The Experts below are selected from a list of 651 Experts worldwide ranked by ideXlab platform
Andrew C Hooker - One of the best experts on this subject based on the ideXlab platform.
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Pharmacometrics meets statistics—A synergy for modern drug development
'Wiley', 2021Co-Authors: Yevgen Ryeznik, Andrew C Hooker, Oleksandr Sverdlov, Elin M. Svensson, Grace Montepiedra, Weng Kee WongAbstract:Abstract Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology‐based drug and disease models are applied in this context. In this paper, we show that statistics and Pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need
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Metaheuristics for Pharmacometrics
'Wiley', 2021Co-Authors: Seongho Kim, Andrew C Hooker, Yu Shi, Grace Hyun J. Kim, Weng Kee WongAbstract:Abstract Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms
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model evaluation of continuous data pharmacometric models metrics and graphics
CPT: Pharmacometrics & Systems Pharmacology, 2017Co-Authors: Thi Huyen Tram Nguyen, Mats O. Karlsson, Nicholas H G Holford, Nidal Alhuniti, I Freedman, Andrew C Hooker, J John, Diane R Mould, J Perez J Ruixo, Elodie L. PlanAbstract: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.
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preconditioning of nonlinear mixed effects models for stabilisation of variance covariance matrix computations
Aaps Journal, 2016Co-Authors: Yasunori Aoki, Rikard Nordgren, Andrew C HookerAbstract: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.
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improved utilization of adas cog assessment data through item response theory based pharmacometric modeling
Pharmaceutical Research, 2014Co-Authors: Sebastian Ueckert, Elodie L. Plan, Mats O. Karlsson, Kaori Ito, Brian Corrigan, Andrew C HookerAbstract:This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.
Elodie L. Plan - One of the best experts on this subject based on the ideXlab platform.
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model evaluation of continuous data pharmacometric models metrics and graphics
CPT: Pharmacometrics & Systems Pharmacology, 2017Co-Authors: Thi Huyen Tram Nguyen, Mats O. Karlsson, Nicholas H G Holford, Nidal Alhuniti, I Freedman, Andrew C Hooker, J John, Diane R Mould, J Perez J Ruixo, Elodie L. PlanAbstract: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.
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Modeling and Simulation of Count Data
CPT: pharmacometrics & systems pharmacology, 2014Co-Authors: Elodie L. PlanAbstract:Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of Pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity.
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improved utilization of adas cog assessment data through item response theory based pharmacometric modeling
Pharmaceutical Research, 2014Co-Authors: Sebastian Ueckert, Elodie L. Plan, Mats O. Karlsson, Kaori Ito, Brian Corrigan, Andrew C HookerAbstract:This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.
Mats O. Karlsson - One of the best experts on this subject based on the ideXlab platform.
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A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism
Journal of Pharmacokinetics and Pharmacodynamics, 2020Co-Authors: Elham Haem, Marziyeh Doostfatemeh, Negar Firouzabadi, Nima Ghazanfari, Mats O. KarlssonAbstract:This manuscript aims to present the first item response theory (IRT) model within a pharmacometric framework to characterize the longitudinal changes of Aberrant Behavior Checklist (ABC) data in children with autism. Data were obtained from 120 patients, which included 20,880 observations of the 58 items for up to three months. Observed scores for each ABC item were modeled as a function of the subject's disability. Longitudinal IRT models with five latent disability variables based on ABC subscales were used to describe the irritability, lethargy, stereotypic behavior, hyperactivity, and inappropriate speech over time. The IRT pharmacometric models could accurately describe the longitudinal changes of the patient's disability while estimating different time-course of disability for the subscales. For all subscales, model-estimated disability was reduced following initiation of therapy, most markedly for hyperactivity. The developed framework provides a description of ABC longitudinal data that can be a suitable alternative to traditional ABC data collected in autism clinical trials. IRT is a powerful tool with the ability to capture the heterogeneous nature of ABC, which results in more accurate analysis in comparison to traditional approaches.
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model evaluation of continuous data pharmacometric models metrics and graphics
CPT: Pharmacometrics & Systems Pharmacology, 2017Co-Authors: Thi Huyen Tram Nguyen, Mats O. Karlsson, Nicholas H G Holford, Nidal Alhuniti, I Freedman, Andrew C Hooker, J John, Diane R Mould, J Perez J Ruixo, Elodie L. PlanAbstract: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.
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improved utilization of adas cog assessment data through item response theory based pharmacometric modeling
Pharmaceutical Research, 2014Co-Authors: Sebastian Ueckert, Elodie L. Plan, Mats O. Karlsson, Kaori Ito, Brian Corrigan, Andrew C HookerAbstract:This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.
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the role of modeling and simulation in development and registration of medicinal products output from the efpia ema modeling and simulation workshop
CPT: Pharmacometrics & Systems Pharmacology, 2013Co-Authors: E Manolis, Mats O. Karlsson, S Rohou, Robert Hemmings, T Salmonson, Peter A MilliganAbstract:Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; 6Pfizer, Pharmacometrics, Global Clinical Pharmacology, Sandwich, UK. Correspondence: E Manolis (Efthymios.Manolis@ema.europa.eu)Received 5 November 2012; accepted 14 January 2013; advance online publication 27 February 2013. doi:10.1038/psp.2013.7
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time for quantitative clinical pharmacology a proposal for a Pharmacometrics curriculum
Clinical Pharmacology & Therapeutics, 2007Co-Authors: Nicholas H G Holford, Mats O. KarlssonAbstract:A formal training program in Pharmacometrics is essential to train clinical pharmacology scientists. A proposal is made for a Pharmacometrics curriculum. The curriculum has components at the undergraduate, graduate and postgraduate levels.
Nicholas H G Holford - One of the best experts on this subject based on the ideXlab platform.
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model evaluation of continuous data pharmacometric models metrics and graphics
CPT: Pharmacometrics & Systems Pharmacology, 2017Co-Authors: Thi Huyen Tram Nguyen, Mats O. Karlsson, Nicholas H G Holford, Nidal Alhuniti, I Freedman, Andrew C Hooker, J John, Diane R Mould, J Perez J Ruixo, Elodie L. PlanAbstract: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.
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a review of mixed effects models of tumor growth and effects of anticancer drug treatment used in population analysis
CPT Pharmacometrics Syst. Pharmacol., 2014Co-Authors: Benjamin Ribba, Paolo Magni, Nicholas H G Holford, Inaki F Troconiz, Ivelina Gueorguieva, P Girard, C Sarr, M Elishmereni, Charlotte Kloft, Lena E FribergAbstract:Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.
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time for quantitative clinical pharmacology a proposal for a Pharmacometrics curriculum
Clinical Pharmacology & Therapeutics, 2007Co-Authors: Nicholas H G Holford, Mats O. KarlssonAbstract:A formal training program in Pharmacometrics is essential to train clinical pharmacology scientists. A proposal is made for a Pharmacometrics curriculum. The curriculum has components at the undergraduate, graduate and postgraduate levels.
Lena E Friberg - One of the best experts on this subject based on the ideXlab platform.
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Pharmacometrics and systems pharmacology 2030
Clinical Pharmacology & Therapeutics, 2020Co-Authors: Lena E Friberg, Stephen B Duffull, Jonathan French, Douglas A Lauffenburger, Donald E Mager, Vikram Sinha, Eric A Sobie, Ping ZhaoAbstract:In 2012, a new journal was launched from the ASCPT family, CPT: Pharmacometrics and Systems Pharmacology (PSP) as both quantitative system pharmacology (QSP) and Pharmacometrics were growing fields in pharmacology, drug development, and drug use. In this Perspective, the present editors and associate editors of PSP want to share their strategic vision of where these two fields, separately and together, should, would, or could be 10 years from now.
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pharmacometric modeling of liver metastases diameter volume and density and their relation to clinical outcome in imatinib treated patients with gastrointestinal stromal tumors
CPT: Pharmacometrics & Systems Pharmacology, 2017Co-Authors: Emilie Schindler, Sreenath M Krishnan, Ron H J Mathijssen, Alessandro Ruggiero, Gaia Schiavon, Lena E FribergAbstract:Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors.
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a review of mixed effects models of tumor growth and effects of anticancer drug treatment used in population analysis
CPT Pharmacometrics Syst. Pharmacol., 2014Co-Authors: Benjamin Ribba, Paolo Magni, Nicholas H G Holford, Inaki F Troconiz, Ivelina Gueorguieva, P Girard, C Sarr, M Elishmereni, Charlotte Kloft, Lena E FribergAbstract:Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.