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."





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