9

MIGALASTAT: A COVID-19 DRUG REPURPOSING STRATEGY

Veena RV¹, Prashant Singh¹, Robin Sinha¹, Preeti P.¹, Trapti Sharma¹ Swarsat Kaushik Nath¹, 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.

CoV DRUGX Software source availability: http://drugx.kamalrawal.in/drugx/

ABSTRACT:

Background:

COVID-19 has already caused billions of deaths all over the world. In the current situation, repositioning of medications may be considered a novel therapeutic option for COVID-19. This approach is useful when new mutant strains appear (omicron, Delta, Gamma, Beta, Alpha). Drug repurposing is a process of identifying new therapeutic uses for approved or investigational drugs and this strategy is considered effective for drug discovery. It involves less time and cost to find a therapeutic agent. In this research, we look at the medication Migalastat to check whether it could reduce the disease progression.

Methods:

Our team has created the CoV-DrugX pipeline, which allows us to comprehend the complete mechanism required for therapeutic candidate identification as well as whether a given medicine can be repurposed against COVID-19. The pipeline comprises modules based on artificial intelligence principles. A drug of interest for repurposing against COVID-19 is migalastat, an oral pharmacologic chaperone. The 14 modules of the pipeline were used to evaluate the characteristics of migalastat. We have considered Migalastat for the repurposing process based on the pipeline's results and scores.

Result and Discussion :

We assessed the CoV-DrugX pipeline outcome created by our research team based on the currently available data. The 200 qualities required for drug repurposing were met by the Migalastat drug. With a binding affinity of -6.7 Kcal/mol, the medication might be employed against targets like Nsp13. It also has a binding affinity of -5.0 for both AAK1 and JAK12, which can impede viral entrance. These findings suggest that migalastat drugs possess several characteristics that are required for medication repurposing against COVID-19.

Keywords: COVID-19, Migalastat, Drug repurposing, Molecular docking, Artificial intelligence

1. INTRODUCTION:

A novel virus emerged in the Chinese city of Wuhan at the end of 2019, causing an unexpected outbreak of uncommon clinical pneumonia (Wu et al., 2020). COVID-19 infection has impacted nearly 318 million people since the SARS-Cov-2 (https://covid19.who.int/). Coronaviruses are enveloped positive single-stranded RNA viruses that belong to the Coronaviridae family. Coronaviruses are classified into four genera: alpha, beta, gamma, and delta (Buchholz U et al., 2013).

The nucleocapsid protein (N), the spike protein (S), a small membrane protein (SM), and the membrane glycoprotein (M) with an extra membrane glycoprotein (HE) is encoded by the four key structural genes (Rottier PJM et al., 2013). SARS-CoV-2 is 96% identical to a bat coronavirus at the complete genome level (Zhou et al., 2020).

The development of any vaccine or treatment for any disease, including COVID-19, takes a long period, and it would take roughly 18-20 months to bring it to market as a ready-to-use product. More than 6000 clinical experiments connected to covid 19 have been registered (https://clinicaltrials.gov/) and more than 1500 articles related to drug repurposing have been found within two years of the pandemic. The hunt for an effective COVID-19 treatment drug is critical (Sertkaya. A et al., 2014). The unexpected finding of pharmacological action on new targets, which would subsequently imply a new prospective indication of use, is one of the primary factors for drug repositioning (Pushpakom et al., 2018). Some classic examples of serendipity-based drug repurposing include thalidomide, which was originally developed for the treatment of morning sickness in pregnant women and is now used to treat multiple myeloma (Jacobson, 2000), sildenafil, which was originally developed for the treatment of angina and hypertension and is now used to treat erectile dysfunction (Ghofrani et al., 2006), and amantadine, an antiviral (Lee and Kim, 2016). Scientists have been able to develop COVID-19 vaccines such as covishield and covaxin, Novavax, COVOVAX. but an antiviral medication is still desperately needed (Singh, A.K et al., 2021). As every virus, including SARS-CoV-2, evolves over time, a fresh strategy is required, especially given the time and cost of developing new medicines. Because every virus, including SARS-CoV-2, evolves over time, a new method known as the repurposing of medications is employed, taking into account the time and money required for new therapeutics (Pushpakom S et al., 2019). Drug repurposing, repositioning, re-tasking, and drug rescue are all terms describing the process of finding new uses for old medications (Huang F et al., 2020). The advantages of using the repurposing method are depicted in (Migalastat supplementary figure 1). Several clinical trials for the long-term treatment of COVID-19 are making progress. SARS-CoV-2 shares certain characteristics with other coronaviruses, such as SARS-CoV and MERS-CoV (Chen B et al., 2020), therefore repurposing current medications to treat COVID-19 is biologically viable. There have also been several successful precedents in repurposing antivirals for new virus targets (Mercorelli B et al., 2018).

Repurposed antiviral medicines, according to (Imran M et al., 2021), can inhibit the RNA-dependent RNA polymerase (RdRp) and are a promising potential therapeutic candidate against COVID-19. It is essential for SARS-CoV-2 replication. The RdRP in nsp12 is the heart of the coronavirus replication and transcription complex, and it's been recommended as a potential therapeutic target because it's a critical enzyme in the virus's life cycle for both viral genome replication and subgenomic mRNA transcription (V’kovski P et al., 2021). The importance of nsp14 in transcription and replication has long been recognized. It functions as a proofreading exoribonuclease and, through its methyltransferase activity, helps to cap viral RNA (Ma Y et al., 2015).

A variety of medicines, including remedesivir, favipiravir, ribavirin, lopinavir, ritonavir, darunavir, arbidol chloroquine, hydroxychloroquine, tocilizumab, and interferons, have exhibited inhibitory activity against the SARS-CoV-2 virus in vitro and in clinical settings. FPV (Favipiravir) is being widely researched for the treatment of several Nipah, Ebola, Lassa, and influenza virus infections (Mims et al., 2020). The drug selectively inhibits the RdRp ( RNA-dependent RNA polymerase) gene of SARS-CoV-2 and prevents genomic RNA replication. The active form of FPV-RTP (Favipiravir- RNA-dependent RNA polymerase) incorporates into the RNA strand and inhibits SARS-CoV-2 replication and transcription. The treatment of a 60-year-old COVID-19 patient with Favipiravir (1800 mg BID on day 1 and 800 mg dose on day 2) resulted in an increase in oxygen level and appetite, a reduction in fever, and an improvement in blood cell counts (Noda A et al., 2020). Remdesivir (RNA polymerase inhibitor) identified considerable anti-coronavirus (MERS and SARS), Ebola, and hepatitis C activity (Mims et al., 2020). Remdesivir identifies the RdRp gene's nsp8 and nsp2 proteins as potential targets for anti-SARS-CoV-2 activity. RDV-triphosphate competes with an equally functional natural ATP template, and the drug's nucleotide precursors inhibit SARS-CoV-2 RNA synthesis (Gordon CJ et al., 2020). Remdesivir is an active NTP analog of the prodrug GS-5734 that inhibits SARS-CoV-2 replication by targeting the RdRp gene. The nucleotide prodrug is transported from the extracellular environment to the intracellular compartment via a membrane, where it initiates a series of metabolic reactions such as ester hydrolysis, cyclization, and phosphorylation, that further results in the active functional moiety for anti-SARS-CoV-2 action (Eastman RT et al., 202)

The eukaryotic proteins Angiotensin-converting enzyme 2 (ACE-2) and (Transmembrane serine protease 2) TMPRSS2 are required for SARS-CoV-2 fusion. Because RdRP (viral protein) is required for viral RNA synthesis in host cells, the simultaneous inhibitions of these Using a single chemical to target enzyme targets could be a convincing technique for treating COVID-19 (Hoffmann M et al, 2020). The numb-associated kinase (NAK) family was discovered to be a therapeutic target for COVID-19 using BenevolentAI. The NAK inhibitor Barcitinib was selected as the most promising candidate medicine from this AI screen due to its strong affinity for AP2 associated protein kinase 1 (AAK-1). SARS-CoV-2 infection of host cells was expected to be inhibited by inhibiting AAK-1, a regulator of clathrin-mediated endocytosis ( Richardson P et al., 2020).

Deep learning algorithms rely on convolutional neural networks and incorporate a variety of types of data, such as transcriptomics, drug structure, and protein sequences, which appeared to work well for discovering SARS-CoV-2 treatments (Wang S et al., 2021) Molecular docking simulations were also used in other methods. Machine learning and AI-based approaches to discovering candidate repurposable medicines for COVID-19 have shown promise as early as February 2020. 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].

GALAFOLD, commonly known as Migalastat, is an alpha-galactosidase A enzyme (alpha-Gal A). Migalastat is a pharmacological chaperone that comprises Migalastat hydrochloride as its active component. It's a low-molecular-weight iminosugar that's an analog of globotriaosylceramide's terminal galactose (GL-3). It's used to treat adults with Fabry's disease with a variation of the galactosidase alpha gene (GLA). The chemical formula for migalastat is C6H14ClNO4, and the 3D structure may be found here (Migalastat Supplementary figure.2). Migalastatat has a PubChem CID of 176077 and a DrugBank accession number of DB05018. The FDA authorized Migalastat for use in the treatment of Fabry's disease on August 10, 2018. ( clinical trial number: NCT03500094).

(https://www.fda.gov/drugs/drug-approvals-and-databases/drug-trials-snapshots-galafold).

Migalastat is a chaperone that binds to the alpha-galactosidase active site and binds it reversibly. In persons with Fabry's disease, this protein is lacking. This binding aids in the stability of alpha- Gala and its transport from the endoplasmic reticulum to the lysosomes. Migalastat detached from alpha-galactosidase A once it entered lysosomes, resulting in a more acidic pH and higher concentration of substrates, allowing the enzyme to break down GL-3. Migalastat is quickly taken from the cell and eliminated after dissociating from the enzyme (Fan J-Q et al., 1999). Migalastat has been demonstrated to be successful in lowering GL-3 build-up and stabilizing renal function in patients with an amenable mutation. In one trial, the patient accepted the drug well and his lysosomal GL-3 levels stayed steady during the transition. Elevated levels of pro-inflammatory cytokines have been linked to Fabry disease (Rozenfeld P et al., 2017) The cytokine storms associated with COVID-19 could potentially raise these levels even higher, putting patients at risk of a clotting event caused by endothelial inflammation and damage (Varga S et al., 2020). GLA is the SARS-CoV-2-interacting target ( Das S et al., 2020).

2. CoV-Drug-X PIPELINE: IMPLEMENTATION

Our laboratory devised the CoV-Drug-X pipeline. This tool (http://drugx.kamalrawal.in/drugx/) is used to acquire a thorough understanding of the full process in order to determine whether a drug candidate can be repurposed against COVID-19. This pipeline is primarily based on artificial intelligence and clinical data. The CoV-Drug-X pipeline was created as a collection of components. It comprises 14 modules for COVID-19 that are based on several computational drug repositioning methodologies that leverage various databases and literature-based approaches. The pipeline's design allows each module to run independently while using SMILES as an input. Modules produce scores ranging from 0 to 1 as a result of their work. The pipelines' workflow is depicted in (Migalastat supplementary figure 3).

2.1) Modules 1&2 based on deep learning: (Drug dl 100 Drug dl 200):

The Drug dl 11 module employs a deep learning method and includes primarily 262 medicines that are related to COVID-19. 11 biological features of COVID-19 are investigated using information from the literature. Drug dl 200, on the other hand, focuses on the 200 chemoinformatics characteristics.

2.2) Drug Docking module 3:

Docking is a crucial approach in COVID-19's medication repurposing investigations. This module performs protein docking with the drug of interest and aids in the comprehensive understanding of medicines and proteins.

2.3) Side effects module 4:

This module works by predicting whether the drug is associated with COVID-19 or not, based on drug side effects. The module predicts its score ranging from 0 to 1.

2.4) Drug Target Module 5: This module is based on the target molecule's similarity to the target molecule of the medications that are linked to COVID-19. If there is any resemblance between a query drug and a target, the module assigns a score of one.

2.5) Gene expression module 6: This gene expression module provides data on gene regulation, such as whether a gene is upregulated or downregulated.

2.6) Phenotype module 7: This module offers the phenotype of the input drug if it is connected with COVID-19, as well as a list of drug phenotypes that match COVID-19.

2.7) Gene-Drug Network Module 8: This module contains information on the interactions between genes, proteins, and drugs of COVID-19.

3. USAGE:

Migalastat is a pharmacological chaperone having the chemical formula C16H14ClNO (supplementary figure 2) a Pubchem CID of 176077, a UniProt ID of P06280, and a drug bank entry number of DB05018. The drug bank has migalastat SMILES. Upload the SMILES file user already saved to the CoV DrugX pipeline server. When we choose the modules all at once or one by one, the pipeline will begin. Then select the submit option. Following submission, the screen will display query details, including the job ID, result for, and status. After a few moments, the page will redirect to the proper result page, which will show the module scores. The scores range from 0 to 1, with 0 indicating that the medicine is not eligible for repurposing research and 1 indicating that it can be used in further investigations. For assessing the drug repurposing parameter, the PI score can be used. The SI score is the total of all modules, whereas the PI score is the average of all modules. For each module, intermediate files are also produced, which provide more precise functional and detailed information on the migalastat drug.

4. RESULTS & DISCUSSION:

The computational drug repurposing approach is used to build pipeline modules. It consists of two modules that use COVID-19's 11 biological features and 200 cheminformatics properties to forecast whether a medication is COVID-19 repurposable. Aside from that, we offer modules on COVID-19-related illnesses, medications with COVID-19-related symptoms, pharmacological side effects, and gene expression-related modules. Another method we employ in our pipeline is docking. We have human targets, viral proteins targets, and protein kinases coupled with human proteins targets in three docking-based modules. A module that estimates the euclidean distance between the drug and COVID-19, as well as a program that checks the query drug for any associations with the SARS-COV-2 circuit, are also included.

Migalastat has no response with the SARS-CoV-2 pathway circuit. (Migalastat Supplementary tables.3). The gene such as AGL (amylo-alpha-1, 6-glucosidase, 4-alpha-glucanotransferase) is a protein-encoding gene that provides instruction for making the glycogen debranching enzyme, GBA (Glucosylceramidase Beta) is a protein-coding gene that encodes the lysosomal enzymes. Disease-associated with this deficiency includes Gaucher Disease. HNRNPH2 (Heterogeneous Nuclear Ribonucleoprotein H2) is a protein-coding gene, associated with pre-mRNAs in the nucleus and influences pre mRNA processing, SI (sucrase-isomaltase), is a gene that provides instruction for producing sucrase-isomaltase enzyme, GLA (Galactosidase alpha A) is a gene that provides instruction for making enzyme galactosidase alpha A , MGAM (Maltase glucoamylase) is a gene which produces the enzyme Maltase glucoamylase and GAA (Acid alpha-glucosidase) is a gene which encodes the lysosomal alpha-glucosidase. These genes have all been found to be down-regulated by Migalastat. GBA2 (Glucosylceramidase Beta 2) is a protein-coding gene and the mutation leads to diseases such as spastic paraplegia, SI (Sucrase isomaltase), GLA (Galactosidase-isomaltase), MGAM (Maltase glucoamylase), and GAA (Acid alpha-glucosidase), on the other hand, are upregulated. For this module, the pipeline assigns a score of 1. (Migalastat supplementary table.4). Migalastat does not affect COVID-19 condition, COVID-19 symptoms, COVID-19 Target, and COVID-19 side effects (Migalastat Supplementary tables.5,6,8,9). According to Migalastat's phenotype module, there are approximately 24 phenotypes and 24 frequent phenotypes. A 1.0 similarity score and a DrugX score of 1 are also shown in the data. Some of the traits include left ventricular hypertrophy, calf hardness, muscle tightness, Neuropathic pain, stroke, diarrhea, and chest discomfort. (Migalastat Supplementary tables.7). The pipeline gives a score of 1 to the deep learning module which contains 200 properties. And hence can be considered for repurposing (Migalastat supplementary table.11).

The results of Migalastatis docking against SARS-CoV-2 viral targets were shown in the form of binding affinities ( strength of binding interaction). Npro, Nsp2, Nsp4, Nsp13, Nsp14, and Nsp15 had the greatest interacting viral proteins with migalastat, with -6.1, -5.4, -5.8, -6.7, -6, and -5.7, respectively. Nsp1, Nsp3, Nsp6, Nsp9s2, Nsp, Nsp12 had the lowest binding energy with -2.7, -4.7, -3.9, -4.2, -4.6, -4.9, respectively (Migalastat supplementary table.14). The distribution of binding affinities for viral proteins is illustrated in a histogram (Migalastat supplementary fig.4).

The docking of migalastat with human targets targeted by SARS-CoV-2 is given a score of 0 by the pipeline. ACE2 and TMPRSS2 are two human targets; TMPRSS2 has a higher binding affinity (-4.9Kcal/mol) than ACE2 (-4.8Kcal/ml). The SARS CoV2 virus enters through the ACE2 receptor. Migalastat has a lower affinity for that specific target. TMPRSS2, on the other hand, is an endothelial surface protein that aids viral entry and propagation of SARS-CoV-2. Migalastat inhibits TMPRSS2 targets, reducing viral entrance and dissemination (Migalastat supplementary table.12).

The migalastat drug has the same binding affinity for both AAK1 and JAK12 kinases. The binding affinity of both kinases is -5.0 Kcal/mol (Migalastat supplementary table.13). AAK1 (AP2 associated kinase 1) is an endocytosis regulator that can prevent the virus from entering the cell. JAK12 is a kinase inhibitor. Because Migalastat has equal affinity for both targets, it could be utilized to prevent covid infection(Migalastat supplementary fig.4.) The drug assay module receives a 1 from the pipeline. HRCE cell type received a hit score of 0.115 out of the three available cell lines (Coca cell, HRCE, and Vero cells). There is no information in the remaining cell lines (Migalastat supplementary table.16).

The pipeline output gives 3 modules a 1 score, 1 module a 0.25 score, and the remaining 10 modules a 0 score. The SI score is 3.25 overall, while the PI and Ti scores are 0.232 and 14, respectively (Migalastat supplementary table 1). The average score is derived using the method Number of modules with score 1 / Total number of modules with drug data. As a result, based on the existing data, the migalastat medication has a 75 percent chance of being selected for repurposing trials. The results of the calculation are tabulated in the (Migalastat supplementary table 18).

5. CONCLUSION:

Researchers are looking for an effective, low-cost, and simple-to-use COVID-19 treatment that could be utilized by everyone, including those in low-income nations. The major treatment direction is still active treatment based on the patient's symptoms. Cyclosporine, doxycycline, chloroquine, and rapamycin are some of the promising repurposing drugs for COVID-19 treatment. We support research suggesting Migalastat be a likely cure for COVID-19 because the results of Migalastat against our CoV-DrugX pipeline, which was extensively designed for repurposing of Covid-19, have shown positive results.

6. ADDITION SUPPLEMENTARY FILES:

1. MIGALASTAT TABLE FILE

2. MIGALASTAT FIGURE FILE

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44. Gupta, R., Soni, Patnaik, Sood, I., Singh, R., Rawal, K., Rani, V. High AU content: A Signature of Upregulated miRNA in Cardiac Diseases, Bioinformation, vol. 3, pp.132-135, 2010

Vaccine Development:

45. Abbasi, B. A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Gupta, P. Rawal, K. (2020, April 8). Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. https://doi.org/10.31219/osf.io/f8zyw

46. Kamal Rawal, v, Abbasi, B. A., et al (2020). Design of a multi-epitope Chagas disease vaccine by computational analysis of the Trypanosoma cruzi CL Brenner proteome. (Communicated)

47. Rawal k, Sinha R et al (2020) Vaxi – DL : A web-based Deep Learning (DL) server to identify Potential Vaccine Candidates, (Bioinformatics- communicated)COVID-19

48. Jethani B., Rawal, K. et al (2020). Clinical Characteristics and Remedy Profile of Patients with COVID-19: Retrospective Cohort Study, Accepted (In Press)

49. Rawal, K., Sinha R et al (2020) To Study the Effect of Unconventional Treatment Protocol on COVID-19 patients in Delhi using AI-based techniques (Lancet- communicated)

Machine Learning:

50. Rawal, K., Khurana T, Sharma H, . 2019. An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7:e27538v1 https://doi.org/10.7287/peerj.preprints.27538v1

51. Established the new framework/portal of "obesity treatment" and other diseases such as type 2 diabetes and hypertension among the people living sedentary lifestyle by engaging relevant sections of community using social networks, data science systems, machine learning, sensors and mobile apps. The system is being used by general public as a social service initiative.

52. Development of a Web Based Weight Loss Programme. K. Rawal, P. Gaur and K. Kashive, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, October, 2014

53. Developing Networks in Obesity using Text Mining. K. Rawal, S. Agarwal and J. Jagannadham, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, September, 2014.

ACKNOWLEDGEMENT

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.

FINANCIAL SUPPORT AND SPONSORSHIP

Dr. Kamal Rawal appreciates the SERB, Department of Science and Technology, for providing support (Grant ID: CVD/2020/000842). The Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India provided computing infrastructure (servers, etc.) for the study (Grant ID: BT/PRI7252/BID/7/708/2016). Grants from the Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA, supported SKN, PP, R, SS, SDS, NG, and TS. We are also grateful to Amity University for their assistance in this research.