key: cord-0707861-83zm334p authors: Azad, A.K.M.; Fatima, Shadma; Capraro, Alexander; Waters, Shafagh A.; Vafaee, Fatemeh title: Integrative resource for network-based investigation of COVID-19 combinatoric drug repositioning and mechanism of action date: 2021-07-14 journal: Patterns (N Y) DOI: 10.1016/j.patter.2021.100325 sha: 1ee4d8193a0fbedb0024db68b656ddba816f3aa0 doc_id: 707861 cord_uid: 83zm334p An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the drug primary targets and the SARS-CoV-2–host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is the first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers. The COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 has caused a grave threat to public health and an unprecedented loss to the global economy. A worldwide scientific attention has been focused on drug repositioning to rapidly identify interventions for COVID-19 prevention and cure 1 . In addition to time effective solutions for disease treatment, drug repositioning provides better value healthcare by reducing cost and avoiding risk as multiple phases of de-novo drug discovery can be bypassed 2 . An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug development, which can be improved by combination therapy 3, 4 . The increased therapeutic efficacy due to combination therapy could result in lower-dose prescribing, reducing risk of side effects and toxicity hazards. However, due to the large number of possible drug pairs, our ability to find and verify effective combinations is limited by this combinatorial explosion 5 . Over the last decade, a variety of computational drug-repurposing methods have been developed. Some of these have been applied to search for new therapeutics against COVID-19 (as recently reviewed 6 ), most of which focused on developing monotherapy strategies. Among these methods, network pharmacology approaches that quantify the interplay between the SARS-CoV-2-host interactome and drug targets in the human protein-protein interaction (PPI) network have offered a ground for prioritizing effective repositioning candidates as both monoand combination therapies [7] [8] [9] . However, most of the former network pharmacology studies focused on prioritising and reporting a few individual drugs or drug pairs (Table S8 ). There has been a lack of an integrative and accessible platform enabling the investigation of a large set of repositioning drug candidates for their putative efficacy and mechanism of action. To address this resource gap, we developed COVID-CDR (COVID-19 Combinatorial Drug Repositioning), an integrative web-based computational platform that prioritises complementary and additive drug combinations for SARS-CoV-2 treatment. COVID-CDR compiles a large set of FDA-approved drugs, investigational compounds previously used to treat COVID-19 symptoms, and drugs in clinical trials for COVID-19 treatment. For a given drug combination, COVID-CDR constructs a multi-level interactome encompassing drug-target/s, target-human and viral-human interactions overlaid on a comprehensive human PPI network. By leveraging this network, COVID-CDR prioritizes drugs with primary host protein targets in close vicinity to SARS-CoV-2 proteins, highlighting those that may have potential to interfere with viral or host-virus functions. Moreover, COVID-CDR prioritizes drug combinations with potential to J o u r n a l P r e -p r o o f act synergistically through different, yet potentially complementary pathways. This networkbased information is complemented with a diverse drug-drug similarity measurement as well as drug pair synergy in cell lines to offer a rational multi-level, multi-evidence solution for investigating drug combination strategies against COVID-19. COVID-CDR also includes a multitude of useful drug information all in one intuitive platform, including drug structure, drug physicochemical properties, therapeutic class, indications, sideeffects, induced pathways, and drug-drug interactions which together form a unique starting point for in silico COVID-19 combinatorial drug repositioning. We demonstrated the utility of COVID-CDR for combination of LY2275796 and cyclosporine and explained the mechanism of action of such combination. To the best of our knowledge, COVID-CDR is the first computational online tool to integrate COVID-19 drug information in the context of virus and human interaction networks, which may facilitate a better understanding of the molecular mechanisms of drug actions for the identification of potentially effective drug combinations and can help in prioritizing therapies of COVID-19 worldwide. Figure 1 shows the COVID-CDR platform content and construction. 867 drugs with reported evidence in treating COVID-19 symptoms or under-investigation in trials were pre-compiled (Table S1) . Of these drugs, 57% were approved for an indication, 41% are investigational, and >2% were veterinary-approved, nutraceutical or withdrawn. These drugs cover a wide range of therapeutic classes (>200 categories) including antivirals, antibiotics, anti-cancer, antiinflammatory, immunomodulatory, immuno-suppressive, and anti-coagulant agents. Multiple drug-related information sources including chemical structure, physiochemical and pharmacological properties, side effects, protein targets, associated pathways, and drug-drug interactions were compiled from diverse resources for each drug (Table 1) and are accessible to explore from the web interface. COVID-CDR constructs a multi-dimensional network ( Figure 1A ) comprising drug-target interactions (867 drugs, 2,228 protein targets, and 4,866 interactions), and high-confidence binding associations between SARS-CoV-2 and human proteins (28 viral proteins, 340 human proteins, and 414 interactions) overlaid on a comprehensive experimentally validated human protein-protein interactome (469,515 PPIs). SARS-CoV-2-host protein-protein interaction J o u r n a l P r e -p r o o f network was curated from literature 10, 11 and relevant interaction databases 12 . In addition, we incorporated the SARS-CoV-1 virus-host protein-protein interaction network which can serve as a valuable reference due to the close similarity between SARS-CoV-1 and SARS-CoV-2 proteins [13] [14] [15] . This multi-dimensional interactome (Table S2) has been used to estimate the topological proximity of drug targets to COVID-19-related proteins and quantify the separation of drug targets on human protein-protein interactome for network-based exploration of efficacious drug combinations ( Figure 1C , c.f., Methods). In addition to network-based topological metrics, the functional relevance of drug targets with COVID-related cellular biological processes were estimated ( Figure 1D ). Furthermore, for each drug pair, structural and functional similarity measures were estimated ( Figure 1B , Table S3 ). Multiple studies suggest that synergy is associated with functional similarity/dissimilarity of drug pairs 16, 17 . Distinct drug-drug similarity matrices were generated based on chemical structures, target protein sequences, induced pathways, and target protein function, i.e., cellular components, biological processes, and molecular functions (see Methods). The size of each matrix is 867 by 867, i.e., 751,689, and values range from zero to one. The individual similarity matrices were then mean-aggregated to form a combined-score similarity matrix and z-transformed for significance assessment (Table S3) . Overall, the network proximity of drug-drug pairs holds negative but insignificant correlation with structural and functional similarities ( Figure S2 ). To provide in-action examples of studies likely to influence clinical practice, 36 different drug combinations were incorporated in the platform involving more than 20 different drugs in various clinical trials designed for treating COVID-19 from Clinicaltrials.gov database ( Figure 1E , Table S5 ). Additionally, 150 pairs of COVID-19-related drugs approved by FDA for other indications were compiled (Table S6) . Table 2 provides statistics and details of external drug combinations included in this platform. COVID-CDR also incorporated the high-throughput viability screening results related to drug combinations assessed on more than 124 immortalized human cancer cell lines ( Figure 1E , Table S7 ) assembled by Liu and colleagues 18 . While reduction in cancer cell proliferation and/or viability may not be associated with antiviral effects, it indicates that at least in a different context/endpoint, the evaluated drugs have shown synergistic interaction. The network-based drug repositioning prioritization is based on the notion that for a drug to be efficacious, its target proteins should be within or in the immediate neighborhood of the corresponding subnetwork of the disease-related proteins in the human interactome 5, 7, [19] [20] [21] [22] . Accordingly, the topological distance of a drug to SARS-CoV-2 proteins was measured as the network-based shortest distance of the drug's primary targets to SARS-CoV-2-related proteins (i.e., disease module) on human PPI network (see Methods). SARS-CoV-2-related proteins considered in this study include viral proteins, human proteins interacting with SARS-CoV-2, and virus entry factors (Table S2 ). To quantify the significance of the shortest distances between drug and disease module, drug-disease proximity measures were then converted to z-scores (z) based on permutation tests as previously explained 5, 7 , and the corresponding p-values were estimated. For z < 0 (and the corresponding p-value <0.05), the drug-target subnetwork (i.e., drug module) and the disease module are significantly proximal and often overlap; while for z ≥ 0, the drug module and the disease module are distal and thus separated 5, 23 . Overall, 543 drugs topologically overlap with SARS-CoV-2 module (z < 0), 118 of them show significant exposure with the disease module (z < 0 and p-value < 0.05, permutation test, Table S1 ). The network-based topological proximity of drug module to the disease module measures the immediate vicinity of drug targets to SARS-CoV-2 proteins on cellular interactome. However, it falls short in capturing the effect of drug's downstream changes in biological processes perturbed under the impact of the SARS-CoV-2 infection. Hence, the topological proximity was complemented with a measure of drug-disease functional proximity that quantifies the similarity between biological processes significantly enriched (FDR < 0.05) by a drug module (drug primary target/s and their direct interactors in PPI) and the disease module (SARS-CoV-2related proteins). The similarity between drug-and disease-associated biological processes was estimated using Gene Ontology-based semantic similarity measure which leverages on the ontology graph structure and information content to estimate similarities among gene ontology terms 24 . Table S4 shows biological processes enriched by SARS-CoV-2 related proteins (FDR < 0.05). Drug-disease functional proximities are ranged between 0 and 1 with the mean value of µ = 0.29 ( Figure S1A ). Overall, the higher the similarity is the greater the effect of the drug would be in perturbing disease-related mechanisms. Similarity measures were standardized to z-scores and the corresponding one-tailed p-values (i.e., [ > ]) were estimated; 306 drugs hold z-score > µ, among them 82 have p-value < 0.05 (Table S3) . SARS-CoV-2 functional J o u r n a l P r e -p r o o f proximities of drugs are inversely corelated to the corresponding topological proximities (Pearson's correlation coefficient = -0.413) and hold relatively weak linear relationship (R 2 = 0.17), indicating that these two measurements are complementary rather than being redundant justifying the integration ( Figure S1B ). For drugs whose known primary targets are topologically and functionally proximal to SARS-CoV-2-related proteins, combinations can be prioritized based on the separation of drug-target modules in PPI. It has been previously hypothesized that different drug-target module has different network-based footprint; two drugs are pharmacologically distinct if the footprints of the drug-target modules are topologically separated 5 We sought to use our platform to identify drug combinations that may provide effective While a number of clinical trials are proposed to test the efficacy of the repurposed drugs against COVID-19, prioritization of many drug candidates has been mostly unstructured 43 (Table S8) . Additionally, while GitHub code is available in some cases, these studies often overlooked providing an accessible implementation to conduct the networkbased proximity analyses of individual or drug pairs as potential COVID-19 mono-or combination therapies. Among these, COVEX 48 is the only online platform that enables a visual exploration of the SARS-CoV-2 virus-host-drug interactome for drug repositioning prediction. COVEX implements several network-based algorithms to prioritize repositionable drugs for COVID-19. The platform however is not intuitively applicable to drug combination prioritization. It lacks in providing an option for the users to start with their own choice of drugs and do not provide comprehensive drug or drug-pair information. Overall, to the best of our knowledge, COVID-CDR is the first computational online platform Full experimental procedures are provided in Supplemental Information. Further information and requests for resources should be directed to the Lead Contact, Fatemeh Vafaee (f.vafaee@unsw.edu.au). No materials were used in this study. To ensure the reproducibility of Supplementary table legends Table S1 . List of all drugs included in this platform along with all drug properties as well as disease topological and functional proximity measures. Table S2 . SARS-CoV-2 interactions incorporated into the multi-dimensional network constructed in this platform (Sheet 1). SARS-CoV interactions incorporated into the multidimensional network constructed in this platform (Sheet 2). We present, COVID-CDR, a web-based computational platform for in silico repositioning of drug combinations against SARS-CoV-2 infection. COVID-CDR constructs a multi-level interactome encompassing drug-target/s, target-human and viral-human interactions overlaid on a human PPI network. By leveraging this interactome, COVID-CDR prioritizes potentially synergistic drug combinations as those whose primary targets are in close vicinity to SARS-CoV-2 proteins but holds distinct PPI footprints. The platform also provides diverse information on drugs/drug-pairs offering a multi-evidence solution for investigating drug combination strategies against COVID-19. 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