Schäfer Samuel, Smelik Martin, Sysoev Oleg, Zhao Yelin, Eklund Desiré, Lilja Sandra, Gustafsson Mika, Heyn Holger, Julia Antonio, Kovács István A, Loscalzo Joseph, Marsal Sara, Zhang Huan, Li Xinxiu, Gawel Danuta, Wang Hui, Benson Mikael
Centre for Personalised Medicine, Linköping University; Linköping, Sweden.
Department of Gastroenterology and Hepatology, University Hospital, Linköping, Sweden.
bioRxiv. 2023 Nov 13:2023.11.08.566249. doi: 10.1101/2023.11.08.566249.
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 , and 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).
对于许多免疫介导的炎症性疾病（IMIDs）患者而言，药物治疗无效是一个主要问题。重要原因在于，基于IMIDs复杂且异质性的细胞和分子变化特征，缺乏用于药物优先级排序和重新利用的系统解决方案。
在此，我们提出了一个计算框架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、改变的细胞间相互作用以及药理特性等信息，用于数千种药物的选择和排名。
scDrugPrio是使用抗原诱导性关节炎小鼠模型开发的，并通过提高已批准药物的精确率/召回率以及对预测但未批准用于所研究疾病的药物进行广泛研究来验证。接下来，scDrugPrio应用于多发性硬化症、克罗恩病和银屑病关节炎，通过对相关和已批准药物进行优先级排序进一步支持了scDrugPrio。然而，与关节炎小鼠模型不同的是，在相同诊断的患者中发现了巨大的个体间细胞和基因表达差异。这种差异可以解释为什么一些患者对治疗有反应而另一些患者没有。将scDrugPrio应用于11名克罗恩病患者的scRNA-seq数据支持了这一解释。分析表明患者之间的药物预测存在很大差异，例如，在一名有反应者中给予抗TNF治疗高排名，而在一名无反应者中给予低排名。
我们提出了一个计算框架scDrugPrio，用于基于IMID疾病的scRNA-seq进行药物优先级排序。应用于个体患者表明scDrugPrio在细胞组、基因组和药物组范围内基于个性化网络的药物筛选方面具有潜力。为此，我们将scDrugPrio制作成一个易于使用的R包（https://github.com/SDTC-CPMed/scDrugPrio）。
