mkin

The R package mkin provides calculation routines for the analysis of chemical degradation data, including multicompartment kinetics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved.

Installation

You can install the latest released version from CRAN from within R:

Background

In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.

Usage

For a start, have a look at the code examples provided for plot.mkinfit and plot.mmkin, and at the package vignettes FOCUS L and FOCUS D.

Documentation

The HTML documentation of the latest version released to CRAN is available at jrwb.de and github. Documentation of the development version is found in the ‘dev’ subdirectory.

Features

  • Highly flexible model specification using mkinmod, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.
  • As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example plot.mmkin.
  • Model solution (forward modelling) in the function mkinpredict is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the deSolve package (default is lsoda).
  • If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see vignette compiled_models. The autogeneration of C code was inspired by the ccSolve package. Thanks to Karline Soetaert for her work on that.
  • By default, kinetic rate constants and kinetic formation fractions are transformed internally using transform_odeparms so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric log-ratio transformation that is now used for the formation fractions.
  • A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.
  • The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.
  • Summary and plotting functions. The summary of an mkinfit object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.
  • The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.
  • When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.
  • Three different error models can be selected using the argument error_model to the mkinfit function.
  • The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as error_model = "obs".
  • A two-component error model similar to the one proposed by Rocke and Lorenzato can be selected using the argument error_model = "tc".
  • Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the nlme.mmkin method.

GUI

There is a graphical user interface that may be useful. Please refer to its documentation page for installation instructions and a manual.

News

There is a ChangeLog, for the latest CRAN release and one for the github master branch.

Credits and historical remarks

mkin would not be possible without the underlying software stack consisting of, among others, R and the package deSolve. In previous version, mkin was also using the functionality of the FME package. Please refer to the package page on CRAN for the full list of imported and suggested R packages. Also, Debian Linux, the vim editor and the Nvim-R plugin have been invaluable in its development.

mkin could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories Ltd (formerly RCC Ltd). mkin greatly profits from and largely follows the work done by the FOCUS Degradation Kinetics Workgroup, as detailed in their guidance document from 2006, slightly updated in 2011 and in 2014.

Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.

The companion package kinfit (now deprecated) was started in 2008 and first published on CRAN on 01 May 2010.

The first mkin code was published on 11 May 2010 and the first CRAN version on 18 May 2010.

In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on mkin, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the FME package.

Somewhat in parallel, Syngenta has sponsored the development of an mkin and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the CAKE website, where you can also find a zip archive of the R scripts derived from mkin, published under the GPL license.

Finally, there is KineticEval, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.

References

Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data Environments 6 (12) 124 doi:10.3390/environments6120124
Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data Environmental Sciences Europe 30 17 doi:10.1186/s12302-018-0145-1

Development

Contributions are welcome!