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COV-Drug Symptoms interaction server for Covid-19 Drug Repurposing

Kamal Rawal^#1, Prashant Singh¹, Robin Sinha¹, Ridhima¹,Priya Kumari¹, Swarsat Kaushik Nath¹, Sukriti Sahai¹, Sweety Dattatraya Shinde¹, Nikita Garg¹ , Preeti P.¹, Trapti Sharma¹

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.

Keywords: Bioinformatics, drug repurposing, COVID-19, disease symptoms

Supplementary Data Website: https://sites.google.com/view/drugx-supplementary

COV-DRUGX Software Pipeline : http://drugx.kamalrawal.in/drugx/

Abstract

During the COVID-19 pandemic, drug repositioning is a promising alternative to the time-consuming process of generating new drugs. This brief study describes a clinical feature driven approach for the drug repurposing process. Here, we put forward information paths that associate COVID-19-related drugs and COVID-19 symptoms with drugs that target the symptoms or that treat diseases that are symptomatically similar to COVID-19. The analysis suggests a list of drugs that we suggest as potential candidates against COVID-19. Further, we have also provided evidence from published studies and in clinical trials that support the therapeutic potential of the drugs in our final list.

1. Introduction

Several strategies have been proposed to identify new drugs for COVID 19 (Pushpakom et al., 2019). Drug repurposing strategy is a promising technique for new pharmacologic therapies to be implemented for COVID-19. After the COVID-19 outbreak, accelerated and diverse research on SARS-CoV-2 infection has generated a huge and sophisticated database that can be approached to identify critical targets and therapies for drug repurposing in COVID-19 treatment. Drug repurposing is a popularly used strategy for finding treatment for diseases as the classical de-novo drug development consumed a lot of time, cost and involved huge risk as well. Numerous drug repurposing strategies have been utilized for drug development which include Knowledge-based, Target-based, Pathway-based, Target mechanism-based, Signature-based, and Phenotype-based repurposing (Park, 2019)

It is inferred that data relevant to biological clinical features, pharmacological responses, drug targets and even drug off-targets can provide unexpected insights for understanding COVID-19 pathology, symptoms and, possibly, identifying treatments (Ciliberto and Cardone, 2020). Symptom based drug repurposing in the paper by Santamaria (Santamaria et al., 2021) similar approach for validating drug-repurposing (DR) methods has been used, utilizing an existing medical data platform, DISNET which holds information about the relations between diseases and symptoms, symptoms and drugs, diseases and genes, and drugs and targets.

2. Implementation

A total of 105 different COVID-19 symptoms were identified from different sources such as Wikipedia texts providing 37 symptoms, ECDC documentation 23, and the Mayo Clinic sources 52, clinical studies. Data for drugs and their therapeutic indications module has been retrieved from SIDER database (http://sideeffects.embl.de/) which includes drug indication data containing drug and its target information. We have listed a total of 105 COVID19 disease related symptoms (Supplementary Table 1) and 25,088 different symptoms associated with various drugs (Supplementary Table 2).

3. Usage

This module matches therapeutic indications of drugs with clinical features of COVID-19. Users are allowed to provide either drug name (seperated by pipe in a text file) or SMILE notation of the drugs (separated by newline character in a text file). The module accepts drug names, or their SMILES as a query and it searches the drug in the drug symptoms dataset. The module picks the symptoms of drug, if found in the dataset and matches them with the COVID-19 symptoms datasets. If the module finds similar symptoms in the COVID-19 dataset, to those obtained from drug symptoms dataset, then the module predicts score 1 otherwise it will give 0. Furthermore, the module provides detailed information about the total number of symptoms associated with a particular drug and the number of symptoms matching to the covid19 symptoms dataset, lists these symptoms as well.

4. Result and Discussion

As a case study, we have collected three drug datasets i.e, 1,000 FDA approved drugs, 261 positive set drugs and 37 drugs in various phases of clinical research (Suvarna et al., 2021). The FDA approved drugs were extracted from the DrugBank database (https://go.drugbank.com/) used for input for the server (Supplementary Table 3). We found that progesterone, oseltamivir, alprazolam, fluoxetine and propranolol were top five FDA approved drugs with maximum number of symptoms matched with COVID-19 (Supplementary Table 4).

The positive drugs dataset (261 drugs) was collected from clinical reports and publications in literature. Those drugs were subjected to analysis with this tool and the intermediate file was obtained (Supplementary Table 5). Oseltamivir, fluoxetine, acetylsalicylic acid, ibuprofen and nifedipine were the top five drugs. (Supplementary table 6).

In another experiment, we have extracted 37 drugs reported by Suvarna et al (2021) (Suvarna et al., 2021). These drugs were used as test examples for our server (Supplementary Table 7). The top 5 approved drugs include captopril, verapamil, valproic acid, ribavirin and loratadine which shows reasonable matches with COVID-19 symptoms (Supplementary Table 8).

The resultant intermediate file consists of 12 columns. The “ID” column represents the serial number of the drugs starting from 0, “DRUG” column represents the drug name, the “IN_DATABASE_VALUE” column describes the availability of the drug (ranges from 1 to -1), the “VALUE” column gives the module prediction (either 0 or 1). The “TOTAL_NUMBER_OF_COVID19_SYMPTOMS”, “NUMBER_OF_SYMPTOMS”, and “NUMBER_OF_COMMON_SYMPTOMS” columns show the total number of targets found from the COVID-19 dataset, the total number of targets found from the SIDER dataset and the total number of common targets found in both datasets, respectively. The “SIMILARITY” column calculates the occurrence of drug’s symptoms with the total symptoms found. The “SIMILARITY_COVID19” column predicts the percentage probability of occurrence of a symptom in COVID-19 database. The “PERCENT_SIMILARITY” column calculates the percentage of the similarity. The “COMMON_SYMPTOMS” and “ALL_SYMPTOMS” columns provide the name of common symptoms and all symptoms associated with that particular drug. The N/A value in the resultant file predicts that the details about that drug are not available in the dataset.

The formula to calculate similarity is as follows:

The formula to calculate similarity is as follows:

The formula to calculate percentage similarity is as follows:

5. Conclusion

Drug-symptom interaction prediction is a new method to screen COVID19 drugs. This approach can be implemented in other drug repurposing studies to identify new drug molecules from large scale analysis..

6. References

1. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, McNamee C, Norris A. Drug repurposing: progress, challenges and recommendations. Nature reviews Drug discovery. 2019 Jan;18(1):41-58.

2. Khan SA, Al-Balushi K. Combating COVID-19: The role of drug repurposing and medicinal plants. Journal of infection and public health. 2021 Apr 1;14(4):495-503.

3. Park K. A review of computational drug repurposing. Translational and Clinical Pharmacology. 2019 Jun 1;27(2):59-63.

4. Ciliberto G, Cardone L. Boosting the arsenal against COVID-19 through computational drug repurposing. Drug discovery today. 2020 Jun;25(6):946.

5. Santamaría LP, Uzquiano MD, Carro EU, Ortiz-Roldán N, Gallardo YP, Rodríguez-González A. Integrating heterogeneous data to facilitate COVID-19 drug repurposing. Drug Discovery Today. 2021 Oct 16.

6. https://www.sciencedirect.com/science/article/pii/S1359644621004384