Detection of an anti-angina therapeutic module in the effective population treated by a multi-target drug Danhong injection: a randomized trial

Abstract

It's a challenge for detecting the therapeutic targets of a polypharmacological drug from variations in the responsed networks in the differentiated populations with complex diseases, as stable coronary heart disease. Here, in an adaptive, 31-center, randomized, double-blind trial involving 920 patients with moderate symptomatic stable angina treated by 14-day Danhong injection(DHI), a kind of polypharmacological drug with high quality control, or placebo (0.9% saline), with 76-day following-up, we firstly confirmed that DHI could increase the proportion of patients with clinically significant changes on angina-frequency assessed by Seattle Angina Questionnaire (ΔSAQ-AF ≥ 20) (12.78% at Day 30, 95% confidence interval [CI] 5.86-19.71%, P = 0.0003, 13.82% at Day 60, 95% CI 6.82-20.82%, P = 0.0001 and 8.95% at Day 90, 95% CI 2.06-15.85%, P = 0.01). We also found that there were no significant differences in new-onset major vascular events (P = 0.8502) and serious adverse events (P = 0.9105) between DHI and placebo. After performing the RNA sequencing in 62 selected patients, we developed a systemic modular approach to identify differentially expressed modules (DEMs) of DHI with the Z_summary value less than 0 compared with the control group, calculated by weighted gene co-expression network analysis (WGCNA), and sketched out the basic framework on a modular map with 25 functional modules targeted by DHI. Furthermore, the effective therapeutic module (ETM), defined as the highest correlation value with the phenotype alteration (ΔSAQ-AF, the change in SAQ-AF at Day 30 from baseline) calculated by WGCNA, was identified in the population with the best effect (ΔSAQ-AF ≥ 40), which is related to anticoagulation and regulation of cholesterol metabolism. We assessed the modular flexibility of this ETM using the global topological D value based on Euclidean distance, which is correlated with phenotype alteration (r²: 0.8204, P = 0.019) by linear regression. Our study identified the anti-angina therapeutic module in the effective population treated by the multi-target drug. Modular methods facilitate the discovery of network pharmacological mechanisms and the advancement of precision medicine. (ClinicalTrials.gov identifier: NCT01681316).

Fig. 1

The flow chart of the trial

Fig. 2

Changes in Seattle Angina Questionnaire scores. a–e Patients with clinically significant improvements from baseline in their Seattle Angina Questionnaire scores; f Freedom from angina over time as assessed by the angina frequency scale of the Seattle Angina Questionnaire (scored out of 100); g Forest plot of the primary outcome in prespecified subgroups (at Day 30). “#” in Figs. 1a and 1g indicates that the primary outcome was calculated from the imputed data, with n = 613 in the DHI group and n = 305 in the control group

Fig. 3

Changes in other secondary outcomes between the two groups. a Comparison of mean Xueyu-Zheng scores at each time point. b The proportion of patients with significant improvements in Xueyu-Zheng scores (%). c Changes in the incidence density of angina (attacks/person-day) according to patients’ diaries. d Changes in the incidence density of nitroglycerin consumption according to patients’ diaries. e The proportion of patients with normal ECG recordings. f The proportion of patients with improved CCS grades

Fig. 4

Differentially express genes targeted by Danhong Injection. a Heatmap of the gene expression pattern (miRNA and mRNA) of the DHI group. Red boxes represent the genes that were upregulated after treatment, while blue ones represent downregulated. b Venn diagram of the numbers of differentially expressed genes (DEGs) before and after treatment. The overlapping area represents the number of genes found to be differentially expressed both at Day 14 or Day 30 after treatment. c Venn diagram of 104 DEG-mRNAs and the predicted target mRNAs of 15 DEG-mRNAs. d The GO biological process categories of the 44 overlapping DEG-mRNAs and the predicted target mRNAs of 15 DEG-mRNAs

Fig. 5

Targeted modular map by the multi-target drug Danhong Injection. a The hierarchic cluster tree (dendrogram) of DHI. Each major tree branch represents a module, and each module is labeled with a color below the dendrogram. b The distribution of modules by Z_summary value. There were 25 differentially expressed modules (DEMs) with negative Z_summary values (<0); these modules were considered the targeted functional modules (TFMs). c The 25-TFM map for DHI at Day 30 with the 25 TFMs as nodes and the connectivity score (CS) as the edge between each pair of modules. The node size indicates the number of genes in the corresponding TFM, and the color of the node shows the degree of the TFM in the network

Fig. 6

The effective target module detected in the responsive population. a The numbers of differentially expressed modules (DEMs) with negative Zsummary values (<0) according to WGCNA in the populations with different responses to DHI treatment at Day 30. ΔAF indicates the change in the Seattle Angina Questionnaire angina frequency scale (SAQ-AF) score from baseline (Day 0) to Day 30. b Comparison of the correlation with ΔAF among DEGs and DEMs in different populations. The significance of the difference between each pair of groups was calculated by ANOVA; ** indicates the notable statistical significance of the correlation with ΔAF between DEGs and the DEMs in the populations with ΔAF ≥ 40, and ## indicates the notable statistical significance of the correlation with ΔAF between the DEMs in the populations with ΔAF ≤ 0 and ΔAF ≥ 40. c Heatmap of the Zsummary value pattern of the 12 DEMs at Day 30 in the populations with ΔAF ≥ 40 compared with the baseline (Day 0) and the control group. d Heatmap of correlation with ΔAF for the 12 DEMs at Day 30 in the populations with ΔAF ≥ 40. e The modular flexibility of the effective therapeutic module in the populations with different responses to DHI treatment. The topological parameters are shown in the table. f The gene distribution in the effective therapeutic module shows the correlation between gene significance to AF and module membership. g, h The linear regression correlation between D value (g) or edge (h) and ΔAF