Population PK/PD modeling software tools | Drug discovery data analysis
Pharmacometrics is a scientific discipline that integrates pharmacokinetics, pharmacodynamics, pharmacology, physiology, knowledge about diseases, and statistics to quantify interactions between drugs and patients using mathematical models. In recent decades, pharmacometric approaches, such as pharmacokinetic-pharmacodynamic modeling and simulation, are increasingly being applied in the drug development process.
Aims to make practical Markov chain Monte Carlo (MCMC) methods available to applied statisticians. WinBUGS allows models to be described using an amended version of the BUGS language, or as graphical representations of models which can be translated to a text-based description. The tool can be used without further reference to any of the BUGS project. It allows researchers to manipulate any output produced by the software.
Fits models to many different types of data. NONMEM is based on classical likelihood methods and Monte Carlo expectation-maximization and Markov Chain Monte Carlo (MCMC) Bayesian methods. It provides parallel computing of a single problem over multiple cores or computers, significantly reducing completion time. The tool offers option to obtain near identical results for repeated runs of Monte Carlo Expectation-Maximization (MCEM) problems regardless of whether job is single CPU processed or parallel processed.
Models population and realizes simulation for scientists with all levels of experience—from the most advanced modelers to novice PK/PD scientists. Phoenix NLME includes integrated data preparation, modeling, and graphics tools. It supports regulatory submissions across the world. The tool allows users to compare models side-by-side to facilitate decision making. It generates automatically diagnostic tables and figures for each model to help users assess model robustness.
Assists users in drawing samples from the joint posterior distribution of the parameters of a Bayesian model. JAGS is a free software package for analysis of Bayesian models. It uses a suite of Markov chain Monte Carlo methods and general-purpose stochastic simulation methods. This approach uses a dialect of the BUGS language to express directed acyclic graphs, a mathematical formalism to define joint densities.
Evaluates and/or optimises population designs based on the expression of the Fisher information matrix (FIM) in nonlinear mixed effects models. PFIM keeps improvements for models with inter-occasion variability (IOV) and models including fixed effects for the influence of discrete covariates on the parameters. It allows Bayesian design for prediction of standard errors and shrinkage of individual parameter using Maximum A Posteriori estimation.
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