Overview of Pumas

Overview of Pumas

Pumas is a collection of a modules that facilitate workflows related to data analysis in the pre-clinical and clinical development stages of drug development. This document provides a general overview of each of these workflows.

The Pumas NLME Workflow

The core Pumas NLME workflow is:

  1. Define a PumasModel. This model defines the structure of the NLME model, but not necessarily the values.
  2. Define a Subject and Population. These structure specify the dosage regimens of the patients, their covariates, and possible observation data to fit against. This can be done programmatically or by reading in tabular input data.
  3. Define a param NamedTuple for the parameters describing the structure of the PumasModel. For simulation, these are the parameter values to simulate with. For estimation, these are the initial conditions.
  4. Call the model API functions (simobs and fit) to perform the simulations and estimation. Both functions act on the pieces defined before.

This workflow directly integrates with usage of the Julia language. For example, one may define Julia functions and use these functions within the definition of a PumasModel. Additionally, one may write loops that defines Subjects or new random params and use these in simulations via simobs. Thus Pumas gives the core tools for handling NLME models but is a lean system that allows the user to seamlessly utilize the larger Julia ecosystem to solve their problem.

After fits and simulations are performed, the returned objects have smart defaults that allow for inspection. Each returned object has a DataFrame overload so that DataFrame(ret) displays the simulation or estimation as tabular data. Additionally, each returned object has a plot overload so that

using Plots
plot(ret)

gives a standardized plot (with options) of the simulation or estimation. All of the internal values are accessible and the fields are documented within the manual.

The Pumas NCA Module Workflow

The NCA submodule works by:

  1. Defining an NCASubject or NCAPopulation, either programmatically or by reading tabular data in the PumasNCADF format.
  2. Running NCA functions on the subject or population, such as NCA.auc, or generating a full NCA.report.

Each of the NCA data objects have smart defaults that allow for inspection. Each of them has a plot overload so that:

using Plots
plot(nca)

gives a standardized plot (with options) of the NCA data.