Converging multi-modality datasets to build efficient drug repositioning pipelines against Alzheimer's disease and related dementias.
Converging multi-modality datasets to build efficient drug repositioning pipelines against Alzheimer's disease and related dementias.
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DOI：
10.1515/mr-2021-0017
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发表时间：
2022-02-01
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阿尔茨海默病和相关痴呆症(AD/ADRD)影响着全世界5000多万人，但没有普通患者人口负担得起的明确治疗方案。最近，以学术机构、医疗中心和医院之间的合作为特色的药物重新定位研究正在产生针对这些毁灭性疾病的新的候选疗法，并填补了制药公司很少代表的医疗保健的一个重要领域。此类药物重新定位研究汇集了生物信息学、化学信息学、医学信息学、人工智能、高通量和高含量筛选以及系统生物学的专业知识。他们还利用多尺度、多模式的数据集，从转录和蛋白质组数据、电子病历和医学成像到患者行为和情感的社交媒体信息以及疾病人群的流行病学概况，以全面了解疾病机制和药物影响。我们提出了一种递归药物重新定位范式，包括建模、预测和验证三个处理步骤的迭代，以识别AD/ADRD的已知药物和生物活性化合物。这种递归范式有可能迅速获得AD/ADRD的一组强大的新药候选药物，并从这些重新定位的候选药物中获得对疾病机制的深入了解，从而提高预测新药命中的成功率。
Alzheimer’s disease and related dementias (AD/ADRD) affects more than 50 million people worldwide but there is no clear therapeutic option affordable for the general patient population. Recently, drug repositioning studies featuring collaborations between academic institutes, medical centers, and hospitals are generating novel therapeutics candidates against these devastating diseases and filling in an important area for healthcare that is poorly represented by pharmaceutical companies. Such drug repositioning studies converge expertise from bioinformatics, chemical informatics, medical informatics, artificial intelligence, high throughput and high-content screening and systems biology. They also take advantage of multi-scale, multi-modality datasets, ranging from transcriptomic and proteomic data, electronical medical records, and medical imaging to social media information of patient behaviors and emotions and epidemiology profiles of disease populations, in order to gain comprehensive understanding of disease mechanisms and drug effects. We proposed a recursive drug repositioning paradigm involving the iteration of three processing steps of modeling, prediction, and validation to identify known drugs and bioactive compounds for AD/ADRD. This recursive paradigm has the potential of quickly obtaining a panel of robust novel drug candidates for AD/ADRD and gaining in-depth understanding of disease mechanisms from those repositioned drug candidates, subsequently improving the success rate of predicting novel hits.