A Bayesian Perspective on Estimation of Variability and Uncertainty in
Mechanism-Based Models
by
Dr. Tarek Leil
Director
Bristol-Myers Squibb
New Date: September 24, 2013
1:00 - 2:00 pm EDT
>From Dr. Leil, "As our understanding of biological systems and pharmacology
>increases, the knowledge required to develop predictive mathematical models
>based on underlying biological/pharmacological processes becomes only a
>mouse-click away. This has led to an increase in the use of mechanism-based
>models for prediction of drug pharmacokinetics (PK) and pharmacodynamics (PD).
>Because of the high dimensionality of these models and the limitations of
>typical non-linear mixed effects (NLME) estimation algorithms, simultaneous
>estimation of fixed and random effect parameters has been difficult. In
>addition, the incorporation of prior information can be subjective, often
>requiring fixing of numerous model parameters. Markov-chain Monte Carlo (MCMC)
>Bayesian estimation algorithms, while relatively computer intensive, are not
>as sensitive to increases in model complexity as traditional estimation
>approaches, permit transparent and robust incorporation of prior information,
>and allow estimation of fixed and random effect parameters for mechanism-based
>models. The concepts of Bayesian NLME approaches will be discussed, and
>examples of Bayesian mechanism-based PK and PD models will be presented."
Dr. Leil's presentation is a part of the monthly world-wide webinar series
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