| アブストラクト | Recent pharmacological studies have been developed based on finding new disease-related genes, accompanied by the production of gene-manipulated disease model animals and high-affinity ligands for the target proteins. However, the emergence of this gene-based strategy in drug development has led to the rapid depletion of drug target molecules. To overcome this, we have attempted to utilize clinical big data to explore a novel and unexpected hypothesis of drug-drug interaction that would lead to drug repositioning. Here, we introduce our data-driven approach in which adverse event self-reports are statistically analyzed and compared in order to find and validate new drug targets. The hypotheses provided by such a data-driven approach will likely impact the style of future drug development and pharmaceutical study. |
|---|
| ジャーナル名 | Biological & pharmaceutical bulletin |
|---|
| Pubmed追加日 | 2020/3/3 |
|---|
| 投稿者 | Kaneko, Shuji; Nagashima, Takuya |
|---|
| 組織名 | Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences,;Kyoto University. |
|---|
| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/32115497/ |
|---|
