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Search: L773:1567 567X OR L773:1573 8744 > (2010-2014)

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1.
  • Alskär, Oskar, et al. (author)
  • A pharmacokinetic model for the glycation of albumin
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 273-282
  • Journal article (peer-reviewed)abstract
    • Glycated haemoglobin (HbA1c) concentrations can be falsely lowered in circumstances when red blood cell (RBC) survival is reduced, e.g. in patients with chronic kidney disease (CKD). Glycated albumin (GA) has been suggested as an alternative marker of glycaemic control in these patients since it is independent of the RBC life span. The primary aim of this work was to develop a pharmacokinetic model that describes the time course of GA. The secondary aim was to assess the performance of GA as marker for glycaemic control in comparison to HbA1c based on simulations. For the second aim, three different scenarios were considered in the simulations: 1) assessment of the effect of large intra-day fluctuations in mean blood glucose on GA concentrations, 2) initiation of antidiabetic treatment on the GA profile, and 3) a hypothetical phase II study for a new antidiabetic compound. The GA model, as well as a previously developed HbA1c model described literature data well. GA concentrations appear to be stable even in the presence of high intra-day fluctuations in mean blood glucose concentrations. Simulation of a decrease in mean blood glucose concentrations resulted in a faster change in GA compared to HbA1c. GA also provided a time to 90 % power of the effect of a hypothetical antidiabetic drug that was 16 days shorter than when using HbA1c. These results indicate that GA could be used as alternative marker to assess blood glucose control in diabetic patients with CKD and also to follow an individual patient over time.
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2.
  • Baverel, Paul G., et al. (author)
  • Two bootstrapping routines for obtaining imprecision estimates for nonparametric parameter distributions in nonlinear mixed effects models
  • 2011
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 38:1, s. 63-82
  • Journal article (peer-reviewed)abstract
    • When parameter estimates are used in predictions or decisions, it is important to consider the magnitude of imprecision associated with the estimation. Such imprecision estimates are, however, presently lacking for nonparametric algorithms intended for nonlinear mixed effects models. The objective of this study was to develop resampling-based methods for estimating imprecision in nonparametric distribution (NPD) estimates obtained in NONMEM. A one-compartment PK model was used to simulate datasets for which the random effect of clearance conformed to a (i) normal (ii) bimodal and (iii) heavy-tailed underlying distributional shapes. Re-estimation was conducted assuming normality under FOCE, and NPDs were estimated sequential to this step. Imprecision in the NPD was then estimated by means of two different resampling procedures. The first (full) method relies on bootstrap sampling from the raw data and a re-estimation of both the preceding parametric (FOCE) and the nonparametric step. The second (simplified) method relies on bootstrap sampling of individual nonparametric probability distributions. Nonparametric 95% confidence intervals (95% CIs) were obtained and mean errors (MEs) of the 95% CI width were computed. Standard errors (SEs) of nonparametric population estimates were obtained using the simplified method and evaluated through 100 stochastic simulations followed by estimations (SSEs). Both methods were successfully implemented to provide imprecision estimates for NPDs. The imprecision estimates adequately reflected the reference imprecision in all distributional cases and regardless of the numbers of individuals in the original data. Relative MEs of the 95% CI width of CL marginal density when original data contained 200 individuals were equal to: (i) -22 and -12%, (ii) -22 and -9%, (iii) -13 and -5% for the full and simplified (n = 100), respectively. SEs derived from the simplified method were consistent with the ones obtained from 100 SSEs. In conclusion, two novel bootstrapping methods intended for nonparametric estimation methods are proposed. In addition of providing information about the precision of nonparametric parameter estimates, they can serve as diagnostic tools for the detection of misspecified parameter distributions.
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3.
  • Bizzotto, Roberto, et al. (author)
  • Multinomial logistic estimation of Markov-chain models for modeling sleep architecture in primary insomnia patients
  • 2010
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:2, s. 137-155
  • Journal article (peer-reviewed)abstract
    • Hypnotic drug development calls for a better understanding of sleep physiology in order to improve and differentiate novel medicines for the treatment of sleep disorders. On this basis, a proper evaluation of polysomnographic data collected in clinical trials conducted to explore clinical efficacy of novel hypnotic compounds should include the assessment of sleep architecture and its drug-induced changes. This work presents a non-linear mixed-effect Markov-chain model based on multinomial logistic functions which characterize the time course of transition probabilities between sleep stages in insomniac patients treated with placebo. Polysomnography measurements were obtained from patients during one night treatment. A population approach was used to describe the time course of sleep stages (awake stage, stage 1, stage 2, slow-wave sleep and REM sleep) using a Markov-chain model. The relationship between time and individual transition probabilities between sleep stages was modelled through piecewise linear multinomial logistic functions. The identification of the model produced a good adherence of mean post-hoc estimates to the observed transition frequencies. Parameters were generally well estimated in terms of CV, shrinkage and distribution of empirical Bayes estimates around the typical values. The posterior predictive check analysis showed good consistency between model-predicted and observed sleep parameters. In conclusion, the Markov-chain model based on multinomial logistic functions provided an accurate description of the time course of sleep stages together with an assessment of the probabilities of transition between different stages.
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4.
  • Björnsson, Marcus A., et al. (author)
  • A two-compartment effect site model describes the bispectral index after different rates of propofol infusion
  • 2010
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:3, s. 243-255
  • Journal article (peer-reviewed)abstract
    • Different estimates of the rate constant for the effect site distribution (k(e0)) of propofol, depending on the rate and duration of administration, have been reported. This analysis aimed at finding a more general pharmacodynamic model that could be used when the rate of administration is changed during the treatment. In a cross-over study, 21 healthy volunteers were randomised to receive a 1 min infusion of 2 mg/kg of propofol at one occasion, and a 1 min infusion of 2 mg/kg of propofol immediately followed by a 29 min infusion of 12 mg kg(-1) h(-1) of propofol at another occasion. Arterial plasma concentrations of propofol were collected up to 4 h after dosing, and BIS was collected before start of infusion and until the subjects were fully awake. The population pharmacokinetic-pharmacodynamic analysis was performed using NONMEM VI. A four-compartment PK model with time-dependent elimination and distribution described the arterial propofol concentrations, and was used as input to the pharmacodynamic model. A standard effect compartment model could not accurately describe the delay in the effects of propofol for both regimens, whereas a two-compartment effect site model significantly improved the predictions. The two-compartment effect site model included a central and a peripheral effect site compartment, possibly representing a distribution within the brain, where the decrease in BIS was linked to the central effect site compartment concentrations through a sigmoidal E-max model.
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6.
  • Choy, Steve, et al. (author)
  • Identification of the primary mechanism of action of an insulin secretagogue from meal test data in healthy volunteers based on an integrated glucose-insulin model
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 40:1, s. 1-10
  • Journal article (peer-reviewed)abstract
    • The integrated glucose–insulin (IGI) model is a previously developed semi-mechanistic model that incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. It has been shown to adequately describe insulin and glucose profiles in both type 2 diabetics and healthy volunteers following various glucose tolerance tests. The aim of this study was to investigate the ability of the IGI model to correctly identify the primary mechanism of action of glibenclamide (Gb), based on meal tolerance test (MTT) data in healthy volunteers. IGI models with different mechanism of drug action were applied to data from eight healthy volunteers participating in a randomized crossover study with five single-dose tests (placebo and four drug arms). The study participants were given 3.5 mg of Gb, intravenously or orally, or 3.5 mg of the two main metabolites M1 and M2 intravenously, 0.5 h prior to a standardized breakfast with energy content of 1800 kJ. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed using NONMEM®. Drug effects that increased insulin secretion resulted in the best model fit, thus identifying the primary mechanism of action of Gb and metabolites as insulin secretagogues. The model also quantified the combined effect of Gb, M1 and M2 to have a fourfold maximal increase on endogenous insulin secretion, with an EC50 of 169.1 ng mL−1 for Gb, 151.4 ng mL−1 for M1 and 267.1 ng mL−1 for M2. The semi-mechanistic IGI model was successfully applied to MTT data and identified the primary mechanism of action for Gb, quantifying its effects on glucose and insulin time profiles.
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8.
  • Delattre, Maud, et al. (author)
  • Analysis of exposure-response of CI-945 in patients with epilepsy : application of novel mixed hidden Markov modeling methodology
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 263-271
  • Journal article (peer-reviewed)abstract
    • We propose to describe exposure-response relationship of an antiepileptic agent, using mixed hidden Markov modeling methodology, to reveal additional insights in the mode of the drug action which the novel approach offers. Daily seizure frequency data from six clinical studies including patients who received gabapentin were available for the analysis. In the model, seizure frequencies are governed by underlying unobserved disease activity states. Individual neighbouring states are dependent, like in reality and they exhibit their own dynamics with patients transitioning between low and high disease states, according to a set of transition probabilities. Our methodology enables estimation of unobserved disease dynamics and daily seizure frequencies in all disease states. Additional modes of drug action are achievable: gabapentin may influence both daily seizure frequencies and disease state dynamics. Gabapentin significantly reduced seizure frequencies in both disease activity states; however it did not significatively affect disease dynamics. Mixed hidden Markov modeling is able to mimic dynamics of seizure frequencies very well. It offers novel insights into understanding disease dynamics in epilepsy and gabapentin mode of action.
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9.
  • Ernest II, Charles, et al. (author)
  • Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:6, s. 639-654
  • Journal article (peer-reviewed)abstract
    • D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIMtotal). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIMtotal was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIMtotal. Through the use of an approximate analytic solution and weighting schemes, the FIMtotal for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
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10.
  • Ernest II, Charles Steven, et al. (author)
  • Simultaneous optimal experimental design for in vitro binding parameter estimation
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science+Business Media B.V.. - 1567-567X .- 1573-8744. ; 40:5, s. 573-585
  • Journal article (peer-reviewed)abstract
    • Simultaneous optimization of in vitro ligand binding studies using an optimal design software package that can incorporate multiple design variables through non-linear mixed effect models and provide a general optimized design regardless of the binding site capacity and relative binding rates for a two binding system. Experimental design optimization was employed with D- and ED-optimality using PopED 2.8 including commonly encountered factors during experimentation (residual error, between experiment variability and non-specific binding) for in vitro ligand binding experiments: association, dissociation, equilibrium and non-specific binding experiments. Moreover, a method for optimizing several design parameters (ligand concentrations, measurement times and total number of samples) was examined. With changes in relative binding site density and relative binding rates, different measurement times and ligand concentrations were needed to provide precise estimation of binding parameters. However, using optimized design variables, significant reductions in number of samples provided as good or better precision of the parameter estimates compared to the original extensive sampling design. Employing ED-optimality led to a general experimental design regardless of the relative binding site density and relative binding rates. Precision of the parameter estimates were as good as the extensive sampling design for most parameters and better for the poorly estimated parameters. Optimized designs for in vitro ligand binding studies provided robust parameter estimation while allowing more efficient and cost effective experimentation by reducing the measurement times and separate ligand concentrations required and in some cases, the total number of samples.
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11.
  • Gabrielsson, Johan (author)
  • A modeling approach for compounds affecting body composition
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40, s. 651-667
  • Journal article (peer-reviewed)abstract
    • Body composition and body mass are pivotal clinical endpoints in studies of welfare diseases. We present a combined effort of established and new mathematical models based on rigorous monitoring of energy intake (EI) and body mass in mice. Specifically, we parameterize a mechanistic turnover model based on the law of energy conservation coupled to a drug mechanism model. Key model variables are fat-free mass (FFM) and fat mass (FM), governed by EI and energy expenditure (EE). An empirical Forbes curve relating FFM to FM was derived experimentally for female C57BL/6 mice. The Forbes curve differs from a previously reported curve for male C57BL/6 mice, and we thoroughly analyse how the choice of Forbes curve impacts model predictions. The drug mechanism function acts on EI or EE, or both. Drug mechanism parameters (two to three parameters) and system parameters (up to six free parameters) could be estimated with good precision (coefficients of variation typically < 20 % and not greater than 40 % in our analyses). Model simulations were done to predict the EE and FM change at different drug provocations in mice. In addition, we simulated body mass and FM changes at different drug provocations using a similar model for man. Surprisingly, model simulations indicate that an increase in EI (e.g. 10 %) was more efficient than an equal lowering of EI. Also, the relative change in body mass and FM is greater in man than in mouse at the same relative change in either EI or EE. We acknowledge that this assumes the same drug mechanism impact across the two species. A set of recommendations regarding the Forbes curve, vehicle control groups, dual action on EI and loss, and translational aspects are discussed. This quantitative approach significantly improves data interpretation, disease system understanding, safety assessment and translation across species.
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12.
  • Gabrielsson, Johan (author)
  • Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40, s. 497-512
  • Journal article (peer-reviewed)abstract
    • Previously, we developed a feedback model to describe the tolerance and oscillatory rebound of non-esterified fatty acid (NEFA) plasma concentrations in male Sprague Dawley rats after intravenous infusions of nicotinic acid (NiAc). This study challenges that model, using the following regimens of intravenous and oral NiAc dosing in male Sprague Dawley rats (n = 95) to create different patterns of exposure: (A) 30 min infusion at 0, 1, 5 or 20 mu mol kg(-1) body weight; (B) 300 min infusion at 0, 5, 10 or 51 mu mol kg(-1); (C) 30 min infusion at 5 mu mol kg(-1), followed by a stepwise decrease in rate every 10 min for 180 min; (D) 30 min infusion at 5 mu mol kg(-1), followed by a stepwise decrease in rate every 10 min for 180 min and another 30 min infusion at 5 mu mol kg(-1) from 210 to 240 min; (E) an oral dose of 0, 24.4, 81.2 or 812 mu mol kg(-1). Serial arterial blood samples were taken for measurement of plasma NiAc and NEFA concentrations. The gradual decrease in infusion rate in (C) and (D) were also designed to test the hypothesis that a gradual reduction in NiAc plasma concentration may be expected to reduce or prevent rebound. The absorption of NiAc was described by parallel linear and non-linear processes and the disposition of NiAc by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. NEFA (R) turnover, which was driven by the plasma concentration of NiAc via an inhibitory drug-mechanism function acting on NEFA formation, was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M (1)) inhibited the formation of R and the last compartment (M (N) ) stimulated the loss of R. All processes regulating the plasma NEFA concentration were assumed to be captured by the moderator function. Data were analyzed using non-linear mixed effects modeling (NONMEM). The potency IC (50) of NiAc was 68 nmol L-1, the fractional turnover rate k (out) 0.27 L mmol(-1) min(-1), and the turnover rate of moderator k (tol) 0.023 min(-1). The lower physiological limit of NEFA, which was modeled as a NiAc-independent release (k (cap) ) of NEFA into plasma, was estimated to 0.023 mmol L-1 min(-1). The parameter estimates derived in this study were consistent with our previous estimates, suggesting that the model may be used for prediction of the NEFA response time-course following different modes and routes administration of NiAc or NiAc analogues. In order to avoid NiAc-induced NEFA rebound, a slow decline in the NiAc exposure pattern is needed at or below IC (50).
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13.
  • Gabrielsson, Johan (author)
  • Dynamics of target-mediated drug disposition: characteristic profiles and parameter identification
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39, s. 429-51
  • Journal article (peer-reviewed)abstract
    • In this paper we present a mathematical analysis of the basic model for target mediated drug disposition (TMDD). Assuming high affinity of ligand to target, we give a qualitative characterisation of ligand versus time graphs for different dosing regimes and derive accurate analytic approximations of different phases in the temporal behaviour of the system. These approximations are used to estimate model parameters, give analytical approximations of such quantities as area under the ligand curve and clearance. We formulate conditions under which a suitably chosen Michaelis-Menten model provides a good approximation of the full TMDD-model over a specified time interval.
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14.
  • Hennig, Stefanie, et al. (author)
  • Concordance between criteria for covariate model building
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:2, s. 109-125
  • Journal article (peer-reviewed)abstract
    • When performing a population pharmacokinetic modelling analysis covariates are often added to the model. Such additions are often justified by improved goodness of fit and/or decreased in unexplained (random) parameter variability. Increased goodness of fit is most commonly measured by the decrease in the objective function value. Parameter variability can be defined as the sum of unexplained (random) and explained (predictable) variability. Increase in magnitude of explained parameter variability could be another possible criterion for judging improvement in the model. The agreement between these three criteria in diagnosing covariate-parameter relationships of different strengths and nature using stochastic simulations and estimations as well as assessing covariate-parameter relationships in four previously published real data examples were explored. Total estimated parameter variability was found to vary with the number of covariates introduced on the parameter. In the simulated examples and two real examples, the parameter variability increased with increasing number of included covariates. For the other real examples parameter variability decreased or did not change systematically with the addition of covariates. The three criteria were highly correlated, with the decrease in unexplained variability being more closely associated with changes in objective function values than increases in explained parameter variability were. The often used assumption that inclusion of covariates in models only shifts unexplained parameter variability to explained parameter variability appears not to be true, which may have implications for modelling decisions.
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15.
  • Johansson, Carl-Christer, et al. (author)
  • Population pharmacokinetic modeling and deconvolution of enantioselective absorption of eflornithine in the rat.
  • 2013
  • In: Journal of pharmacokinetics and pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 40:1, s. 117-28
  • Journal article (peer-reviewed)abstract
    • Enantioselective pharmacokinetics and absorption of eflornithine in the rat was investigated using population pharmacokinetic modeling and a modified deconvolution method. Bidirectional permeability of L- and D-eflornithine was investigated in Caco-2 cells. The rat was administered racemic eflornithine hydrochloride as a single oral dose [40-3,000 mg/kg bodyweight (BW)] or intravenously (IV) (100-2,700 mg/kg BW infused over 60-400 min). Serial arterial blood samples were collected and L- and D-eflornithine were quantitated with a previously published chiral bioanalysis method. The D:L concentration ratio was determined in rat faeces. Intravenous L-and D-eflornithine plasma concentration-time data was analyzed using population pharmacokinetic modeling and described with a 3-compartment pharmacokinetic model with saturable binding to one of the peripheral compartments. Oral plasma concentration-time data was analyzed using a modified deconvolution method accounting for nonlinearities in the eflornithine pharmacokinetics. Clearance was similar for both enantiomers (3.36 and 3.09 mL/min). Oral bioavailability was estimated by deconvolution at 30 and 59% for L- and D-eflornithine. The D:L concentration ratio in feces was 0.49 and the Caco-2 cell permeability was similar for both enantiomers (6-10 × 10(-8) cm/s) with no evident involvement of active transport or efflux. The results presented here suggest that the difference in the bioavailability between eflornithine enantiomers is caused by a stereoselective difference in extent rather than rate of absorption. The presented modified deconvolution method made it possible to account for the non-linear component in the suggested three-compartment pharmacokinetic model thus rapidly estimating eflornithine oral bioavailability.
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16.
  • Johansson, Åsa M., 1983-, et al. (author)
  • Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:3, s. 223-238
  • Journal article (peer-reviewed)abstract
    • NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation algorithms to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7.The algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.
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18.
  • Keizer, Ron J., et al. (author)
  • A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080
  • 2010
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:4, s. 347-363
  • Journal article (peer-reviewed)abstract
    • Hypertension and proteinuria are commonly observed side-effects for anti-angiogenic drugs targeting the VEGF pathway. In most cases, hypertension can be controlled by prescription of anti-hypertensive (AH) therapy, while proteinuria often requires dose reductions or dose delays. We aimed to construct a pharmacokinetic-pharmacodynamic (PK-PD) model for hypertension and proteinuria following treatment with the experimental VEGF-inhibitor E7080, which would allow optimization of treatment, by assessing the influence of anti-hypertensive medication and dose reduction or dose delays in treating and avoiding toxicity. Data was collected from a phase I study of E7080 (n = 67), an inhibitor of multiple tyrosine kinases, among which VEGF. Blood pressure and urinalysis data were recorded weekly. Modeling was performed in NONMEM, and direct and indirect response PK-PD models were evaluated. A previously developed PK model was used. An indirect response PK-PD model described the increase in BP best, while the probability of developing proteinuria toxicity in response to exposure to E7080, was best described by a Markov transition model. This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.
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20.
  • Korell, Julia, et al. (author)
  • A semi-mechanistic red blood cell survival model provides some insight into red blood cell destruction mechanisms
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:4, s. 469-478
  • Journal article (peer-reviewed)abstract
    • Most mathematical models developed for the survival of haematological cell populations, in particular red blood cells (RBCs), follow the principle of parsimony. They focus on the predominant destruction mechanism of age-related cell death (senescence) and do not account for within subject variability in the RBC lifespan. However, assessment of the underlying physiological destruction mechanisms can be of interest in pathological conditions that affect RBC survival, for example sickle cell anaemia or anaemia of chronic kidney disease. We have previously proposed a semi-mechanistic RBC survival model which accounts for four different types of RBC destruction mechanisms. In this work, it is shown that the proposed model in combination with informative RBC survival data is able to provide a deeper insight into RBC destruction mechanisms. The proposed model was applied in a non-linear mixed effect modelling framework to biotin derived RBC survival data available from literature. Three mechanisms were estimable based on the available data of twelve subjects, including random destruction, senescence and destruction due to delayed failure. It was possible to identify three subjects with a decreased RBC survival in the study population. These three subjects all showed differences in the contribution of the estimated destruction mechanisms: an increased random destruction, versus an accelerated senescence, versus a combination of both.
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22.
  • Kroon, Tobias, et al. (author)
  • Feedback modeling of non-esterified fatty acids in obese Zucker rats after nicotinic acid infusions
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40, s. 623-638
  • Journal article (peer-reviewed)abstract
    • This study investigates the impact of disease on nicotinic acid (NiAc)-induced changes in plasma concentrations of non-esterified fatty acids (NEFA). NiAc was given by constant intravenous infusion to normal Sprague-Dawley and obese Zucker rats, and arterial blood samples were taken for analysis of NiAc, NEFA, insulin and glucose plasma concentrations. The intravenous route was intentionally selected to avoid confounding processes, such as absorption, following extravascular administration. Data were analyzed using nonlinear mixed effects modeling (NONMEM, version VI). The disposition of NiAc in the normal rats was described by a two-compartment model with endogenous synthesis of NiAc and two parallel capacity-limited elimination processes. In the obese rats disposition was described by a one-compartment model with endogenous synthesis of NiAc and one capacity-limited elimination process. The plasma concentration of NiAc drove NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M (1) ) inhibited the formation of R and the last compartment (M (N) ) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. Differences in the pharmacodynamic response of the two strains included, in the obese animals, an increased NEFA baseline, diminished rebound and post-rebound oscillation, and a more pronounced slowly developing tolerance during the period of constant drug exposure. The feedback model captured the NiAc-induced changes in NEFA response in both the normal and obese rats. Differences in the parameter estimates between the obese and normal rats included, in the former group, increases in R (0) , k (in) and p by 44, 41 and 78 %, respectively, and decreases in k (out) and gamma by 64 and 84 %, respectively. The estimates of k (tol) and IC (50) were similar in both groups. The NiAc-NEFA concentration-response relationship at equilibrium was substantially different in the two groups, being shifted upwards and to the right, and being shallower in the obese rats. The extent of such shifts is important, as they demonstrate the impact of disease at equilibrium and, if ignored, will lead to erroneous dose predictions and, in consequence, poorly designed studies. The proposed models are primarily aimed at screening and selecting candidates with the highest potential of becoming a viable drug in man.
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23.
  • Lacroix, Brigitte D., et al. (author)
  • Evaluation of IPPSE, an alternative method for sequential population PKPD analysis
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:2, s. 177-193
  • Journal article (peer-reviewed)abstract
    • The aim of this study is to present and evaluate an alternative sequential method to perform population pharmacokinetic-pharmacodynamic (PKPD) analysis. Simultaneous PKPD analysis (SIM) is generally considered the reference method but may be computationally burdensome and time consuming. Evaluation of alternative approaches aims at speeding up the computation time and stabilizing the estimation of the models, while estimating the model parameters with good enough precision. The IPPSE method presented here uses the individual PK parameter estimates and their uncertainty (SE) to propagate the PK information to the PD estimation step, while the IPP method uses the individual PK parameters only and the PPP&D method utilizes the PK data. Data sets (n = 200) with various study designs were simulated according to a one-compartment PK model and a direct Emax PD model. The study design of each dataset was randomly selected. The same PK and PD models were fitted to the simulated observations using the SIM, IPP, PPP&D and IPPSE methods. The performances of the methods were compared with respect to estimation precision and bias, and computation time. Estimated precision and bias for the IPPSE method were similar to that of SIM and PPP&D, while IPP had higher bias and imprecision. Compared with the SIM method, IPPSE saved more computation time (61%) than PPP&D (39%), while IPP remained the fastest method (86% run time saved). The IPPSE method is a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.
  •  
24.
  • Lledó-García, Rocío, et al. (author)
  • A semi-mechanistic model of the relationship between average glucose and HbA1c in healthy and diabetic subjects
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:2, s. 129-142
  • Journal article (peer-reviewed)abstract
    • HbA1c is the most commonly used biomarker for the adequacy of glycemic management in diabetic patients and a surrogate endpoint for anti-diabetic drug approval. In spite of an empirical description for the relationship between average glucose (AG) and HbA1c concentrations, obtained from the A1c-derived average glucose (ADAG) study by Nathan et al., a model for the non-steady-state relationship is still lacking. Using data from the ADAG study, we here develop such models that utilize literature information on (patho)physiological processes and assay characteristics. The model incorporates the red blood cell (RBC) aging description, and uses prior values of the glycosylation rate constant (KG), mean RBC life-span (LS) and mean RBC precursor LS obtained from the literature. Different hypothesis were tested to explain the observed non-proportional relationship between AG and HbA1c. Both an inverse dependence of LS on AG and a non-specificity of the National Glycohemoglobin Standardization Program assay used could well describe the data. Both explanations have mechanistic support and could be incorporated, alone or in combination, in models allowing prediction of the time-course of HbA1c changes associated with changes in AG from, for example dietary or therapeutic interventions, and vice versa, to infer changes in AG from observed changes in HbA1c. The selection between the alternative mechanistic models require gathering of new information.
  •  
25.
  • Lledo-Garcia, Rocio, et al. (author)
  • Modeling of red blood cell life-spans in hematologically normal populations
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:5, s. 453-462
  • Journal article (peer-reviewed)abstract
    • Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction-in the above mentioned areas- was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.
  •  
26.
  • Maloney, Alan, et al. (author)
  • An example of optimal phase II design for exposure response modelling
  • 2010
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:5, s. 475-491
  • Journal article (peer-reviewed)abstract
    • This paper presents an example of how optimal design methodology was used to help design a phase II clinical study. The planned analysis would relate the clinical endpoint to exposure (measured via the area under the curve (AUC)), rather than dose. Optimal design methodology was used to compare a number of candidate phase II designs, and an algorithm for finding optimal designs was employed. The sigmoidal E-max with baseline (E-0) model was used to relate the clinical endpoint to individual subject AUCs, and the primary metrics were D optimality and the standard error (SE) of the AUC required to yield a clinically relevant change in the clinical endpoint. The performance of the candidate designs were compared across four different 'true' exposure response relationships (determined from the analysis of an earlier proof of concept (PoC) study). The results suggested the total sample size should be increased from the planned 540 individuals, and that the optimal design with 700 individuals would be equivalent to 812 individuals with the reference design (a 16% gain). The performance with this design was considered acceptable, although all designs performed poorly if the true exposure response relationship was very flat. This work allowed a prospective assessment of the likely performance and precision from the exposure response modelling prior to the start of the phase II study, and hence allowed the design to be revised to ensure the subsequent analysis would be of most value.
  •  
27.
  • Maloney, Alan, 1972-, et al. (author)
  • D Optimal Designs for Three Poisson Dose-Response Models
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - Springer : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:2, s. 201-211
  • Journal article (peer-reviewed)abstract
    • The objective of this paper was to find and investigate the performance of the D optimal designs for three Poisson dose-response models. Phase II dose ranging studies are pivotal in the drug development program, being used to select dose(s) for phase III. Count data is encountered in a number of clinical areas. The Poisson distribution provides an intuitive platform for modelling such data, especially when combined with random effects which allow subjects to differ in their response rates. This work investigated three Poisson dose-response models of increasing complexity. A simple Emax model was used to describe the drug effect, and D optimal designs under a range of different parameter values (scenarios) were found. The relative performances between scenarios were assessed using: the precision of all parameters, the precision of the drug effect parameters, and the percent coefficient of variation (%CV) of the ED50 parameter. The results showed that the D optimal designs were similar across models and scenarios, with the D optimal designs consisting of placebo, the maximum dose, and a dose just below the ED50. However the relative performance of the optimal designs was very different. For example, with 1000 subjects, the %CV of the ED50 parameter ranged from 1.4% to 91%. Performance typically improved with higher baseline counts, smaller random effects, and larger Emax. This work introduces a framework for determining and evaluating the performance of D optimal designs for phase II dose ranging studies with count data as the primary endpoint.
  •  
28.
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29.
  • Nyberg, Joakim, et al. (author)
  • Serial correlation in optimal design for nonlinear mixed effects models
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 239-249
  • Journal article (peer-reviewed)abstract
    • In population modeling two sources of variability are commonly included; inter individual variability and residual variability. Rich sampling optimal design (more samples than model parameters) using these models will often result in a sampling schedule where some measurements are taken at exactly the same time point, thereby maximizing the signal-to-noise ratio. This behavior is a result of not appropriately taking into account error generation mechanisms and is often clinically unappealing and may be avoided by including intrinsic variability, i.e. serially correlated residual errors. In this paper we extend previous work that investigated optimal designs of population models including serial correlation using stochastic differential equations to optimal design with the more robust, and analytic, AR(1) autocorrelation model. Further, we investigate the importance of correlation strength, design criteria and robust designs. Finally, we explore the optimal design properties when estimating parameters with and without serial correlation. In the investigated examples the designs and estimation performance differs significantly when handling serial correlation.
  •  
30.
  • Petersson, Klas J. F., et al. (author)
  • Transforming parts of a differential equations system to difference equations as a method for run-time savings in NONMEM
  • 2010
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:5, s. 493-506
  • Journal article (peer-reviewed)abstract
    • Computer models of biological systems grow more complex as computing power increase. Often these models are defined as differential equations and no analytical solutions exist. Numerical integration is used to approximate the solution; this can be computationally intensive, time consuming and be a large proportion of the total computer runtime. The performance of different integration methods depend on the mathematical properties of the differential equations system at hand. In this paper we investigate the possibility of runtime gains by calculating parts of or the whole differential equations system at given time intervals, outside of the differential equations solver. This approach was tested on nine models defined as differential equations with the goal to reduce runtime while maintaining model fit, based on the objective function value. The software used was NONMEM. In four models the computational runtime was successfully reduced (by 59-96%). The differences in parameter estimates, compared to using only the differential equations solver were less than 12% for all fixed effects parameters. For the variance parameters, estimates were within 10% for the majority of the parameters. Population and individual predictions were similar and the differences in OFV were between 1 and -14 units. When computational runtime seriously affects the usefulness of a model we suggest evaluating this approach for repetitive elements of model building and evaluation such as covariate inclusions or bootstraps.
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31.
  • Rekić, Dinko, 1984, et al. (author)
  • Model based design and analysis of phase II HIV-1 trials
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:4, s. 487-496
  • Journal article (peer-reviewed)abstract
    • This work explores the advantages of a model based drug development (MBDD) approach for the design and analysis of antiretroviral phase II trials. Two different study settings were investigated: (1) a 5-arm placebo-controlled parallel group dose-finding/proof of concept (POC) study and (2) a comparison of investigational drug and competitor. Studies were simulated using a HIV-1 dynamics model in NONMEM. The Monte-Carlo Mapped Power method determined the sample size required for detecting a dose-response relationship and a significant difference in effect compared to the competitor using a MBDD approach. Stochastic simulation and re-estimation were used for evaluation of model parameter precision and bias given different sample sizes. Results were compared to those from an unpaired, two-sided t test and ANOVA (p a parts per thousand currency sign 0.05). In all scenarios, the MBDD approach resulted in smaller study sizes and more precisely estimated treatment effect than conventional statistical analysis. Using a MBDD approach, a sample size of 15 patients could be used to show POC and estimate ED50 with a good precision (relative standard error, 25.7 %). A sample size of 10 patients per arm was needed using the MBDD approach for detecting a difference in treatment effect of a parts per thousand yen20 % at 80 % power, a 3.4-fold reduction in sample size compared to a t test. The MBDD approach can be used to achieve more precise dose-response characterization facilitating decision making and dose selection. If necessitated, the sample size needed to reach a desired power can potentially be reduced compared to traditional statistical analyses. This may allow for comparison against competitors already in early clinical studies.
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32.
  • Röshammar, Daniel, 1979, et al. (author)
  • Non-linear mixed effects modeling of antiretroviral drug response after administration of lopinavir, atazanavir and efavirenz containing regimens to treatment-naive HIV-1 infected patients
  • 2011
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 38:6, s. 727-742
  • Journal article (peer-reviewed)abstract
    • The objective of this analysis was to compare three methods of handling HIV-RNA data below the limit of quantification (LOQ) when describing the time-course of antiretroviral drug response using a drug-disease model. Treatment naive Scandinavian HIV-positive patients (n = 242) were randomized to one of three study arms. Two nucleoside reverse transcriptase inhibitors were administrated in combination with 400/100 mg lopinavir/ritonavir twice daily, 300/100 mg atazanavir/ritonavir once a day or 600 mg efavirenz once a day. The viral response was monitored at screening, baseline and at 1, 2, 3, 4, 12, 24, 48, 96, 120, and 144 weeks after study initiation. Data up to 400 days was fitted using a viral dynamics non-linear mixed effects drug-disease model in NONMEM. HIV-RNA data below LOQ of 50 copies/ml plasma (39%) was omitted, replaced by LOQ/2 or included in the analysis using a likelihood-based method (M3 method). Including data below LOQ using the M3 method substantially improved the model fit. The drug response parameter expressing the fractional inhibition of viral replication was on average (95% CI) estimated to 0.787 (0.721-0.864) for lopinavir and atazanavir treatment arms and 0.868 (0.796-0.923) for the efavirenz containing regimen. At 400 days after treatment initiation 90% (76-100) of the lopinavir and atazanavir treated patients were predicted to have undetectable viral levels and 96% (89-100%) for the efavirenz containing treatment. Including viral data below the LOQ rather than omitting or replacing data provides advantages such as better model predictions and less biased parameter estimates which are of importance when quantifying antiretroviral drug response.
  •  
33.
  • Schneck, Karen B., et al. (author)
  • Assessment of glycemic response to an oral glucokinase activator in a proof of concept study : application of a semi-mechanistic, integrated glucose-insulin-glucagon model
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:1, s. 67-80
  • Journal article (peer-reviewed)abstract
    • A proof of concept study was conducted to investigate the safety and tolerability of a novel oral glucokinase activator, LY2599506, during multiple dose administration to healthy volunteers and subjects with Type 2 diabetes mellitus (T2DM). To analyze the study data, a previously established semi-mechanistic integrated glucose-insulin model [1-5] was extended to include characterization of glucagon dynamics. The model captured endogenous glucose and insulin dynamics, including the amplifying effects of glucose on insulin production and of insulin on glucose elimination, as well as the inhibitory influence of glucose and insulin on hepatic glucose production. The hepatic glucose production in the model was increased by glucagon and glucagon production was inhibited by elevated glucose concentrations. The contribution of exogenous factors to glycemic response, such as ingestion of carbohydrates in meals, was also included in the model. The effect of LY2599506 on glucose homeostasis in subjects with T2DM was investigated by linking a one-compartment, pharmacokinetic model to the semi-mechanistic, integrated glucose-insulin-glucagon system. Drug effects were included on pancreatic insulin secretion and hepatic glucose production. The relationships between LY2599506, glucose, insulin, and glucagon concentrations were described quantitatively and consequently, the improved understanding of the drug-response system could be used to support further clinical study planning during drug development, such as dose selection.
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34.
  • Shivva, Vittal, et al. (author)
  • Parameterisation affects identifiability of population models
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:1, s. 81-86
  • Journal article (peer-reviewed)abstract
    • Identifiability is an important aspect of model development. In this work, using a simple one compartment population pharmacokinetic model, we show that identifiability of the variances of the random effects parameters are affected by the parameterisation of the fixed effects parameters.
  •  
35.
  • Stevens, Jasper, et al. (author)
  • Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge : quantitative extrapolation to humans
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:5, s. 463-477
  • Journal article (peer-reviewed)abstract
    • The aim of this investigation was to develop a mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model for the biological system prolactin response following a dopamine inhibition challenge using remoxipride as a paradigm compound. After assessment of baseline variation in prolactin concentrations, the prolactin response of remoxipride was measured following (1) single intravenous doses of 4, 8 and 16 mg/kg and (2) following double dosing of 3.8 mg/kg with different time intervals. The mechanistic PK-PD model consisted of: (i) a PK model for remoxipride concentrations in brain extracellular fluid; (ii) a pool model incorporating prolactin synthesis, storage in lactotrophs, release into- and elimination from plasma; (iii) a positive feedback component interconnecting prolactin plasma concentrations and prolactin synthesis; and (iv) a dopamine antagonism component interconnecting remoxipride brain extracellular fluid concentrations and stimulation of prolactin release. The most important findings were that the free brain concentration drives the prolactin release into plasma and that the positive feedback on prolactin synthesis in the lactotrophs, in contrast to the negative feedback in the previous models on the PK-PD correlation of remoxipride. An external validation was performed using a dataset obtained in rats following intranasal administration of 4, 8, or 16 mg/kg remoxipride. Following simulation of human remoxipride brain extracellular fluid concentrations, pharmacodynamic extrapolation from rat to humans was performed, using allometric scaling in combination with independent information on the values of biological system specific parameters as prior knowledge. The PK-PD model successfully predicted the system prolactin response in humans, indicating that positive feedback on prolactin synthesis and allometric scaling thereof could be a new feature in describing complex homeostatic mechanisms.
  •  
36.
  • Svensson, Elin M., et al. (author)
  • Use of a linearization approximation facilitating stochastic model building
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:2, s. 153-158
  • Journal article (peer-reviewed)abstract
    • The objective of this work was to facilitate the development of nonlinear mixed effects models by establishing a diagnostic method for evaluation of stochastic model components. The random effects investigated were between subject, between occasion and residual variability. The method was based on a first-order conditional estimates linear approximation and evaluated on three real datasets with previously developed population pharmacokinetic models. The results were assessed based on the agreement in difference in objective function value between a basic model and extended models for the standard nonlinear and linearized approach respectively. The linearization was found to accurately identify significant extensions of the model's stochastic components with notably decreased runtimes as compared to the standard nonlinear analysis. The observed gain in runtimes varied between four to more than 50-fold and the largest gains were seen for models with originally long runtimes. This method may be especially useful as a screening tool to detect correlations between random effects since it substantially quickens the estimation of large variance-covariance blocks. To expedite the application of this diagnostic tool, the linearization procedure has been automated and implemented in the software package PsN.
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37.
  • Taneja, A, et al. (author)
  • Application of ED-optimality to screening experiments for analgesic compounds in an experimental model of neuropathic pain.
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:6, s. 673-681
  • Journal article (peer-reviewed)abstract
    • In spite of the evidence regarding high variability in the response to evoked pain, little attention has been paid to its impact on the screening of drugs for inflammatory and neuropathic pain. In this study, we explore the feasibility of introducing optimality concepts to experimental protocols, enabling estimation of parameter and model uncertainty. Pharmacokinetic (PK) and pharmacodynamic data from different experiments in rats were pooled and modelled using nonlinear mixed effects modelling. Pain data on gabapentin and placebo-treated animals were generated in the complete Freund's adjuvant model of neuropathic pain. A logistic regression model was applied to optimise sampling times and dose levels to be used in an experimental protocol. Drug potency (EC(50)) and interindividual variability (IIV) were considered the parameters of interest. Different experimental designs were tested and validated by SSE (stochastic simulation and estimation) taking into account relevant exposure ranges. The pharmacokinetics of gabapentin was described by a two-compartment PK model with first order absorption (CL = 0.159 l h(-1), V(2) = 0.118 l, V(3) = 0.253 l, Ka = 0.26 h(-1), Q = 1.22 l h(-1)). Drug potency (EC(50)) for the anti-allodynic effects was estimated to be 1400 ng ml(-1). Protocol optimisation improved bias and precision of the EC50 by 6 and 11.9. %, respectively, whilst IIV estimates showed improvement of 31.89 and 14.91 %, respectively. Our results show that variability in behavioural models of evoked pain response leads to uncertainty in drug potency estimates, with potential impact on the ranking of compounds during screening. As illustrated for gabapentin, ED-optimality concepts enable analysis of discrete data taking into account experimental constraints.
  •  
38.
  • Taneja, A, et al. (author)
  • Optimised protocol design for the screening of analgesic compounds in neuropathic pain.
  • 2012
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:6, s. 661-671
  • Journal article (peer-reviewed)abstract
    • We have previously shown how screening experiments for neuropathic pain can be optimised taking into account parameter and model uncertainty. Here we demonstrate how optimised protocols can be used to screen and rank candidate molecules. The concept is illustrated by pregabalin as a new chemical entity and gabapentin as a reference compound. ED-optimality was applied to a logistic regression model describing the relationship between drug exposure and response to evoked pain in the complete Freund's adjuvant (CFA) model in rats. Design variables for optimisation of the experimental protocol included dose levels and sampling times. Prior information from the reference compound was used in conjunction with relative in vitro potency as priors. Results from simulated scenarios were then combined with fitting of experimental data to estimate precision and bias of model parameters for the empirical and optimised designs. The pharmacokinetics of pregabalin was described by a two-compartment model. The expected value of EC(50) of pregabalin was 637.5 ng ml(-1). Model-based analysis of the data yielded median (range) of EC(50) values of 1,125 (898-2412) ng ml(-1) for the empirical protocol and 755 (189-756) ng ml(-1) for the optimised design. In contrast to current practice, optimal design entails different sampling schedule across dose levels. ED-optimised designs should become standard practice in the screening of candidate molecules. It ensures lower bias when estimating the drug potency, facilitating accurate ranking and selection of compounds for further development.
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39.
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40.
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41.
  • Ueckert, Sebastian, et al. (author)
  • Optimizing disease progression study designs for drug effect discrimination
  • 2013
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40:5, s. 587-596
  • Journal article (peer-reviewed)abstract
    • Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.
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42.
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43.
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44.
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45.
  • Vong, Camille, et al. (author)
  • Handling Below Limit of Quantification Data in Optimal Trial Design
  • 2014
  • In: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744.
  • Journal article (other academic/artistic)abstract
    • Methods that perform well in handling limit of quantification (LOQ) data exist in estimation of parameters for non-linear mixed effect models but are not well developed in experimental design.  The aim of this work was to evaluate existing methods and to explore new methods of handling LOQs in Optimal Design (OD). Seven different methods were implemented in PopED 2.13: D1 (Ignore LOQ), D2 (Non-informative Fisher information matrix (FIM) for median response below LOQ), new D3 (Non-informative FOCE linearized FIM for individual response below LOQ), D4 (Addition of a homoscedastic variance), new D5 (Simulation & Rescaling), new D6 (Integration & Rescaling) and new D7 (joint likelihood using the Laplace approximation). Predictive performance of D1-D7 was first assessed and sample time optimization was performed for a number of different LOQ levels. Resulting designs were evaluated for bias and imprecision, robustness and predictability from multiple stochastic simulations and estimations (SSE) in NONMEM using the M3 method. Evaluated determinants of the FIM for all methods, except D1 and D4, were in good agreement with SSE-derived covariance. In optimization, D6 provided the most accurate and precise parameter estimates and the designs with the best predictive performance under the M3 method. Methods D1 and D2 resulted in the least robust designs for estimation. Method D4 was shown to be insensitive to LOQ levels. For the scenarios investigated, method D6 showed the best compromise in terms of speed and accuracy. The use of OD methods anticipating LOQ data in planned designs allows better parameter estimation.
  •  
46.
  • Ahlström, Christine, et al. (author)
  • Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions
  • 2011
  • In: JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. - 1567-567X. ; 38:1, s. 1-24
  • Journal article (peer-reviewed)abstract
    • A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 μmol kg(-1) of body weight) or over 300 min (0, 5, 10 or 51 μmol kg(-1) of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M (1)) inhibited the formation of R and the last compartment (M ( N )) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC (50), of NiAc was 45 nmol L(-1), the fractional turnover rate k ( out ) was 0.41 L mmol(-1) min(-1) and the turnover rate of moderator k ( tol ) was 0.027 min(-1). A lower physiological limit of NEFA was modeled as a NiAc-independent release (k ( cap )) of NEFA into plasma and was estimated to 0.032 mmol L(-1) min(-1). This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues.
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