The drug cocktail network
© Xu et al.; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
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© Xu et al.; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
Combination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations.
In this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred (Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach.
The drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations.
Drug combination is the combination of different agents that can achieve better efficacy with less side effects compared to its single components. Recently, it is becoming a popular and promising strategy to new drug discovery, especially for treating complex diseases, e.g. cancer [1–3]. For example, Moduretic is the combination of Amiloride and Hydrochlorothiazide, which is an approved combination used to treat patients with hypertension [4, 5]. Chan et al. identified a combination drug, namely Tri-Luma, for combating melasma (dark skin patches) of the face based on efficacy and safety experiments . Agrawal et al. found two effective combinatorial drug regimens to treat Huntington disease based on prescreening in Drosophila . In addition, through the synergistic antiangiogenic effects, very low-dose combinatorial use of vinblastine (VBL) and rapamycin (RAP) was demonstrated to inhibit the proliferation of the endothelial cells much more effectively than single drug treatment both in vitro and in vivo . Recently, Lehar et al. found that synergistic drug combinations may have less side effects, because synergistic drug combinations are generally more selective to particular cellular contexts than single agents, and the dosage of each compound in combination will be reduced comparatively . Despite of the extensive efforts that have been made to discover new drug combinations in the past few decades, the majority of effective combinatorial drugs used in clinic were discovered through experiences, which generally require labor-intensive and time-consuming "brute force" screening of all possible combinations among the approved individual drugs . In a drug combination, a drug may promote or suppress the effect of another one. For instance, cyclosporine increases the effect of sirolimus, while bupropion decreases the effect of cyclosporine. As a result, two drugs may have a totally new effect that is different from the ones of either individual drugs [11, 12]. Accordingly, the presence of potential drug-drug interactions (DDIs) and the possibility of pharmacokinetic interventions between the drugs could confound the identification of effective drug combinations . Furthermore, the number of possible combinations will increase exponentially with the increasing availability of single drugs. For example, in the case of four drugs, there will be six possible combinations. This number would be enormous considering the fact that there are thousands of approved drugs. Due to the huge search space of possible combinations between known drugs, the identification of optimal and effective drug combinations is a non-trivial and challenging task.
Therefore, it is necessary to develop effective in silico methods that are capable of discovering new drug combinations prior to combination synthesis and practical test in the lab. Owing to the completion of human genome sequencing projects and the advancement of molecular medicine, extensive system biology efforts have been made to discover new combinations based on molecular interaction networks [14, 15] in the past few years [16–19]. Nevertheless, there is still a long way to go before we reach the stage of devising generally applicable and effective prediction models. Recently, there have been considerable progresses in developing new approaches for identifying drug-drug interactions and even drug combinations . In this context, Geva-Zatorsky et al. have recently found that the protein dynamics in response to drug combination can be accurately described by a linear superposition of the dynamics under the corresponding individual drugs . Their study indicated that protein dynamics of three- and four-drug combinations can be predicted based on the drug combination pairs, thereby providing a useful way for reducing the search space of possible drug combinations. Calzolari et al. devised an efficient search algorithm originated from information theory for optimization of drug combinations based on the sequential decoding algorithms . More recently, researchers have also developed computational frameworks for predicting drug combinations and synergistic effects based on high-throughput data [18–20].
In this work, we study the drug combinations in terms of their therapeutic similarity and the network topology of a drug cocktail network constructed from the effective drug combinations deposited in the Drug Combination Database (DCDB) . We find that the drugs in an effective combination tend to have more similar therapeutic effects and share more interaction partners in the context of drug cocktail network. We further develop a statistical approach called DCPred to predict possible drug combinations and validate this approach based on a benchmark dataset with all the known effective drug combinations. As a result, DCPred achieves the overall best AUC (Area Under the receiver operating characteristic Curve) score of 0.92, demonstrating the predictive capability of the proposed approach and its potential value in identifying new possible drug combinations.
where c = 2.1 and α = 1.9 in this case.
The enriched ATC codes for child networks
Number of drugs
Enriched ATC codes: Frequency
L:40, J:24, A:16, S:11
The comparisons between drug cocktail network and random networks
ATC code level
In various networks, the hub nodes are generally considered to play important roles . Therefore, we next studied the 14 hub drugs in the drug cocktail network, all of which have more than 6 neighbor drugs. The largest two hub drugs are DB00999 (Hydrochlorothiazide) and DB00072 (Trastuzumab). Hydrochlorothiazide is used to treat high blood pressure and edema [26, 27]. According to the annotations in DrugBank and DCDB, we found that all the 18 drug neighbors of hydrochlorothiazide can be used to cure hypertension while all the drug combinations involving hydrochlorothiazide have been used to treat hypertension. Among these 18 combinations, 11 combinatorial drugs target different but related pathways while the other 7 ones target unrelated pathways (Additional file 1). In the case of Trastuzumab used to treat HER2-positive metatsatic breast cancer [28, 29], 5 of its 10 neighbor drugs are used to treat breast cancer, while the other 5 have pesticide effects on neoplasm or other cancers. All the 10 drug combinations are used to treat breast cancer except the one used for treating gastric cancer. Additionally, 8 drug combinations target related pathways, while the other two target different unrelated pathways or cross-talking pathways (Additional file 2). Finally, these results, together with the consistent findings shown in Figure 3, strongly indicate that star drugs tend to have similar therapeutic characteristics as their neighbors.
As shown in Figure 3, 82% of the combinations between star drugs and their neighbors have therapeutic similarity, and most of the star drugs have therapeutic similarity to the majority of their neighbors in the drug cocktail network. Additionally, most of the effective combinations are observed to be located in the vicinity of drug pairs with similar ATC codes. Hence, it is possible to predict drug combinations from the set of drug pairs with similar ATC codes. Nonetheless, we found that there are only 74 known effective combinations in all of the 1181 possible combinations with similar ATC codes. Since the number of effective drug combinations is considerably smaller than that of random combinations between drugs having similar ATC codes, it is a challenging but crucial task to discover the effective combinations from the pool with a vast number of random combinations.
The novel predictions of DCPred2
Reported effective combinations?
If we only considered the combinations whose drug components have at least 3 neighbors, termed as DCPred3 (the blue curve in Figure 6), we predicted 40 combinations and 379 negative ones (Additional file 4). DCPred3 achieves an AUC score of 0.92. Compared with the aforementioned two models DCPred1 and DCPred2, based on the information of at least 3 neighor drugs, DCPred3 leads to the overall best performance. In this work, we considered the results by DCPred2 as the final results because only few drugs have more than two neighbors in the drug cocktail network. We hope that the DCPred models developed in this study can be used to facilitate the in silico identification of effective drug combinations and speed up the future discovery process.
Drug combination is a promising strategy for combating complex disease, but our complete understanding of the underlying mechanisms of drug combination is largely lacking at present. It is therefore imperative to develop efficient computational methods to infer effective drug combinations in order to reduce the labor-intensive, time consuming trial-and-error experiments. In this article, we extracted all the known effective drug combinations from DCDB and constructed a drug cocktail network, which includes 215 drugs and 239 effective drug combinations. Based on this cocktail network, we observed that the star drugs tend to have therapeutic similarity with their drug neighbors, and two drugs having similar therapy and sharing neighbors tend to be employed in drug combination. Our analysis also revealed that: 1) hub drugs usually have similar and even the same therapeutic effects as their neighbors; 2) target proteins of the hub drugs are often membrane or membrane-associated proteins; 3) the components in effective drug combinations usually have more similar therapeutic effects, making the drug cocktail network significantly different from the random combination networks.
From the above observations, we consequently developed a new statistical approach to infer and rank possible effective drug combinations by taking into account drugs with at least two or three drug neighbors. As a result, our DCPred2 and DCPred3 models achieved the AUC scores of 0.88 and 0.92, respectively, demonstrating a good performance. We further applied these models to rank all the possible drug combinations and found that the top ranked combinations are more likely to be effective combinations, according to the cross-reference to the literature or the similarity of their ATC codes. In particular, four combinations in the top 35 rankings have been verified as effective combinations by the literature search. We also show that there is a better chance for another 3 combinations to be effective combinations in terms of the pharmacological similarity. Our results in this study provide useful insights into the underlying mechanisms of effective drug combinations and hence important clues for efficiently reducing the search space of possible combinations within the approved drugs. Our approach may be further useful for developing more accurate models. The DCPred models are anticipated to be applied to screen more effective drug combinations with clinical importance.
Furthermore, the concentration of each drug in a combination is a crucial factor in the study of drug combination. However, it is currently difficult to utilize the dosage information of drugs without the knowledge of their quantitative dose-response profiles (e.g. drug induced gene/protein expression data) under different drug concentrations, due to the limited availability of such data. We will investigate drug combinations from this perspective in the future, when more data regarding drug concentrations become available.
The annotations of drug combinations were retrieved from a newly released Drug Combination Database (DCDB) . This is a major resource for collecting effective drug combinations from the literature. The target protein information, the Anatomical Therapeutic Chemical (ATC) code annotation of the drugs and protein subcellular localizations, were extracted from DrugBank . Drug combinations that do not have ATC codes for the corresponding drug components and combinations with none or unclear efficacy were discarded. Finally, 194 effective drug combinations were obtained, including 76 approved combinations, 64 clinical combinations and 54 preclinical combinations. We then split the combinations with more than two drug components into combination pairs, resulting in 239 drug combination pairs. These drug combinations were used to construct a drug cocktail network (Figure 1), where the nodes represent drugs and the edges represent combinations, respectively. In the drug cocktail network, the size of each node denotes its degree and the width of each edge denotes the therapeutic similarity (TS) between the two drugs linked by the edge. The gray edge means that there is no therapeutic similarity between the two drugs.
Human protein-protein interactions (PPIs) with high confidence from STRING  were used to annotate this drug cocktail network, which includes 169,603 interactions between 11,289 proteins after removing pairs with low scores ( < 700).
where n ranges from 1 to 5. In this study, n = 3 is adopted considering that only a few drugs have the same ATC codes at the 5th level.
If two drugs share more common drugs compared with all of their neighbors, the p-value computed by equation (4) will be closer to 0, which means they are more likely to be combined. We use the equation (4) to compute the p-values for all possible combinations and then rank the values in ascending order. As drug pairs with lower p-values are more likely to be combined, the prediction of effective drug combinations can be made given a certain p-value threshold. We term this framework that explores the drug cocktail network and predicts possible drug combination as DCPred (Drug Combination Predictor) and assess its performance for inferring effective drug combinations based on the curated drug combinations dataset.
XMZ was partly supported by the Innovation Program of Shanghai Municipal Education Commission (10YZ01), Shanghai Rising-Star Program (10QA1402700), and National Natural Science Foundation of China (61103075, 91130032). JS was supported by the National Health and Medical Research Council of Australia (NHMRC) Peter Doherty Fellowship and the Hundred Talents Program of the Chinese Academy of Sciences (CAS). KJX was supported by the Innovation Program of Shanghai University (SHUCX112015).
This article has been published as part of BMC Systems Biology Volume 6 Supplement 1, 2012: Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/6/S1.