Drug Repurposing Using Modularity Clustering in DrugDrug Similarity Networks Based on Drug–Gene Interactions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Databases
2.2. Building the Drug–Drug Similarity Network
2.3. Network Clustering Analysis
2.4. Tuning Resolution $\lambda $
Algorithm 1 Find the parameter $\lambda $, such that the clustering $\mathcal{C}$ of nodes/drugs ${D}_{i}$ in $\mathcal{G}$ with modularity resolution $\lambda $ (i.e., $\mathrm{Clustering}\left(\mathcal{G},\lambda \right)$) produces the biggest number of repositionings confirmed with the level 1 ATC codes in DrugBank 5.1.8. 

2.5. Generating New Repurposing Hints
Algorithm 2 Generate the list of drug repurposing hints by clustering the DDSN $\mathcal{G}$ with the tuned modularity resolution. 

3. Results
3.1. DDSN Using Drug–Gene Interactions from DrugBang 5.0.9
3.2. DDSN Using Drug–Gene Interactions from DrugBang 5.1.8
3.3. Repositioning Confirmations
3.3.1. Confirmed Drug Repositionings in DrugBank 5.0.9
Modularity Cluster ${C}_{0}$
Modularity Cluster ${C}_{2}$
3.3.2. Drug Repositioning Hints in DrugBank 5.1.8
4. Discussion
4.1. Drug–Gene Interactions
4.2. Method Limitations
4.3. Labeling and Validation with ATC Codes
4.4. Method Application
5. Conclusions
 (i)
 A new method to build weighted drug–drug similarity networks based on drug–gene interactions;
 (ii)
 An automated procedure to optimize the modularity resolution such that network clustering maximizes the number of identified drug repurposings. A known/ confirmed drug repurposing is a drug with more level 1 ATC codes in the latest drug database, compared with the earlier database—used to generate the drug–drug similarity network;
 (iii)
 A new drug repurposing list was generated with our pipeline from the latest DrugBank 5.1.8 by analyzing the three most representative clusters.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ATC  Anatomical Therapeutic Chemical; 
COPD  Chronic Obstructive Pulmonary Disease; 
COX2  Cyclooxygenase2; 
DDSN  Drug–Drug Similarity Network; 
NSCLC  NonSmall Cell Lung Cancer. 
Appendix A. Repositionings and Statistics for DrugBank 5.0.9 DDSN
Appendix A.1. DDSN Zoomed Details
Appendix A.2. DDSN Cluster Histograms
Appendix B. Repositionings and Statistics for DrugBank 5.1.8 DDSN
Appendix B.1. DDSN Zoomed Details
Appendix B.2. DDSN Cluster Histograms
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Drug  Cluster  Current Level 1 ATC  Predicted Level 1 ATC  References 

Pyridoxal phosphate  ${C}_{0}$  A  H  [44,45] 
Albendazole  ${C}_{1}$  P  J  [46,47] 
Methotrexate  ${C}_{1}$  L  J  [48,49,50] 
$\left[\begin{array}{l}\mathrm{Simvastatin}\\ \mathrm{Fluvastatin}\\ \mathrm{Lovastatin}\\ \mathrm{Atorvastatin}\end{array}\right.$  ${C}_{1}$  C  J  [51,52] 
Theophylline  ${C}_{2}$  R  L  [14,53] 
Meloxicam  ${C}_{2}$  M  L  [54,55,56] 
$\left[\begin{array}{l}\mathrm{Cholecalciferol}\\ \mathrm{Ergocalciferol}\\ \mathrm{Calcifediol}\end{array}\right.$  ${C}_{2}$  M, A  L  [57,58] 
Chloroquine  ${C}_{2}$  P  L  [59,60,61,62,63] 
$\left[\begin{array}{l}\mathrm{Mecasermin}\\ \mathrm{Mecasermin\; rinfabate}\end{array}\right.$  ${C}_{4}$  H  A  [64,65,66] 
Ornithine  ${C}_{25}$  A  N  [67] 
Drug Name  Gene Name  Interaction Type 

Alteplase  PLG  activator 
Hydromorphone  OPRK1  agonist 
Varenicline  CHRNB2  partial agonist 
Prazosin  ADRA1B  antagonist 
Ascorbic acid  EGLN1  chaperone 
Pyridoxal phosphate  GAD1  cofactor 
Vardenafil  PDE6G  allosteric modulator 
Trastuzumab  ERBB2  antibody 
Nusinersen  SMN2  antisense oligonucleotide 
Methysergide  HTR1F  binder 
Tiapride  DRD2  blocker 
Carvedilol  KCNJ4  inhibitor 
Clobetasol propionate  ANXA1  inducer 
Clofazimine  PPARG  modulator 
Cerliponase alfa  IGF2R  ligand 
Filgrastim  CSF3R  stimulator 
Dalteparin  SERPINC1  potentiator 
Vitamin A  RDH13  substrate 
Nedocromil  CYSLTR1  suppressor 
Belimumab  TNFSF13B  neutralizer 
Esmirtazapine  HRH1  inverse agonist 
Procainamide  DNMT1  other 
Haloperidol  HTR2A  other/unknown 
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Groza, V.; Udrescu, M.; Bozdog, A.; Udrescu, L. Drug Repurposing Using Modularity Clustering in DrugDrug Similarity Networks Based on Drug–Gene Interactions. Pharmaceutics 2021, 13, 2117. https://doi.org/10.3390/pharmaceutics13122117
Groza V, Udrescu M, Bozdog A, Udrescu L. Drug Repurposing Using Modularity Clustering in DrugDrug Similarity Networks Based on Drug–Gene Interactions. Pharmaceutics. 2021; 13(12):2117. https://doi.org/10.3390/pharmaceutics13122117
Chicago/Turabian StyleGroza, Vlad, Mihai Udrescu, Alexandru Bozdog, and Lucreţia Udrescu. 2021. "Drug Repurposing Using Modularity Clustering in DrugDrug Similarity Networks Based on Drug–Gene Interactions" Pharmaceutics 13, no. 12: 2117. https://doi.org/10.3390/pharmaceutics13122117