Centre for Personalised Medicine, Linköping University, Linköping, Sweden.
Department of Gastroenterology and Hepatology, University Hospital, Linköping, Sweden.
Genome Med. 2024 Mar 20;16(1):42. doi: 10.1186/s13073-024-01314-7.
Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.
Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.
scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.
We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).
免疫介导的炎症性疾病（IMID）患者的药物治疗效果不佳是一个主要问题。造成这种情况的重要原因是缺乏基于 IMID 中复杂和异质的细胞和分子变化特征来进行药物优先排序和再利用的系统解决方案。
在这里，我们提出了一种计算框架 scDrugPrio，它基于单细胞 RNA 测序（scRNA-seq）数据构建炎症性疾病的网络模型。scDrugPrio 构建了详细的炎症性疾病网络模型，整合了细胞类型特异性表达变化、改变的细胞串扰以及药物选择和排序的药理学特性的信息。
scDrugPrio 是使用抗原诱导的关节炎小鼠模型开发的，并通过提高批准药物的精确性/召回率进行了验证，以及对预测但未批准用于研究疾病的药物进行了广泛的体外、体内和计算机模拟研究。接下来，scDrugPrio 被应用于多发性硬化症、克罗恩病和银屑病关节炎，通过对相关和批准药物进行优先级排序，进一步支持了 scDrugPrio。然而，与关节炎的小鼠模型不同，在具有相同诊断的患者中发现了个体间细胞和基因表达的巨大差异。这种差异可以解释为什么有些患者对治疗有反应，而有些患者没有。这一解释得到了 scDrugPrio 在 11 名克罗恩病患者的 scRNA-seq 数据中的应用的支持。分析表明，药物预测在患者之间存在很大差异，例如，在对 TNF 治疗有反应的患者中给予高优先级，而在对该治疗无反应的患者中则给予低优先级。
我们提出了一种基于 IMID 疾病的 scRNA-seq 的药物优先排序的计算框架 scDrugPrio。在个体患者中的应用表明，scDrugPrio 具有在细胞组、基因组和药物组范围内进行基于网络的个性化药物筛选的潜力。为此，我们将 scDrugPrio 制作成一个易于使用的 R 包（https://github.com/SDTC-CPMed/scDrugPrio）。
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