DrugX-Drug Side-effect interaction based drug Repositioning
Kamal Rawal#1, Prashant Singh1, Robin Sinha1, Priya Kumari1, Swarsat Kaushik Nath1, Ridhima1, Sukriti Sahai1, Sweety Dattatraya Shinde1, Nikita Garg1 , Preeti P.1, Trapti Sharma1
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, artificial intelligence, COVID-19, molecular targets
Supplementary Data Website:
COV-DRUGX Software Pipeline : http://drugx.kamalrawal.in/cov_se/
Abstract
1. Introduction
Molecular promiscuity can be described as the situation in which small molecules and proteins participate in molecular interactions. Promiscuity can play a role in the appearance of side-effects, but could also be leveraged in polypharmacological strategies or repurposing. Experimental assays of 72 inhibitors (11 are FDA-approved drugs) with 442 kinases showed that 64% of the compounds bind 20% of kinases with an affinity threshold of 3 μM (Davis et al., 2011). Another inter-family large-scale study with data for 238 655 compounds and 2876 targets, showed that promiscuity is often within the same protein family, but also among members of different protein families (Paolini et al., 2006). Promiscuity is often perceived negatively because of side-effects that can occur when the drug modulates the activity of off-targets. Toxicity issues are responsible for nearly 30% of failures in drug development programs (Whitebread et al., 2005). The major and important fact before recommending any drug to the patient is its after effect in the human body (Aygün et al., 2020).
Research shows that multiple drug usage (polypharmacy) significantly increases drug side effects (Guthrie et al., 2015; (Colley and Lucas, 1993). Therefore, it is vital to predict adverse drug reactions for the drugs to be used in the treatment of a disease (Aygün et al., 2020). Many drugs were suggested and still going in progress to increase the success in Covid 19 treatment. Some clinical studies are shared in the literature where drugs are being used in the combinations beside the fact that using more than one drug together may cause serious side effects (Aygün et al., 2020). Although some current therapeutic agents have shown potential prevention or treatment, a growing number of associated adverse events have occurred in patients with COVID-19 in the course of medical treatment (Wu et al., 2020). Coronavirus disease (COVID-19) vaccine-related side effects have a determinant role in the public decision regarding vaccination (Riad et al., 2021). Therefore, a comprehensive assessment of the safety profile of therapeutic agents against COVID-19 is urgently needed. Chartier et al., performed large scale analysis using 400 drugs against 7895 different known protein structures. The study predicted Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target (Chartier et al., 2017). In another study The interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. The study revealed that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Heparin and Atazanavir appear to cause more adverse reactions than other drugs (Aygün et al., 2020).
In this study, we have prepared a framework to identify potential drug candidates by comparing side effects of drugs with side effects of covid-19 drugs. Study of adverse drug reactions (ADR) is vital for the drugs to be used in the treatment of Covid-19 (Wu et al., 2020). The Drug Side-effect module 5 scores a drug candidate on the basis of the similarity of their side effects to the side effects of drugs associated with COVID-19.
2. Implementation
Side effect database associated with Covid-19
Using text mining and a deep curation approach, we have collected the list of drugs that are associated with Covid-19. Total of 393 drugs were found (Supplementary Table 1). Duplicate drugs and drugs with no SMILES were removed and a final list of 263 drugs was made (Supplementary Table 2). We collected over 6000+ side effects of these drugs from various databases (Supplementary Table 3).
3. Usage
The module predicts if the drug is associated with COVID-19 or not on the basis of drug- effect association . Users are allowed to provide SMILE notation of the drugs (separated by newline characters in a text file). The module changes the drug SMILE notation into a drug name.
The module accepts drug names as a query and it searches for its Side- effect in the SIDER and offside datasets. The module picks the Side-effect of the provided drug and moves towards the list of Side effects associated with Covid-19. If the module got the side effect in the listed dataset then the module predicts score 1 otherwise it will give 0.
4. Result and Discussion
As a case study, we have collected three drug datasets i.e, 1,000 FDA approved drugs, 261 positive drugs and 37 drugs from machine learning study (Suvarna et al., 2021). The FDA approved drugs were extracted from the DrugBank database (https://go.drugbank.com/) used to input data for the server (Supplementary Table 5). In the given 1000 drugs, there were 913 drugs which have score 1 and 86 drugs which have score 0. The intermediate result file has been analysed and found that Hydrocortisone, Cholecalciferol, Thiamine, Ergocalciferol and Isosorbide dinitrate were top five FDA approved drugs maximum number of common side-effects (Supplementary Table 6). The distribution of the FDA approved drugs revealed that the drugs' score 1 were found in the range of 10-25 percent similarity(Supplementary Figure 1).
The positive drugs dataset (261 drugs) was collected from various literature. Those drugs were subjected to analysis with this tool and the intermediate file was obtained (Supplementary Table).In the given 264 drugs, there were 195 drugs which have score 1 and 69 drugs which have score 0. Colchicine, Tamoxifen, Epinephrine, Mycophenolic Acid and Sirolimus were the top five drugs found after the analysis of the intermediate file (Supplementary Table). We have plotted the distribution of the percentage similarity (Supplementary Figure 4).
In another experiment, we have extracted 37 drugs from the study reported by Suvarna et al in the year 2021 (Suvarna et al., 2021). Suverna et al predicted 37 drugs as the prognostic markers for the COVID-19 using proteomics and machine learning approaches. Those drugs were used as samples for our server and the predicted intermediate file is collected (Supplementary Table). In the given 37 drugs, there were 24 drugs which have score 1 and 13 drugs which have score 0. The resultant file was analyzed and top drugs were found including Mycophenolic Acid, Rapamycin, Captopril, Indomethacin and linezolid which consist of a maximum number of common side-effects(Supplementary Table). Further, we have plotted the distribution of the drugs with the percentage similarity score (Supplementary Figure).
Top drugs with respect to percentage similarity were also calculated. Formula used for percentage similarity was :
Percentage similarity = Common drugs side effects X 100
Total number of side effects
Top 5 drugs reported through percentage similarity are netupitant, cinoxacin, chlortetracycline, lenvatinib and dipivefrin with the topmost drug having 75% similarity score [S3].
The intermediate file of results consists of 11 columns. The “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_SE” and “NUMBER_OF_SIDE_EFFECTS” columns provide the no. of total side effect related to covid19 associated drugs and the total no. of the side effect for that particular drug. The “NUMBER_OF_COMMON_SIDE_EFFECTS” column contains the no. of side effects of that drug matched with the Covid-19 associated drugs. The “SIMILARITY” and “SIMILARITY_COVID19” columns contain the scores for the Drug . The “COMMON_SIDE_EFFECTS” and “ALL_SIDE_EFFECTS” contains the name of the side effect common with covid associated drugs and total no. of the side effect of that particular drug respectively. The user can observe N/A or 0 value in some columns which means the given drugs are not available in the database columns and 0 value in IN_DATABASE_VALUE column.
5. Conclusion
Drug-Gene interaction is an important part of most of the rational drug repositioning approaches. In fact, different biochemical, physical, and mathematical techniques have been designed and optimized to accurately infer links between ligands and Genes or associated proteins from these genes. In this work we utilize the drug gene interaction database (DGIdb database) and Covid-19 DEGs for the prediction of off-target effects to suggest potential cases of drug repurposing and determine the molecular mechanism responsible for changes in gene- expression. There are a number of ways to find out the drug-off target drugs. Here we have combined the experimental data and system biology approach to yield a promising tool to better understand the biological response of the drug.
Contribution of Authors
This study was conducted under the overall guidance of KR, who contributed in protocol, critical evaluation of data and manuscript. The pipeline was designed, constructed and validated by RS and PS. Manuscript writing was done by PS, PK, and PP. All the authors are responsible for the content of the manuscript.
Acknowledgement
We extend our sincere gratitude to Amity University for providing administrative and technical support required in the conduct of this study.
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 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.
Supplementary Information
References
Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, Hocker M, Treiber DK, Zarrinkar PP. Comprehensive analysis of kinase inhibitor selectivity. Nature biotechnology. 2011 Nov;29(11):1046-51.
Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nature biotechnology. 2006 Jul;24(7):805-15.
Whitebread S, Hamon J, Bojanic D, Urban L. Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug discovery today. 2005 Nov 1;10(21):1421-33.
Aygün İ, Kaya M, Alhajj R. Identifying side effects of commonly used drugs in the treatment of Covid 19. Scientific Reports. 2020 Dec 9;10(1):1-4.
Guthrie B, Makubate B, Hernandez-Santiago V, Dreischulte T. The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010. BMC medicine. 2015 Dec;13(1):1-0.
Colley CA, Lucas LM. Polypharmacy. Journal of general internal medicine. 1993 May;8(5):278-83.
Kuhn, Michael, et al. "The SIDER database of drugs and side effects." Nucleic acids research 44.D1 (2016): D1075-D1079.
Von Eichborn, Joachim, et al. "PROMISCUOUS: a database for network-based drug-repositioning." Nucleic acids research 39.suppl_1 (2010): D1060-D1066.
Tatonetti, Nicholas P., et al. "Data-driven prediction of drug effects and interactions." Science translational medicine 4.125 (2012): 125ra31-125ra31.
Aygün İ, Kaya M, Alhajj R. Identifying side effects of commonly used drugs in the treatment of Covid 19. Scientific Reports. 2020 Dec 9;10(1):1-4.
Wu Q, Fan X, Hong H, Gu Y, Liu Z, Fang S, Wang Q, Cai C, Fang J. Comprehensive assessment of side effects in COVID-19 drug pipeline from a network perspective. Food and Chemical Toxicology. 2020 Nov 1;145:111767.
Riad A, Schünemann H, Attia S, Peričić TP, Žuljević MF, Jürisson M, Kalda R, Lang K, Morankar S, Yesuf EA, Mekhemar M. COVID-19 Vaccines Safety Tracking (CoVaST): Protocol of a multi-center prospective cohort study for active surveillance of COVID-19 vaccines’ side effects. International journal of environmental research and public health. 2021 Jan;18(15):7859.
Chartier M, Morency LP, Zylber MI, Najmanovich RJ. Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects. BMC Pharmacology and Toxicology. 2017 Dec;18(1):1-6.