  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 (ML) applications within
pharmacology. One such application not yet given close study is the
unsupervised clustering of plasma concentration-time curves, hereafter,
pharmacokinetic (PK) curves. In this paper, we present our findings on how to
cluster PK curves by their similarity. Specifically, we find clustering to be
effective at identifying similar-shaped PK curves and informative for
understanding patterns within each cluster of PK curves. Because PK curves are
time series data objects, our approach utilizes the extensive body of research
related to the clustering of time series data as a starting point. As such, we
examine many dissimilarity measures between time series data objects to find
those most suitable for PK curves. We identify Euclidean distance as generally
most appropriate for clustering PK curves, and we further show that dynamic
time warping, Fr\'{e}chet, and structure-based measures of dissimilarity like
correlation may produce unexpected results. As an illustration, we apply these
methods in a case study with 250 PK curves used in a previous pharmacogenomic
study. Our case study finds that an unsupervised ML clustering with Euclidean
distance, without any subject genetic information, is able to independently
validate the same conclusions as the reference pharmacogenomic results. To our
knowledge, this is the first such demonstration. Further, the case study
demonstrates how the clustering of PK curves may generate insights that could
be difficult to perceive solely with population level summary statistics of PK
metrics.
