Mycophenolic Acid: Repurposing approach with CoV-DrugX Pipeline
Sakshi Singh1, Robin Sinha1, Trapti Sharma1, Preeti P.1, Kamal Rawal#1
Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India #Corresponding Author
Email ID: kamal.rawal@gmail.com
Centre for Computational Biology and Bioinformatics,
AIB Amity University, Noida
Website: http://drugx.kamalrawal.in/drugx/
Abstract
COVID -19 is a global epidemic that has piqued the interest of healthcare providers all over the world. Scientists all across the world are seeking out a new method of drug repurposing because de novo drug development is more expensive and time-consuming. In this research, we have examined how the mycophenolic acid drug could be used to treat the COVID-19 pandemic. We are doing this through the CoV-DrugX pipeline, which has 13 different types of modules that describe 13 different attributes to explain whether this drug can be repurposed for COVID-19. We have employed the CoV-DrugX pipeline, which contains various modules that should be addressed when repurposing pharmaceuticals for COVID-19. Through the pipeline, we have analyzed the properties of Mycophenolic Acid, which is a well-known drug that is used to combat several infections. H. The pipeline predicts and provides outcomes in the form of scores for individual modules (either 0 or 1), as well as a cumulative PI score that combines the scores from all modules. Mycophenolic acid contains qualities that are regarded for repurposing against COVID-19 with a likelihood of 78 percent, according to the pipeline.
Keywords: de novo, drug repurposing, artificial intelligence, bioinformatics, Covid -19, CoV-DrugX, mycophenolic acid.
1. Introduction
The COVID-19 disease has piqued the interest of healthcare providers around the world. SARS-CoV-2 is a positive-sense single-stranded RNA virus with 29,891 bases that is 96 percent identical to a bat coronavirus at the whole-genome level and shares 79.6% sequence identity with SARS-CoV (Wu et al. 2020 T). As a result, deciding on choice medication regimens to prevent and cure COVID-19 for those who are critically ill remains a huge difficulty. To tackle the highly contagious SARS-CoV-2, effective vaccinations and treatment are required. Extensive research is currently being done around the world in search of effective treatments for this disease. Individuals with COVID-19 have been treated with "repurposed" drugs until SARS-CoV-2 vaccines or treatments are discovered.
Previously, we have developed several machine learning and bioinformatics platforms. These include text mining and network biology-based systems [Jagannadham et al., 2016], vaccine discovery system [Rawal et al. 2021], next-generation sequencing analysis system for cancer and other genomes [Preeti et al.,2021, Rawal et al 2011, Mandal et al 2006].
The application of an existing therapy to a new illness indication, known as drug repurposing, promises to have a faster clinical impact at a cheaper cost than de novo drug development, as demonstrated in Supplementary Figure 1. Given the significant expense and time required for drug development on average, a decade or more repurposing is appealing and practical (Corsello et al., 2017). Even medications with unfavorable side effects are worth reconsidering, for example, the antiemetic thalidomide has been successfully repurposed to treat multiple myeloma (Corsello et al., 2017). Due to the lack of a definitive physical drug collection, poor quality drug annotations, and insufficient display of therapeutic activity from which new indications might be expected, systematic, large-scale repurposing initiatives have proved impossible.
Mycophenolic acid (MPA), the active immunosuppressive form of the prodrug mycophenolate mofetil (MMF), is a typical component of organ transplant immunosuppressive regimens(Shaw et al., 2003). MPA exposure is lowered early following liver and small bowel transplantation when compared to the same MMF dosage used in renal transplantation(Kuypers et al., 2010). Since the early 1970s, oral mycophenolic acid therapy has been studied in the treatment of moderate to severe psoriasis and is both safe and effective (Kitchin et al., 1997). MPA inhibits the coronaviral papain-like protease, and a deeper understanding of how it works could aid in the development of new anti-SARS-CoV-2 medicines.MPA has antiviral action against MERS-CoV, human coronavirus (HCoV)-OC43, HCoV-NL63, and mouse hepatitis virus 2, 3, and IMD-0354 shows antiviral activity against porcine transmissible gastroenteritis virus, according to the researchers. Supplementary Figure 2 depicts the drug's three-dimensional structure.
MPA treatment of the Huh7 reporter cell line results in considerable overexpression of IFN regulatory factors 1 and 9 as well as IFITM3. Researchers have also discovered that the combination of MPA and IFN-a has a synergistic antiviral impact against hepatitis C virus infection and interferon-stimulated gene expression. MPA and its derivative, mycophenolate mofetil, have previously been shown to have potent antiviral activity in vitro against four coronavirus infections (HCoV-OC43, HCoV-NL63, MERS-CoV, and MHV-A59) (Dowran et al. 2020).
Patients who have had organ transplants may be able to change their clinical course and lower the rate and severity of lung injury. Although transplanted patients may be more susceptible to SARS-CoV-2 infection with unusual symptoms, long-term immunosuppressive therapy may operate as a "protective factor" against the disease's significant clinical complications. This theory might also explain why patients with lymphopenia have the worst prognosis. Lymphopenia could be caused by hyper-activated T cells being trapped in the lungs, and immunosuppressive drugs could help to mitigate this effect. Except for the case reports mentioned above, no data on the incidence and outcome of COVID-19 in this group of patients could be obtained. Furthermore, immunosuppressive medicines may be a viable "therapeutic" option for lowering T cell immune system activity and preventing organ harm (Romanelli and Mascolo 2020).
2. Implementation
The Covid-DrugX pipeline (http://drugx.kamalrawal.in/) is an online tool that uses 13 modules to forecast whether the drug is a suitable candidate for repurposing. Drug_dl_11, Drug_dl_200, drug_condition, drug_phenotype, drug_side_effects, drug_targets, drug_human_gene_expression, docking_with_SARS-CoV-2_targets, docking_with_human_targets, docking_with_knowledge_graph_targets, pathway_circuit, euclidean_distance, and drug_symptoms are the modules that were built in this pipeline.These modules display the findings of the query drug as a score of 0 or 1 and the SMILES text file format of the drug can be used as the input. The implementation Supplementary Figure 3 depicts the pipeline's quick implementation.
2.1 Drug Feature Module:
The Drug_dl_11 module investigates COVID-19's 11 biological characteristics. The Drug_dl_200 module examines 200 chemoinformatics features linked to COVID-19 of the query drug on a similar dataset. RDkit (https://rdkit.org/) was used to retrieve these attributes. The DGIdb database is also used to generate drug-gene interaction data for the pathway_circuit module's source files. In addition, a literature search is conducted to gather information on the circuits connected with genes.
2.2 Docking modules:
Docking_with_SARS-CoV-2_targets, docking_with_human_targets, and docking_with_knowledge_graph_targets are the three docking modules in the pipeline. In docking with SARS-CoV-2 targets, 23 viral proteins from SARS-COV-2 are included. Spike protein, membrane protein, envelop small membrane protein, nucleocapsid protein, main protease, papain-like protease, nsp3 (207–379), RNA dependent RNA polymerase (RdRp, nsp12/7/8 complex), helicase, nsp7, nsp8, nsp12, nsp14, nsp15 (endoribonuclease) (Supplementary Figure 4).
We added the human proteins ACE2 and TMPRSS for docking against the query medications in docking_with_human_targets since they are considered as the major targets for viral entry. AAK1, GAK, and JAK12, which have been found to have a role in viral endocytosis(Luo et al. 2020), have also been included in docking_with_knowledge_graph_targets (Supplementary Figure 5). TMPRSS2 is highly expressed in the subsegmental bronchial branches, whereas ACE2 is seen primarily in transitory secretory cells. Surprisingly, pathways related to RHO GTPase function and viral activities are overrepresented in these transiently differentiated cells, implying a higher risk of SARS-CoV-2 infection (Lukassen et al. 2020)
2.3 Side effect module:
We have assembled the 6,123 side effects of the drugs associated with COVID-19 in the drug_side_effect_module. In addition, we have compiled a list of 3,052 medications with varied adverse effects. Based on the drug-side effect association, this module predicts if the drug is connected with COVID-19 or not. If the module detects any adverse effects from the COVID-19 dataset, it will score 1; otherwise, it will score 0. The databases utilized in this module are Sider (http://sideeffects.embl.de/) and OFFSIDES (http://tatonettilab.org/offsides/).
2.4 Target Module:
The targets were obtained utilizing medicines that have a functional role in COVID-19 treatment (positive drug dataset). If the query medication is related to COVID-19, the drug target module returns the target. If the target name is found in the COVID-19 dataset, the module predicts a score of 1, otherwise, it predicts a score of 0. This module is based on the TTD database (http://db.idrblab.net/ttd/), which has 31,359 medications and their targets, with 378 targets linked to covid 19 accumulating in a separate file based on literature searches.
2.5 Circuit module:
The pathway_circuit module's function is to offer the circuit for an input query drug that is associated with COVID-19, either by supplying the drug name (separated by pipe in a text file) or the medicines' SMILE notation (separated by newline character in a text file). The module would accept drug names as a query and search the datasets provided for relevant interactions (genes) and further associated circuit information. If the module has an associated circuit with our query medication in the dataset, it predicts a score of 1; otherwise, it predicts a score of 0. The pathway circuit module is based on the DGIdb database (https://www.dgidb.org/downloads), which contains information on 100,274 genes and their linked medicines.
2.6 Phenotype Module:
WebMD (https://www.kaggle.com/rohanharode07/webmd-drug-reviews-dataset) is the source of the Drug_phenotype module's data. If the input drug is associated with COVID-19, an expression-related drug phenotype module returns the phenotype of the drug, as well as the number of matching phenotypes of the drug with COVID-19. In this module, 1952 COVID-19 phenotypes have been documented. It also mentions the query's and COVID-19 disease's shared traits.
2.7 Gene Expression Module:
The drug_human_gene_expression module contains information about the gene symbol as well as gene expression-related data, such as whether the gene is upregulated or downregulated by the drug. The module would offer information on genes linked to the medicine, as well as their expression levels, whether elevated or downregulated. If the query medicine interacts with the genes linked to COVID-19, it will return a score of 1, but if it does not, it will return a score of 0.
3. Usage
Mycophenolic acid, with the drug bank id DB01024, is an immunosuppressant used to prevent organ transplant rejection. It can also be used in research to find animal cells that express the XGPRT gene from E. coli (xanthine guanine phosphoribosyltransferase). COC1=C(CC=C(/C)CCC(O)=O)C(O)=C2C(=O)OCC2=C1C are SMILES from the drug bank database. The text file format was used to save these grins. The text file is then opened in the pipeline, and all 13 modules are selected, including drug_dl_11, drug_dl_200, drug_condition, drug_phenotype, drug_side_effects, drug_targets, drug_human_gene_expression, docking_with_SARS-CoV-2_targets, docking_with_human_targets, docking_with_knowledge graph_targets, pathway_circuit, euclidean_distance and drug symptoms.As a result, the output is in the form of 0 and 1 scores, with 0 indicating that the medicine should not be considered for drug repurposing and 1 indicating that it should be. Furthermore, these modules generate two types of files: tsv files and intermediating files. The tsv files contain all of the modules' information in one file, whereas the intermediate files contain the detailed information created for each module of the input drug.
4. Result and Discussion
To determine whether MPA is a repurposable medication for Covid-19, a total of 13 modules are used. To begin, we have two modules, drug_dl_11 (Supplementary Table 3) and drug_dl_200 (Supplementary Table 4) that describe 11 biological properties and 200 attributes of the SARS-Covid-19 virus, respectively. The drug that has cured any COVID-19 condition, the drug that has shown any COVID-19 phenotype, the drug that has shown any COVID-19 side effect, the drug that has shown any COVID-19 interaction, the drug that has shown any abnormal human gene expression during COVID-19, and the drug that has shown any COVID-19 symptom are all described in other modules. Then this pipeline has inbuilt 3 docking modules where we have a human target-based docking module, viral proteins target-based module, and protein kinases associated with human proteins as targets for the query drugs. Lastly, we have two modules describing the drug that has shown any response with the SARS-CoV-2 pathway circuit and Check the euclidean distance between the drug and SARS-CoV-2 disease.
We interpret the data in the docking modules as binding affinities, which show the strength of the binding interactions. Docking with SARS-CoV-2 targets is done against 23 viral proteins, with Npro, Nsp14, Nsp15, S trimmer, and Nsp4 having the highest binding energies of -8.2, -7.9, -7.7, -7.3, -7.3 KCal/mol and Npro, Nsp14, Nsp15, S trimmer, and Nsp4 having the greatest binding energies of -8.2, -7. (in decreasing order)(Rawal et al. ). ORF6 and Nsp1 are the two least interacting viral proteins, with binding energies of -4.4 and -3.1 (in decreasing order), respectively, as shown in Supplementary Table 1. These proteins have an average binding affinity of -6.1 KCal/mol. Supplementary Figure 4 shows a column chart of MPA drug docking data against 23 viral proteins.
The output binding affinity values of human proteins and their associated protein kinases when the mycophenolic acid medication is docked to human proteins (ACE2, TMPRSS2) and their associated protein kinases. The average binding affinity is 6.82 KCal/mol, in this case, Supplementary Table 2. Supplementary Figure 5 shows a column chart examining the binding affinity values obtained. COVID-19 has 377 targets specified in the drug targets module (shown in Supplementary Table 5). Only 364 of the common adverse effects of the mycophenolic acid medication are obtained out of the 6123 side effects available for Covid-19 in the pipeline (shown in Supplementary Table 6).
Supplementary Table 12 contains tsv file format data containing combined data from all 13 modules that were performed for the Mycophenolic Acid medication. Except for the two modules, all of the scores are either 0 or 1. Docking with SARS-Cov-2 targets and Docking with knowledge graph targets with values between 0 and 1 are the two modules in question. Only four modules have a score of zero, indicating that mycophenolic acid has a good chance of being repurposed as a medicine against the Covid-19 virus.
5. ADDITIONAL INFORMATION
6. Conclusion
The modules with a score of 1 are rated as 7 and those with a score of 0 to 1 are rated as 2, indicating that these modules perceive the medication as repurposable against COVID-19. With a 78 percent score in the running 13 modules, mycophenolic acid medicine has a good chance of being a viable curative or repurposing therapy against the Covid-19 virus, according to the Covid- Drugx pipeline.
7. Acknowledgment
Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016) and Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.
8. Contribution of Authors
This study was conducted under the overall guidance of KR, who contributed to the protocol, critical evaluation of data, and manuscript. The pipeline was designed, constructed, and validated by RS and PS. Manuscript writing was done by Sakshi Singh.
9. Financial Support and Sponsorship
Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016). SKN, PP, R, SS, SDS, NG, and TS have received financial support from grants obtained from Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.
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Built a new system for finding role of microRNAs in heart development and heart diseases by integration of large scale experimental data with computational and comparative approaches. We detected 353 known and 703 novel miRNAs involved in heart development. The target mRNAs were appeared to be enriched with genes related to cell cycle, apoptosis, signaling pathways, extracellular remodeling, metabolism, chromatin remodeling and transcriptional regulators
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