Expand description
PMcore is a framework for developing and running non-parametric algorithms for population pharmacokinetic modelling
The framework is designed to be modular and flexible, allowing for easy integration of new algorithms and methods. It is heavily designed around the specifications for Pmetrics, a package for R, and is designed to be used in conjunction with it. However, as a general rust library, it can be used for a wide variety of applications, not limited to pharmacometrics.
§Configuration
PMcore is configured using a TOML file, which specifies the settings for the algorithm. The settings file is divided into sections, each of which specifies a different aspect of the algorithm. They are further described in the routines::settings module.
§Model definition
As PMcore is provided as a library, the user must define the model to be used. Some algebraic models are provided, and more will be added, but the user is free to define their own model. The model must implement the [routines::simulation::predict] trait, which specifies the methods that the model must implement. For more information on how to define models with ordinary differential equations, please look at at the examples.
§Data format
Data is provided in a CSV file, and the format is described in the table below. For each subject, there must be at least one dose event.
| Column | Description | Conditions |
|---|---|---|
ID | Unique subject ID | |
EVID | Event type; 0 = observation, 1 = dose, 4 = reset | |
TIME | Time of event | |
DUR | Duration of an infusion | Must be provided if EVID = 1 |
DOSE | The dose amount | Must be provided if EVID = 1 |
ADDL | The number of additional doses to be given at the interval II | |
II | Interval for additional doses | |
INPUT | The compartment the dose should be delivered to | |
OUT | The observed value | Must be provided if EVID = 0 |
OUTEQ | Denotes the output equation for which OUT is provided | |
C0 | Optional override of the error polynomial coefficient | |
C1 | Optional override of the error polynomial coefficient | |
C2 | Optional override of the error polynomial coefficient | |
C3 | Optional override of the error polynomial coefficient | |
COV... | Any additional columns are assumed to be covariates, one per column | Must be present for the first event for each subject |
§Examples
A couple of examples are provided in the examples directory. The settings.toml file contains the settings for the algorithm, and the data.csv file contains the data.
They can be run using the following command
cargo run --release --example `example_name`
where example_name is the name of the example to run. Currently, the following examples are available:
bimodal_ke: A simple, one-compartmental model following an intravenous infusion. The example is named by the bimodal distribution of one of two parameters,Ke, the elimination rate constant. The example is designed to demonstrate the ability of the algorithm to handle multimodal distributions, and detect outliers.simple_covariates: A simple example with a single subject and a single dose event, with covariates.
Modules§
- Provides the various algorithms used within the framework
- Logger functionality for the framework using [tracing]
- A collection of commonly used items to simplify imports.
- Routines for the crate
Structs§
- A hash map implemented with quadratic probing and SIMD lookup.
Type Aliases§
Result<T, Error>