Discussion
Models characterising drug actions and biological systems can have
different degrees of complexity. Compartmental models, that also
incorporate empirical pharmacodyanmic functions, e.g. the Emax model,
for instance, are built chiefly on the observed data with the aim of
being utilised for predictions to new scenarios. However, their
predictive performance can be limited [18,19,55] when used for
extrapolation. Such models when incorporating sufficient population data
usually provide good insights for answering many drug development and
utilisation questions. However, extrapolating beyond the available data
remains problematic. In contrast, QSP models (including PBPK models) are
built upon mechanistic knowledge of biological systems and drug actions
and therefore allow for predictions of outcomes in previously untested
scenarios. Despite the potential benefits of these mechanistic models,
the issue of model complexity can make them intractable for many
applications. There remains the need for a balance between model
usability and complexity. Model-order reduction can act as a bridging
approach that provides a means of producing intermediary scale models
that bring together the strengths of both simplicity and mechanistic
predictive ability. Model-order reduction can also be used to estimate
between-subject variability in some parameters using population approach
[22] which could then be used to feedback into the original QSP
model.
Despite the benefits that model-order reduction provides, careful
consideration of the limitations of different methods should be given.
Most model-order reduction methods will incur some degree of information
loss relative to the full-order model. The greater extent of model
reduction means, in most situations, greater loss of information. The
optimal balance of the trade-off between model simplicity and accuracy
will largely depend on the intended use of the reduced model. It is also
important to realise the local nature of many model-order reduction
methods, i.e., the reduced model may only be valid for a specific set of
parameter values. Of note, most model-order reduction techniques incur a
large upfront computational cost for producing a reduced model. This
should be balanced against the expected computational speed-up gained by
the reduced model in order to determine whether a particular model-order
reduction technique is worthwhile.
In this work, we have developed an overall framework for considering
various methods of model-order reduction that can be useful in the
context of QSP models (figure 2 & table 1). The framework provides a
natural categorisation of various methods based on their utility. We
hope this can guide modellers to choose the best model-order reduction
method that suits their needs.
In summary, model-order reduction can have a significant impact on the
future of modelling and simulation in drug development and clinical use
[56]. The vast repository of information contained in QSP models can
act as source library from which smaller models to describe a specific
input-output relationship(s) can be extracted through model-order
reduction methods. The existence of automated tools for model-order
reduction can streamline the process of model building saving time and
effort. It is therefore anticipated that pharmacometricians and clinical
pharmacologists would benefit from being familiar with various
model-order reduction techniques and there benefits and potential uses.