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Benefits of Non-Linear Mixed Effect Modeling and Optimal Design: Pre-Clinical and Clinical Study Applications
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Despite the growing promise of pharmaceutical research, inferior experimentation or interpretation of data can inhibit breakthrough molecules from finding their way out of research institutions and reaching patients. This thesis provides evidence that better characterization of pre-clinical and clinical data can be accomplished using non-linear mixed effect modeling (NLMEM) and more effective experiments can be conducted using optimal design (OD). 

To demonstrate applicability of NLMEM and OD in pre-clinical applications, in vitro ligand binding studies were examined. NLMEMs were used to evaluate precision and accuracy of ligand binding parameter estimation from different ligand binding experiments using sequential (NLR) and simultaneous non-linear regression (SNLR). SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.  OD of these ligand binding experiments for one and two binding site systems including commonly encountered experimental errors was performed.  OD was employed using D- and ED-optimality.  OD demonstrated that reducing the number of samples, measurement times, and separate ligand concentrations provides robust parameter estimation and more efficient and cost effective experimentation.

To demonstrate applicability of NLMEM and OD in clinical applications, a phase advanced sleep study formed the basis of this investigation. A mixed-effect Markov-chain model based on transition probabilities as multinomial logistic functions using polysomnography data in phase advanced subjects was developed and compared the sleep architecture between this population and insomniac patients. The NLMEM was sufficiently robust for describing the data characteristics in phase advanced subjects, and in contrast to aggregated clinical endpoints, which provide an overall assessment of sleep behavior over the night, described the dynamic behavior of the sleep process. OD of a dichotomous, non-homogeneous, Markov-chain phase advanced sleep NLMEM was performed using D-optimality by computing the Fisher Information Matrix for each Markov component.  The D-optimal designs improved the precision of parameter estimates leading to more efficient designs by optimizing the doses and the number of subjects in each dose group. 

This thesis provides examples how studies in drug development can be optimized using NLMEM and OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. , 65 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 181
Keyword [en]
Pharmacometrics, optimal design, nonlinear mixed effects models, population models
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-209247ISBN: 978-91-554-8779-9 (print)OAI: oai:DiVA.org:uu-209247DiVA: diva2:656669
Public defence
2013-12-06, B22, BMC, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Note

My name should be listed as "Charles Steven Ernest II" on cover.

Available from: 2013-11-14 Created: 2013-10-15 Last updated: 2017-09-11Bibliographically approved
List of papers
1. Methodological Comparison of In Vitro Binding Parameter Estimation: Sequential vs. Simultaneous Non-linear Regression
Open this publication in new window or tab >>Methodological Comparison of In Vitro Binding Parameter Estimation: Sequential vs. Simultaneous Non-linear Regression
2010 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 27, no 5, 866-877 p.Article in journal (Refereed) Published
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.

Keyword
BEV, between-experiment variability, B-NS, non-specific binding, NLR, sequential non-linear regression, SNLR, simultaneous non-linear regression, alpha, proportional constant relating non-specific binding to ligand concentration
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-137011 (URN)10.1007/s11095-010-0082-1 (DOI)000276512500014 ()
Available from: 2010-12-14 Created: 2010-12-14 Last updated: 2017-12-11Bibliographically approved
2. Simultaneous optimal experimental design for in vitro binding parameter estimation
Open this publication in new window or tab >>Simultaneous optimal experimental design for in vitro binding parameter estimation
2013 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 5, 573-585 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2013
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-209246 (URN)10.1007/s10928-013-9330-4 (DOI)000325263800003 ()
Available from: 2013-10-15 Created: 2013-10-15 Last updated: 2017-12-06Bibliographically approved
3. Multinomial Markov-chain model of sleep architecture in Phase Advanced Subjects
Open this publication in new window or tab >>Multinomial Markov-chain model of sleep architecture in Phase Advanced Subjects
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-208582 (URN)
Available from: 2013-10-03 Created: 2013-10-03 Last updated: 2014-01-23Bibliographically approved
4. Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model
Open this publication in new window or tab >>Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model
2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 6, 639-654 p.Article in journal (Refereed) Published
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.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-208583 (URN)10.1007/s10928-014-9391-z (DOI)000344800200008 ()25308776 (PubMedID)
Available from: 2013-10-03 Created: 2013-10-03 Last updated: 2017-12-06Bibliographically approved

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