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1.	Ahn, Jae Eun, et al. (författare) Likelihood based approaches to handling data below the quantification limit using NONMEM VI 2008 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:4, s. 401-421 Tidskriftsartikel (refereegranskat)abstract PURPOSE: To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. METHODS: A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. RESULTS: For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. CONCLUSIONS: Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
2.	Albitar, Orwa, et al. (författare) Pharmacometric modeling of drug adverse effects : an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine 2023 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Nature. - 1567-567X .- 1573-8744. ; 50:1, s. 21-31 Tidskriftsartikel (refereegranskat)abstract Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients’ medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m2 increase every week over a baseline of 24.7 kg/m2 until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.
3.	Almquist, Joachim, 1980, et al. (författare) Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood 2015 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209 Tidskriftsartikel (refereegranskat)abstract The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.
4.	Alskär, Oskar, et al. (författare) A pharmacokinetic model for the glycation of albumin 2012 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 273-282 Tidskriftsartikel (refereegranskat)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.
5.	Andersson, R., et al. (författare) Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 44:3, s. 203-222 Tidskriftsartikel (refereegranskat)abstract Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc-FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague-Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 , respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be 2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.
6.	Aoki, Yasunori, 1982-, et al. (författare) Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:6, s. 581-597 Tidskriftsartikel (refereegranskat)abstract Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.
7.	Aoki, Yasunori, et al. (författare) Second order Taylor expansion of likelihood-based models 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 44, s. S72-S72 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
8.	Arrington, Leticia, et al. (författare) Performance of longitudinal item response theory models in shortened or partial assessments. 2020 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 47:5, s. 461-471 Tidskriftsartikel (refereegranskat)abstract This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models' ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.
9.	Arshad, Usman, et al. (författare) Development of visual predictive checks accounting for multimodal parameter distributions in mixture models 2019 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 46:3, s. 241-250 Tidskriftsartikel (refereegranskat)abstract The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.
10.	Baverel, Paul, et al. (författare) Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data 2009 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 297-315 Tidskriftsartikel (refereegranskat)abstract The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).
11.	Baverel, Paul G., et al. (författare) Two bootstrapping routines for obtaining imprecision estimates for nonparametric parameter distributions in nonlinear mixed effects models 2011 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 38:1, s. 63-82 Tidskriftsartikel (refereegranskat)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.
12.	Bergström, Mats, et al. (författare) Blood-brain barrier penetration of zolmitriptan--modelling of positron emission tomography data 2006 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 33:1, s. 75-91 Tidskriftsartikel (refereegranskat)abstract Positron emission tomography (PET) with the drug radiolabelled allows a direct measurement of brain or other organ kinetics, information which can be essential in drug development. Usually, however, a PET-tracer is administered intravenously (i.v.), whereas the therapeutic drug is mostly given orally or by a different route to the PET-tracer. In such cases, a recalculation is needed to make the PET data representative for the alternative administration route. To investigate the blood-brain barrier penetration of a drug (zolmitriptan) using dynamic PET and by PK modelling quantify the brain concentration of the drug after the nasal administration of a therapeutic dose. [11C]Zolmitriptan at tracer dose was administered as a short i.v. infusion and the brain tissue and venous blood kinetics of [11C]zolmitriptan was measured by PET in 7 healthy volunteers. One PET study was performed before and one 30 min after the administration of 5 mg zolmitriptan as nasal spray. At each of the instances, the brain radioactivity concentration after subtraction of the vascular component was determined up to 90 min after administration and compared to venous plasma radioactivity concentration after correction for radiolabelled metabolites. Convolution methods were used to describe the relationship between arterial and venous tracer concentrations, respectively between brain and arterial tracer concentration. Finally, the impulse response functions derived from the PET studies were applied on plasma PK data to estimate the brain zolmitriptan concentration after a nasal administration of a therapeutic dose. The studies shows that the PET data on brain kinetics could well be described as the convolution of venous tracer kinetics with an impulse response including terms for arterial-to-venous plasma and arterial-to-brain impulse responses. Application of the PET derived impulse responses on the plasma PK from nasal administration demonstrated that brain PK of zolmitriptan increased with time, achieving about 0.5 mg/ml at 30 min and close to a maximum of 1.5 mg/ml after 2 hr. A significant brain concentration was observed already after 5 min. The data support the notation of a rapid brain availability of zolmitriptan after nasal administration.
13.	Bizzotto, Roberto, et al. (författare) Multinomial logistic estimation of Markov-chain models for modeling sleep architecture in primary insomnia patients 2010 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:2, s. 137-155 Tidskriftsartikel (refereegranskat)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.
14.	Björkman, Sven (författare) A comment on the application of drug tissue-plasma partition coefficients Kp in eliminating organs to calculation of volume of distribution at steady state 2005 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 32:5-6, s. 655-658 Tidskriftsartikel (refereegranskat)
15.	Björnsson, Marcus A., et al. (författare) A two-compartment effect site model describes the bispectral index after different rates of propofol infusion 2010 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:3, s. 243-255 Tidskriftsartikel (refereegranskat)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.
16.	Bonate, Peter L., et al. (författare) Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses : results from the International Society of Pharmacometrics (ISOP) Working Group 2016 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:2, s. 123-135 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.
17.	Bonate, Peter L., et al. (författare) Training the next generation of pharmacometric modelers : a multisector perspective 2023 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Nature. - 1567-567X .- 1573-8744. ; 51:1, s. 5-31 Tidskriftsartikel (refereegranskat)abstract The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970’s and early 1980’s and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.
18.	Bouchene, Salim, et al. (författare) Whole Body Physiologically-Based Pharmacokinetic Model for Colistin and Colistimethate Sodium (CMS) in Six Different Species : Mouse, Rat, Rabbit, Baboon, Pig and Human 2013 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 40:S1, s. S115-S116 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
19.	Brekkan, Ari, et al. (författare) Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach 2024 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 51:1, s. 65-75 Tidskriftsartikel (refereegranskat)abstract Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.
20.	Brekkan, Ari, et al. (författare) Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM 2019 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 46:6, s. 591-604 Tidskriftsartikel (refereegranskat)abstract Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.
21.	Brekkan, Ari, et al. (författare) Reduced and optimized trial designs for drugs described by a target mediated drug disposition model 2018 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 45:4, s. 637-647 Tidskriftsartikel (refereegranskat)abstract Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA-IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ae 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.
22.	Buatois, S., et al. (författare) Modelling approaches in dose finding clinical trial : Simulation-based study comparing predictive performances of model averaging and model selection. 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 44, s. S16-S17 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
23.	Buatoisi, Simon, et al. (författare) A pharmacometric extension of MCP-MOD in dose finding studies 2018 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 45:Suppl. 1, s. S106-S106 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
24.	Cardilin, Tim, 1989, et al. (författare) Exposure-response modeling improves selection of radiation and radiosensitizer combinations 2022 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 49:2, s. 167-178 Tidskriftsartikel (refereegranskat)abstract A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.
25.	Chasseloup, Estelle, et al. (författare) Comparison of covariate selection methods with correlated covariates : prior information versus data information, or a mixture of both? 2020 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : SPRINGER/PLENUM PUBLISHERS. - 1567-567X .- 1573-8744. ; 47:5, s. 485-492 Tidskriftsartikel (refereegranskat)abstract The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: 2(*) ln(Pr(X)/(1 - Pr(X))), Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates (r = 0.9), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is < 80%, PACS when > 80% but < 100%, and SCM when the expected power is 100%.
26.	Chasseloup, Estelle, et al. (författare) Generation and application of avatars in pharmacometric modelling 2023 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 50, s. 411-423 Tidskriftsartikel (refereegranskat)
27.	Chen, Chunli, et al. (författare) The multistate tuberculosis pharmacometric model : a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:2, s. 133-141 Tidskriftsartikel (refereegranskat)abstract The Multistate Tuberculosis Pharmacometric (MTP) model, a pharmacokinetic-pharmacodynamic disease model, has been used to describe the effects of rifampicin on Mycobacterium tuberculosis (M. tuberculosis) in vitro. The aim of this work was to investigate if the MTP model could be used to describe the rifampicin treatment response in an acute tuberculosis mouse model. Sixty C57BL/6 mice were intratracheally infected with M. tuberculosis H37Rv strain on Day 0. Fifteen mice received no treatment and were sacrificed on Days 1, 9 and 18 (5 each day). Twenty-five mice received oral rifampicin (1, 3, 9, 26 or 98 mg·kg-1·day-1; Days 1–8; 5 each dose level) and were sacrificed on Day 9. Twenty mice received oral rifampicin (30 mg·kg-1·day-1; up to 8 days) and were sacrificed on Days 2, 3, 4 and 9 (5 each day). The MTP model was linked to a rifampicin population pharmacokinetic model to describe the change in colony forming units (CFU) in the lungs over time. The transfer rates between the different bacterial states were fixed to estimates from in vitro data. The MTP model described well the change in CFU over time after different exposure levels of rifampicin in an acute tuberculosis mouse model. Rifampicin significantly inhibited the growth of fast-multiplying bacteria and stimulated the death of fast- and slow-multiplying bacteria. The data did not support an effect of rifampicin on non-multiplying bacteria possibly due to the short duration of the study. The pharmacometric modelling framework using the MTP model can be used to perform investigations and predictions of the efficacy of anti-tubercular drugs against different bacterial states.
28.	Choy, Steve, et al. (författare) 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 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 40:1, s. 1-10 Tidskriftsartikel (refereegranskat)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.
29.	Clewe, Oskar, et al. (författare) A model-based analysis identifies differences in phenotypic resistance between in vitro and in vivo : implications for translational medicine within tuberculosis. 2020 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 47:5, s. 421-430 Tidskriftsartikel (refereegranskat)abstract Proper characterization of drug effects on Mycobacterium tuberculosis relies on the characterization of phenotypically resistant bacteria to correctly establish exposure-response relationships. The aim of this work was to evaluate the potential difference in phenotypic resistance in in vitro compared to murine in vivo models using CFU data alone or CFU together with most probable number (MPN) data following resuscitation with culture supernatant. Predictions of in vitro and in vivo phenotypic resistance i.e. persisters, using the Multistate Tuberculosis Pharmacometric (MTP) model framework was evaluated based on bacterial cultures grown with and without drug exposure using CFU alone or CFU plus MPN data. Phenotypic resistance and total bacterial number in in vitro natural growth observations, i.e. without drug, was well predicted by the MTP model using only CFU data. Capturing the murine in vivo total bacterial number and persisters during natural growth did however require re-estimation of model parameter using both the CFU and MPN observations implying that the ratio of persisters to total bacterial burden is different in vitro compared to murine in vivo. The evaluation of the in vitro rifampicin drug effect revealed that higher resolution in the persister drug effect was seen using CFU and MPN compared to CFU alone although drug effects on the other bacterial populations were well predicted using only CFU data. The ratio of persistent bacteria to total bacteria was predicted to be different between in vitro and murine in vivo. This difference could have implications for subsequent translational efforts in tuberculosis drug development.
30.	Clewe, Oskar, et al. (författare) A Model Predicting Penetration of Rifampicin from Plasma to Epithelial Lining Fluid and Alveolar Cells 2013 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - 1567-567X .- 1573-8744. ; 40:S1, s. S68-S69 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
31.	Clewe, Oskar, et al. (författare) Evaluation of optimized bronchoalveolar lavage sampling designs for characterization of pulmonary drug distribution 2015 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:6, s. 699-708 Tidskriftsartikel (refereegranskat)abstract Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid a parts per thousand yen LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs.
32.	Conrado, DJ, et al. (författare) Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy 2019 Ingår i: Journal of pharmacokinetics and pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 46:5, s. 441-455 Tidskriftsartikel (refereegranskat)
33.	Dansirikul, Chantaratsamon, et al. (författare) Approaches to handling pharmacodynamic baseline responses 2008 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:3, s. 269-283 Tidskriftsartikel (refereegranskat)abstract A few approaches for handling baseline responses are available for use in pharmacokinetic (PK)-pharmacodynamic (PD) analysis. They include: (method 1-B1) estimation of the typical value and interindividual variability (IIV) of baseline in the population, (B2) inclusion of the observed baseline response as a covariate acknowledging the residual variability, (B3) a more general version of B2 as it also takes the IIV of the baseline in the population into account, and (B4) normalization of all observations by the baseline value. The aim of this study was to investigate the relative performance of B1-B4. PD responses over a single dosing interval were simulated from an indirect response model in which a drug acts through stimulation or inhibition of the response according to an Emax model. The performance of B1-B4 was investigated under 22 designs, each containing 100 datasets. NONMEM VI beta was used to estimate model parameters with the FO and the FOCE method. The mean error (ME, %) and root mean squared error (RMSE, %) of the population parameter estimates were computed and used as an indicator of bias and imprecision. Absolute ME (\|ME\|) and RMSE from all methods were ranked within the same design, the lower the rank value the better method performance. Average rank of each method from all designs was reported. The results showed that with B1 and FOCE, the average of \|ME\| and RMSE across all typical individual parameters and all conditions was 5.9 and 31.8%. The average rank of \|ME\| for B1, B2, B3, and B4 was 3.7, 3.8, 3.3, and 5.2 for the FOCE method, and 4.6, 4.3, 4.7, and 6.4 for the FO method. The smallest imprecision was noted with the use of B1 (rank of 3.1 for FO, and 2.9 for FOCE) and increased, in order, with B3 (3.9-FO and 3.6-FOCE), B2 (4.8-FO; 4.7-FOCE), and B4 (6.4-FO; 6.5-FOCE). We conclude that when considering both bias and imprecision B1 was slightly better than B3 which in turn was better than B2. Differences between these methods were small. B4 was clearly inferior. The FOCE method led to a smaller bias, but no marked reduction in imprecision of parameter estimates compared to the FO method.
34.	de Vries Schultink, Aurelia H M, et al. (författare) Pharmacodynamic modeling of cardiac biomarkers in breast cancer patients treated with anthracycline and trastuzumab regimens. 2018 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 45:3, s. 431-442 Tidskriftsartikel (refereegranskat)abstract Trastuzumab is associated with cardiotoxicity, manifesting as a decrease of the left-ventricular ejection fraction (LVEF). Administration of anthracyclines prior to trastuzumab increases risk of cardiotoxicity. High-sensitive troponin T and N-terminal-pro-brain natriuretic peptide (NT-proBNP) are molecular markers that may allow earlier detection of drug-induced cardiotoxicity. In this analysis we aimed to quantify the kinetics and exposure-response relationships of LVEF, troponin T and NT-proBNP measurements, in patients receiving anthracycline and trastuzumab. Repeated measurements of LVEF, troponin T and NT-proBNP and dosing records of anthracyclines and trastuzumab were available from a previously published clinical trial. This trial included 206 evaluable patients with early breast cancer. Exposure to anthracycline and trastuzumab was simulated based on available dosing records and by using a kinetic-pharmacodynamic (K-PD) and a fixed pharmacokinetic (PK) model from literature, respectively. The change from baseline troponin T was described with a direct effect model, affected by simulated anthracycline concentrations, representing myocyte damage. The relationship between trastuzumab and LVEF was described by an indirect effect compartment model. The EC50 for LVEF decline was significantly affected by the maximum troponin T concentration after anthracycline treatment, explaining 15.1% of inter-individual variability. In this cohort, NT-proBNP changes could not be demonstrated to be related to anthracycline or trastuzumab treatment. Pharmacodynamic models for troponin T and LVEF were successfully developed, identifying maximum troponin T concentration after anthracycline treatment as a significant determinant for trastuzumab-induced LVEF decline. These models can help identify patients at risk of drug-induced cardiotoxicity and optimize cardiac monitoring strategies.
35.	Delattre, Maud, et al. (författare) Analysis of exposure-response of CI-945 in patients with epilepsy : application of novel mixed hidden Markov modeling methodology 2012 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:3, s. 263-271 Tidskriftsartikel (refereegranskat)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.
36.	Deng, Chenhui, et al. (författare) Approaches for modeling within subject variability in pharmacometric count data analysis : dynamic inter-occasion variability and stochastic differential equations 2016 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:3, s. 305-314 Tidskriftsartikel (refereegranskat)abstract Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
37.	Dodds, Michael G, et al. (författare) Robust population pharmacokinetic experiment design. 2005 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:1, s. 33-64 Tidskriftsartikel (refereegranskat)abstract The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.
38.	Dosne, Anne-Gaëlle, et al. (författare) A strategy for residual error modeling incorporating scedasticity of variance and distribution shape 2016 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:2, s. 137-151 Tidskriftsartikel (refereegranskat)abstract Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. dTBS always led to significant improvements in objective function value, with most examples displaying some degree of right-skewness and variances proportional to predictions raised to powers between 0 and 1. The t-distribution led to significant improvement for 5 out of 10 models with degrees of freedom between 3 and 9. Six models were most improved by the t-distribution while four models benefited more from dTBS. Changes in other model parameter estimates were observed. In conclusion, the use of dTBS and/or t-distribution models provides a flexible and easy-to-use framework capable of characterizing all commonly encountered residual error distributions.
39.	Dosne, Anne-Gaëlle, et al. (författare) An Automated Sampling Importance Resampling Procedure For Estimating Parameter Uncertainty 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:6, s. 509-520 Tidskriftsartikel (refereegranskat)abstract Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.
40.	Dosne, Anne-Gaëlle, et al. (författare) dOFV distributions : A New Diagnostic For The Adequacy Of Parameter Uncertainty In Nonlinear Mixed-Effects Models Applied To The Bootstrap 2016 Ingår i: Journal Of Pharmacokinetics And Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:6, s. 597-608 Tidskriftsartikel (refereegranskat)abstract Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20-200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific "effective" sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used.
41.	Dosne, Anne-Gaëlle, et al. (författare) Improving The Estimation Of Parameter Uncertainty Distributions In Nonlinear Mixed Effects Models Using Sampling Importance Resampling 2016 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 43:6, s. 583-596 Tidskriftsartikel (refereegranskat)abstract Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.
42.	Duffull, Stephen B., et al. (författare) Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models 2017 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:6, s. 611-616 Tidskriftsartikel (refereegranskat)abstract Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.
43.	Elsherbiny, Doaa A, et al. (författare) A model based assessment of the CYP2B6 and CYP2C19 inductive properties by artemisinin antimalarials: implications for combination regimens. 2008 Ingår i: Journal of pharmacokinetics and pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:2, s. 203-17 Tidskriftsartikel (refereegranskat)abstract The study aim was to assess the inductive properties of artemisinin antimalarials using mephenytoin as a probe for CYP2B6 and CYP2C19 enzymatic activity. The population pharmacokinetics of S-mephenytoin and its metabolites S-nirvanol and S-4'-hydroxymephenytoin, including enzyme turn-over models for induction, were described by nonlinear mixed effects modeling. Rich data (8-16 samples/occasion/subject) were collected from 14 healthy volunteers who received mephenytoin before and during ten days of artemisinin administration. Sparse data (3 samples/occasion/subject) were collected from 74 healthy volunteers who received mephenytoin before, during and after five days administration of artemisinin, dihydroartemisinin, arteether, artemether or artesunate. The production rate of CYP2B6 was increased 79.7% by artemisinin, 61.5% by arteether, 76.1% by artemether, 19.9% by dihydroartemisinin and 16.9% by artesunate. The production rate of CYP2C19 increased 51.2% by artemisinin, 14.8% by arteether and 24.9% by artemether. In conclusion, all studied artemisinin derivatives induced CYP2B6. CYP2C19 induction by arteether and artemether as well as CYP2B6 and CYP2C19 induction by artemisinin was confirmed. The inductive capacity is different among the artemisinin drugs, which is of importance when selecting drugs to be used in antimalarial combination therapy such that the potential for drug-drug interactions is minimized.
44.	Ernest, C. Steven, et al. (författare) Population pharmacokinetics and pharmacodynamics of prasugrel and clopidogrel in aspirin-treated patients with stable coronary artery disease 2008 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:6, s. 593-618 Tidskriftsartikel (refereegranskat)abstract The aim of the current analysis was to characterize the population PK of prasugrel and clopidogrel metabolites, the resulting PD response, and identification of covariates for key PK/PD parameters. Aspirin-treated subjects with coronary artery disease were randomized to double-blind treatment with clopidogrel 600 mg loading dose (LD) followed by daily 75 mg maintenance dose (MD) or prasugrel 60 mg LD and daily 10 mg MD for 28 days. Plasma concentrations of prasugrel active metabolite (Pras-AM) and prasugrel's inactive thiolactone metabolite (Pras-thiolactone) were simultaneously fit to a multicompartmental model; a similar model adequately described clopidogrel's active metabolite (Clop-AM) PK. By linking to the PK model through the active metabolite concentrations, the PK/PD model characterized the irreversible inhibition of platelet aggregation through a sigmoidal Emax model. Although dose, sex, and weight were identified as significant covariates in the prasugrel PK model, only the effect of body weight produced significant changes in Pras-AM exposure. Generally, these factors resulted in only minor changes in Pras-AM exposures such that, overall, the change in the resulting maximal platelet aggregation (MPA) was predicted to be < or =10% points on average. The clopidogrel PK model included dose as a covariate indicating that a significantly less-than-proportional increase in Clop-AM exposure is expected over the dose range of 75-600 mg, thus, the model-predicted PD response is lower than might be anticipated given an 8-fold difference in dose and lower than that typically achieved following prasugrel 60 mg LD. The greater PD response with prasugrel compared with clopidogrel was accounted for by greater conversion of dose to active metabolite.
45.	Ernest II, Charles, et al. (författare) Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model 2014 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:6, s. 639-654 Tidskriftsartikel (refereegranskat)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.
46.	Ernest II, Charles Steven, et al. (författare) Simultaneous optimal experimental design for in vitro binding parameter estimation 2013 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science+Business Media B.V.. - 1567-567X .- 1573-8744. ; 40:5, s. 573-585 Tidskriftsartikel (refereegranskat)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.
47.	Friberg Hietala, Sofia, 1973, et al. (författare) Population pharmacokinetics of amodiaquine and desethylamodiaquine in pediatric patients with uncomplicated falciparum malaria 2007 Ingår i: JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 12, s. 222-222 Tidskriftsartikel (refereegranskat)abstract The study aimed to characterize the population pharmacokinetics of amodiaquine (AQ) and its major metabolite N-desethylamodiaquine (N-DEAQ), and to assess the correlation between exposure to N-DEAQ and treatment outcome. Blood samples from children in two studies in Zanzibar and one in Papua New Guinea were included in the pharmacokinetic analysis (n = 86). The children had been treated with AQ in combination with artesunate or sulphadoxine-pyrimethamine. The population pharmacokinetics of AQ and N-DEAQ were modeled using the non-linear mixed effects approach as implemented in NONMEM. Bayesian post-hoc estimates of individual pharmacokinetic parameters were used to generate individual profiles of N-DEAQ exposure. The correlation between N-DEAQ exposure and effect was studied in 212 patients and modeled with logistic regression in NONMEM. The pharmacokinetics of AQ and N-DEAQ were best described by two parallel two-compartment models with a central and a peripheral compartment for each compound. The systemic exposure to AQ was low in comparison to N-DEAQ. The t (1/2lambda) of N-DEAQ ranged from 3 days to 12 days. There was a statistically significant, yet weak, association between N-DEAQ concentration on day 7 and treatment outcome. The age-based dosing schedule currently recommended in Zanzibar appeared to result in inadequate exposure to N-DEAQ in many patients.
48.	Gabrielsson, Johan (författare) A modeling approach for compounds affecting body composition 2013 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40, s. 651-667 Tidskriftsartikel (refereegranskat)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.
49.	Gabrielsson, Johan (författare) Challenge model of TNF alpha turnover at varying LPS and drug provocations 2019 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 46, s. 223-240 Tidskriftsartikel (refereegranskat)abstract A mechanism-based biomarker model of TNF-response, including different external provocations of LPS challenge and test compound intervention, was developed. The model contained system properties (such as k(t), k(out)), challenge characteristics (such as k(s), k(LPS), K-m,K-LPS, S-max, SC50) and test-compound-related parameters (I-max, IC50). The exposure to test compound was modelled by means of first-order input and Michaelis-Menten type of nonlinear elimination. Test compound potency was estimated to 20 nM with a 70% partial reduction in TNF alpha-response at the highest dose of 30 mg.kg(-1). Future selection of drug candidates may focus the estimation on potency and efficacy by applying the selected structure consisting of TNF alpha system and LPS challenge characteristics. A related aim was to demonstrate how an exploratory (graphical) analysis may guide us to a tentative model structure, which enables us to better understand target biology. The analysis demonstrated how to tackle a biomarker with a baseline below the limit of detection. Repeated LPS-challenges may also reveal how the rate and extent of replenishment of TNF alpha pools occur. Lack of LPS exposure-time courses was solved by including a biophase model, with the underlying assumption that TNF alpha-response time courses, as such, contain kinetic information. A transduction type of model with non-linear stimulation of TNF alpha release was finally selected. Typical features of a challenge experiment were shown by means of model simulations. Experimental shortcomings of present and published designs are identified and discussed. The final model coupled to suggested guidance rules may serve as a general basis for the collection and analysis of pharmacological challenge data of future studies.
50.	Gabrielsson, Johan (författare) Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats 2013 Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 40, s. 497-512 Tidskriftsartikel (refereegranskat)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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