Overview of Pumas
Pumas is a collection of modules that facilitate workflows related to data analysis in the pre-clinical and clinical 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
via@model
orPumasEMModel
via@emmodel
. Both of these constructions define the structure of the NLME model. - Define
Subject
s andPopulation
. These structures 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
param
NamedTuple
for the parameters describing the structure of thePumasModel
or thePumasEMModel
. For simulation, these are the parameter values to simulate with. For estimation, these are the initial parameter estimates. - Call the model interface functions,
simobs
orfit
, to perform the simulations or estimation respectively. Both functions act on the objects defined above. - Post-process the results of a model or compare between different models using automated workflows.
- Generate automated reports of your model.
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 define Subject
s or new random param
s and use these in simulations via simobs
. Thus, Pumas supplies the core tools for handling NLME models while remaining 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 plotting function that yields a standardized plot (with options) of the simulation or estimation. All the internal values are accessible, and the fields are documented within the manual.
The Pumas NCA Module Workflow
The NCA submodule works by:
- Defining a
NCASubject
orNCAPopulation
, either programmatically or by reading tabular data in thePumasNCADF
format. - Running NCA functions on the subject or population, such as
NCA.auc
, or generating a full NCA report usingrun_nca
followed byreport
.
Each of the NCA data objects have smart defaults that allow for inspection. Each of them has a plotting function that provides a standardized plot (with options) of the NCA data and results.
The Pumas Bioequivalence Workflow
The Bioequivalence module works by:
- Reading in tabular data that support various designs.
- Performing bioequivalence analysis on the tabular data using
pumas_be
.
An additional Bioequivalence module is BioequivalencePower
which allows one to analyze power and determine sample sizes for bioequivalence studies, prior to a study, or after a pilot study and prior to a main study.
The Pumas Optimal Design Workflow
The optimal design module of Pumas currently includes sample time optimization. The sample time optimization workflow in Pumas is:
- Define the model and subject template or population.
- Define time windows in human-readable times and dates.
- Define resource and logistic constraints.
- Choose the optimality type (D/A/T optimality).
- Optimize the sample times.
- View the optimal sample times for each subject in human-readable times and dates.