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Methodological Comparison of In Vitro Binding Parameter Estimation: Sequential vs. Simultaneous Non-linear Regression
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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.

Place, publisher, year, edition, pages
2010. Vol. 27, no 5, 866-877 p.
Keyword [en]
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: urn:nbn:se:uu:diva-137011DOI: 10.1007/s11095-010-0082-1ISI: 000276512500014OAI: oai:DiVA.org:uu-137011DiVA: diva2:377705
Available from: 2010-12-14 Created: 2010-12-14 Last updated: 2014-01-23Bibliographically approved
In thesis
1. Benefits of Non-Linear Mixed Effect Modeling and Optimal Design: Pre-Clinical and Clinical Study Applications
Open this publication in new window or tab >>Benefits of Non-Linear Mixed Effect Modeling and Optimal Design: Pre-Clinical and Clinical Study Applications
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
Pharmacometrics, optimal design, nonlinear mixed effects models, population models
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-209247 (URN)978-91-554-8779-9 (ISBN)
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

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Hooker, Andrew C.Karlsson, Mats O.

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