Bayesian estimation of six different sets of carbamazepine pharmacokinetic parameters in Egyptian adult epileptic patients.

The individualization of carbamazepine (CBZ) dosage regimen based on estimation of pharmacokinetic (PK) parameters and measurement of serum drug concentration in epileptic patients can help to control epilepsy. In a retrospective study, the predictive performance of six different sets of CBZ PK parameters selected according to the literature was evaluated in 60 adult epileptic patients. Patients were administered controlled release CBZ (dose range: 200-1200 mg day(-1)) as monotherapy and one steady state serum concentration of the drug was available for each patient. The Bayesian Program of Abbott (PKS system; Abbott Laboratories, Wiesbaden, Germany) was used in the prediction process. Predictive measures included estimation of mean prediction error (mpe) for bias, mean squared prediction error (mspe) and root mean squared prediction error (rmspe) for precision. The analysis showed that three of the investigated six sets achieved the best predictive performance in Egyptian patients and consequently, the PK parameters of any of these three sets can be used by the Bayesian approach as prior information for CBZ dose optimization among the Egyptian adult population.     

Application of near infrared spectroscopy for rapid analysis of intermediates of Tanreqing injection

A method for rapid quantitative analysis of four kinds of Tanreqing injection intermediates was developed based on Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS) algorithm. The NIR spectra of 120 samples were collected in transflective mode. The concentrations of chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid (UDCA), and chenodeoxycholic acid (CDCA) were determined with the HPLC–DAD/ELSD as reference method. In the PLS calibration, the NIR spectra were pretreated with different methods and the number of PLS factors used in the model calibration was optimized by leave-one-out cross-validation. The performance of the final PLS models was evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and correlation coefficients (R). The R values in the prediction sets were all higher than 0.93, and the SEPs for the 6 compounds are 1.18, 6.02, 2.71, 155, 126, 30.0 mg/l, respectively. The established models were used for the liquid preparation process analysis of Tanreqing injection in three batches, and a model updating method was proposed for the long-term usage of the established models. This work demonstrated that NIR spectroscopy is more rapid and convenient than the conventional methods to analyze the intermediates of Tanreqing injection, and the presented method is helpful to the implementation of process analytical technology (PAT) in pharmaceutical industry of Chinese Medicines Injections.     

Multiple-dose non-linear regression analysis program. Aminoglycoside dose prediction

The ability of a new multiple-dose non-linear regression analysis program to predict steady-state aminoglycoside peak and trough serum concentrations was evaluated. 30 patients receiving either amikacin (7), gentamicin (10) or tobramycin (13) were studied. A standard method of prediction which requires the collection of 3 or 4 serum samples during a dosing interval and a predictive method which relies upon population-based estimates of pharmacokinetic parameters were compared with the new approach which requires the collection of 2 serum samples. There were no significant differences between the methods which utilised serum concentration data with regard to predictive precision (mean prediction error of about 10%). These methods were more precise than the population-based method (p less than 0.01, mean prediction error 29.1%). None of the methods produced biased estimates. These results indicate that when the regression program is employed, valid estimates of pharmacokinetic parameters and prediction of steady-state serum concentrations can be obtained with fewer serum samples than have been recommended.     

Moisture assay of an antifungal by near-infrared diffuse reflectance spectroscopy

Near-infrared (NIR) diffuse reflectance spectroscopy was employed in the method development and validation of a moisture assay for the novel antifungal caspofungin acetate. Spectra were obtained over the entire spectral region available (950-1650 nm) using an InGaAs photodiode array detector equipped with a diffuse reflectance probe. No sample pre-treatment was required and the analysis time was less than 1 min. Primary reference data were obtained using a Karl Fischer (KF) titration (coulometric, volumetric or both). The investigated range of water content was 2.6-9.9% (w/w) with a standard error of prediction (SEP) of 0.2%. The predictive capabilities of the partial least-squares (PLS) regression calibration model used in the moisture assay were verified using independent test sets. The NIR predicted values of the developed method were equivalent to the reference method sets and the prediction error was equivalent to the reference method error. These results reveal that the predictive model constructed by means of a PLS regression is valid, rugged and could be used to determine moisture levels on-line in caspofungin acetate drug substance.     

Measuring the predictive performance of computer-controlled infusion pumps.

Current measures of the performance of computer-controlled infusion pumps (CCIPs) are poorly defined, of little use to the clinician using the CCIP, and pharmacostatistically incorrect. We propose four measures be used to quantitate the performance of CCIPs: median absolute performance error (MDAPE), median performance error (MDPE), divergence, and wobble. These measures offer several significant advantages over previous measures. First, their definitions are based on the performance error as a fraction of the predicted (rather than measured) drug concentration, making the measures much more useful to the clinician. Second, the measures are defined in a way that addresses the pharmacostatistical issue of appropriate estimation of population parameters. Finally, the measure of inaccuracy, MDAPE, is defined in a way that is consistent with iteratively reweighted least squares nonlinear regression, a commonly used method of estimating pharmacokinetic parameters. These measures make it possible to quantitate the overall performance of a CCIP or to compare the predictive performance of CCIPs which differ in either general approach (e.g., compartmental model driven vs. plasma efflux approach), pump mechanics, software algorithms, or pharmacokinetic parameter sets.     

Measuring the predictive performance of computer-controlled infusion pumps

Current measures of the performance of computer-controlled infusion pumps (CCIPs) are poorly defined, of little use to the clinician using the CCIP, and pharmacostatistically incorrect. We propose four measures be used to quantitate the performance of CCIPs: median absolute performance error (MDAPE), median performance error (MDPE), divergence, and wobble. These measures offer several significant advantages over previous measures. First, their definitions are based on the performance error as a fraction of the predicted (rather than measured) drug concentration, making the measures much more useful to the clinician. Second, the measures are defined in a way that addresses the pharmacostatistical issue of appropriate estimation of population parameters. Finally, the measure of inaccuracy, MDAPE, is defined in a way that is consistent with iteratively reweighted least squares nonlinear regression, a commonly used method of estimating pharmacokinetic parameters. These measures make it possible to quantitate the overall performance of a CCIP or to compare the predictive performance of CCIPs which differ in either general approach (e.g., compartmental model driven vs. plasma efflux approach), pump mechanics, software algorithms, or pharmacokinetic parameter sets.     

Prediction of Human Glomerular Filtration Rate from Preterm Neonates to Adults: Evaluation of Predictive Performance of Several Empirical Models

The objective of this study was to evaluate the predictive performance of several allometric empirical models (body weight dependent, age dependent, fixed exponent 0.75, a data-dependent single exponent, and maturation models) to predict glomerular filtration rate (GFR) in preterm and term neonates, infants, children, and adults without any renal disease. In this analysis, the models were developed from GFR data obtained from inulin clearance (preterm neonates to adults; n?=?93) and the predictive performance of these models were evaluated in 335 subjects (preterm neonates to adults). The primary end point was the prediction of GFR from the empirical allometric models and the comparison of the predicted GFR with measured GFR. A prediction error within ±30% was considered acceptable. Overall, the predictive performance of the four models (BDE, ADE, and two maturation models) for the prediction of mean GFR was good across all age groups but the prediction of GFR in individual healthy subjects especially in neonates and infants was erratic and may be clinically unacceptable.     

Rapid quantification of phenolic acids in Radix Salvia Miltrorrhiza extract solutions by FT-NIR spectroscopy in transflective mode

A rapid method for simultaneous determination of main phenolic acids in Radix Salvia Miltrorrhiza extract solutions was developed using Fourier transform near infrared spectroscopy in transflective mode and multivariate calibration and HPLC-UV as the reference method. Partial least squares (PLS) algorithm was conducted on the calibration of regression models. The multiplicative scatter correction, Norris derivative and second derivative were adopted for the spectral pre-processing, and the number of PLS factors were optimized by leave-one-out cross-validation. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R). The R values achieved in the prediction set were above 0.93. The developed models were used for analysis of unknown samples and routine monitoring with satisfactory results. This work demonstrated that NIR spectroscopy combined with PLS algorithm could be used for the rapid determination of the main phenolic acids of Salvia Miltrorrhiza extract solutions.     

The relative predictive performance of two theophylline pharmacokinetic dosing programs

The predictive performance of 2 theophylline pharmacokinetic dosing programs (Abbott and Simkin) was evaluated using a group of 44 inpatients who had 2 serum concentrations (TSC) measured during hospitalization. Bias was assessed with the median prediction error (PE) and precision was assessed with the median absolute PE. The Abbott program was significantly less biased than the Simkin program in predicting the first TSC (PEs 0.1 and -1.3 micrograms/ml, respectively; p less than 0.05). No significant difference in bias was observed in predicting the second TSC, or in precision in predicting either the first or second TSC. Both programs exhibited small improvements in prediction precision when the first TSC was used to predict the second. Correlations of predicted versus measured TSC also improved with the second prediction. These programs may be useful in dosing theophylline; however, TSC monitoring and the application of sound clinical judgment are warranted.     

Bayesian pharmacokinetic/pharmacodynamic forecasting of prothrombin response to warfarin therapy: preliminary evaluation

The ability of a pharmacokinetic/pharmacodynamic Bayesian forecasting computer program to predict prothrombin response to warfarin therapy was investigated. The performance of the program was evaluated retrospectively in an inpatient study population of 45 subjects. Predictions of prothrombin response at discharge, based on zero to five serially measured prothrombin ratios, were compared. Precision of prediction was measured by root mean squared error (rmse), bias was measured by average prediction error, and significance (p less than 0.05) was determined by 95% confidence intervals and correlation coefficients. Eleven (3.8%) predictions exceeded established limitations of the pharmacokinetic/pharmacodynamic model and were excluded from data analysis. Correlations between measured and predicted prothrombin ratios for all methods were significant. The five prothrombin ratio feedbacks provided the most accurate predictions (rmse 0.219). These predictions were significantly better than the population parameter (rmse 0.418), one (rmse 0.401), and two (rmse 0.459) prothrombin ratio feedback predictions. The predictions based on population parameters and one prothrombin ratio feedback were significantly biased. When provided with sufficient feedback, the bias was not apparent and the predictive performance improved with each additional prothrombin ratio. The predictive performance of the four and five prothrombin ratio feedbacks is sufficient to provide clinically useful dosage guidelines early in the course of warfarin therapy. The population parameter estimates require further delineation in order to improve the performance of limited prothrombin ratio feedback predictions.     

An evaluation of Bayesian microcomputer predictions of theophylline concentrations in newborn infants

Determination of appropriate theophylline maintenance doses in preterm infants is confounded by interpatient variability. This study evaluated the performance of an IBM PC computer program applying Bayesian regression before and during steady state in 37 preterm infants. Prior population estimates of clearance and distribution volume in preterm infants and Bayesian estimates of clearance and distribution volume based on one to three theophylline plasma concentrations were used to predict subsequent concentrations (drawn 1-17 days later). We assessed the accuracy and precision of the predictive performance of the Bayesian program with the mean prediction error and the mean absolute prediction error. The absolute prediction error (mean absolute error +/- SEM) significantly decreased with increasing feedback concentrations from 3.54 +/- 0.45 micrograms/ml (population estimates) to 2.74 +/- 0.42 (one feedback) and 2.02 +/- 0.35 micrograms/ml (two feedback concentrations). Mean prediction errors (+/- SEM) based on one to three feedbacks (-1.5 +/- 0.40 micrograms/ml) were significant improvements over population predictions (-2.63 +/- 0.72 micrograms/ml, p less than 0.05), although a small but significant average overprediction remained. Absolute prediction error was correlated with postconceptional and postnatal age when zero or one but not two feedback concentrations were available. Computer program predictions based on one measured feedback concentration were more accurate and precise than population-based predictions. Refinement of population parameters or two feedback concentrations further improved performance.     

Predictive capacity of carbamazepine pharmacokinetic parameters in a Portuguese outpatient population

The individualization of anticonvulsant therapy regimens can contribute to the implementation of appropriate carbamazepine (CBZ) maintenance doses in epileptic patients. An accurate method for the prediction of concentrations based on a determination of parameters and serum concentrations could be of clinical relevance in the management of epilepsy. In this study, we retrospectively evaluated the predictive performance in an adult outpatient population of six different methods, representing six sets of CBZ pharmacokinetic parameters selected according to the literature using a Bayesian computer program (PKS System; Abbott Laboratories, Abbott Park, IL, USA). The study involved 50 patients with two or more available concentrations selected under several inclusion criteria. The patients were taking CBZ (between 200 and 1600 mg/d) in monotherapy or polytherapy regimens and had no hepatic or renal disease. Steady state concentrations were predicted according to the use of prior information and using one and two feedback patient concentrations. Accuracy and precision were assessed by mean prediction error (ME), mean squared prediction error (MSE) and root mean squared prediction error (RMSE). The analysis showed CL = 0.067 L/hour/kg and Vd = 1.19 L/kg as the most accurate and precise set of pharmacokinetic parameters, presenting the highest percentage of clinically acceptable estimates (error < 2 microg/mL). Additionally, predictions based on one measured feedback concentration were found to be more accurate and precise than prior population-based predictions; the use of two previous patient concentrations further improved predictive capacity but failed to show a significant difference when compared with predictions based on one measured feedback concentration. In conclusion, the adoption of the previously mentioned set of parameters as population estimates and the use of at least one feedback concentration through the Bayesian approach seems to be essential for a better CBZ use in clinical practice. Finally, despite the obtained results, we believe that the Portuguese pharmacokinetic parameter determination of antiepileptics should be carried out to improve the rationale and cost-effectiveness of anticonvulsant therapy.     

Bayesian forecasting of serum gentamicin concentrations in intensive care patients

This study retrospectively evaluated the predictive performance of a 1-compartment Bayesian forecasting program in adult intensive care unit (ICU) patients with stable renal function. A comparison was made of the reliability of 3 sets of population-based parameter estimates and 2 serum concentration monitoring strategies. A larger mean error for prediction of peak gentamicin concentrations was seen with literature-derived parameters than when ICU population-based parameter estimates were used. Bias and precision improved when non-steady-state peak and trough concentrations were used to predict those at steady-state; the addition of steady-state values did not provide additional information for predictions once non-steady-state feedback concentrations were incorporated. The addition of 4 serial gentamicin concentrations obtained at both non-steady-state and steady-state did not noticeably improve the predictive performance. The results demonstrate that initial ICU pharmacokinetic parameter estimates for a 1-compartment Bayesian model provide accurate prediction of steady-state gentamicin concentrations. Prediction bias and precision showed the greatest improvement when non-steady-state gentamicin concentrations were used to determine individualised pharmacokinetic parameters.     

Accuracy of Bayesian, Sawchuk-Zaske, and nomogram dosing methods for vancomycin

The predictive performance of a Bayesian method for vancomycin dosing was compared with that of two nomogram-based methods and the Sawchuk-Zaske method. Prospectively collected serum concentration data were evaluated retrospectively in patients who had at least two steady-state peak and trough serum vancomycin concentrations obtained during two different dosage regimens. The methods evaluated were a Bayesian program that uses a one-compartment weighted-sum-of-squares expression; the nomogram methods of Moellering and Matzke, which derive vancomycin clearance from urinary creatinine clearance; and the Sawchuk-Zaske method, which uses equations for one-compartment, first-order elimination. The ability of each method to predict the second set of serum concentrations when given the first set of concentrations was evaluated using mean prediction error (ME), mean absolute error (MAE), and root mean squared error (RMSE). To compare the predictions made by each of the four methods, the differences in mean error and the differences in the natural logarithm of mean absolute error and their 95% confidence intervals were compared. No significant difference in ME (bias) or MAE (precision) was found between the Moellering and Matzke methods. The Sawchuk-Zaske method was significantly more precise than the Matzke method in predicting peak serum concentrations and more precise than the Moellering or Matzke method in predicting trough concentrations. The Bayesian program was significantly more precise and less biased than the Moellering and Matzke methods and less biased than the Sawchuk-Zaske method in predicting both peak and trough concentrations.(ABSTRACT TRUNCATED AT 250 WORDS)     

Quantification of lean bodyweight

BACKGROUND: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. OBJECTIVE: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. PATIENTS AND METHODS: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18-82 years), bodyweight (40.7-216.5 kg) and BMI values (17.1-69.9 kg/m2). Patients in population B had BMI values of 18.7-38.4 kg/m2. A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. RESULTS: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r2 = 0.78, mean error [ME] = 2.30 x 10(-3), root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r2 = 0.93, ME = -0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r2 = 0.85, ME = -0.04, RMSE = 4.39 [approximately 7% of mean]). CONCLUSIONS: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.     

Predictive performance of population pharmacokinetic parameters of tianeptine as applied to plasma concentrations from a post-marketing study

Summary The predictive ability of population pharmacokinetic parameters of tianeptine, obtained from a mixed effect analysis of pre-marketing pharmacokinetic studies, was evaluated using tianeptine plasma concentrations obtained during a large multi-center post-marketing surveillance study.The mean prediction error was 7.8 ng·ml^–1 and the root mean square prediction error was 52.1 ng/ml when initial estimates of population pharmacokinetic parameters were used to predict drug concentrations in one half of the post-marketing data. When the population parameters were revised to reflect the data collected in the first half of the post-marketing study, the mean prediction error was reduced to –3.2 ng·ml^–1 and the root mean square prediction error was reduced to 29.5 ng·ml^–1.These results suggest that population pharmacokinetic parameters obtained from pre-marketing data may not accurately predict drug concentrations in patients receiving the drug in the post-marketing setting. Once the population parameters are updated to reflect data from the post-marketing period, the predictive ability of the database increases, but substantial variability in the prediction error remains.     

近红外漫反射光谱法测定诺氟沙星胶囊的含量

目的：利用近红外漫反射技术和化学计量学的方法对诺氟沙星胶囊进行定量分析。方法：通过偏最小二乘法建立数学模型，对预测集进行预测，且对实际样品的含量进行测定。结果：30个样品经内部交叉验证建立预测模型，内部交叉验决定系数R^2=99.70，内部交叉验证均方差RMSECV＝0.203。用10个样品进行外部验证，外部验证预测均方差RMSEP=0.602，预测值与真值的相关系数达0.9948。预测值的平均回收率为99.7%，方法精密度RSD＝0.45%（n=7)。结论：建立预测模型对诺氟沙星胶囊进行含量分析是可行的，该法的样品不需预处理，分析快速简便，结果准确。     

近红外漫反射光谱法测定头孢氨苄胶囊的含量

目的:应用近红外漫反射技术和化学计量学的方法对头孢氨苄胶囊进行定量分析.方法:通过偏最小二乘法建立数学模型,对预测集进行预测,并对实际样品的含量进行分析测定.结果:25个样品经内部交叉验证建立预测模型,内部交叉验证的均方差为0.40,确定系数R2=1.00.用15个样品进行外部验证,外部验证的均方差为0.59,确定系数R2=0.999.预测值与真值的确定系数为0.9995.预测值的平均回收率为100.4%(RSD为0.50%,n=15).方法精密度RSD为0.85%(n=7).结论:建立的预测模型对头孢氨苄胶囊进行含量测定是可行和有效的,样品不需预处理,分析快速、简便、环保,结果准确.     

Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates

The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat.     

复方氨酚烷胺片中对乙酰氨基酚的近红外漫反射光谱法快速检测

采用偏最小二乘法建立复方氨酚烷胺片中对乙酰氨基酚含量与其近红外漫反射光谱间的相关模型,并采用所建立的模型对预测集样品中对乙酰氨基酚含量进行预测。结果表明,所建的相关模型回归系数(R_c)为0.98520,对模型进行交互验证所得的预测值与测定值间的相关系数(R_v)为0.98089,交互验证均方根误差(RMSECV)为0.00557,对预测集样品中的对乙酰氨基酚含量进行预测,预测均方根误差(RMSEP)为0.00416。