Input estimation for drug discovery using optimal control and Markov chain Monte Carlo approaches.

ABSTRACT: Input estimation is employed in cases where it is desirable to recover the form of an input function which cannot be directly observed and for which there is no model for the generating process. In pharmacokinetic and pharmacodynamic modelling, input estimation in linear systems (deconvolution) is well established, while the nonlinear case is largely unexplored. In this paper, a rigorous definition of the input-estimation problem is given, and the choices involved in terms of modelling assumptions and estimation algorithms are discussed. In particular, the paper covers Maximum a Posteriori estimates using techniques from optimal control theory, and full Bayesian estimation using Markov Chain Monte Carlo (MCMC) approaches. These techniques are implemented using the optimisation software Cas

SUBMITTER: Tragardh M 

PROVIDER: S-EPMC4791487 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature