ArXiv Preprint
Pharmaceutical researchers are continually searching for techniques to
improve both drug development processes and patient outcomes. An area of recent
interest is the potential for machine learning applications within
pharmacology. One such application not yet given close study is the
unsupervised clustering of plasma concentration-time curves, hereafter,
pharmacokinetic (PK) curves. This can be done by treating a PK curve as a time
series object and subsequently utilizing the extensive body of research related
to the clustering of time series data objects. In this paper, we introduce
hierarchical clustering within the context of clustering PK curves and find it
to be effective at identifying similar-shaped PK curves and informative for
understanding patterns of PK curves via its dendrogram data visualization. We
also examine many dissimilarity measures between time series objects to
identify Euclidean distance as generally most appropriate for clustering PK
curves. We further show that dynamic time warping, Fr\'echet, and
structure-based measures of dissimilarity like correlation may produce
unexpected results. Finally, we apply these methods to a dataset of 250 PK
curves as an illustrative case study to demonstrate how the clustering of PK
curves can be used as a descriptive tool for summarizing and visualizing
complex PK data, which may enhance the study of pharmacogenomics in the context
of precision medicine.
Jackson P. Lautier, Stella Grosser, Jessica Kim, Hyewon Kim, Junghi Kim
2022-10-24
