Pumas (PharmaceUtical Modeling And Simulation) is a suite of tools to perform quantitative analytics of various kinds across the horizontal 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 1.0 currently includes:
- Non-compartmental Analysis
- Specification of Nonlinear Mixed Effects (NLME) Models
- Simulation of NLME model using differential equations or analytical solutions
- Deep control over the differential equation solvers for high efficiency
- Estimation of NLME parameters via Maximum Likelihood and Bayesian methods
- Parallelization capabilities for both simulation and estimation
- Mixed analytical and numerical problems
- Interfacing with global optimizers for more accurate parameter estimates
- Simulation and estimation diagnostics for model post-processing
- Global and local sensitivity analysis routines for multi-scale models
- Bioequivalence analysis
Additional features are under development, with the central goal being a 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.
Pumas is covered by the Julia Computing 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 email@example.com
Pumas can be downloaded from https://pumas.ai/products/pumas/download
One can start using Pumas by invoking it from the REPL as below.
To start understanding the package in more detail, please checkout the tutorials at the start of this manual. We highly suggest that all new users start with the Introduction to Pumas tutorial! If you find any example where there seems to be an error, please open an issue.
If you have questions about usage, please join the official Pumas Discourse and take part in the discussion there. There is also a #pumas channel on the JuliaLang Slack for more informal discussions around Pumas.jl usage.
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.
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/
- Introduction to Pumas
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
- Dosage Regimens, Subjects, and Populations
- Simulation of Pumas Models
- Estimating Parameters of Pumas Models
- Defining Data for Estimation
- Maximum Likelihood Estimation
- Bayesian Estimation
- Maximum A Posteriori (MAP) Estimation
- Noncompartmental Analysis (NCA)
- Bioequivalence Analysis (BE)
- Frequently Asked Questions (FAQ)
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.
- Matching Parameter Types and Domains
- RealDomain and VectorDomain
- Positive-Definite Matrix Domains
- Distributional Domains
- Dosing Control Parameters (DCP)
- Dynamical Problem Types
- Error models
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.
Please visit https://pumas.ai/ to know more about our team and capabilities