This Research Topic is part of a series with:
Drug Repositioning: Current Advances and Future Perspectives, Volume IIDrug repositioning is the process of identifying new indications for existing drugs. At present, the conventional de novo drug discovery process requires an average of about 14 years and US$2.5 billion to approve and launch a drug. Drug repositioning can reduce the time and cost of this process because it takes advantage of drugs already in clinical use for other indications or drugs that have cleared phase I safety trials but have failed to show efficacy in the intended diseases.
Historically, drug repositioning has been realized through serendipitous clinical observations or improved understanding of disease mechanisms. For example, sildenafil, a phosphodiesterase 5 inhibitor originally developed to treat angina pectoris, was subsequently found to be an effective treatment for erectile dysfunction. Similarly, monoclonal antibodies against vascular endothelial growth factor, which were originally developed as angiogenesis inhibitors for cancer treatment, have been successfully employed for age-related macular degeneration and diabetic retinopathy, in which angiogenesis is the main pathophysiological mechanism. However, recent technological advances have enabled a more systematic approach to drug repositioning. Genome editing technologies such as the CRISPR/Cas-9 system, combined with small animals such as the rodent, fish, fly, and worm, have been used to model human diseases and to perform in vivo screening of existing drugs for effects on disease-related phenotypes. Likewise, induced pluripotent stem cell technology can be used to generate patient-derived cells for high-throughput in vitro screening of existing drugs for effects on patient-specific cellular phenotypes. Many types of data from a variety of sources are publicly available, including genome-wide association studies, transcriptome datasets, chemical structures, phenome-wide association studies, clinical adverse effect profiles, and literature-derived biochemical data. These collections are a massive source of information useful for analyzing the mechanisms of diseases and the clinical effects of drugs. Thanks to these databases and advances in systems biology, computational drug repositioning has emerged as a powerful and promising strategy. Artificial intelligence is likely to stimulate computational drug repositioning by enabling predictive polypharmacology optimized for individual patients.
We welcome authors to contribute articles focusing on current advances and future perspectives in drug repositioning. This Research Topic will maximize the knowledge for drug repositioning, with the hope of identifying drugs that can be exploited to prevent and/or treat diseases for which effective medications are currently lacking.
This Research Topic is part of a series with:
Drug Repositioning: Current Advances and Future Perspectives, Volume IIDrug repositioning is the process of identifying new indications for existing drugs. At present, the conventional de novo drug discovery process requires an average of about 14 years and US$2.5 billion to approve and launch a drug. Drug repositioning can reduce the time and cost of this process because it takes advantage of drugs already in clinical use for other indications or drugs that have cleared phase I safety trials but have failed to show efficacy in the intended diseases.
Historically, drug repositioning has been realized through serendipitous clinical observations or improved understanding of disease mechanisms. For example, sildenafil, a phosphodiesterase 5 inhibitor originally developed to treat angina pectoris, was subsequently found to be an effective treatment for erectile dysfunction. Similarly, monoclonal antibodies against vascular endothelial growth factor, which were originally developed as angiogenesis inhibitors for cancer treatment, have been successfully employed for age-related macular degeneration and diabetic retinopathy, in which angiogenesis is the main pathophysiological mechanism. However, recent technological advances have enabled a more systematic approach to drug repositioning. Genome editing technologies such as the CRISPR/Cas-9 system, combined with small animals such as the rodent, fish, fly, and worm, have been used to model human diseases and to perform in vivo screening of existing drugs for effects on disease-related phenotypes. Likewise, induced pluripotent stem cell technology can be used to generate patient-derived cells for high-throughput in vitro screening of existing drugs for effects on patient-specific cellular phenotypes. Many types of data from a variety of sources are publicly available, including genome-wide association studies, transcriptome datasets, chemical structures, phenome-wide association studies, clinical adverse effect profiles, and literature-derived biochemical data. These collections are a massive source of information useful for analyzing the mechanisms of diseases and the clinical effects of drugs. Thanks to these databases and advances in systems biology, computational drug repositioning has emerged as a powerful and promising strategy. Artificial intelligence is likely to stimulate computational drug repositioning by enabling predictive polypharmacology optimized for individual patients.
We welcome authors to contribute articles focusing on current advances and future perspectives in drug repositioning. This Research Topic will maximize the knowledge for drug repositioning, with the hope of identifying drugs that can be exploited to prevent and/or treat diseases for which effective medications are currently lacking.