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Allows users to automate the data analysis pipeline of Poly-Pharmacokinetics (PK) strategy. polyPK uses metabolomics approach for pharmacokinetic analysis and visualization on multi-component drugs. It supplies over 10 functions for: data pre-processing, differential compound identification and grouping, traditional PK parameters calculation, multivariate statistical analysis, correlations, cluster analyses, and resulting visualization. This tool is an independant platform and can be applied in the PK studies of Puerh tea and many other multi-component drugs.
gPKPDSim / Genentech PKPD Simulator
Aims to create models. gPKPDSim offers a means to simplify the use of preclinical and translational pharmacokinetic-pharmacodynamic (PKPD) models in drug development. It can serve to corroborate the results from published papers, compare the results with other scenarios that may not have been included in the original publications and use those to informal internal decisions. This tool aims to reduce animal utilization and assist with decision-makings.
NONMEM / NONlinear Mixed Effects Modeling
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.
Phoenix NLME
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.
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