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PubMed2003

Validation of a new intraarterial microdialysis shunt probe for the estimation of pharmacokinetic parameters.

The aim of our study was to compare pharmacokinetic parameters of a highly bound protein drug, irbesartan, obtained from microdialysis data (MD) of arterial blood and conventional blood samples (BS). A new vascular shunt microdialysis probe was inserted into the carotid artery and one femoral vein was cannulated for i.v. administration of irbesartan. Microdialysis samples were collected every 15 min. Blood samples were taken every 15 min. Levels of drug were measured by HPLC. Pharmacokinetic parameters were estimated using TOPFIT program. Corrected MD were compared with BS taken at same time to determine protein binding. The irbesartan protein binding did not change during the experiment. The estimated Ke from MD and BS were similar (MD: 1.8+/-0.3 h(-1), n=5; BS: 1.7+/-0.2 h(-1), n=5). After protein binding correction for the MD, the estimated values of volume of distribution (Vd) (MD: 1.2+/-0.4 l, n=5; BS: 1.1+/-0.4 l, n=5), clearance (Cl) (MD: 32.3+/-7.3 ml min(-1), n=5; BS: 30.7+/-8.2 ml min(-1), n=5) and AUC (MD: 7.7+/-3.2 microg x ml(-1) h, n=5; BS: 8.8+/-3.4 microg x ml(-1) h, n=5) were similar between MD and BS. In conclusion, these results show that our new probe inserted in the carotid artery provides accurate MD to estimate pharmacokinetic parameters of a highly bound protein drug like irbesartan. On the other hand, MD were also useful to the in vivo study of drug protein binding and saturation in protein binding.

Animals↗

PubMed1989

A nonlinear least squares program, MULTI(FILT), based on fast inverse Laplace transform for microcomputers.

A nonlinear curve fitting program MULTI(FILT) into which the fast inverse Laplace transform (FILT) is incorporated was developed on a microcomputer. FILT is an algorithm for the numerical inversion of Laplace-transformed equations (image equations) to generate the corresponding real time courses. The pharmacokinetic models can be defined in the form of Laplace-transformed equations as a subroutine in MULTI(FILT). MULTI(FILT) achieves the numerical inversion of the defined image equations according to FILT and the subsequent curve-fitting of the inverse-transformed time courses to the experimental data points to estimate the pharmacokinetic parameters by the nonlinear least-squares method. MULTI(FILT) has a function to impose constraints on the pharmacokinetic parameters. In order to verify the reliability of MULTI(FILT), the pharmacokinetic parameters estimated by MULTI(FILT) were compared with those by MULTI using 100 time courses which were artificially generated according to the Monte Carlo method, based on data for theophylline and bishydroxycoumarin. The estimated pharmacokinetic parameters by MULTI(FILT) agreed with those by MULTI. Thus, it is suggested that FILT, developed in the field of electronic technology, is also useful in the pharmacokinetic field.

Mathematics↗

PubMed1979

Ultrasonographic measurement of fetal growth parameters for estimation of gestational age and fetal weight.

The present study was undertaken to establish accurate, convenient, simple and rapid ultrasonographic measurements of fetal parameters and their correlation with gestational age and fetal weight. Relationship with gestational age was studied in 60 fetuses from 348 measurements of the biparietal diameter (BPD) and was also correlated with eight other parameters. There is no doubt that the BPD is the most reliable parameter for estimation of gestational age. In 34 infants, 306 measurements of nine fetal parameters taken within four days before delivery were correlated with their birth weights. Abdominal area gave the highest correlation, compared with other parameters or their combinations. Since combined measurements of several parameters gave only a slightly higher multiple correlation (R = 0.89) with fetal weight than the measurement of abdominal area alone (r = 0.85), their use is not justified as a routine procedure.

Body Weight↗

PubMed1993

Bayesian forecasting of gentamicin pharmacokinetics in pediatric intensive care unit patients.

The predictive performance of a one compartment Bayesian forecasting program was evaluated in pediatric intensive care unit patients with normal renal function. Gentamicin pharmacokinetic parameters were determined in 44 PICU patients (0.8 month to 14 years old) from all available serum concentrations and doses by nonlinear least squares regression. Population pharmacokinetic parameter estimates were established from 27 of the PICU patients. Mean prediction error (ME) and mean absolute error (MAE) for 2 future sets of peak and trough gentamicin serum concentrations with the use of the population parameter estimates with and without feedback were evaluated in the remaining 17 patients. Mean clearance (+/- SD) and volume of distribution for all 44 patients were 0.123 +/- 0.041 liter/hour/kg and 0.424 +/- 0.116 liter/kg, respectively. Bayesian forecasting of the second set of peak and trough concentrations with feedback from the first set of peak and trough concentrations resulted in smaller bias (peak ME, -0.15 mg/liter; trough ME, 0.13 mg/liter) and better accuracy (peak MAE, 0.91 mg/liter; trough MAE, 0.28 mg/liter) compared with the population parameter estimates alone (peak ME, 0.4 mg/liter; trough ME, 0.28 mg/liter; peak MAE, 1.21 mg/liter; trough MAE, 0.57 mg/liter). This study indicates that gentamicin volume of distribution in PICU patients is larger than non-PICU literature values. The Bayesian program, with specific population parameter estimates for PICU patients, provides accurate initial and subsequent predictions of gentamicin serum concentrations.

Adolescent↗

PubMed2000

Strategies for estimating the parameters needed for different test-day models.

Currently, most analyses of parameters in test-day models involve two types of models: random regression, where various functions describe variability of (co)variances with regard to days in milk, and multiple traits, where observations in adjacent days in milk are treated as one trait. The methodologies used for estimation of parameters included Bayesian via Gibbs sampling, and REML in the form of derivative-free, expectation-maximization, or average-information algorithms. The first method is simpler and uses less memory but may need many rounds to produce posterior samples. In REML, however, the stopping point is well established. Because of computing limitations, the largest estimations of parameters were on fewer than 20,000 animals. The magnitude and pattern of heritabilities varied widely, which could be caused by simplifications in the model, overparameterization, small sample size, and unrepresentative samples. Patterns of heritability differ among random regression and multiple-trait models. Accurate parameters for large multi-trait random regression models may be difficult to obtain at the present time. Parameters that are sufficiently accurate in practice may be obtained outside the complete prediction model by a constructive approach, where parameters averaged over the lactation would be combined with several typical curves for (co)variances for days in milk. Obtained parameters could be used for any model, and could also aid in comparison of models.

Analysis of Variance↗

PubMed2006

On-line estimation of kinetic parameters in anaerobic digestion using observer-based estimators and multiwavelength fluorometry.

Observer-based estimators (OBE) were used for estimation of state variables and kinetic parameters in an anaerobic digestion (AD) process. A simplified first-order model with time-varying kinetic parameters was used to design an OBE for kinetic parameter estimation. This approach was validated on a laboratory-scale anaerobic reactor equipped with a multiwavelength fluorometer for on-line measurements of COD and VFA concentrations in the reactor effluent. The proposed estimators provide continuous adjustment of kinetic parameters and can be used for predictions of state variables between samples acquisition and during sensor failure.

Bacteria, Anaerobic↗

PubMedDate not supplied

Fast adaptive alpha-particle spectrum fitting algorithm based on genetically estimated initial parameters.

This work presents a high performance procedure for the unfolding of alpha particle spectra based on genetically estimated initial parameters. The process starts with the search for a globally optimized set of fitting parameters from a population of randomly generated solutions. The solution found with the genetic algorithm is then transferred to a Levenberg-Marquardt procedure in order to calculate the covariance matrix for the fit. The proposed method provides the set of final parameters and their associated standard deviation. Several fitted spectra demonstrate the effectiveness of the proposed method in searching for and finding initial conditions in a fast and automated process.

Algorithms↗

PubMed1963

Estimation of parameters of rotatory dispersion curves of proteins.

Estimation of the constants a, b, and lambda(0) by means of the standard Moffitt-Yang plot is evaluated. It is found that the method is very insensitive as an estimation procedure and that large errors in b may be expected. Expressions for the maximum-likelihood estimates of the constants are derived.

Likelihood Functions↗

PubMedDate not supplied

[Statistical estimation of parameters in allometric equations].

An algorithm for estimating allometric coefficients widely used in biological studies is presented. The coefficients can be estimated only when the relationship between logarithms of the approximated data meets the linearity criterion. The proposed algorithm was applied for the brain-body weight relationship in mammals and oxygen consumption rate-body weight relationship in amphibians.

Amphibians↗

PubMed2002

Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals.

The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity (S(I)). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic subjects, NLS implemented in MINMOD software often predicts S(I)=0 (the so-called "zero" S(I) problem), whereas general purpose modeling software systems, e.g., SAAM II, provide a very small S(I) but with a very large uncertainty, which produces unrealistic negative values in the confidence interval. To overcome these difficulties, in this article we resort to Bayesian parameter estimation implemented by a Markov chain Monte Carlo (MCMC) method. This approach provides in each individual the S(I) a posteriori probability density function, from which a point estimate and its confidence interval can be determined. Although NLS results are not acceptable in four out of the ten studied subjects, Bayes estimation implemented by MCMC is always able to determine a nonzero point estimate of S(I) together with a credible confidence interval. This Bayesian approach should prove useful in reanalyzing large databases of epidemiological studies.

Bayes Theorem↗

PubMed1997

The effect of adding further traits in index selection.

The effect on genetic and economic response of adding further traits in index selection was studied. This was done first using the true parameters and then using simulated parameter estimates and with "bending" to ensure consistent matrixes. The extra responses obtained in the full aggregate genotype were expressed as a percentage of the response before adding the trait. For simplicity the cases studied were limited to 2 to 4 traits, and to a range of simple parameter sets. The extra response from adding a further trait in index selection was very variable, but was often very large. As a simple rule, adding a trait with a high relative product (ah2) of the standardized economic weight (a) and heritability (h2) gave large extra response. Adding a trait with lower ah2 value gave less extra response. The extra responses were smaller if the correlations between the added trait and other traits with high relative ah2 were favorable and were larger if the correlations were unfavorable. With estimated parameters, the results from adding a further trait in index selection were more variable. For many cases the increase in the response was still large but for some cases the response was reduced. With poor parameter estimates the number of traits in index selection should be limited to those with the larger values of ah2. As the parameter estimates improve, more traits can be added without reducing overall genetic response. Even though some general tendencies have been discerned, there was much variation in the extra responses obtained.

Animals↗

PubMed2003

Stochastic EM for estimating the parameters of a multilevel IRT model.

An item response theory (IRT) model is used as a measurement error model for the dependent variable of a multilevel model. The dependent variable is latent but can be measured indirectly by using tests or questionnaires. The advantage of using latent scores as dependent variables of a multilevel model is that it offers the possibility of modelling response variation and measurement error and separating the influence of item difficulty and ability level. The two-parameter normal ogive model is used for the IRT model. It is shown that the stochastic EM algorithm can be used to estimate the parameters which are close to the maximum likelihood estimates. This algorithm is easily implemented. The estimation procedure will be compared to an implementation of the Gibbs sampler in a Bayesian framework. Examples using real data are given.

Educational Status↗

PubMed1997

The influence of cryopreservation on murine oocyte water permeability and osmotically inactive volume.

Osmotic experiments were performed on unfrozen (N = 18) and cryopreserved (N = 21) ICR murine oocytes in order to determine whether a standard cryopreservation process alters membrane water permeability (hydraulic conductivity, Lp) and/or osmotically inactive volume (Vb). Oocytes, initially in an isotonic (288 mOsm) NaCl solution, were exposed to 900 mOsm NaCl in a microdiffusion chamber. Cell size changes were videotaped and analyzed using a parameter estimation program. Best estimates for a two-parameter model (Lp and Vb) which includes the osmotically inactive volume as a fitting parameter are presented for the first time. The cryopreservation process produced no significant difference between the mean Lp or the mean Vb values for the unfrozen control population (Lp = 0.64 +/- 0.15 micron/min/atm, Vb = 24.7 +/- 2.9%) and the cryopreserved population (Lp = 0.63 +/- 0.12 micron/min/atm, Vb = 28.0 +/- 10.8%). While the cryopreservation process did not cause significant changes in the mean values of Lp, Vb, or the variability of Lp, it did produce more variability of Vb. The cause of the increased variability of Vb produced by cryopreservation is unknown. These results suggest that the osmotic properties of unfrozen control oocytes can be used as a reasonable approximation for frozen-thawed oocytes. They also suggest that multiple parameter models and parameter estimation methods may be useful in developing a more comprehensive understanding of the more subtle alterations in osmotic properties that were detected here. Statistical tests were also used for the first time to confirm the assumption that all of the experimental populations were derived from normal distributions.

Animals↗

PubMed2000

Salivary mucin as related to oral Streptococcus mutans in elderly people.

MG1 (MUC5b and MUC4) and MG2 (MUC7), predominant mucins in human whole saliva, provide lubrication and antimicrobial protection for oral tissues. This study examines potential relationships between Streptococcus mutans titers in the oral cavity and the following: mucin concentrations; unstimulated and stimulated whole saliva flow rates; decayed, missing, and filled tooth surfaces; and age of 24 elderly patients. S. mutans titers were determined using Denticult SM. Mucin concentrations were determined using Stains-all, sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Logistic regression was used to identify potential relationships between the above variables. S. mutans classification served as the dependent variable. The remaining variables were possible predictor variables. The best model for predicting S. mutans category contained log MG2 as a predictor variable for all of its parameter estimates. No other set of parameter estimates were statistically significant. These results suggest that elevated S. mutans titers are significantly associated with diminished concentrations of MG2 in unstimulated whole saliva, as quantified in mucin-dye binding units.

Aged↗

PubMed2001

A solution to the problem of monotone likelihood in Cox regression.

The phenomenon of monotone likelihood is observed in the fitting process of a Cox model if the likelihood converges to a finite value while at least one parameter estimate diverges to +/- infinity. Monotone likelihood primarily occurs in small samples with substantial censoring of survival times and several highly predictive covariates. Previous options to deal with monotone likelihood have been unsatisfactory. The solution we suggest is an adaptation of a procedure by Firth (1993, Biometrika 80, 27-38) originally developed to reduce the bias of maximum likelihood estimates. This procedure produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald-type tests and confidence intervals are available, but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. An empirical study of the suggested procedures confirms satisfactory performance of both estimation and inference. The advantage of the procedure over previous options of analysis is finally exemplified in the analysis of a breast cancer study.

Biometry↗

PubMed2000

Population pharmacokinetics and pharmacodynamic modeling of abacavir (1592U89) from a dose-ranging, double-blind, randomized monotherapy trial with human immunodeficiency virus-infected subjects.

Abacavir (formerly 1592U89) is a carbocyclic nucleoside analog with potent anti-human immunodeficiency virus (anti-HIV) activity when administered alone or in combination with other antiretroviral agents. The population pharmacokinetics and pharmacodynamics of abacavir were investigated in 41 HIV type 1 (HIV-1)-infected, antiretroviral naive adults with baseline CD4(+) cell counts of >/=100/mm(3) and plasma HIV-1 RNA levels of >30,000 copies/ml. Data for analysis were obtained from patients who received randomized, blinded monotherapy with abacavir at 100, 300, or 600 mg twice-daily (BID) for up to 12 weeks. Plasma abacavir concentrations from sparse sampling were analyzed by standard population pharmacokinetic methods, and the effects of dose, combination therapy, gender, weight, and age on parameter estimates were investigated. Bayesian pharmacokinetic parameter estimates were calculated to determine the peak concentration of abacavir in plasma (C(max)) and the area under the concentration-time curve from time zero to infinity (AUC(0-infinity)) for individual subjects. The pharmacokinetics of abacavir were dose proportional over the 100- to 600-mg dose range and were unaffected by any covariates. No significant correlations were observed between the incidence of the five most common adverse events (headache, nausea, diarrhea, vomiting, and malaise or fatigue) and AUC(0-infinity). A significant correlation was observed between C(max) and nausea by categorical analysis (P = 0.019), but this was of borderline significance by logistic regression (odds ratio, 1.45; 95% confidence interval, 0.95 to 2.32). The log(10) time-averaged AUC(0-infinity) minus baseline (AAUCMB) values for HIV-1 RNA and CD4(+) cell count correlated significantly with C(max) and AUC(0-infinity), but with better model fits for AUC(0-infinity). The increase in AAUCMB values for CD4(+) cell count plateaued early for drug exposures that were associated with little change in AAUCMB values for plasma HIV-1 RNA. There was less than a 0.4 log(10) difference over 12 weeks in the HIV-1 RNA levels with the doubling of the abacavir AUC(0-infinity) from 300 to 600 mg BID dosing. In conclusion, pharmacodynamic modeling supports the selection of abacavir 300 mg twice-daily dosing.

Adolescent↗

PubMed2004

Using conservation of pattern to estimate spatial parameters from a single snapshot.

Rapid reaction in the face of an epidemic is a key element in effective and efficient control; this is especially important when the disease has severe public health or economic consequences. Determining an appropriate level of response requires rapid estimation of the rate of spread of infection from limited disease distribution data. Generally, the techniques used to estimate such spatial parameters require detailed spatial data at multiple time points; such data are often time-consuming and expensive to collect. Here we present an alternative approach that is computationally efficient and only requires spatial data from a single time point, hence saving valuable time at the start of the epidemic. By assuming that fundamental spatial statistics are near equilibrium, parameters can be estimated by minimizing the expected rate of change of these statistics, hence conserving the general spatial pattern. Although applicable to both ecological and epidemiological data, here we focus on disease data from computer simulations and real epidemics to show that this method produces reliable results that could be used in practical situations.

Animals↗

PubMed2004

Evolutionary optimization with data collocation for reverse engineering of biological networks.

MOTIVATION: Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. RESULTS: We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

Algorithms↗