SIMPATHIC: Accelerating drug repurposing for rare diseases by exploiting SIMilarities in clinical and molecular PATHology is a research paper published in Molecular Genetics and Metabolism (2025). On theSindex it has a DataRank of 0.165. It has been cited 2 times.
Rare diseases affect over 400 million people worldwide, with approved treatment available for less than 6 % of these diseases. Drug repurposing is a key strategy in the development of therapies for rare disease patients with large unmet medical needs. The process of repurposing drugs compared to novel drug development is a time-saving and cost-efficient method potentially resulting in higher success rates. To accelerate and ensure sustainability in therapy development for rare neurometabolic, neurological, and neuromuscular diseases, an international consortium SIMilarities in clinical and molecular PATHology (SIMPATHIC) has been established where we move away from the one drug one disease concept and move towards one drug targeting a pathomechanism shared between diseases, by applying parallel preclinical and clinical drug development. Here the consortium describes accelerators of drug repurposing pursued by the consortium, including 1) co-creation, 2) patient empowerment, 3) use of standardized induced pluripotent stem cell (iPSC)-derived disease models and cellular and molecular profiling, 4) high-throughput drug screening in neurons, 5) innovative clinical trial design, and 6) selection of appropriate exploitation and patient access models. In this way, a fast and effective drug repurposing pathway for several rare diseases will be established to reduce time from discovery to patient access.
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Base Score Contribution
0.165
From this paper's citation signal
Citation Network Contribution
0
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This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
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