Network target for screening synergistic drug combinations with application to traditional Chinese medicine

Abstract

Background: Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.

Results: In this work, we proposed for the first time a "network target"-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.

Conclusions: NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

Figure 1

Pipeline of NIMS: ranking the synergistic effect of n agents paired with a given agent. For a given agent (Agent_x) and n candidate agents (Agent₁, …, Agent_n), all agent genes are collected and mapped to a disease network target. For each agent (Agent₁, …, Agent_n) combined with Agent_x, TS (Topology Score) is obtained by calculating the node importance of both sets of agent genes and the shortest path between them. TS is subsequently weighed by the AS (Agent Score) of each agent pair to ultimately produce S (Synergy score), which is used to rank the synergy strength for the n candidate agents matched with the given Agent_x.

Figure 2

Anti-angiogenesis synergistic effects of five agent pairs. a-e. The red line denotes the inhibition rate of Human Umbilical Vein Endothelial Cells (HUVEC) proliferation in a dose-dependent manner. The blue line denotes the additive effects calculated by the Bliss independence model. The gray column denotes the optimal dose and ratio of each pair. f. The value of the maximum increased inhibition rate (MIIR) for the synergistic effects produced by five agent pairs corresponds well with the NIMS ranks against the angiogenesis network. The proportion of two agents is determined by following the same ratio of the two agent’s IC50 values.

Figure 3

Permutation tests to assess the robust performance of NIMS. The permutations are performed by evaluating fluctuations of (a) TS (Topology Score), (b) AS (Agent Score), and (c) the background network (angiogenesis network) and calculated by the average SRCC (Spearman rank correlation coefficient) between the permutation outputs and the original scores.

Figure 4

Features of synergistic agent combinations on the angiogenesis network target. a. 5-flourourcil and Vinblastine with known synergy. b. Sinomenine and Matrine with the high NIMS synergy score. c. Sinomenine and Paeoniflorin with the low NIMS synergy score. The nodes with red or blue colour denote responsive genes associated with two agents respectively.

Figure 5

A network target perspective for understanding the mechanisms of multicomponent synergy. a. Two agents targeting a protein complex, the feedback loop or crosstalk in a signaling network (the left figure) may have the shortest path distance and obtain high NIMS synergy score compared to those do not (the right figure). b. Two agents targeting hub or betweenness nodes (the left figure) may produce higher synergism than the combinations targeting peripheral nodes (the right figure). c. Two agents targeting two compensatory modules related to one disease or the similar diseases (the left figure) may produce higher synergism than those targeting two unrelated modules from unrelated diseases (the right figure). Dashed lines represent direct or indirect connections in a network. Blue or red nodes denote the responsive genes of two agents respectively.