Institute for Next Generation Healthcare, Mount Sinai School of Medicine, New York, NY, USA.
Courant Institute for Mathematical Sciences, New York University, New York, NY, USA.
CPT Pharmacometrics Syst Pharmacol. 2021 May;10(5):500-510. doi: 10.1002/psp4.12626. Epub 2021 May 2.
Rare diseases affect 10% of the first-world population, yet over 95% lack even a single pharmaceutical treatment. In the present age of information, we need ways to leverage our vast data and knowledge to streamline therapeutic development and lessen this gap. Here, we develop and implement an innovative informatic approach to identify therapeutic molecules, using the Connectivity Map and LINCS L1000 databases and disease-associated transcriptional signatures and pathways. We apply this to cystic fibrosis (CF), the most common genetic disease in people of northern European ancestry leading to chronic lung disease and reduced lifespan. We selected and tested 120 small molecules in a CF cell line, finding 8 with activity, and confirmed 3 in primary CF airway epithelia. Although chemically diverse, the transcriptional profiles of the hits suggest a common mechanism associated with the unfolded protein response and/or TNFα signaling. This study highlights the power of informatics to help identify new therapies and reveal mechanistic insights while moving beyond target-centric drug discovery.
罕见病影响了第一世界人口的 10%，但超过 95%的罕见病甚至缺乏单一的药物治疗。在当今的信息时代，我们需要利用我们庞大的数据和知识的方法来简化治疗的开发，并缩小这一差距。在这里，我们开发并实施了一种创新的信息学方法，利用 Connectivity Map 和 LINCS L1000 数据库以及与疾病相关的转录特征和途径来识别治疗分子。我们将其应用于囊性纤维化（CF），这是北欧血统人群中最常见的遗传疾病，导致慢性肺部疾病和寿命缩短。我们在 CF 细胞系中选择并测试了 120 种小分子，发现有 8 种具有活性，并在原代 CF 气道上皮细胞中证实了 3 种。尽管化学性质多样，但命中化合物的转录谱表明与未折叠蛋白反应和/或 TNFα 信号相关的共同机制。这项研究强调了信息学在帮助识别新疗法和揭示机制见解方面的力量，同时超越了以靶点为中心的药物发现。
