Task view: Analysis of Pharmacokinetic Data

Last updated on 2019-07-04 by Bill Denney

Analysis of pharmacokinetic (PK) data is concerned with defining the relationship between the dosing regimen and the body's exposure to drug as indicated by the concentration time curve to determine a dose. To analyze PK data, there are three categories of packages within CRAN: noncompartmental analysis (NCA), modeling (typically using compartmental analysis), and reporting (typically for NCA). NCA is used as method of description of PK with minimal assumptions of the rates of distribution of the drug through the body. NCA is typically used to describe the PK of a drug in clinical studies with many samples per subject on the same and sequential days. The NCA packages are:

ncappc
Performs traditional NCA and simulation-based posterior predictive checks for a population PK model using NCA metrics. It targets summarizing data from model-fit or simulated sources.
NonCompart
Provides basic computational functions for NCA.
PK
Allows estimation of pharmacokinetic parameters using non-compartmental theory. Both complete sampling and sparse sampling designs are implemented. The package provides methods for hypothesis testing and confidence intervals related to superiority and equivalence.
PKNCA
Computes standard NCA parameters and summarizes them with the goal of taking in observed clinical data and providing summaries ready for study reports and regulatory submission.
Modeling of PK data typically uses compartmental methods which assume that the drug enters the body either through an intravenous (IV) or extravascular (often oral or subcutaneous, SC) dose. Packages listed below are restricted to packages that have specific interest to PK modeling and not the (many) packages that support modeling that could be used for PK data. The PK modeling and simulation packages are:
clinPK
Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.
cpk
Provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care.
dfpk
Provides statistical methods involving PK measures for dose finding in Phase 1 clinical trials.
mrgsolve
Facilitates simulation from hierarchical, ordinary differential equation (ODE) based models typically employed in drug development.
nmw
Is a package to understand the algorithms of NONMEM.
PKgraph
Provides a graphical user interface for population pharmacokinetic model diagnosis from a variety of modeling fitting software, including NONMEM, Monolix, SAS, and R.
PKPDmodels
Provides functions to evaluate common pharmacokinetic/pharmacodynamic models and their gradients.
pmxTools
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008)); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
scaRabee
Provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level.
Communication of results is as important (or more important) than actually completing an analysis. While many users are currently using rmarkdown and knitr for general reporting, the features of packages which are important for reporting PK data are:
ncar
Provides NCA for a report writer generating rtf and pdf output.
pkr
Generates NCA data sets compliant to CDISC and other pharmacokinetic functions for reviewer.
PKreport
Provides automatic pipeline for users to visualize data and models with an archive-oriented management tool for users to store, retrieve and modify figures and graph generation based on lattice and ggplot2.
xpose
Diagnostics for non-linear mixed-effects (population) models from 'NONMEM'. 'xpose' facilitates data import, creation of numerical run summary and provide 'ggplot2'-based graphics for data exploration and model diagnostics.
Packages that focus on a single pharmacokinetic model or dataset include:
caffsim
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015)
Packages related to PK study design include:
microsamplingDesign
Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a general simulation methodology. This methodology consist of (1) specifying a pharmacokinetic model including variability among animals; (2) generating possible sampling times; (3) evaluating performance of each time point choice on simulated data; (4) generating possible schemes given a time point choice and additional constraints and finally (5) evaluating scheme performance on simulated data. The default settings differ from the article of Barnett and others, in the default pharmacokinetic model used and the parameterization of variability among animals.
PharmPow
PharmPow contains functions performing power calculations for mixed (sparse/dense sampled) pharmacokinetic study designs. The input data for these functions is tailored for NONMEM .phi files.

Packages

caffsim — 0.2.2

Simulation of Plasma Caffeine Concentrations by Using Population Pharmacokinetic Model

clinPK — 0.9.0

Clinical Pharmacokinetics Toolkit

cpk — 1.3-1

Clinical Pharmacokinetics

dfpk — 3.5.1

Bayesian Dose-Finding Designs using Pharmacokinetics (PK) for Phase I Clinical Trials

microsamplingDesign — 1.0.6

Finding Optimal Microsampling Designs for Non-Compartmental Pharmacokinetic Analysis

mrgsolve — 0.10.0

Simulate from ODE-Based Models

ncappc — 0.3.0

NCA Calculations and Population Model Diagnosis

ncar — 0.4.2

Noncompartmental Analysis for Pharmacokinetic Report

nmw — 0.1.4

Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics

NonCompart — 0.4.5

Noncompartmental Analysis for Pharmacokinetic Data

PharmPow — 1.0

Pharmacometric Power calculations for mixed study designs

PK — 1.3-4

Basic Non-Compartmental Pharmacokinetics

PKgraph — 1.7

Model diagnostics for population pharmacokinetic models

PKNCA — 0.9.1

Perform Pharmacokinetic Non-Compartmental Analysis

PKPDmodels — 0.3.2

Pharmacokinetic/pharmacodynamic models

pmxTools — 0.1.1

Pharmacometric and Pharmacokinetic Toolkit

pkr — 0.1.2

Pharmacokinetics in R

PKreport — 1.5

A reporting pipeline for checking population pharmacokinetic model assumption

scaRabee — 1.1-3

Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models

xpose — 0.4.5

Diagnostics for Pharmacometric Models


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