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Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident.

Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study.

This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration.

This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2011. , 74 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 149
Keyword [en]
Pharmacometrics, optimal design, nonlinear mixed effects models, robust design, optimizing drug development, population models
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
URN: urn:nbn:se:uu:diva-160481ISBN: 978-91-554-8202-2 (print)OAI: oai:DiVA.org:uu-160481DiVA: diva2:451173
Public defence
2011-12-09, B21, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-18 Created: 2011-10-24 Last updated: 2011-11-23Bibliographically approved
List of papers
1. Simultaneous optimal experimental design on dose and sample times
Open this publication in new window or tab >>Simultaneous optimal experimental design on dose and sample times
2009 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 2, 125-145 p.Article in journal (Refereed) Published
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.

Keyword
Optimal design, simultaneous optimization, dose optimization, pharmacometrics
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-122156 (URN)10.1007/s10928-009-9114-z (DOI)000265825000002 ()19319484 (PubMedID)
Available from: 2010-04-06 Created: 2010-04-06 Last updated: 2017-12-12Bibliographically approved
2. Trial treatment length optimization with an emphasis on disease progression studies
Open this publication in new window or tab >>Trial treatment length optimization with an emphasis on disease progression studies
2009 (English)In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 49, no 3, 323-335 p.Article in journal (Refereed) Published
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.

Keyword
optimal design, disease progression studies, clinical trial design, pharmacometrics
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-122158 (URN)10.1177/0091270008329560 (DOI)000263695600007 ()19246730 (PubMedID)
Available from: 2010-04-06 Created: 2010-04-06 Last updated: 2017-12-12Bibliographically approved
3. Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin
Open this publication in new window or tab >>Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin
Show others...
2012 (English)In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 52, no 3, 347-360 p.Article in journal (Refereed) Published
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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-160467 (URN)10.1177/0091270010397731 (DOI)000300755600006 ()21543664 (PubMedID)
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2017-12-08Bibliographically approved
4. Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design
Open this publication in new window or tab >>Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design
2009 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 36, no 3, 281-295 p.Article in journal (Refereed) Published
Abstract [en]

Intravenous glucose tolerance test (IVGTT) provocations are informative, but complex and laborious, for studying the glucose-insulin system. The objective of this study was to evaluate, through optimal design methodology, the possibilities of more informative and/or less laborious study design of the insulin modified IVGTT in type 2 diabetic patients.

A previously developed model for glucose and insulin regulation was implemented in the optimal design software PopED 2.0. The following aspects of the study design of the insulin modified IVGTT were evaluated; (1) glucose dose, (2) insulin infusion, (3) combination of (1) and (2), (4) sampling times, (5) exclusion of labeled glucose. Constraints were incorporated to avoid prolonged hyper- and/or hypoglycemia and a reduced design was used to decrease run times. Design efficiency was calculated as a measure of the improvement with an optimal design compared to the basic design.

The results showed that the design of the insulin modified IVGTT could be substantially improved by the use of an optimized design compared to the standard design and that it was possible to use a reduced number of samples. Optimization of sample times gave the largest improvement followed by insulin dose. The results further showed that it was possible to reduce the total sample time with only a minor loss in efficiency. Simulations confirmed the predictions from PopED. The predicted uncertainty of parameter estimates (CV) was low in all tested cases, despite the reduction in the number of samples/subject. The best design had a predicted average CV of parameter estimates of 19.5%.

We conclude that improvement can be made to the design of the insulin modified IVGTT and that the most important design factor was the placement of sample times followed by the use of an optimal insulin dose. This paper illustrates how complex provocation experiments can be improved by sequential modeling and optimal design.

Keyword
PopED, NONMEM, glucose, insulin, optimal design
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-99117 (URN)10.1007/s10928-009-9123-y (DOI)000267541600005 ()19554431 (PubMedID)
Available from: 2009-03-10 Created: 2009-03-09 Last updated: 2017-12-13Bibliographically approved
5. Optimal experimental design for assessment of enzyme kinetics in a drug discovery screening environment
Open this publication in new window or tab >>Optimal experimental design for assessment of enzyme kinetics in a drug discovery screening environment
Show others...
2011 (English)In: Drug Metabolism And Disposition, ISSN 0090-9556, E-ISSN 1521-009X, Vol. 39, no 5, 858-863 p.Article in journal (Refereed) Published
Abstract [en]

A penalized ED-optimal design with a discrete parameter distribution was used to find an optimal experimental design for assessment of enzyme kinetics in a screening environment. A data set for enzyme kinetic data (Vmax and Km) was collected from previously reported studies and every Vmax/Km pair (n=76) was taken to represent a unique drug compound. The design was restricted to 15 samples, an incubation time of up to 40 min and starting concentrations (C0) for the incubation between 0.01 and 100 µM. The optimization was performed by finding the sample times and C0 returning the lowest uncertainty (SE) of the model parameter estimates. Individual optimal designs (I-OD), one general optimal design (G-OD) and one for laboratory practice pragmatically modified design (OD) were obtained. In addition, a standard design (STD-D), representing a commonly applied approach for metabolic stability investigations, was constructed. Simulations were performed for OD and STD-D using the Michaelis-Menten (MM) equation and enzyme kinetic parameters were estimated both with MM and a mono exponential (EXP) decay. OD generated a better result (RSE) for 99% of the compounds and an equal or better result (RMSE) for 78% of the compounds. Furthermore, high-quality estimates (RMSE <30%) of both Vmax and Km could be obtained for a considerable number (26%) of the investigated compounds. The results presented in this study demonstrate that the output could generally be improved when compared to that obtained from the standard approaches used today.

Keyword
enzyme kinetics, drug discovery screen, optimal experimental design, CLint, Vmax, Km
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-132347 (URN)10.1124/dmd.110.037309 (DOI)000289619600017 ()21289074 (PubMedID)
Available from: 2010-10-21 Created: 2010-10-18 Last updated: 2017-12-12Bibliographically approved
6. Serial correlation in optimal design for nonlinear mixed effects models
Open this publication in new window or tab >>Serial correlation in optimal design for nonlinear mixed effects models
Show others...
2012 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 39, no 3, 239-249 p.Article in journal (Refereed) Published
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.

Keyword
Population optimal design, Serial correlation, ED-optimal designs, Robust designs, Autocorrelation
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-160470 (URN)10.1007/s10928-012-9245-5 (DOI)000304617500002 ()
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2017-12-08Bibliographically approved
7. Optimal design in nonlinear mixed effects models with discrete type data including Categorical, Count, Dropout and Markov models
Open this publication in new window or tab >>Optimal design in nonlinear mixed effects models with discrete type data including Categorical, Count, Dropout and Markov models
(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-160479 (URN)
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2012-04-02
8. PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
Open this publication in new window or tab >>PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
Show others...
2012 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 108, no 2, 789-805 p.Article in journal (Refereed) Published
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. 

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-160475 (URN)10.1016/j.cmpb.2012.05.005 (DOI)000310828200030 ()
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2017-12-08

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