Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, 'drug repurposing' (or 'drug repositioning') has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.

Park K. Transl Clin Pharmacol. 2021 Sep;29(3):117-124. https://doi.org/10.12793/tcp.2021.29.e18

Park K. Yonsei Med J. 2017 Jan;58(1):1-8. https://doi.org/10.3349/ymj.2017.58.1.1

Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose requirements in the real‐world setting. Here we use IRT to extract two latent variables that represent tremor and non‐tremor‐related symptoms from baseline assessments of UPDRS Part III scores. We show that relative magnitudes of the two latent variables are strong predictors of the progressive increase of levodopa equivalent dose (LED). Retrospectively collected item‐level UPDRS Part III scores and longitudinal records of prescribed medication doses of 128 patients with de novo PD extracted from the electronic medical records were used for model building. Supplementary analysis based on a subset of 36 patients with at least three serial assessments of UPDRS Part III scores suggested that the two latent variables progress at significantly different rates. A web application was developed to facilitate the use of our model in making individualized predictions of future LED and disease progression.