Welcome to Pumas
Pumas (PharmaceUtical Modeling And Simulation) is a suite of tools used to perform quantitative analytics of various kinds across the horizon of pharmaceutical drug development. The purpose of this framework is to bring efficient implementations of all aspects of the analytics in this domain under one cohesive package. Pumas 2.5 currently includes:
- Non-compartmental Analysis
- Specification of Nonlinear Mixed Effects (NLME) Models
- Simulation of NLME models using differential equations or analytical solutions
- Deep control over the differential equation solvers for high efficiency
- Estimation of NLME parameters via Maximum Likelihood, Expectation Maximization and Bayesian methods
- Automated covariate selection
- Parallelization capabilities for both simulation and estimation
- Mixed analytical and numerical problems
- Simulation and estimation diagnostics for model post-processing
- Interactive model exploration and diagnostics tools through webapps
- Automated report generation for models and non-compartmental analysis
- Global and local sensitivity analysis routines for multi-scale models
- Bioequivalence analysis
- Bioequivalence Power computation and sample size determination
- Optimal design of experiments
- Evaluation of model identifiability
Additional features are under development, with the central goal of providing a comprehensive parameter estimation tool, complete clinical trial simulation engine which combines efficiency with a standardized workflow, consistent nomenclature, and automated report generation. All of this takes place in the high level interactive Julia programming language and integrates with the other packages in the Julia ecosystem for a robust user experience.
License
Pumas is covered by the JuliaHub EULA. Pumas is a proprietary product developed by Pumas-AI, Inc. It is available free of cost for educational and research institutes. For commercial use, please contact sales@pumas.ai or to know more visit Pumas for Enterprises.
Getting Started: Accessing Pumas
Pumas is available via JuliaHub, or as desktop or server application. The desktop and server applications are made available free of charge for academics, see Pumas for Academia. The onboarding video playlist on YouTube will guide you through the platform and help you get started.
For additional information and resources on using Pumas in the cloud, refer to JuliaHub's documentation. The documentation provides comprehensive tutorials and guides covering essential features. See, for example, the following guide on how to create batch jobs
One can start using Pumas by invoking it from the REPL (Read-Eval-Print Loop, the command line environment for Julia) as below.
using Pumas
To start understanding the Pumas suite of products in more detail, we have our user-friendly notebook tutorials at tutorials.pumas.ai, along with video courses at Pumas Labs. We highly suggest that all new users start with the Onboarding Video Playlist!
If you have questions about usage, please join the official Pumas Discourse and take part in the discussion there.
Annotated Table of Contents
Below is an annotated table of contents with summaries to help guide you to the appropriate page. The materials shown here are links to the same materials in the sidebar. Additionally, you may use the search bar provided on the left to directly find the manual pages with the appropriate terms.
Tutorials
These tutorials give an "example first" approach to learning Pumas and establish the standardized nomenclature for the package. Additionally, ways of interfacing with the rest of the Julia ecosystem for visualization and statistics are demonstrated. Thus, we highly recommend new users check out these tutorials before continuing into the manual.
More tutorials can be found at https://tutorials.pumas.ai/. If you are looking for Optimal Design tutorials, please visit the Optimal Design Tutorial.
Basics
The basics are the core principles of using Pumas. An overview introduces the user to the basic design tenants, and manual pages proceed to give details on the central functions and types used throughout Pumas.
- Overview of Pumas
- Defining NLME models in Pumas
- Model Representation
- Dosage Regimens, Subjects, and Populations
- Data Representation
- Simulation of Pumas Models
- Estimating Parameters of Pumas Models
- Estimating Parameters using SAEM
Model Components
This section of the documentation goes into more detail on the model components, specifying the possible domain types, dosage control parameters (DCP), and the various differential equation types for specifying problems with analytical solutions and alternative differential equations such as delay differential equations (DDEs), stochastic differential equations (SDEs), etc.
PumasModel
Domains- PumasEMModel Domains
- Dose Control Parameters (DCP)
- Analytical Solutions and Differential Equations
- PumasModel-Error models
- PumasEMModel-Error models
Analysis
This section of the documentation defines the analysis tooling. Essential tools such as diagnostics, plotting, report generation, and sensitivity analysis are discussed in detail in this portion.
- Inference
- Inference from asymptotic variance-covariance matrices
- Bootstrap-based inference
- Sampling importance resampling (SIR)
- Inference based on samples from the marginal likelihood using
MarginalMCMC
- Calculating mean values and variance-covariance matrices from Bootstrap and SIR
- Extracting standard errors (
stderror
) - Condition number
- Simulation and Estimation Diagnostics
- Global Sensitivity Analysis
- Visual Predictive Check (VPC)
- Plotting
- PDF Reports
Noncompartmental Analysis
This section of the documentation covers the use of Noncompartmental Analysis tooling available with Pumas and independently.
- Non-Compartmental Analysis (NCA)
- Data Format and parsing for NCA
- Handling Missing and BLQ Data
- NCA Functions
- Analysis and Reporting
Bioequivalence
Bioequivalence analysis is available in Pumas through the Bioequivalence
package.
Pumas Development Team
Please visit https://pumas.ai/company to know more about our team and capabilities.
Citing Pumas
If you use Pumas within scientific reports, please cite Pumas via the following paper:
Rackauckas, Chris, et al. "Accelerated predictive healthcare analytics with pumas, a high performance pharmaceutical modeling and simulation platform." (2020).
or in Bibtex form:
@article{rackauckas2020accelerated,
title={Accelerated predictive healthcare analytics with pumas, a high performance pharmaceutical modeling and simulation platform},
author={Rackauckas, Chris and Ma, Yingbo and Noack, Andreas and Dixit, Vaibhav and Mogensen, Patrick Kofod and Byrne, Simon and Maddhashiya, Shubham and Santiago Calder{\'o}n, Jos{\'e} Bayo{\'a}n and Nyberg, Joakim and Gobburu, Jogarao VS and others},
year={2020}
}