Abstract:
Therapy resistance to single agents has led to the realization that combination therapies could become the cornerstone of cancer treatment. To operationalize the selection of effective and safe multitarget therapies, we propose to integrate chemical and preclinical therapeutic information with clinical efficacy and toxicity data, allowing a new perspective on the drug target landscape. To assess the feasibility of this approach, we evaluated the publicly available chemical, preclinical, and clinical therapeutic data, and we addressed some potential limitations while integrating the data. First, by mapping available structured data from the main biomedical resources, we noticed that there is only a 1.7% overlap between drugs in chemical, preclinical, or clinical databases. Especially, the limited amount of structured data in the clinical domain hinders linking drugs to clinical aspects such as efficacy and side effects. Second, to overcome the abovementioned knowledge gap between the chemical, preclinical, and clinical domain, we suggest information extraction from scientific literature and other unstructured resources through natural language processing models, where BioBERT and PubMedBERT are the current state-of-the-art approaches. Finally, we propose that knowledge graphs can be used to link structured data, scientific literature, and electronic health records, to come to meaningful interpretations. Together, we expect this richer knowledge will lower barriers toward clinical application of personalized combination therapies with high efficacy and limited adverse events.
摘要:
对单一药物的治疗抵抗导致人们认识到联合疗法可能成为癌症治疗的基石。实施有效和安全的多靶点治疗的选择,我们建议将化学和临床前治疗信息与临床疗效和毒性数据相结合,为药物目标领域提供了新的视角。为了评估这种方法的可行性,我们评估了公开的化学物质,临床前,和临床治疗数据,我们在整合数据的同时解决了一些潜在的限制。首先,通过映射来自主要生物医学资源的可用结构化数据,我们注意到化学药物之间只有1.7%的重叠,临床前,或临床数据库。尤其是,临床领域中有限的结构化数据阻碍了药物与临床方面的联系,如疗效和副作用。第二,为了克服上述化学品之间的知识差距,临床前,和临床领域,我们建议通过自然语言处理模型从科学文献和其他非结构化资源中提取信息,其中Biobert和PubMedBERT是当前最先进的方法。最后,我们建议知识图可以用来链接结构化数据,科学文献,和电子健康记录,得出有意义的解释。一起,我们预计这一更丰富的知识将降低个性化联合疗法的临床应用障碍,这些疗法具有高疗效和有限的不良事件.