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:
- Define a
PumasModel. This model defines the structure of the NLME model, but not necessarily the values.
- Define a
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.
- Define a
NamedTuplefor 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.
- Call the model API functions (
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:
- Defining an
NCAPopulation, either programmatically or by reading tabular data in the
- Running NCA functions on the subject or population, such as
NCA.auc, or generating a full
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.