Interpretation of Simulation Studies for Efficient Estimation of Population Pharmacokinetic Parameters

1993 ◽  
Vol 27 (9) ◽  
pp. 1034-1039 ◽  
Author(s):  
Ene I. Ette ◽  
Andrew W. Kelman ◽  
Catherine A. Howie ◽  
Brian Whiting
Keyword(s):  
Parameter Estimation ◽  
Efficient Estimation ◽  
Experimental Unit ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Good Precision ◽  
Simulation Studies ◽  
Individual Parameter

OBJECTIVE: To develop new approaches for evaluating results obtained from simulation studies used to determine sampling strategies for efficient estimation of population pharmacokinetic parameters. METHODS: One-compartment kinetics with intravenous bolus injection was assumed and the simulated data (one observation made on each experimental unit [human subject or animal]), were analyzed using NONMEM. Several approaches were used to judge the efficiency of parameter estimation. These included: (1) individual and joint confidence intervals (CIs) coverage for parameter estimates that were computed in a manner that would reveal the influence of bias and standard error (SE) on interval estimates; (2) percent prediction error (%PE) approach; (3) the incidence of high pair-wise correlations; and (4) a design number approach. The design number (Φ) is a new statistic that provides a composite measure of accuracy and precision (using SE). RESULTS: The %PE approach is useful only in examining the efficiency of estimation of a parameter considered independently. The joint CI coverage approach permitted assessment of the accuracy and reliability of all model parameter estimates. The Φ approach is an efficient method of achieving an accurate estimate of parameter(s) with good precision. Both the Φ for individual parameter estimation and the overall Φ for the estimation of model parameters led to optimal experimental design. CONCLUSIONS: Application of these approaches to the analyses of the results of the study was found useful in determining the best sampling design (from a series of two sampling times designs within a study) for efficient estimation of population pharmacokinetic parameters.

scholarly journals 1319. Pharmacokinetics of Ceftolozane/Tazobactam in Patients with Burns

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S670-S671
Author(s):  
Ronald G Hall ◽  
Jotam Pasipanodya ◽  
William C Putnam ◽  
John Griswold ◽  
Sharmila Dissanaike ◽  
...  
Keyword(s):  
Human Plasma ◽  
Kidney Injury ◽  
Compartment Model ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Full Thickness ◽  
Severe Burns

Abstract Background Antimicrobial dosing in moderate/severe burns patients is complicated due to the potential unpredictable hyperdynamic pathophysiologic states including 1) hypoproteinemia, 2) acute kidney injury and 3) onset of septicemia. Therefore, distribution assumptions about the population pharmacokinetic (PopPK) profiles of either endogenous or xenobiotic pharmacophores in this patient population can lead to biased parameter estimates. In order to prevent potential bias an agnostic nonparametric adaptive grid approach to describe ceftolozane/tazobactam (C/T) PopPK profiles in patients with partial- and full-thickness burns was employed. Methods A human clinical PK study in burn patients was conducted using the standard approved dose of C/T (2 grams/1 gram). A single intravenous dose was administered over 60 minutes. Whole blood was obtained pre-dose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, 16, and 24 hours following the start of infusion. LC-MS/MS bioanalytical methods were developed, validated and employed to determine C/T concentrations in human plasma. PopPK were modeled using Pmetrics package for R. One-, two- and three-compartment models were examined and compared. The influence of several parameters, including %body surface area burns, creatinine clearance (CrCL), weight, albumin and age were tested. Results The bioanalytical method for determination of C/T in human plasma met all recommended criteria of the LC-MS/MS. Five males and one female (ages 24 to 66 years), contributed 148 plasma PK samples. The female had 35% partial-thickness burns. The males had full-thickness burns ranging from 27 to 66%. The median CrCL was 104 mL/min (range 73-148 mL/min). Two-compartment model with absorption (Ka) from compartment 1 to 2 and elimination from compartment 2 (Ke), with nonlinear interactions between C/T elimination and CrCL best described the data. Figure A show that bias was minimal. Importantly, both drugs exhibited marked variability for both volume and elimination (Table), since volume was bimodally distributed (Figure B). A) Observation-versus-Prediction; B) Estimated Ke, V and Ka population parameter densities Summary of pharmacokinetic parameters Conclusion C/T exhibited high variability surpassing that observed with severe infections, suggesting that dose adjustment and/or may be therapeutic drug monitoring may be needed to balance target attainment from dose-related toxicities. Disclosures Ronald G. Hall, II, PharmD, MSCS, Medical Titan Group (Grant/Research Support)Merck (Research Grant or Support)

Integrating Parameter Estimation Solutions From the Time and Time-Scale Domains

2009 ◽  
Author(s):  
James R. McCusker ◽  
Kourosh Danai
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Time Scale ◽  
Prediction Error ◽  
Nonlinear Least Squares ◽  
Integrated Approach ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Single Model ◽  
Iterative Estimation

A method of parameter estimation was recently introduced that separately estimates each parameter of the dynamic model [1]. In this method, regions coined as parameter signatures, are identified in the time-scale domain wherein the prediction error can be attributed to the error of a single model parameter. Based on these single-parameter associations, individual model parameters can then be estimated for iterative estimation. Relative to nonlinear least squares, the proposed Parameter Signature Isolation Method (PARSIM) has two distinct attributes. One attribute of PARSIM is to leave the estimation of a parameter dormant when a parameter signature cannot be extracted for it. Another attribute is independence from the contour of the prediction error. The first attribute could cause erroneous parameter estimates, when the parameters are not adapted continually. The second attribute, on the other hand, can provide a safeguard against local minima entrapments. These attributes motivate integrating PARSIM with a method, like nonlinear least-squares, that is less prone to dormancy of parameter estimates. The paper demonstrates the merit of the proposed integrated approach in application to a difficult estimation problem.

scholarly journals Population Pharmacokinetics of Atazanavir in Patients with Human Immunodeficiency Virus Infection

2006 ◽  
Vol 50 (11) ◽  
pp. 3801-3808 ◽  
Author(s):  
Sara Colombo ◽  
Thierry Buclin ◽  
Matthias Cavassini ◽  
Laurent A. Décosterd ◽  
Amalio Telenti ◽  
...  
Keyword(s):  
Plasma Levels ◽  
Population Analysis ◽  
Pharmacokinetic Profile ◽  
Relative Bioavailability ◽  
Fixed Dose ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Genetic Traits

ABSTRACT Atazanavir (ATV) is a new azapeptide protease inhibitor recently approved and currently used at a fixed dose of either 300 mg once per day (q.d.) in combination with 100 mg ritonavir (RTV) or 400 mg q.d. without boosting. ATV is highly bound to plasma proteins and extensively metabolized by CYP3A4. Since ATV plasma levels are highly variable and seem to be correlated with both viral response and toxicity, dosage individualization based on plasma concentration monitoring might be indicated. This study aimed to assess the ATV pharmacokinetic profile in a target population of HIV patients, to characterize interpatient and intrapatient variability, and to identify covariates that might influence ATV disposition. A population analysis was performed with NONMEM with 574 plasma samples from a cohort of 214 randomly selected patients receiving ATV. A total of 346 randomly collected ATV plasma levels and 19 full concentration-time profiles at steady state were available. The pharmacokinetic parameter estimates were an oral clearance (CL) of 12.9 liters/h (coefficient of variation [CV], 26%), a volume of distribution of 88.3 liters (CV, 29%), an absorption rate constant of 0.405 h−1 (CV, 122%), and a lag time of 0.88 h. A relative bioavailability value was introduced to account for undercompliance due to infrequent follow-ups (0.81; CV, 45%). Among the covariates tested, only RTV significantly reduced CL by 46%, thereby increasing the ATV elimination half-life from 4.6 h to 8.8 h. The pharmacokinetic parameters of ATV were adequately described by a one-compartment population model. The concomitant use of RTV improved the pharmacokinetic profile. However, the remaining high interpatient variability suggests the possibility of an impact of unmeasured covariates, such as genetic traits or environmental influences. This population pharmacokinetic model, together with therapeutic drug monitoring and Bayesian dosage adaptation, can be helpful in the selection and adaptation of ATV doses.

Cardiopulmonary Bypass Has Minimal Effects on the Pharmacokinetics of Fentanyl in Adults

2003 ◽  
Vol 99 (4) ◽  
pp. 847-854 ◽  
Author(s):  
Robert J. Hudson ◽  
Ian R. Thomson ◽  
Rajive Jassal ◽  
David J. Peterson ◽  
Aaron D. Brown ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Prediction Error ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Compartment Model ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Model Parameters ◽  
Final Model ◽  
Three Compartment Model

Background Although fentanyl has been widely used in cardiac anesthesia, no complete pharmacokinetic model that has assessed the effect of cardiopulmonary bypass (CPB) and that has adequate predictive accuracy has been defined. The aims of this investigation were to determine whether CPB had a clinically significant impact on fentanyl pharmacokinetics and to determine the simplest model that accurately predicts fentanyl concentrations during cardiac surgery using CPB. Methods Population pharmacokinetic modeling was applied to concentration-versus-time data from 61 patients undergoing coronary artery bypass grafting using CPB. Predictive ability of models was assessed by calculating bias (prediction error), accuracy (absolute prediction error), and measured:predicted concentration ratios versus time. The predictive ability of a simple three-compartment model with no covariates was initially compared to models with premedication (lorazepam vs. clonidine), sex, or weight as covariates. This simple model was then compared to 18 CPB-adjusted models that allowed for step changes in pharmacokinetic parameters at the start and/or end of CPB. The predictive ability of the final model was assessed prospectively in a second group of 29 patients. Results None of the covariate (premedication, sex, weight) models nor any of the CPB-adjusted models significantly improved prediction error or absolute prediction error, compared to the simple three-compartment model. Thus, the simple three-compartment model was selected as the final model. Prospective assessment of this model yielded a median prediction error of +3.8%, with a median absolute prediction error of 15.8%. The model parameters were as follows: V1, 14.4 l; V2, 36.4 l; V3, 169 l; Cl1, 0.82 l. min-1; Cl2, 2.31 l x min-1; Cl3, 1.35 l x min-1. Conclusions Compared to other factors that cause pharmacokinetic variability, the effect of CPB on fentanyl kinetics is clinically insignificant. A simple three-compartment model accurately predicts fentanyl concentrations throughout surgery using CPB.

scholarly journals Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming

Processes ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 231 ◽  
Author(s):  
Ernie Che Mid ◽  
Vivek Dua
Keyword(s):  
Wastewater Treatment ◽  
Parameter Estimation ◽  
Fault Detection ◽  
Normal Operation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Algebraic Equations ◽  
Multiparametric Programming ◽  
Nonlinear Algebraic Equations ◽  
Wastewater Treatment Systems

In this work, a methodology for fault detection in wastewater treatment systems, based on parameter estimation, using multiparametric programming is presented. The main idea is to detect faults by estimating model parameters, and monitoring the changes in residuals of model parameters. In the proposed methodology, a nonlinear dynamic model of wastewater treatment was discretized to algebraic equations using Euler’s method. A parameter estimation problem was then formulated and transformed into a square system of parametric nonlinear algebraic equations by writing the optimality conditions. The parametric nonlinear algebraic equations were then solved symbolically to obtain the concentration of substrate in the inflow, , inhibition coefficient, , and specific growth rate, , as an explicit function of state variables (concentration of biomass, ; concentration of organic matter, ; concentration of dissolved oxygen, ; and volume, ). The estimated model parameter values were compared with values from the normal operation. If the residual of model parameters exceeds a certain threshold value, a fault is detected. The application demonstrates the viability of the approach, and highlights its ability to detect faults in wastewater treatment systems by providing quick and accurate parameter estimates using the evaluation of explicit parametric functions.

Perioperative Pharmacodynamics of Acetaminophen Analgesia in Children 

1999 ◽  
Vol 90 (2) ◽  
pp. 411-421 ◽  
Author(s):  
Brian J. Anderson ◽  
Nicholas H. G. Holford ◽  
Gerald A. Woollard ◽  
Suchitra Kanagasundaram ◽  
Murali Mahadevan
Keyword(s):  
Coefficient Of Variation ◽  
Half Life ◽  
Lag Time ◽  
Hill Coefficient ◽  
Pharmacokinetic Parameters ◽  
Effect Compartment ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
First Order ◽  
Emax Model

Background There are no adequate pharmacodynamic data relating concentrations of acetaminophen in serum to analgesia. Methods Children undergoing outpatient tonsillectomy were administered acetaminophen either orally, 0.5-1.0 h preoperatively (n = 20), or per rectum at induction of anesthesia (n = 100). No other analgesic agents were administered. Individual concentrations of acetaminophen in serum and pain scores (0-10) measured over a 4-h postoperative period were analyzed using a nonlinear mixed-effects model (NONMEM). Results Mean (% CV) estimates of population pharmacokinetic parameters with percent coefficient of variation, standardized to a 70-kg person, for a one-compartment model with first-order input, lag time, and first order-elimination were a volume of distribution of 60 (21) 1 and a clearance of 13.5 (46) 1/h. Rectally administered acetaminophen had an absorption half-life of 35 (63) min with a lag time of 40 min. The absorption half-life for the oral preparation was 4.5 (63) min without a detectable lag time. The relative bioavailability of the rectal compared with the oral formulation was 0.54. The equilibration half-time of an effect compartment was 1.6 (131) h. Pharmacodynamic population parameter estimates (percent coefficient of variation) for a fractional sigmoidal Emax model, in which the greatest possible pain relief equates to an Emax of 1, were Emax = 1, EC50 (the concentration producing 50% of Emax) = 3.4 (94) mg/l, and Hill coefficient = 0.54 (42). Conclusions The pharmacodynamics of acetaminophen can be described using a sigmoidal Emax model with a low Hill coefficient. To achieve a mean posttonsillectomy pain score of 3.6 of 10, an effect compartment concentration of 10 mg/l is necessary.

scholarly journals Integrated Population Pharmacokinetic Analysis of Voriconazole in Children, Adolescents, and Adults

2012 ◽  
Vol 56 (6) ◽  
pp. 3032-3042 ◽  
Author(s):  
Lena E. Friberg ◽  
Patanjali Ravva ◽  
Mats O. Karlsson ◽  
Ping Liu
Keyword(s):  
Pediatric Population ◽  
Compartment Model ◽  
Population Pharmacokinetic Analysis ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Loading Dose ◽  
Time Curve ◽  
Individual Parameter ◽  
Dosing Regimens

ABSTRACTTo further optimize the voriconazole dosing in the pediatric population, a population pharmacokinetic analysis was conducted on pooled data from 112 immunocompromised children (2 to <12 years), 26 immunocompromised adolescents (12 to <17 years), and 35 healthy adults. Different maintenance doses (i.e., 3, 4, 6, 7, and 8 mg/kg of body weight intravenously [i.v.] every 12 h [q12h]; 4 mg/kg, 6 mg/kg, and 200 mg orally q12h) were evaluated in these children. The adult dosing regimens (6 mg/kg i.v. q12h on day 1, followed by 4 mg/kg i.v. q12h, and 300 mg orally q12h) were evaluated in the adolescents. A two-compartment model with first-order absorption and mixed linear and nonlinear (Michaelis-Menten) elimination adequately described the voriconazole data. Larger interindividual variability was observed in pediatric subjects than in adults. Deterministic simulations based on individual parameter estimates from the final model revealed the following. The predicted total exposure (area under the concentration-time curve from 0 to 12 h [AUC0-12]) in children following a 9-mg/kg i.v. loading dose was comparable to that in adults following a 6-mg/kg i.v. loading dose. The predicted AUC0-12s in children following 4 and 8 mg/kg i.v. q12h were comparable to those in adults following 3 and 4 mg/kg i.v. q12h, respectively. The predicted AUC0-12in children following 9 mg/kg (maximum, 350 mg) orally q12h was comparable to that in adults following 200 mg orally q12h. To achieve voriconazole exposures comparable to those of adults, dosing in 12- to 14-year-old adolescents depends on their weight: they should be dosed like children if their weight is <50 kg and dosed like adults if their weight is ≥50 kg. Other adolescents should be dosed like adults.

scholarly journals An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Q. Feltgen ◽  
J. Daunizeau
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Reaction Times ◽  
Mechanistic Explanation ◽  
Added Value ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Drift Diffusion ◽  
Neural Noise ◽  
Computational Bottleneck

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

scholarly journals Practical unidentifiability of a simple vector-borne disease model: implications for parameter estimation and intervention assessment

2017 ◽  
Author(s):  
Yu-Han Kao ◽  
Marisa C. Eisenberg
Keyword(s):  
Parameter Estimation ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Disease Incidence ◽  
Environmental Drivers ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Vector Borne Disease ◽  
Vector Borne ◽  
The Basic Reproduction Number

AbstractBackgroundMathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many of these studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset.Methodology/Principle FindingsIdentifiability methods allow us to examine whether model parameters can be estimated uniquely—a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquitoborne disease, using 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, which can lead to incorrect predictions of the effects of interventions. However, in spite of unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field.ConclusionsWhile we only consider the simplest case for the model, without explicit environmental drivers, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions, and particularly highlights the importance of combining experimental, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.Author SummaryMathematical models have seen increasing use in understanding transmission processes, developing interventions, and predicting disease incidence and prevalence. Vector-borne diseases in particular present both a challenge and an opportunity for modeling, due to the complex interactions between host and vector species. A key step in many of these studies is connecting transmission models with data to infer parameters and make useful predictions, which requires careful consideration of identifiability and uncertainty of the model parameters. Whether due to intrinsic limitations of the model structure, or practical limitations of the data collected, is common that many different parameter values may yield the same or very similar fits to the data, making it impossible to successfully estimate the parameters. This issue of parameter unidentifiability can have broad implications for our ability to draw conclusions from mechanistic models—in some cases making it difficult or impossible to generate specific predictions, forecasts, or parameter estimates from a given model and data. Here, we evaluate these questions for a commonly-used model of vectorborne disease, examining how parameter uncertainty and unidentifiability can affect intervention predictions, estimation of the basic reproduction number, and other public health conclusions drawn from the model.

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