Non-linear mixed effects modeling : From methodology and software development to driving implementation in drug development science

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effec...

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Published inJournal of pharmacokinetics and pharmacodynamics Vol. 32; no. 2; pp. 161 - 183
Main Authors PILLAI, Goonaseelan, MENTRE, France, STEIMER, Jean-Louis
Format Journal Article
LanguageEnglish
Published Dordrecht Springer 01.04.2005
Springer Nature B.V
Subjects
Algorithms
Anticoagulants - pharmacokinetics
Biological and medical sciences
Cardiotonic Agents - pharmacokinetics
Clinical medicine
Digoxin - pharmacokinetics
Drug Industry
Medical sciences
Models, Statistical
Nonlinear Dynamics
Pharmacokinetics
Pharmacology - statistics & numerical data
Pharmacology - trends
Pharmacology. Drug treatments
Software
Warfarin - pharmacokinetics
algorithms
Methodology
Vehicle driving
model-based
PKPD
Algorithm
Modeling
Biological activity
Mixture
Research and development
NLME
Mixed
Development
Software
Models
PK
pharmacokinetics: pharmacodynamics
Non linear effect
Pharmacokinetics
NONMEM
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Summary:Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940-2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making.
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SourceType-Scholarly Journals-1
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ISSN:1567-567X
1573-8744
DOI:10.1007/s10928-005-0062-y