as induces bias in parameter estimation. We present estimators for several population-genetic parameters (gene and genotype frequencies, within- and between-population heterozygosities, degree of inbreeding and population subdivision, and degree of individual relatedness) along with expressions for their sampling variances. Although completely unbiased estimators do not appear to be possible with RAPDs, several steps are suggested that will insure that the bias in parameter estimates is negligible. To achieve the same degree of statistical power, on the order of 2 to 10 times more individuals need to be sampled per locus when dominant markers are relied upon, as compared to codominant (RFLP, isozyme) markers. Moreover, to avoid bias in parameter estimation, the marker alleles for most of these loci should be in relatively low frequency. Due to the need for pruning loci with low-frequency null alleles, more loci also need to be sampled with RAPDs than with more conventional markers, and some problems of bias cannot be completely eliminated.

Animals↗

PubMed2000

Population pharmacokinetic analysis of amikacin and validation on neonates using Monte Carlo method.

AIM: To make programs for population pharmacokinetic analysis and to assess the ability of this method in pharmacokinetic parameter estimation and in the prediction of serum concentrations. METHODS: Data of amikacin as a model drug were collected from 42 neonates with 142 serum samples. A one-compartment open model was used to describe the kinetics of amikacin after the intravenous infusion. Following Sheiner's idea of population pharmacokinetics, we made the programs to evaluate population parameter and individual parameter. The target function minimality was obtained from Monte Carlo algorithm. The validation of the population analysis was performed using classic pharmacokinetic program 3p87 for antithesis. The predictability of the developed method was evaluated by computing precision and accuracy of serum concentration predicted using the parameter estimates. RESULTS: The stability of our self-made program was good. The population parameters obtained from this approach were in conformity with those from 3p87, and the interindividual variability was relatively small. For the learning sample and the validation sample, predicted and observed concentrations were all close with correlation coefficient 0.995 and 0.990, respectively. Most of predicted errors were found < +/- 1 mg/L, and RMSD and BIAS were 0.58 and -0.07 for the validation sample, respectively. The choice of blood sampling time was an important factor for the predictive performance. An early sampling time after the infusion was observed to be the best sampling time. CONCLUSION: The estimation program of population parameter and individual parameter made by us ran stably, and allowed us to use sparse data to estimate population pharmacokinetic parameters. It provided accurate estimates of these parameters and satisfactory ability of serum concentration prediction. Therefore, it can be used for the population pharmacokinetic analysis and individualization of dosage regimen.

Amikacin↗

PubMed2002

Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data.

The following two factors significantly influence estimates of the maximum specific growth rate ( micro (max)) and the lag-phase duration (lambda): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of micro (max) and lambda when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of lambda.

Colony Count, Microbial↗