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  • 1.
    Acharya, Chayan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Turkyilmaz, Gulbeyaz Yildiz
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Ege Univ, Fac Pharm, Dept Biopharmaceut & Pharmacokinet, TR-35100 Izmir, Turkey..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R2016In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 127, p. 83-93Article in journal (Refereed)
    Abstract [en]

    Background and objective: Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. Methods: The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. Results: The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. Conclusions: The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://crantoprojectorg/web/packages/ncappc/index.html/) and GitHub (https://github.comicacha0227/ncappc/). 

  • 2.
    Aoki, Yasunori
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Monika, Sundqvist
    AstraZeneca, Cardiovasc & Metab Dis, Innovat Med & Early Dev Biotech Unit, Pepparedsleden 1, S-43183 Molndal, Sweden.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gennemark, Peter
    AstraZeneca, Cardiovasc & Metab Dis, Innovat Med & Early Dev Biotech Unit, Pepparedsleden 1, S-43183 Molndal, Sweden.
    PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies2016In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 127, p. 126-143Article in journal (Refereed)
    Abstract [en]

    Background and Objective

    Optimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies.

    Methods

    Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented.

    Results

    The software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper.

    Conclusions

    PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools.

  • 3.
    Aoki, Yasunori
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Department of Mathematics, Uppsala University.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations2016In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 18, no 2, p. 505-518Article in journal (Refereed)
    Abstract [en]

    As the importance of pharmacometric analysis increases, more and more complex mathematical models are introduced and computational error resulting from computational instability starts to become a bottleneck in the analysis. We propose a preconditioning method for non-linear mixed effects models used in pharmacometric analyses to stabilise the computation of the variance-covariance matrix. Roughly speaking, the method reparameterises the model with a linear combination of the original model parameters so that the Hessian matrix of the likelihood of the reparameterised model becomes close to an identity matrix. This approach will reduce the influence of computational error, for example rounding error, to the final computational result. We present numerical experiments demonstrating that the stabilisation of the computation using the proposed method can recover failed variance-covariance matrix computations, and reveal non-identifiability of the model parameters.

  • 4.
    Aoki, Yasunori
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Natl Inst Informat, Tokyo, Japan..
    Roshammar, Daniel
    AstraZeneca, IMED Biotech Unit, Quantitat Clin Pharmacol Innovat Med & Early Dev, Gothenburg, Sweden.;SGS Exprimo, Mechelen, Belgium..
    Hamren, Bengt
    AstraZeneca, IMED Biotech Unit, Quantitat Clin Pharmacol Innovat Med & Early Dev, Gothenburg, Sweden..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 581-597Article in journal (Refereed)
    Abstract [en]

    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.

  • 5.
    Aoki, Yasunori
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
    Sundqvist, Monika
    AstraZeneca R&D.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Gennemark, Peter
    AstraZeneca R&D.
    PopED lite: an optimal design software for preclinical pharmacokinetic and pharmacodynamic studiesManuscript (preprint) (Other academic)
    Abstract [en]

    Optimal experimental design approaches are seldom used in pre-clinical drug discovery. Main reasons for this lack of use are that available software tools require relatively high insight in optimal design theory, and that the design-execution cycle of in vivo experiments is short, making time-consuming optimizations infeasible. We present the publicly available software PopED lite in order to increase the use of optimal design in pre-clinical drug discovery. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit the short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Key functionality of PopED lite is demonstrated by three case studies from real drug discovery projects. 

  • 6.
    Bergstrand, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wallin, Johan E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 2, p. 143-151Article in journal (Refereed)
    Abstract [en]

    Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.

  • 7. Bizzotto, Roberto
    et al.
    Zamuner, Stefano
    Mezzalana, Enrica
    De Nicolao, Giuseppe
    Gomeni, Roberto
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture: Internal and External Validation and Covariate Analysis2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 3, p. 445-463Article in journal (Refereed)
    Abstract [en]

    Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.

  • 8.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Reduced and optimized trial designs for drugs described by a target mediated drug disposition model2018In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 45, no 4, p. 637-647Article in journal (Refereed)
    Abstract [en]

    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<currency> 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.

  • 9.
    Brekkan, Ari
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Lopez-Lazaro, Luis
    Dr Reddys Labs, Basel, Switzerland.
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden;Uppsala Univ, Dept Pharmaceut Biosci, Pharmacometr Res Grp, Uppsala, Sweden.
    Plan, Elodie L.
    Pharmetheus, Uppsala, Sweden.
    Acharya, Chayan
    Pharmetheus, Uppsala, Sweden.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    Kankanwadi, Suresh
    Dr Reddys Labs, Basel, Switzerland.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus, Uppsala, Sweden.
    A Population Pharmacokinetic-Pharmacodynamic Model of Pegfilgrastim2018In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 5, article id 91Article in journal (Refereed)
    Abstract [en]

    Neutropenia and febrile neutropenia (FN) are serious side effects of cytotoxic chemotherapy which may be alleviated with the administration of recombinant granulocyte colony-stimulating factor (GCSF) derivatives, such as pegfilgrastim (PG) which increases absolute neutrophil count (ANC). In this work, a population pharmacokinetic-pharmacodynamic (PKPD) model was developed based on data obtained from healthy volunteers receiving multiple administrations of PG. The developed model was a bidirectional PKPD model, where PG stimulated the proliferation, maturation, and margination of neutrophils and where circulating neutrophils in turn increased the elimination of PG. Simulations from the developed model show disproportionate changes in response with changes in dose. A dose increase of 10% from the 6 mg therapeutic dose taken as a reference leads to area under the curve (AUC) increases of similar to 50 and similar to 5% for PK and PD, respectively. A full random effects covariate model showed that little of the parameter variability could be explained by sex, age, body size, and race. As a consequence, little of the secondary parameter variability (C-max and AUC of PG and ANC) could be explained by these covariates.

  • 10. Dodds, Michael G
    et al.
    Hooker, Andrew C.
    Vicini, Paolo
    Robust population pharmacokinetic experiment design.2005In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 32, no 1, p. 33-64Article in journal (Refereed)
    Abstract [en]

    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.

  • 11.
    Duffull, Stephen B.
    et al.
    Univ Otago, Sch Pharm, 18 Frederick St, Dunedin, New Zealand..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models2017In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 611-616Article in journal (Refereed)
    Abstract [en]

    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.

  • 12.
    Ernest, C. Steven, II
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Methodological Comparison of In Vitro Binding Parameter Estimation: Sequential vs. Simultaneous Non-linear Regression2010In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 27, no 5, p. 866-877Article in journal (Refereed)
    Abstract [en]

    Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation. Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B-NS), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters. The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B-NS from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV. SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.

  • 13.
    Ernest II, Charles
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bizzotto, Roberto
    DeBrota, David J.
    Ni, Lan
    Harris, Cynthia J.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Multinomial Markov-chain model of sleep architecture in Phase Advanced SubjectsManuscript (preprint) (Other academic)
  • 14.
    Ernest II, Charles
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model2014In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 6, p. 639-654Article in journal (Refereed)
    Abstract [en]

    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.

  • 15.
    Ernest II, Charles Steven
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simultaneous optimal experimental design for in vitro binding parameter estimation2013In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 5, p. 573-585Article in journal (Refereed)
    Abstract [en]

    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.

  • 16.
    Fang, Lanyan
    et al.
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    Kim, Myong-Jin
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    Li, Zhichuan
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    Wang, Yaning
    US FDA, Off Clin Pharmacol, Off Translat Sci, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    DiLiberti, Charles E.
    Montclair Bioequivalence Serv LLC, Montclair, NJ USA.
    Au, Jessie
    Optimum Therapeut LLC, Carlsbad, CA USA.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ducharme, Murray P.
    Learn & Confirm Inc, St Laurent, PQ, Canada.
    Lionberger, Robert
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    Zhao, Liang
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA.
    Model-Informed Drug Development and Review for Generic Products: Summary Of FDA Public Workshop2018In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 104, no 1, p. 27-30Article in journal (Refereed)
    Abstract [en]

    On October 2nd and 3rd, 2017, the US Food and Drug Administration (FDA) hosted a public workshop titled “Leveraging Quantitative Methods and Modeling to Modernize Generic Drug Development and Review.”1 This report summarizes Session 2 of the public workshop: “Model Informed Drug Development and Review for Generic Products.” The session focused on the application of quantitative methods and modeling in modernizing the generic drug development and review.

  • 17. Foracchia, Marco
    et al.
    Hooker, Andrew C.
    Vicini, Paolo
    Ruggeri, Alfredo
    POPED, a software for optimal experiment design in population kinetics.2004In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 74, no 1, p. 29-46Article in journal (Refereed)
    Abstract [en]

    Population kinetic analysis is the methodology used to quantify inter-subject variability in kinetic studies. It entails the collection of (possibly sparse) data from dynamic experiments in a group of subjects and their quantitative interpretation by means of a mathematical model. This methodology is widely used in the pharmaceutical industry (where it is termed "pharmacokinetic population analysis") and recently it is becoming increasingly used in other areas of biomedical research. Unlike traditional kinetic studies, where the number of subjects can be quite small, population kinetic studies require large numbers of subjects. It is, therefore, of great interest to design these studies in the most efficient manner possible, to maximize the information content provided by the data. In this paper we propose an algorithm and a computer program, POPED, for the optimal design of a population kinetic experiment. In particular, the number of samples for each subject and the design of the individual sampling strategies, i.e. the number and location of the time points at which the output variable is sampled, will be considered. Among the various criteria proposed in the literature, D and ED optimality are the ones implemented in our software program, since they are the most widely used. A brief description of the techniques employed to perform design optimization is given, together with some details on their actual implementation. Some examples are then presented to show the program usage and the results provided.

  • 18.
    Gennemark, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Danis, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Tucker, Warwick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Applied Mathematics.
    Optimal Design in Population Kinetic Experiments by Set-Valued Methods2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 4, p. 495-507Article in journal (Refereed)
    Abstract [en]

    We propose a new method for optimal experimental design of population pharmacometric experiments based on global search methods using interval analysis; all variables and parameters are represented as intervals rather than real numbers. The evaluation of a specific design is based on multiple simulations and parameter estimations. The method requires no prior point estimates for the parameters, since the parameters can incorporate any level of uncertainty. In this respect, it is similar to robust optimal design. Representing sampling times and covariates like doses by intervals gives a direct way of optimizing with rigorous sampling and dose intervals that can be useful in clinical practice. Furthermore, the method works on underdetermined problems for which traditional methods typically fail.

  • 19.
    Hennig, Stefanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Fanta, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Backman, Janne T.
    Department of Clinical Pharmacology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.
    Hoppu, Kalle
    Department of Clinical Pharmacology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin2012In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 52, no 3, p. 347-360Article in journal (Refereed)
    Abstract [en]

    A time and sampling intensive pretransplant test dose design was to be reduced, but at the same time optimized so that there was no loss in the precision of predicting the individual pharmacokinetic (PK) estimates of posttransplant dosing. The following variables were optimized simultaneously: sampling times, ciclosporin dose, time of second dose, infusion duration, and administration order, using a published ciclosporin population PK model as prior information. The original design was reduced from 22 samples to 6 samples/patient and both doses (intravenous oral) were administered within 8 hours. Compared with the prior information given by the published ciclosporin population PK model, the expected standard deviations (SDs) of the individual parameters for clearance and bioavailability could be reduced by, on average, 40% under the optimized sparse designs. The gain of performing the original rich design compared with the optimal reduced design, considering the standard errors of the parameter estimates, was found to be minimal. This application demonstrates, in a practical clinical scenario, how optimal design techniques may be used to improve diagnostic procedures given available software and methods.

  • 20.
    Hennig, Stefanie
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Trial treatment length optimization with an emphasis on disease progression studies2009In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 49, no 3, p. 323-335Article in journal (Refereed)
    Abstract [en]

    Optimal design has been used in the past mainly to optimize sampling schedules for clinical trials. Optimization on design variables other than sampling times has been published in the literature only once before. This study shows, as an example, optimization on the length of treatment periods to obtain reliable estimates of drug effects on longterm disease progression studies. Disease progression studies are high in cost, effort, and time; therefore, optimization of treatment length is highly recommended to avoid failure or loss of information. Results are provided for different drug effects (eg, protective and symptomatic) and for different lengths of studies and sampling schedules. The merits of extending the total study length versus inclusion of more samples per participants are investigated. The authors demonstrate that if no observations are taken during the washout period, a trial can lose up to 40% of its efficiency. Furthermore, when optimization of treatment length is performed using multiple possible drug effect models simultaneously, these studies show high power in discriminating between different drug effect models.

  • 21. Hooker, Andrew C
    et al.
    Foracchia, Marco
    Dodds, Michael G
    Vicini, Paolo
    An evaluation of population D-optimal designs via pharmacokinetic simulations.2003In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 31, no 1, p. 98-111Article in journal (Refereed)
    Abstract [en]

    One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.

  • 22. Hooker, Andrew C.
    et al.
    Greene, Chris H.
    Clark, William
    Classical examination of the Stark effect in hydrogen1997In: Physical Review A. Atomic, Molecular, and Optical Physics, ISSN 1050-2947, E-ISSN 1094-1622, Vol. 55, no 6, p. 4609-4612Article in journal (Refereed)
  • 23.
    Hooker, Andrew C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Staatz, Christine E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method2007In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 24, no 12, p. 2187-2197Article in journal (Refereed)
    Abstract [en]

    Purpose  Population model analyses have shifted from using the first order (FO) to the first-order with conditional estimation (FOCE) approximation to the true model. However, the weighted residuals (WRES), a common diagnostic tool used to test for model misspecification, are calculated using the FO approximation. Utilizing WRES with the FOCE method may lead to misguided model development/evaluation. We present a new diagnostic tool, the conditional weighted residuals (CWRES), which are calculated based on the FOCE approximation. Materials and Methods  CWRES are calculated as the FOCE approximated difference between an individual’s data and the model prediction of that data divided by the root of the covariance of the data given the model. Results  Using real and simulated data the CWRES distributions behave as theoretically expected under the correct model. In contrast, in certain circumstances, the WRES have distributions that greatly deviate from the expected, falsely indicating model misspecification. CWRES/WRES comparisons can also indicate if the FOCE estimation method will improve the results of an FO model fit to data. Conclusions  Utilization of CWRES could improve model development and evaluation and give a more accurate picture of if and when a model is misspecified when using the FO or FOCE methods.

  • 24.
    Hooker, Andrew C
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Ten Tije, A J
    Carducci, M A
    Weber, J
    Garrett-Mayer, E
    Gelderblom, H
    McGuire, W P
    Verweij, J
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy.
    Baker, S D
    Population pharmacokinetic model for docetaxel in patients with varying degrees of liver function: incorporating cytochrome P4503A activity measurements2008In: Clinical Pharmacology and Therapeutics, ISSN 0009-9236, E-ISSN 1532-6535, Vol. 84, no 1, p. 111-118Article in journal (Refereed)
    Abstract [en]

    The relationship between cytochrome P4503A4 (CYP3A4) activity and docetaxel clearance in patients with varying degrees of liver function (LF) was evaluated. Docetaxel 40, 50, or 75 mg/m(2) was administered to 85 patients with advanced cancer; 23 of 77 evaluable patients had abnormalities in LF tests. Baseline CYP3A activity was assessed using the erythromycin breath test (ERMBT). Pharmacokinetic studies and toxicity assessments were performed during cycle 1 of therapy and population modeling was performed using NONMEM. Docetaxel unbound clearance was lower (317 vs. 470 l/h) and more variable in patients with LF abnormalities compared to patients with normal LF. Covariates evaluated accounted for 83% of variability on clearance in patients with liver dysfunction, with CYP3A4 activity accounting for 47% of variation; covariates accounted for only 23% of variability in patients with normal LF. The clinical utility of the ERMBT may lie in identifying safe docetaxel doses for patients with LF abnormalities.

  • 25. Hooker, Andrew C.
    et al.
    Vicini, Paolo
    Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments.2005In: The AAPS journal, ISSN 1550-7416, Vol. 7, no 4, p. E759-85Article in journal (Refereed)
    Abstract [en]

    Multiple outputs or measurement types are commonly gathered in biological experiments. Often, these experiments are expensive (such as clinical drug trials) or require careful design to achieve the desired information content. Optimal experimental design protocols could help alleviate the cost and increase the accuracy of these experiments. In general, optimal design techniques ignore between-individual variability, but even work that incorporates it (population optimal design) has treated simultaneous multiple output experiments separately by computing the optimal design sequentially, first finding the optimal design for one output (eg, a pharmacokinetic [PK] measurement) and then determining the design for the second output (eg, a pharmacodynamic [PD] measurement). Theoretically, this procedure can lead to biased and imprecise results when the second model parameters are also included in the first model (as in PK-PD models). We present methods and tools for simultaneous population D-optimal experimental designs, which simultaneously compute the design of multiple output experiments, allowing for correlation between model parameters. We then apply these methods to simulated PK-PD experiments. We compare the new simultaneous designs to sequential designs that first compute the PK design, fix the PK parameters, and then compute the PD design in an experiment. We find that both population designs yield similar results in designs for low sample number experiments, with simultaneous designs being possibly superior in situations in which the number of samples is unevenly distributed between outputs. Simultaneous population D-optimality is a potentially useful tool in the emerging field of experimental design.

  • 26.
    Johansson, Åsa M.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 72014In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 3, p. 223-238Article in journal (Refereed)
    Abstract [en]

    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.

  • 27.
    Karlsson, Kristin C.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Savić, Radojka M.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making2009In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 11, no 1, p. 148-154Article in journal (Refereed)
    Abstract [en]

    In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.

  • 28. Keenan, Thomas M
    et al.
    Hooker, Andrew C.
    Spilker, Mary E
    Li, Nianzhen
    Boggy, Gregory J
    Vicini, Paolo
    Folch, Albert
    Automated identification of axonal growth cones in time-lapse image sequences.2006In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 151, no 2, p. 232-8Article in journal (Refereed)
    Abstract [en]

    The isolation and purification of axon guidance molecules has enabled in vitro studies of the effects of axon guidance molecule gradients on numerous neuronal cell types. In a typical experiment, cultured neurons are exposed to a chemotactic gradient and their growth is recorded by manual identification of the axon tip position from two or more micrographs. Detailed and statistically valid quantification of axon growth requires evaluation of a large number of neurons at closely spaced time points (e.g. using a time-lapse microscopy setup). However, manual tracing becomes increasingly impractical for recording axon growth as the number of time points and/or neurons increases. We present a software tool that automatically identifies and records the axon tip position in each phase-contrast image of a time-lapse series with minimal user involvement. The software outputs several quantitative measures of axon growth, and allows users to develop custom measurements. For, example analysis of growth velocity for a dissociated E13 mouse cortical neuron revealed frequent extension and retraction events with an average growth velocity of 0.05 +/- 0.14 microm/min. Comparison of software-identified axon tip positions with manually identified axon tip positions shows that the software's performance is indistinguishable from that of skilled human users.

  • 29.
    Keizer, Ron J
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose2013In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 2, p. e50-Article in journal (Refereed)
    Abstract [en]

    Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.

  • 30. Kerwin, William
    et al.
    Hooker, Andrew C.
    Spilker, Mary
    Vicini, Paolo
    Ferguson, Marina
    Hatsukami, Thomas
    Yuan, Chun
    Quantitative magnetic resonance imaging analysis of neovasculature volume in carotid atherosclerotic plaque.2003In: Circulation, ISSN 0009-7322, E-ISSN 1524-4539, Vol. 107, no 6, p. 851-6Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Neovasculature within atherosclerotic plaques is believed to be associated with infiltration of inflammatory cells and plaque destabilization. The aim of the present investigation was to determine whether the amount of neovasculature present in advanced carotid plaques can be noninvasively measured by dynamic, contrast-enhanced MRI.

    METHODS AND RESULTS: A total of 20 consecutive patients scheduled for carotid endarterectomy were recruited to participate in an MRI study. Images were obtained at 15-second intervals, and a gadolinium contrast agent was injected coincident with the second of 10 images in the sequence. The resulting image intensity within the plaque was tracked over time, and a kinetic model was used to estimate the fractional blood volume. For validation, matched sections from subsequent endarterectomy were stained with ULEX and CD-31 antibody to highlight microvessels. Finally, all microvessels within the matched sections were identified, and their total area was computed as a fraction of the plaque area. Results were obtained from 16 participants, which showed fractional blood volumes ranging from 2% to 41%. These levels were significantly higher than the histological measurements of fractional vascular area. Nevertheless, the 2 measurements were highly correlated, with a correlation coefficient of 0.80 (P<0.001).

    CONCLUSIONS: Dynamic contrast-enhanced MRI provides an indication of the extent of neovasculature within carotid atherosclerotic plaque. MRI therefore provides a means for prospectively studying the link between neovasculature and plaque vulnerability.

  • 31.
    Khandelwal, Akash
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jonsson, Niclas E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A Fast Method for Testing Covariates in Population PK/PD Models2011In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 13, no 3, p. 464-472Article in journal (Refereed)
    Abstract [en]

    The development of covariate models within the population modeling program like NONMEM is generally a time-consuming and non-trivial task. In this study, a fast procedure to approximate the change in objective function values of covariate-parameter models is presented and evaluated. The proposed method is a first-order conditional estimation (FOCE)-based linear approximation of the influence of covariates on the model predictions. Simulated and real datasets were used to compare this method with the conventional nonlinear mixed effect model using both first-order (FO) and FOCE approximations. The methods were mainly assessed in terms of difference in objective function values (Delta OFV) between base and covariate models. The FOCE linearization was superior to the FO linearization and showed a high degree of concordance with corresponding nonlinear models in Delta OFV. The linear and nonlinear FOCE models provided similar coefficient estimates and identified the same covariate-parameter relations as statistically significant or non-significant for the real and simulated datasets. The time required to fit tesaglitazar and docetaxel datasets with 4 and 15 parameter-covariate relations using the linearization method was 5.1 and 0.5 min compared with 152 and 34 h, respectively, with the nonlinear models. The FOCE linearization method allows for a fast estimation of covariate-parameter relations models with good concordance with the nonlinear models. This allows a more efficient model building and may allow the utilization of model building techniques that would otherwise be too time-consuming.

  • 32. Kumpulainen, Elina
    et al.
    Valitalo, Pyry
    Kokki, Merja
    Lehtonen, Marko
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ranta, Veli-Pekka
    Kokki, Hannu
    Plasma and cerebrospinal fluid pharmacokinetics of flurbiprofen in children2010In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 70, no 4, p. 557-566Article in journal (Refereed)
    Abstract [en]

    Flurbiprofen is a commonly used non-steroidal anti-inflammatory drug in children to treat pain and fever. There is limited information on the pharmacokinetics of flurbiprofen in children and no data on the cerebrospinal fluid permeation of flurbiprofen. WHAT THIS STUDY ADDS Our population pharmacokinetic model indicates that weight-based dosing of flurbiprofen is appropriate in children older than 6 months. The bioavailability of oral flurbiprofen syrup is high, 71-91%, and thus, the oral syrup provides accurate dosing in paediatric patients. Cerebrospinal fluid concentrations of flurbiprofen are markedly higher than the unbound plasma concentrations. AIMS This study was designed to characterize paediatric pharmacokinetics and central nervous system exposure of flurbiprofen. METHODS The pharmacokinetics of flurbiprofen were studied in 64 healthy children aged 3 months to 13 years, undergoing surgery with spinal anaesthesia. Children were administered preoperatively a single dose of flurbiprofen intravenously as prodrug (n = 27) or by mouth as syrup (n = 37). A single cerebrospinal fluid (CSF) sample (n = 60) was collected at the induction of anaesthesia, and plasma samples (n = 304) before, during and after the operation (up to 20 h after administration). A population pharmacokinetic model was built using the NONMEM software package. RESULTS Flurbiprofen concentrations in plasma were well described by a three compartment model. The apparent bioavailability of oral flurbiprofen syrup was 81%. The estimated clearance (CL) was 0.96 l h-1 70 kg-1. Age did not affect the clearance after weight had been included as a covariate. The estimated volume of distribution at steady state (V-ss) was 8.1 l 70 kg-1. Flurbiprofen permeated into the CSF, reaching concentrations that were seven-fold higher compared with unbound plasma concentrations. CONCLUSIONS Flurbiprofen pharmacokinetics can be described using only weight as a covariate in children above 6 months, while more research is needed in neonates and in younger infants.

  • 33.
    Kågedal, Matts
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Cselenyi, Zsolt
    Nyberg, Svante
    Raboisson, Patrick
    Ståhle, Lars
    Stenkrona, Per
    Varnäs, Katarina
    Halldin, Christer
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A positron emission tomography study in healthy volunteers to estimate mGluR5 receptor occupancy of AZD2066-Estimating occupancy in the absence of a reference region2013In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 82, p. 160-169Article in journal (Refereed)
    Abstract [en]

    AZD2066 is a new chemical entity pharmacologically characterized as a selective, negative allosteric modulator of the metabotropic glutamate receptor subtype 5 (mGluR5). Antagonism of mGluR5 has been implicated in relation to various diseases such as anxiety, depression, and pain disorders. To support translation from preclinical results and previous experiences with this target in man, a positron emission tomography study was performed to estimate the relationship between AZD2066 plasma concentrations and receptor occupancy in the human brain, using the mGluR5 radioligand [C-11]-ABP688. The study involved PET scans on 4 occasions in 6 healthy volunteers. The radioligand was given as a tracer dose alone and following oral treatment with different doses of AZD2066. The analysis was based on the total volume of distribution derived fro m each PET-assessment. A non-linear mixed effects model was developed where ten delineated brain regions of interest from all PET scans were included in one simultaneous fit. For comparison the analysis was also performed according to a method described previously by Lassen et al. (1995). The results of the analysis showed that the total volume of distribution decreased with increasing drug concentrations in all regions with an estimated Kipl of 1170 nM. Variability between individuals and occasions in non-displaceable volume of distribution could explain most of the variability in the total volume of distribution. The Lassen approach provided a similar estimate for Kipl, but the variability was exaggerated and difficult to interpret.

  • 34.
    Kågedal, Matts
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. AstraZeneca R&D, SE-151 85 Södertälje, Sweden.
    Cselényi, Zsolt
    Nyberg, Svante
    AstraZeneca R&D, SE-151 85 Södertälje, Sweden.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Raboisson, Patrick
    Stenkrona, Per
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Non-linear mixed effects modelling of positron emission tomography data for simultaneous estimation of radioligand kinetics and occupancy in healthy volunteers2012In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 4, p. 849-856Article in journal (Refereed)
    Abstract [en]

    The aim of this work was to develop a model simultaneously estimating (11)C-AZD9272 radioligand kinetics and the relationship between plasma concentration of AZD9272 and receptor occupancy in the human brain.

    AZD9272 is a new chemical entity pharmacologically characterised as a noncompetitive antagonist at the metabotropic glutamate receptor subtype 5 (mGluR5). Positron emission tomography (PET) was used to measure the time course of ((11)C-AZD9272) in the brain. The study included PET measurements in six healthy volunteers where the radioligand was given as a tracer dose alone as well as post oral treatment with different doses of unlabelled AZD9272. Estimation of radioligand kinetics, including saturation of receptor binding was performed by use of non-linear mixed effects modelling. Data from the regions with the highest (ventral striatum) and lowest (cerebellum) radioligand concentrations were included in the analysis. It was assumed that the extent of non-displaceable brain uptake was the same in both regions while the rate of CNS uptake and the receptor density differed.

    The results of the analysis showed that AZD9272 binding at the receptor is saturable with an estimated plasma concentration corresponding to 50% occupancy of approximately 200nM. The density of the receptor binding sites was estimated to 800nM and 200nM in ventral striatum and cerebellum respectively. By simultaneously analysing data from several PET measurements and different brain regions in a non-linear mixed effects framework it was possible to estimate parameters of interest that would otherwise be difficult to quantify.

  • 35.
    Kågedal, Matts
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Improved precision of exposure-response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment2015In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 42, no 3, p. 211-224Article in journal (Refereed)
  • 36.
    Kågedal, Matts
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Varnäs, Katarina
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Estimation of drug receptor occupancy when non-displaceable binding differs between brain regions: extending the simplified reference tissue model2015In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 80, no 1, p. 116-127Article in journal (Refereed)
    Abstract [en]

    AIM: The simplified reference tissue model (SRTM) is used for estimation of receptor occupancy assuming that the non-displaceable binding in the reference region is identical to the brain regions of interest. The aim of this work was to extended the SRTM to also account for inter-regional differences in non-displaceable concentrations, and to investigate if this model allowed estimation of receptor occupancy using white matter as reference. It was also investigated if an apparent higher affinity in caudate compared to other brain regions, could be better explained by a difference in the extent of non-displaceable binding.

    METHODS: The analysis was based on a PET study in 6 healthy volunteers using the 5-HT1B receptor radioligand [(11) C]AZ10419369. The radioligand was given intravenously as a tracer dose alone and following different oral doses of the 5-HT1B receptor antagonist AZD3783. Nonlinear mixed effects models were developed where differences between regions in non-specific concentrations were accounted for. The properties of the models were also evaluated by means of simulation studies.

    RESULTS: The estimate (95% CI) of KiPL was 10.2 ng/ml (5.4-15) and 10.4 ng/ml (8.1-13.6) based on the extended SRTM with white matter as reference and based on the SRTM using cerebellum as reference respectively. The estimate (95% CI) of KiPL for caudate relative to other brain regions was 55% ( 48% -62%).

    CONCLUSIONS: The extended SRTM allows consideration of white matter as reference region when no suitable grey matter region exists. The AZD3783 affinity appears to be higher in caudate compared with other brain regions.

  • 37.
    Lledó-García, Rocío
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hennig, Stefanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ethically Attractive Dose-Finding Designs for Drugs With a Narrow Therapeutic Index2012In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 52, no 1, p. 29-38Article in journal (Refereed)
    Abstract [en]

    A simulation-based comparison study on the relative merits of dose-control trials (DCTs) with exposure-response analysis versus concentration-control trials (CCTs) for drugs with narrow therapeutic index showed that DCT designs are more informative about the exposure-response relationship. The authors revisit the question employing optimal design methodology and propose strategies for designing ethically attractive trials for these drugs, balancing between individual-collective risk and informativeness. An optimal study was performed considering a hypothetical immunosuppressant agent with 2 clinical end points. Different scenarios were optimized applying cost-based designs (unwanted events vs number of sub-jects/trial or maximal individual risk). Dose/exposure targets and number of subjects per trial/arm were optimized. Prior information inclusion on baseline risks was evaluated. DCTs were more informative, needing smaller studies to provide the same information as CCTs. Using the number of unwanted events-rather than subjects-as cost resulted in ethically more attractive designs. Including prior baseline risk information reduced the number of subject/events and allowed the use of targets closer to the optimal. Designing dose-finding trials for some narrow therapeutic index drugs may be improved by using DCTs with exposure-response analysis, cost-based designs, prior information, and optimal design analysis providing information on the ethical trade-off between individual risk and information gain.

  • 38.
    Marklund, Matti
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism.
    Strömberg, Eric A
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hammarlund-Udenaes, Margareta
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Åman, Per
    Landberg, Rikard
    Kamal-Eldin, Afaf
    Chain length of dietary alkylresorcinols affects their in vivo elimination kinetics in rats2013In: Journal of Nutrition, ISSN 0022-3166, E-ISSN 1541-6100, Vol. 143, no 10, p. 1573-1578Article in journal (Refereed)
    Abstract [en]

    Two phenolic acids, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)- propanoic acid (DHPPA), are the major metabolites of cereal alkylresorcinols (ARs). Like their precursors, AR metabolites have been suggested as biomarkers for intake of whole-grain wheat and rye and as such could aid the understanding of diet-disease associations. This study estimated and compared pharmacokinetic parameters of ARs and their metabolites in rats and investigated differences in metabolite formation after ingestion of different AR homologs. Rats were i.v. infused for 30 min with 2, 12, or 23 μmol/kg DHBA or DHPPA or orally given the same amounts of the AR homologs, C17:0 and C25:0. Repeated plasma samples, obtained from rats for 6 h (i.v.) or 36 h (oral), were simultaneously analyzed for ARs and their metabolites by GC-mass spectrometry. Pharmacokinetic parameters were estimated by population-based compartmental modeling and noncompartmental calculation. A 1-compartment model best described C25:0 pharmacokinetics, whereas C17:0 and AR metabolites best fitted 2-compartment models. Combined models for simultaneous prediction of AR and metabolite concentration were more complex, with less reliable estimates of pharmacokinetic parameters. Although the AUC of C17:0 was lower than that of C25:0 (P < 0.05), the total amount and composition of AR metabolites did not differ between rats given C17:0 or C25:0. The elimination half-life of ARs and their metabolites increased with length of the side chain (P-trend < 0.001) and ranged from 1.2 h (DHBA) to 8.8 h (C25:0). The formation of AR metabolites was slower than their elimination, indicating that the rate of AR metabolism and not excretion of DHBA and DHPPA determines their plasma concentrations in rats.

  • 39.
    Marklund, Matti
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism.
    Strömberg, Eric A
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lærke, Helle N
    Knudsen, Knud E Bach
    Kamal-Eldin, Afaf
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Landberg, Rikard
    Simultaneous pharmacokinetic modeling of alkylresorcinols and their main metabolites indicates dual absorption mechanisms and enterohepatic elimination in humans2014In: Journal of Nutrition, ISSN 0022-3166, E-ISSN 1541-6100, Vol. 144, no 11, p. 1674-1680Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Alkylresorcinols have proven to be useful biomarkers of whole-grain wheat and rye intake in many nutritional studies. To improve their utility, more knowledge regarding the fate of alkylresorcinols and their metabolites after consumption is needed.

    OBJECTIVE: The objective of this study was to develop a combined pharmacokinetic model for plasma concentrations of alkylresorcinols and their 2 major metabolites, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-propanoic acid (DHPPA).

    METHODS: The model was established by using plasma samples collected from 3 women and 2 men after a single dose (120 g) of rye bran and validated against fasting plasma concentrations from 8 women and 7 men with controlled rye bran intake (23, 45, or 90 g/d). Alkylresorcinols in the lymph and plasma of a pig fed a single alkylresorcinol dose (1.3 mmol) were quantified to assess absorption. Human ileostomal effluent and pig bile after high and low alkylresorcinol doses were analyzed to evaluate biliary alkylresorcinol metabolite excretion.

    RESULTS: The model contained 2 absorption compartments: 1 that transferred alkylresorcinols directly to the systematic circulation and 1 in which a proportion of absorbed alkylresorcinols was metabolized before reaching the systemic circulation. Plasma concentrations of alkylresorcinols and their metabolites depended on absorption and formation, respectively, and the mean ± SEM terminal elimination half-life of alkylresorcinols (1.9 ± 0.59 h), DHPPA (1.5 ± 0.26 h), and DHBA (1.3 ± 0.22 h) did not differ. The model accurately predicted alkylresorcinol and DHBA concentrations after repeated alkylresorcinol intake but DHPPA concentration was overpredicted, possibly because of poorly modeled enterohepatic circulation. During the 8 h following administration, <2% of the alkylresorcinol dose was recovered in the lymph. DHPPA was identified in both human ileostomal effluent and pig bile, indicating availability of DHPPA for absorption and enterohepatic circulation.

    CONCLUSION: Intact alkylresorcinols have advantages over DHBA and DHPPA as plasma biomarkers for whole-grain wheat and rye intake because of lower susceptibility to factors other than alkylresorcinol intake.

  • 40. Mentre, France
    et al.
    Duffull, Steven
    Gueorguieva, Iva
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Leonov, Sergei
    Ogungbenro, K
    Retout, Sylvie
    Software for optimal design in population pharmacokinetics and pharmacodynamics: a comparison2007Conference paper (Refereed)
    Abstract [en]

    Introduction:

    Following the first theoretical work on optimal design for nonlinear mixed effect models, this research theme has rapidly grown both  in methodological and application developments. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PK and PD models and proposed optimization of the experimental designs. In 2006, the Population Optimal Design of Experiments workshop was created with a meeting every year in May (www.maths.qmul.ac.uk/~bb/PODE/PODE2007.html). This year at PODE07 a special session was organized to present different software tools for population PK/PD optimal design and to compare them with respect to their statistical methodology.

    Objectives:

    1) To present the different software tools; 2) To compare the statistical methods implemented in these tools; 3) To report the conclusion of the PODE07 meeting with respect to future software development in population PK/PD design.

    Methods:

    The software tools will be compared with respect to: a) their  availability, b) required language, c) library of PK or PD models, d) ability to deal with multiresponse models and/or with models defined by differential equations, e) approximations made to compute the Fisher information matrix, f) optimisation criteria, g)optimisation algorithms, h) ability to optimize design structure, i) ability to deal with constraints in sampling times, j) availability of optimisation trough sampling windows, k) assessment of user specified designs,  l) ability to deal with unbalanced multiresponse designs, m) ability to deal with correlations between random effects, o) provided outputs ...

    Results:

    The five software tools discussed at PODE07 are (in alphabetical order): PFIM (S. Retout & F. Mentré), PkStaMP (S. Leonov), PopDes (K. Ogungbenro & I. Gueorguieva) PopED (A. Hooker), and WinPOPT (S. Duffull). Tables comparing the software with respect to the different aspects described in the method section will be reported. The conclusions of the PODE07 meeting regarding future software development for optimal design in population PK/PD will be presented.

  • 41. Mentré, F
    et al.
    Chenel, M
    Comets, E
    Grevel, J
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lavielle, M
    Gueorguieva, I
    Current Use and Developments Needed for Optimal Design in Pharmacometrics: A Study Performed Among DDMoRe's European Federation of Pharmaceutical Industries and Associations Members2013In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 2, p. e46-Article in journal (Refereed)
    Abstract [en]

    Methods and software tools for optimal design in nonlinear mixed effect models, based on the Fisher information matrix, have been developed for a decade.1,2 Academic groups regularly proposed new versions.3–5 Present tools do not incorporate adaptive designs for these models although prior information is needed and adaptive designs are increasingly used in drug development.6 We conducted a study among drug companies of the Drug and Disease Model Resources consortium7 to identify current practices and expectations.

  • 42.
    Musuamba, F. T.
    et al.
    EMA Modelling & Simulat Working Grp, London, England.;Fed Agcy Med & Hlth Prod, Brussels, Belgium.;Univ Limoges, INSERM, UMR850, Limoges, France..
    Manolis, E.
    EMA Modelling & Simulat Working Grp, London, England.;European Med Agcy, London, England..
    Holford, N.
    Univ Auckland, Dept Pharmacol & Clin Pharmacol, Auckland, New Zealand..
    Cheung, S. Y. A.
    AstraZeneca UK Ltd, London, England..
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ogungbenro, K.
    Univ Manchester, Manchester, Lancs, England..
    Posch, M.
    Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Vienna, Austria..
    Yates, J. W. T.
    AstraZeneca UK Ltd, London, England..
    Berry, S.
    Berry Consultants, Austin, TX USA..
    Thomas, N.
    Pfizer, London, England..
    Corriol-Rohou, S.
    AstraZeneca UK Ltd, London, England..
    Bornkamp, B.
    Novartis, London, England..
    Bretz, F.
    Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Vienna, Austria.;Novartis, London, England..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Van der Graaf, P. H.
    Leiden Acad Ctr Drug Res, Leiden, Netherlands.;Certara QSP, Canterbury, Kent, England..
    Standing, J. F.
    EMA Modelling & Simulat Working Grp, London, England.;UCL, London, England..
    Hay, J.
    EMA Modelling & Simulat Working Grp, London, England.;Med & Healthcare Prod Regulatory Agcy, London, England..
    Cole, S.
    EMA Modelling & Simulat Working Grp, London, England.;Med & Healthcare Prod Regulatory Agcy, London, England..
    Gigante, V.
    EMA Modelling & Simulat Working Grp, London, England.;Agenzia Italiana Farmaco, Rome, Italy..
    Karlsson, K.
    EMA Modelling & Simulat Working Grp, London, England.;Med Prod Agcy, Uppsala, Sweden..
    Dumortier, T.
    Novartis, London, England..
    Benda, N.
    EMA Modelling & Simulat Working Grp, London, England.;Bundesinst Arzneimittel & Med Prod, Bonn, Germany..
    Serone, F.
    EMA Modelling & Simulat Working Grp, London, England.;Agenzia Italiana Farmaco, Rome, Italy..
    Das, S.
    AstraZeneca UK Ltd, London, England..
    Brochot, A.
    ABLYNX, Ghent, Belgium..
    Ehmann, F.
    European Med Agcy, London, England..
    Hemmings, R.
    Med & Healthcare Prod Regulatory Agcy, London, England..
    Rusten, I. Skottheim
    EMA Modelling & Simulat Working Grp, London, England.;Norvegian Med Agcy, Oslo, Norway..
    Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 7, p. 418-429Article in journal (Refereed)
    Abstract [en]

    Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dosefinding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

  • 43.
    Nguyen, T. H. T.
    et al.
    INSERM, IAME, UMR 1137, Paris, France.;Univ Paris Diderot, Sorbonne Paris Cite, Paris, France..
    Mouksassi, M-S
    Certara Strateg Consulting, Montreal, PQ, Canada..
    Holford, N.
    Univ Auckland, Dept Pharmacol & Clin Pharmacol, Auckland, New Zealand..
    Al-Huniti, N.
    AstraZeneca, Quantitat Clin Pharmacol, Waltham, MA USA..
    Freedman, I.
    Dr Immanuel Freedman Inc, Harleysville, PA USA..
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    John, J.
    US FDA, Ctr Drug Evaluat & Res, Washington, DC USA..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mould, D. R.
    Project Res Inc, Phoenixville, PA USA..
    Ruixo, J. J. Perez
    Janssen Pharmaceut Companies Johnson, Beerse, Belgium..
    Plan, E. L.
    Pharmetheus, Uppsala, Sweden..
    Savic, R.
    Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA USA..
    van Hasselt, J. G. C.
    Leiden Univ, Leiden Acad Ctr Drug Res, Div Pharmacol, Leiden, Netherlands..
    Weber, B.
    Boehringer Ingelheim Pharmaceut Inc, Ridgefield, CT USA..
    Zhou, C.
    Genentech Inc, San Francisco, CA USA..
    Comets, E.
    INSERM, IAME, UMR 1137, Paris, France.;Univ Paris Diderot, Sorbonne Paris Cite, Paris, France.;Univ Rennes 1, INSERM, CIC 1414, Rennes, France..
    Mentre, F.
    INSERM, IAME, UMR 1137, Paris, France.;Univ Paris Diderot, Sorbonne Paris Cite, Paris, France..
    Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics2017In: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, ISSN 2163-8306, Vol. 6, no 2, p. 87-109Article in journal (Refereed)
    Abstract [en]

    This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

  • 44.
    Nyberg, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bazzoli, Caroline
    Ogungbenro, Kay
    Aliev, Alexander
    Leonov, Sergei
    Duffull, Stephen
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mentré, France
    Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies2015In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 79, no 1, p. 6-17Article in journal (Refereed)
    Abstract [en]

    Population Pharmacokinetic (PK)-Pharmacodynamic (PD) (PKPD) models are increasingly used in drug development and in academic research. Hence designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed effect models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated five software tools: PFIM, PkStaMP, PopDes, PopED, and POPT. The comparisons were performed using two models: i) a simple one compartment warfarin PK model; ii) a more complex PKPD model for Pegylated-interferon (peg-interferon) with both concentration and response of viral load of hepatitis C virus (HCV) data. The results of the software were compared in terms of the standard error values of the parameters (SE) predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the peg-interferon PKPD model all software gave similar results. Of interest it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

  • 45.
    Nyberg, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Höglund, Richard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Serial correlation in optimal design for nonlinear mixed effects models2012In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 39, no 3, p. 239-249Article in journal (Refereed)
    Abstract [en]

    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.

  • 46.
    Nyberg, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simultaneous optimal experimental design on dose and sample times2009In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 2, p. 125-145Article in journal (Refereed)
    Abstract [en]

    Optimal experimental design can be used for optimizing new pharmacokinetic (PK)-pharmacodynamic (PD) studies to increase the parameter precision. Several methods for optimizing non-linear mixed effect models has been proposed previously but the impact of optimizing other continuous design parameters, e.g. the dose, has not been investigated to a large extent. Moreover, the optimization method (sequential or simultaneous) for optimizing several continuous design parameters can have an impact on the optimal design. In the sequential approach the time and dose where optimized in sequence and in the simultaneous approach the dose and time points where optimized at the same time. To investigate the sequential approach and the simultaneous approach; three different PK-PD models where considered. In most of the cases the optimization method did change the optimal design and furthermore the precision was improved with the simultaneous approach.

  • 47.
    Nyberg, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Svensson, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Optimal design in nonlinear mixed effects models with discrete type data including Categorical, Count, Dropout and Markov modelsManuscript (preprint) (Other academic)
  • 48.
    Nyberg, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Strömberg, Eric
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hennig, Stefanie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool2012In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 108, no 2, p. 789-805Article in journal (Refereed)
    Abstract [en]

    Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of theoptimal design software PopED, which incorporates many of these recent advances into aneasily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate designperformance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments. 

  • 49.
    Papathanasiou, Theodoros
    et al.
    Univ Copenhagen, Fac Hlth & Med Sci, Dept Drug Design & Pharmacol, Copenhagen, Denmark; Novo Nordisk AS, Quantitat Clin Pharmacol, Søborg, Denmark.
    Strathe, Anders
    Novo Nordisk AS, Quantitat Clin Pharmacol, Søborg, Denmark.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lund, Trine Meldgaard
    Univ Copenhagen, Fac Hlth & Med Sci, Dept Drug Design & Pharmacol, Copenhagen, Denmark.
    Overgaard, Rune Viig
    Novo Nordisk AS, Quantitat Clin Pharmacol, Søborg, Denmark.
    Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations2018In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 3, article id UNSP 64Article in journal (Refereed)
    Abstract [en]

    The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

  • 50.
    Papathanasiou, Theodoros
    et al.
    Univ Copenhagen, Fac Hlth & Med Sci, Dept Drug Design & Pharmacol, Copenhagen, Denmark;Novo Nordisk AS, Quantitat Clin Pharmacol, Soborg, Denmark.
    Strathe, Anders
    Novo Nordisk AS, Quantitat Clin Pharmacol, Soborg, Denmark.
    Overgaard, Rune Viig
    Novo Nordisk AS, Quantitat Clin Pharmacol, Soborg, Denmark.
    Lund, Trine Meldgaard
    Univ Copenhagen, Fac Hlth & Med Sci, Dept Drug Design & Pharmacol, Copenhagen, Denmark.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models2019In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 5, article id 95Article in journal (Refereed)
    Abstract [en]

    Combinations of pharmacological treatments are increasingly being investigated for potentially higher clinical benefit, especially when the combined drugs are expected to act via synergistic interactions. The clinical development of combination treatments is particularly challenging, particularly during the dose-selection phase, where a vast range of possible combination doses exists. The purpose of this work was to evaluate the added value of using optimal design for guiding the dose allocation in drug combination dose-finding studies as compared with a typical drug-combination trial. Optimizations were performed using local [D(s)-optimality] and global [ED(s)-optimality] optimal designs to maximize the precision of model parameters in a number of potential exposure-response (E-R) surfaces. A compound criterion [D(s)/V-optimality] was used to optimize the precision of model predictions in specific parts of the E-R surfaces. Optimal designs provided unbiased estimates and significantly improved the accuracy of results relative to the typical design. It was possible to improve the efficiency and overall parameter precision up to 7832% and 96.6% respectively. When the compound criterion was used, the probability to accurately identify the optimal dose-combination increased from 71% for the typical design up to 91%. These results indicate that optimal design methodology in tandem with E-R analyses is a beneficial tool that can be used for appropriate dose allocation in dose-finding studies for drug combinations.

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