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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