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Background:
Hypothesis

Drug Investigation to Dampen the Comorbidity of Rheumatoid Arthritis and Osteoporosis via Molecular Docking Test

by
Ki-Kwang Oh
,
Md. Adnan
and
Dong-Ha Cho
*
Department of Bio-Health Convergence, College of Biomedical Science, Kangwon National University, Chuncheon 24341, Korea
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2022, 44(3), 1046-1061; https://doi.org/10.3390/cimb44030069
Submission received: 16 December 2021 / Revised: 8 February 2022 / Accepted: 21 February 2022 / Published: 23 February 2022
(This article belongs to the Collection Application of Natural and Pseudo Natural Products in Drug Discovery and Development)
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Abstract

:
At present, most rheumatoid arthritis (RA) patients are at risk of osteoporosis (OP), which is increased by 1.5 times compared to non-RA individuals. Hence, we investigated overlapping targets related directly to the occurrence and development of RA and OP through public databases (DisGeNET, and OMIM) and literature. A total of 678 overlapping targets were considered as comorbid factors, and 604 out of 678 were correlated with one another. Interleukin 6 (IL-6), with the highest degree of value in terms of protein–protein interaction (PPI), was considered to be a core target against comorbidity. We identified 31 existing small molecules (< 1000 g/mol) as IL-6 inhibitors, and 19 ligands were selected by the 3 primary criteria (Lipinski’s rule, TPSA, and binding energy). We postulated that MD2-TLR4-IN-1 (PubChem ID: 138454798), as confirmed by the three criteria, was the key ligand to alleviate comorbidity between RA and OP. In conclusion, we described a promising active ligand (MD2-TLR4-IN-1), and a potential target (IL-6) against comorbidity of RA and OP, providing scientific evidence for a further clinical trial.

1. Introduction

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease that mainly causes severe pain and is associated with physical unfitness, diverse comorbidities, and diminished quality of life [1,2]. The main symptoms of RA are musculoskeletal pain, swelling, and stiffness of affected joints, linked deeply to synovial inflammation [3,4]. RA can present at all ages, and around 1% of this population suffers intractable pain, which entails enormous emotional stress and economic burden for the individual, and even for society [5]. Inflammation is the main driving factor that causes joint impairment, disorder, and unexpected comorbidity in RA patients, and anti-inflammation is the most significant therapeutic strategy [6]. At present, there are seven antibody drugs (biologics) for the treatment of RA: infliximab, adalimumab, etanercept, golimumab, tocilizumab, certolizumab, and abatacept [7]. In particular, disease-modifying anti-rheumatic drugs (DMARDs) are targeted against RA inflammation, limited to connective tissue damage [8]. Moreover, biological DMARDs are ineffective for improving bone density involved in the development of OP [9].
Osteoporosis (OP) is a severe health condition that weakens bones, making them fragile and more easily destroyed [10]. OP symptoms include back pain, loss of height, bone fractures, and change in posture [11]. Similarly, OP can also occur at all ages; mainly, primary osteoporosis develops ~10–15 years after menopause in women, and in elderly men between 75 and 80 years old [12]. In 2017, the International Osteoporosis Foundation announced that around 33% of women over 50 years old and 20% of men would experience OP in their lifespan [13]. Most recently, an emerging significant factor in OP was found to be inflammation that occurs upon bone turnover [14]. Thus, blockage of inflammation is a key clinical approach in OP patients [15]. Denosumab and odanacatib were used as biologics in the treatment of OP by enhancing bone mineral density [16,17]. Collectively, all biologics dampen the immune system, making it susceptible to common infections such as pneumonia, respiratory infections, urinary tract infections, and skin infections [18]. In addition, all antibody drugs are parenteral preparations for the patient, which have multiple risks, including hypersensitivity responses, risk of infection and emboli, and the absence of drug reversal [19,20,21]. Additionally, non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to relieve the pain related to RA and OP [22]. All NSAIDs are targeted to cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2)—the two cyclooxygenase (COX) isoforms in tissues, which have different expression levels [23]. Moreover, the inhibition of COX interferes with bone formation, angiogenesis, and soft tissue regeneration, which obstructs the bone healing process [24]. This implies that NSAIDs function merely as an analgesic for a short period of time.
Most commonly, OP has been considered to be a classical comorbidity in RA [25]. According to one report, in a cohort of 47,000 RA patients, the risk of osteoporotic destruction increased by 1.5 times compared with non-RA individuals [26]. Both RA and OP represent chronic inflammatory responses against cytokines such as interleukin 1 (IL-1), interleukin 6 (IL-6), and interleukin 17 (IL-17) [27]. This implies that inhibition of interleukin(s) might be a crucial strategy to alleviate the liaisons between RA and OP. The application of drug repositioning analysis on a data-driven approach is the most efficient methodology to obtain promising compounds [28].
Furthermore, drug repositioning is the procedure of obtaining new therapies for already-existing drugs [29]. This process has great synergistic effects, diminishing the cost of new drug development as well as securing its safety [30]. Previously, the output of drug repositioning was mainly due to fortuitous findings of unexpected therapeutic effects identified after testing with a given agent [31]. However, at present, the development of computational methodologies from holistic perspectives provides us with critical hints to reevaluate the additional efficacy of existing drugs [32].
Thus, the aim of this work was to discover the hierarchical target by which to manage both RA and OP via the computational approach method, thereby unveiling the most significant small molecule (<1000 g/mol) against the comorbidity of the two diseases.

2. Hypothesis

The identified overlapping targets between RA and OP were used to construct protein–protein interaction (PPI). We hypothesized that a target with the highest degree of value would be the most promising therapeutic point [33], while a ligand with the lowest binding energy would be the most significant compound against the comorbidity of RA and OP.

3. Methods

3.1. Retrieval of RA or OP Targets and Identification of Overlapping Targets

The targets linked to the occurrence and development of RA and OP were retrieved from DisGeNET (https://www.disgenet.org/) (accessed on 24 July 2021), OMIM (https://www.omim.org/) (accessed on 26 July 2021), and previous literature. InteractiVenn was utilized to identify the overlapping targets between RA and OP.

3.2. PPI Network Analysis

The overlapping targets analyzed by STRING (https://string-db.org/) (accessed on 29 July 2021) had their PPI constructed via an R package. One target with the highest degree of value was obtained via PPI analysis; we considered it to be the most significant target to manage the comorbidity of RA and OP.

3.3. Collection of Ligands

Based on the target, we prepared for its known ligands on a small molecule screen (<1000 g/mol), which can facilitate its biological activity or modify a target [34]. The ligands were retrieved from the website of the chemical supplier Selleckchem (https://www.selleckchem.com/) (accessed on 2 August 2021), which had input them into PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 2 August 2021) for identification in the SMILES (simplified molecular-input line-entry system) format.

3.4. The Screening of Ligands

The screening methodology of the selected ligands was based on three criteria (Lipinski’s rule, TPSA, and binding energy), which were filtered using SwissADME (http://www.swissadme.ch/) (accessed on 4 August 2021). The three detailed selective conditions were as follows: (1) Lipinski’s rule violation (≤1) [35], (2) TPSA (<140 Å2) [36], and (3) binding energy (<−6.0 kcal/mol) [37].

3.5. The Preparation of Ligands and a Target for MDT

The identified ligands were converted from .sdf on PubChem into .pdb format via PyMOL; finally, the ligands were converted into .pdbqt format using AutoDock. Likewise, the PDB ID of the target was identified via RCSB PDB (https://www.rcsb.org/) (accessed on 7 August 2021), which was selected as .pdb format and converted to .pdbqt format via AutoDock (http://autodock.scripps.edu/ 15 December 2021). The existing positive ligands were docked with a target on AutoDock 4 by setting up 4 energy ranges and 8 levels of exhaustiveness as default to obtain 10 different poses of ligand molecules [38]. The grid box size was set to 40 Å × 40 Å × 40 Å. The 2D binding interactions were utilized on LigPlot+ v.2.2 (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) (accessed on 8 August 2021). After the molecular docking test (MDT), a key ligand accepted by three criteria with the lowest binding energy (highest affinity) was selected to visualize the ligand–target complex in PyMOL.

3.6. The Prediction of Toxicological Properties of the Key Ligand in Silico

Finally, we established the toxicological properties of the key ligand via the admetSAR web service tool (http://lmmd.ecust.edu.cn/admetsar1/predict/) (accessed on 10 August 2021) to develop new medication [39]. The workflow of this study is represented in Figure 1.

4. Results

A total of 3369 targets associated with the occurrence and development of RA and a total of 1416 targets associated with OP were identified from DisGeNET (https://www.disgenet.org/ 15 December 2021), OMIM (https://www.omim.org/ 15 December 2021), and the literature (Supplementary Table S1). A total of 678 targets were overlapped between RA (3369 targets) and OP (1416 targets) (Figure 2) (Supplementary Table S2). Based on STRING analysis, 604 out of 678 targets were directly associated with comorbidity of RA and OP, suggesting 604 nodes and 16,705 edges (Figure 3); the 74 removed targets had no connectivity to the overlapping 678 targets. The nodes represented the total number of targets, while the edges stood for the number of relationships of each node. In PPI networks, IL-6 (432 degrees) had the greatest degree of value, and was considered a hierarchical target to manage the comorbidity of RA and OP (Table 1). The IL-6 (PDB ID: 4NI9) structure was revealed as two bound forms: apo-bound and receptor-bound [40,41]. The full length of IL-6 consisted of 212 amino acids linked to specific signal peptides with 29 amino acids, with a four-helix structure organized topologically [41,42]. In particular, 20 residues of the N-terminal did not form any secondary structure, and only the last 7 amino acids were identified as the crystal structure [43]. In contrast, the C–D loop of 10 residues (131–140 amino acids) was invisible in the crystal structure, and 17 (amino acids 44–60) out of 37 residues (amino acids 43–79) were unresolved in the crystal structure [43]. Then, IL-6 inhibitors of 31 small compounds (<1000 g/mol) to be retrieved from the Selleckchem website were screened by 3 criteria (Lipinski’s rule, TPSA, and binding energy) (Table 2); 19 out of 31 compounds were sorted by the three criteria. The MDT profiling of 31 known IL-6 inhibitors is given in Table 3.
Particularly, forsythoside B (PubChem ID: 23928102) and pectolinarin (PubChem ID: 168849), with a higher affinity for IL-6 than MD2-TLR4-IN-1 (PubChem ID: 138454798), were not accepted by the Lipinski’s rule (Lipinski’s violations ≤ 1) and TPSA (<140 Å2) criteria.
A total of 19 out of 31 compounds were accepted by Lipinski’s rule (Lipinski’s violations ≤ 1), TPSA (<140 Å2), and binding energy (<−6.0 kcal/mol), among which MD2-TLR4-IN-1 (PubChem ID: 138454798) (Figure 4), with the highest binding energy (−9.9 kcal/mol), was selected as the most important ligand to dampen comorbidity of RA and OP. MDT studies suggest that aprepitant (PubChem ID: 135413536), NE 52-QQ57 (PubChem ID: 68379135), and madecassic acid (PubChem ID: 73412) could be potential ligands with positive effects in alleviating comorbidity. The MDT of MD2-TLR4-IN-1 (PubChem ID: 138454798) on IL-6 (PDB ID: 4NI9) is displayed in Figure 5. The MDT results showed that the complex of IL-6 (PDB ID: 4NI9) with MD2-TLR4-IN-1 (PubChem ID: 138454798) had two hydrophobic interactions (Glu110 and Ala114), and there was no hydrogen bonding; this implies that hydrophobic interactions exert a strong binding effect on the complex of IL-6 (PDB ID: 4NI9) with MD2-TLR4-IN-1 (PubChem ID: 138454798). Furthermore, MD2-TLR4-IN-1 (PubChem ID: 138454798) on IL-6 had 4–22 donors and 3–7 acceptors; however, aprepitant (PubChem ID: 135413536), with the second highest affinity for IL-6, had 7–23 donors and 1–13 acceptors, while NE 52-QQ57 (PubChem ID: 6837915), with the third highest affinity for IL-6, had 11–24 donors and 2–7 acceptors. In parallel, these results shed light on the significance of the number of acceptors involved in target–ligand interactions. Finally, we demonstrated the toxicity of MD2-TLR4-IN-1 (PubChem ID: 138454798) via the admetSAR web-based tool. Our results showed that MD2-TLR4-IN-1 (PubChem ID: 138454798) had no Ames toxicity, carcinogenic properties, acute oral toxicity, or rat acute toxicity properties (Table 4).

5. Discussion

A total of 678 targets were involved in the occurrence and development of the liaison of comorbidity between RA and OP. IL-6, with the highest degree of value in PPI, was considered to be a core target to alleviate the level of pathological severity.
A report demonstrated that IL-6 inhibition prevents the progression of joint destruction in RA patients and interferes with bone resorption by blocking osteoclast formation [44]. IL-6 plays important roles in inflammatory processing—a continuation of autoimmunity via B-cell and T-helper 17 (Th17) differentiation [45]. Tocilizumab (TCZ), as a representative antagonist of IL-6, has been used to treat RA; however, its serious adverse side effect is that infectious disease related to C-reactive protein (CRP) cannot be recognized during TCZ treatment [46]; this implies that stealth infections without any specific signals can wreak havoc on patients’ condition.
Another report revealed that the expression level of IL-6 in OA patients is elevated noticeably, and that there is a considerable correlation between CRP and bone mineral density (BMD) [47]. Moreover, IL-6 expression levels were increased in the synovial fluid of RA patients, and a significant stimulator of bone resorption in OA patients [48]. Particularly, in an in vivo test, IL-6 aggravated the severity of osteoporosis, due to fewer osteoclasts and increased bone destruction [49]; this suggests that IL-6 plays a pivotal role in alleviating osteoporotic inflammation reactions.
Collectively, IL-6 inhibitors may be promising ligands to overcome comorbidity of RA and OP. Among the known IL-6 inhibitor ligands, MD2-TLR4-IN-1 (PubChem ID: 138454798), which is a derivative of indazole with a heterocyclic aromatic organic compound, was accepted by all three criteria (Lipinski’s rule, TPSA, and binding energy). The derivatives consist of a benzene ring and a pyrazole ring, which exert diverse biological activities, including antitumor, antibacterial, antifungal, antiarrhythmic, anti-HIV, and anti-inflammation activities [50,51]. A report demonstrated that derivatives of indazoles have potent anti-inflammatory efficacy, including anti-RA and anti-OP [52,53]. Thus, we suggest that MD2-TLR4-IN-1 (PubChem ID: 138454798) might be a potent ligand to alleviate the comorbidity of RA and OP.

6. Conclusions

To sum things up, the most significant targets—the key ligands for alleviation of the comorbidity of RA and OP—were IL-6 (PDB ID: 4NI9) and MD2-TLR4-IN-1 (PubChem ID: 138454798), respectively. This study gives us a hint at the value of imidazole derivatives to develop new medications against RA and OP.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cimb44030069/s1: Table S1: The 3377 rheumatoid arthritis (RA)-related targets, and 1426 osteoporosis (OP)-related targets; Table S2: The 678 overlapping targets.

Author Contributions

Conceptualization, methodology, formal analysis, investigation, visualization, data curation, and writing—original draft preparation, K.-K.O.; software, investigation, and data curation, K.-K.O. and M.A.; validation, writing—review and editing, M.A.; supervision, project administration, D.-H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

International Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Materials).

Acknowledgments

This study has been worked with the support of a research grant of Kangwon National University in 2022.

Conflicts of Interest

There are no conflicts of interest to be declared.

Abbreviations

BMDBone mineral density
COXCyclooxygenase
COX-1Cyclooxygenase-1
COX-2Cyclooxygenase-2
CRPC-reactive protein
DMARDsDisease-modifying anti-rheumatic drugs
IL-1Interleukin 1
IL-6Interleukin 6
IL-17Interleukin 17
NSAIDsNon-steroidal anti-inflammatory drugs
OPOsteoporosis
PPIProtein–protein interaction
RARheumatoid arthritis
TCZTocilizumab
TPSATopological polar surface area

References

  1. Llorente, I.; García-Castañeda, N.; Valero, C.; González-Álvaro, I.; Castañeda, S. Osteoporosis in Rheumatoid Arthritis: Dangerous Liaisons. Front. Med. 2020, 802. [Google Scholar] [CrossRef] [PubMed]
  2. McInnes, I.B.; Schett, G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet 2017, 389, 2328–2337. [Google Scholar] [CrossRef] [Green Version]
  3. Bullock, J.; Rizvi, S.A.A.; Saleh, A.M.; Ahmed, S.S.; Do, D.P.; Ansari, R.A.; Ahmed, J. Rheumatoid Arthritis: A Brief Overview of the Treatment. Med. Princ. Pract. 2019, 27, 501. [Google Scholar] [CrossRef] [PubMed]
  4. Sweeney, E.S.; Firestein, G.S. Rheumatoid arthritis: Regulation of synovial inflammation. Int. J. Biochem. Cell Biol. 2004, 36, 372–378. [Google Scholar] [CrossRef]
  5. Jiang, Y.; Zhong, M.; Long, F.; Yang, R.; Zhang, Y.; Liu, T. Network Pharmacology-Based Prediction of Active Ingredients and Mechanisms of Lamiophlomis rotata (Benth.) Kudo Against Rheumatoid Arthritis. Front. Pharmacol. 2019, 1435. [Google Scholar] [CrossRef] [Green Version]
  6. Smolen, J.S.; Aletaha, D.; Koeller, M.; Weisman, M.H.; Emery, P. New therapies for treatment of rheumatoid arthritis. Lancet 2007, 370, 1861–1874. [Google Scholar] [CrossRef]
  7. Stevenson, M.; Archer, R.; Tosh, J.; Simpson, E.; Everson-Hock, E.; Stevens, J.; Hernandez, M.; Paisley, S.; Dickinson, K.; Scott, D.; et al. Adalimumab, etanercept, infliximab, certolizumab pegol, golimumab, tocilizumab and abatacept for the treatment of rheumatoid arthritis not previously treated with disease-modifying antirheumatic drugs and after the failure of conventional disease-modifying antirheumatic drugs only: Systematic review and economic evaluation. Health Technol. Assess. 2016, 20, 1–610. [Google Scholar] [CrossRef] [Green Version]
  8. Betts, K.A.; Griffith, J.; Ganguli, A.; Li, N.; Douglas, K.; Wu, E.Q. Economic Burden and Treatment Patterns of Cycling between Conventional Synthetic Disease-modifying Antirheumatic Drugs among Biologic-treated Patients with Rheumatoid Arthritis. Clinical Therapeutics 2016, 38, 1205–1216. [Google Scholar] [CrossRef]
  9. Tanaka, Y. Managing Osteoporosis and Joint Damage in Patients with Rheumatoid Arthritis: An Overview. J. Clin. Med. 2021, 10, 1241. [Google Scholar] [CrossRef]
  10. Osterhoff, G.; Morgan, E.F.; Shefelbine, S.J.; Karim, L.; McNamara, L.M.; Augat, P. Bone mechanical properties and changes with osteoporosis. Injury 2016, 47, S11. [Google Scholar] [CrossRef] [Green Version]
  11. Alexandru, D.; So, W. Evaluation and Management of Vertebral Compression Fractures. Perm. J. 2012, 16, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Ji, M.-X.; Yu, Q. Primary osteoporosis in postmenopausal women. Chronic Dis. Transl. Med. 2015, 1, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Sözen, T.; Özışık, L.; Başaran, N.Ç. An overview and management of osteoporosis. Eur. J. Rheumatol. 2017, 4, 46. [Google Scholar] [CrossRef] [PubMed]
  14. Ginaldi, L.; Di Benedetto, M.C.; Martinis, M. De Osteoporosis, inflammation and ageing. Immun. Ageing I A 2005, 2, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Hardy, R.; Cooper, M.S. Bone loss in inflammatory disorders. J. Endocrinol. 2009, 201, 309–320. [Google Scholar] [CrossRef] [PubMed]
  16. Tella, S.H.; Gallagher, J.C. Biological agents in management of osteoporosis. Eur. J. Clin. Pharmacol. 2014, 70, 1291–1301. [Google Scholar] [CrossRef] [PubMed]
  17. Lewiecki, E.M. New targets for intervention in the treatment of postmenopausal osteoporosis. Nat. Rev. Rheumatol. 2011, 7, 631–638. [Google Scholar] [CrossRef]
  18. Ginosyan, K.; Jndoyan, Z.; Ghazinyan, I.; Vardanyan, V. Benefits and disadvantages of biologic agents in chronic inflammatory arthritis. Int. J. Clin. Rheumatol. 2020, 15, 6–9. [Google Scholar] [CrossRef]
  19. Thong, B.Y.-H.; Tan, T.-C. Epidemiology and risk factors for drug allergy. Br. J. Clin. Pharmacol. 2011, 71, 684–700. [Google Scholar] [CrossRef]
  20. Parker, S.E.; Davey, P.; Davey, P.G. Pharmacoeconomics of intravenous drug administration. Pharm. 1992, 1, 103–115. [Google Scholar] [CrossRef]
  21. Gomes, E.; Demoly, P. Epidemiology of hypersensitivity drug reactions. Curr. Opin. Allergy Clin. Immunol. 2005, 5, 309–316. [Google Scholar] [CrossRef] [PubMed]
  22. Crofford, L.J. Use of NSAIDs in treating patients with arthritis. Arthritis Res. Ther. 2013, 15, S2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Brooks, P.; Emery, P.; Evans, J.F.; Fenner, H.; Hawkey, C.J.; Patrono, C.; Smolen, J.; Breedveld, F.; Day, R.; Dougados, M.; et al. Interpreting the clinical significance of the differential inhibition of cyclooxygenase-1 and cyclooxygenase-2. Rheumatology 1999, 38, 779–788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Lisowska, B.; Kosson, D.; Domaracka, K. Positives and negatives of nonsteroidal anti-inflammatory drugs in bone healing: The effects of these drugs on bone repair. Drug Des. Dev. Ther. 2018, 12, 1809. [Google Scholar] [CrossRef]
  25. Gaubitz, M. Osteoporose–häufige Komorbidität bei Rheumapatienten. Z. Für Rheumatol. 2019, 78, 249–254. [Google Scholar] [CrossRef] [PubMed]
  26. Kim, S.Y.; Schneeweiss, S.; Liu, J.; Daniel, G.W.; Chang, C.-L.; Garneau, K.; Solomon, D.H. Risk of osteoporotic fracture in a large population-based cohort of patients with rheumatoid arthritis. Arthritis Res. Ther. 2010, 12, R154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Castañeda, S.; Garcés-Puentes, M.V.; Bernad Pineda, M. Pathophysiology of osteoporosis in chronic inflammatory joint diseases. Rev. De Osteoporos. Y Metab. Miner. 2021, 13, 32–38. [Google Scholar] [CrossRef]
  28. Pushpakom, S.; Iorio, F.; Eyers, P.A.; Escott, K.J.; Hopper, S.; Wells, A.; Doig, A.; Guilliams, T.; Latimer, J.; McNamee, C.; et al. Drug repurposing: Progress, challenges and recommendations. Nat. Rev. Drug Discov. 2018, 18, 41–58. [Google Scholar] [CrossRef]
  29. Somolinos, F.J.; León, C.; Guerrero-Aspizua, S. Drug repurposing using biological networks. Processes 2021, 9, 1057. [Google Scholar] [CrossRef]
  30. Rudrapal, M.; Khairnar, S.J.; Jadhav, A.G. Drug Repurposing (DR): An Emerging Approach in Drug Discovery. In Drug Repurposing-Hypothesis, Molecular Aspects and Therapeutic Applications; IntechOpen: London, UK, 2020. [Google Scholar] [CrossRef]
  31. Gil, C.; Martinez, A. Is drug repurposing really the future of drug discovery or is new innovation truly the way forward? Expert Opin. Drug Discov. 2021, 16, 829–831. [Google Scholar] [CrossRef]
  32. Hodos, R.A.; Kidd, B.A.; Shameer, K.; Readhead, B.P.; Dudley, J.T. Computational Approaches to Drug Repurposing and Pharmacology. Wiley Interdiscip. Reviews. Syst. Biol. Med. 2016, 8, 186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Yu, G.; Wang, W.; Wang, X.; Xu, M.; Zhang, L.; Ding, L.; Guo, R.; Shi, Y. Network pharmacology-based strategy to investigate pharmacological mechanisms of Zuojinwan for treatment of gastritis. BMC Complementary Altern. Med. 2018, 18, 1–12. [Google Scholar] [CrossRef] [PubMed]
  34. Yip, K.W.; Liu, F.-F. Small Molecule Screens. Encycl. Cancer 2011, 3451–3455. [Google Scholar] [CrossRef]
  35. Zhang, M.-Q.; Wilkinson, B. Drug discovery beyond the “rule-of-five”. Curr. Opin. Biotechnol. 2007, 18, 478–488. [Google Scholar] [CrossRef]
  36. Matsson, P.; Kihlberg, J. How Big Is Too Big for Cell Permeability? J. Med. Chem. 2017, 60, 1662–1664. [Google Scholar] [CrossRef] [Green Version]
  37. Shityakov, S.; Förster, C. In silico predictive model to determine vector-mediated transport properties for the blood–brain barrier choline transporter. Adv. Appl. Bioinform. Chem. AABC 2014, 7, 23. [Google Scholar] [CrossRef] [Green Version]
  38. Khanal, P.; Patil, B.M.; Chand, J.; Naaz, Y. Anthraquinone Derivatives as an Immune Booster and their Therapeutic Option Against COVID-19. Nat. Prod. Bioprospecting 2020, 10, 325. [Google Scholar] [CrossRef]
  39. Yang, H.; Lou, C.; Sun, L.; Li, J.; Cai, Y.; Wang, Z.; Li, W.; Liu, G.; Tang, Y. admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 2019, 35, 1067–1069. [Google Scholar] [CrossRef]
  40. Boulanger, M.J.; Chow, D.-c.; Brevnova, E.E.; Garcia, K.C. Hexameric structure and assembly of the interleukin-6/IL-6 alpha-receptor/gp130 complex. Science (New York N.Y.) 2003, 300, 2101–2104. [Google Scholar] [CrossRef]
  41. Somers, W.; Stahl, M.; Seehra, J.S. 1.9 A crystal structure of interleukin 6: Implications for a novel mode of receptor dimerization and signaling. EMBO J. 1997, 16, 989–997. [Google Scholar] [CrossRef] [Green Version]
  42. Yokota, T.; Arai, N.; De Vries, J.; Spits, H.; Banchereau, J.; Zlotnik, A.; Rennick, D.; Howard, M.; Takebe, Y.; Miyatake, S.; et al. Molecular Biology of Interleukin 4 and Interleukin 5 Genes and Biology of their Products that Stimulate B Cells, T Cells and Hemopoietic Cells. Immunol. Rev. 1988, 102, 137–187. [Google Scholar] [CrossRef] [PubMed]
  43. Gelinas, A.D.; Davies, D.R.; Edwards, T.E.; Rohloff, J.C.; Carter, J.D.; Zhang, C.; Gupta, S.; Ishikawa, Y.; Hirota, M.; Nakaishi, Y.; et al. Crystal Structure of Interleukin-6 in Complex with a Modified Nucleic Acid Ligand. J. Biol. Chem. 2014, 289, 8720. [Google Scholar] [CrossRef] [Green Version]
  44. Hashizume, M.; Mihara, M. The roles of interleukin-6 in the pathogenesis of rheumatoid arthritis. Arthritis 2011, 2011, 765624. [Google Scholar] [CrossRef]
  45. Scheller, J.; Chalaris, A.; Schmidt-Arras, D.; Rose-John, S. The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochim. Et Biophys. Acta (BBA)-Mol. Cell Res. 2011, 1813, 878–888. [Google Scholar] [CrossRef] [Green Version]
  46. Ogata, A.; Kato, Y.; Higa, S.; Yoshizaki, K. IL-6 inhibitor for the treatment of rheumatoid arthritis: A comprehensive review. Mod. Rheumatol. 2019, 29, 258–267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Li, X.; Zhou, Z.; Zhang, Y.; Yang, H. IL-6 Contributes to the Defective Osteogenesis of Bone Marrow Stromal Cells from the Vertebral Body of the Glucocorticoid-Induced Osteoporotic Mouse. PLoS ONE 2016, 11, 154677. [Google Scholar] [CrossRef] [Green Version]
  48. Roodman, G.D. Perspectives: Interleukin-6: An osteotropic factor? J. Bone Miner. Res. 1992, 7, 475–478. [Google Scholar] [CrossRef] [PubMed]
  49. Ohshima, S.; Saeki, Y.; Mima, T.; Sasai, M.; Nishioka, K.; Nomura, S.; Kopf, M.; Katada, Y.; Tanaka, T.; Suemura, M.; et al. Interleukin 6 plays a key role in the development of antigen-induced arthritis. Proc. Natl. Acad. Sci. USA 1998, 95, 8222–8226. [Google Scholar] [CrossRef] [Green Version]
  50. Denya, I.; Malan, S.F.; Joubert, J. Indazole derivatives and their therapeutic applications: A patent review (2013–2017). Expert Opin. Ther. Pat. 2018, 28, 441–453. [Google Scholar] [CrossRef]
  51. Zhang, S.-G.; Liang, C.-G.; Zhang, W.-H. Recent Advances in Indazole-Containing Derivatives: Synthesis and Biological Perspectives. Molecules 2018, 23, 2783. [Google Scholar] [CrossRef] [Green Version]
  52. Cheekavolu, C.; Muniappan, M. In vivo and In vitro Anti-Inflammatory Activity of Indazole and Its Derivatives. J. Clin. Diagn. Res. JCDR 2016, 10, FF01. [Google Scholar] [CrossRef] [PubMed]
  53. Cerecetto, H.; Gerpe, A.; González, M.; Arán, V.J.; De Ocáriz, C.O. Pharmacological properties of indazole derivatives: Recent developments. Mini Rev. Med. Chem. 2005, 5, 869–878. [Google Scholar] [CrossRef] [PubMed]
Cimb 44 00069 g001 550
Figure 1. The workflow of this study.
Figure 1. The workflow of this study.
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Cimb 44 00069 g002 550
Figure 2. The overlapping targets between RA (3369 targets) and OP (1416 targets).
Figure 2. The overlapping targets between RA (3369 targets) and OP (1416 targets).
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Cimb 44 00069 g003 550
Figure 3. PPI network of 604 overlapping targets.
Figure 3. PPI network of 604 overlapping targets.
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Figure 4. 2D structure of MD2-TLR4-IN-1 (PubChem ID: 138454798).
Figure 4. 2D structure of MD2-TLR4-IN-1 (PubChem ID: 138454798).
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Figure 5. The molecular docking between IL-6 (PDB ID: 4NI9) and MD2-TLR4-IN-1 (PubChem ID: 138454798).
Figure 5. The molecular docking between IL-6 (PDB ID: 4NI9) and MD2-TLR4-IN-1 (PubChem ID: 138454798).
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Table
Table 1. Degrees of value of the top 20 targets from PPI.
Table 1. Degrees of value of the top 20 targets from PPI.
No.TargetDegrees of Value
1IL-6432
2INS317
3AKT1312
4TNF289
5GAPDH288
6TP53267
7VEGFA266
8MAPK3242
9EGFR237
10STAT3236
11CXCL8216
12JUN216
13MAPK1215
14SRC215
15MMP9215
16IGF1209
17IL-10206
18CASP3195
19IL-1B195
20TLR4194
Table
Table 2. The physicochemical properties and classification of 31 compounds as IL-6 antagonists.
Table 2. The physicochemical properties and classification of 31 compounds as IL-6 antagonists.
CompoundsLipinski Rules
MWHBAHBDMLogPLipinski’s violationsBioavailability ScoreTPSACompound Classification
No.<500<10≤5≤4.15≤1>0.1<140 Å2
1Forsythoside B756.70 1911−3.9330.17304.21 Oligosaccharides
2Pectolinarin622.57 157−3.0330.17227.20 Flavonoid-7-O-glycosides
3MD2-TLR4-IN-1421.28 234.0100.5573.57 Indazole
4Aprepitant534.43 1224.0510.5583.24 Phenylmorpholines
5Mulberroside A568.52 1410−2.9730.17239.22 Stilbene glycosides
6Homoplantaginin462.40 116−1.8920.17179.28 Flavonoid-7-O-glycosides
7NE 52-QQ57416.52 613.4600.5581.14 Pyrazolo[1,5-a]pyrimidines
8Madecassic acid504.70 653.3310.55118.22 Triterpenoids
9GSK583398.45 523.3600.5596.12 Aminoquinolines and derivatives
10IQ 3341.32 60200.5577.58 Quinoxalines
11Methylprednisolone374.47 531.5200.5594.83 21-Hydroxysteroids
12Hydrocortisone hemisuccinate462.53 831.2900.55138.20 Gluco/mineralocorticoids, progestogens, and derivatives
1320(S)-Ginsenoside Rh1638.87 971.7720.17160.07 Triterpene saponins
14Stylopine323.34 502.5600.55323.34 Protoberberine alkaloids and derivatives
15Methylprednisolone Acetate416.51 621.8600.55100.90 Gluco/mineralocorticoids, progestogens, and derivatives
16Gardenoside404.37 116−2.6220.11175.37 Iridoid O-glycosides
174-Methylesculetin192.17 420.7600.5570.67 6,7-Dihydroxycoumarins
18Auraptene298.38 303.5100.5539.44 Terpene lactones
19AX-024 HCl375.86 403.8600.5521.70 Neoflavenes
20APX-115 free base279.34 212.6600.5550.68 Pyrazolylpyridines
21Resatorvid361.82 512.4400.5580.85 Sulfanilides
22Myrislignan374.43 621.9700.5577.38 Lignans, neolignans, and related compounds
23Muscone238.41 103.9200.5517.07 Cyclic ketones
242′,5′-Dihydroxyacetophenone152.15 320.5100.5557.53 Alkyl-phenylketones
25α-Cyperone218.33 103.4600.5517.07 Eudesmane, isoeudesmane, or cycloeudesmane sesquiterpenoids
26Veratric acid182.17 411.0600.8555.76 P-methoxybenzoic acids and derivatives
27Triolein885.43 609.4920.1778.90 Triacylglycerols
28Methylthiouracil142.18 12−0.3500.5580.74 Pyrimidones
29Falcarindiol260.37 223.3300.5540.46 Long-chain fatty alcohols
30Diethyl phosphate154.10 41−0.4300.8565.57 Dialkyl phosphates
31Sodium thiocyanate81.07 10−1.0100.5523.79 Metal thiocyanates
Table
Table 3. Binding energy of 31 known IL-6 inhibitors (<1000 g/mol).
Table 3. Binding energy of 31 known IL-6 inhibitors (<1000 g/mol).
Grid BoxHydrogen Bond InteractionsHydrophobic Interactions
ProteinLigandPubChem IDBinding Energy (kcal/mol)CenterDimensionAmino Acid ResidueAmino Acid Residue
IL6 (PDB ID: 4NI9)Forsythoside B23928102−11.4x = 11.213size_x = 40Asp34,Tyr31,Glu110Gly35,Gln111,Ala114
y = 33.474size_y = 40
z = 11.162size_z = 40
Pectolinarin168849−10.4x = 11.213size_z = 41Asp34,Gln111Ala38
y = 33.474size_z = 42
z = 11.162size_z = 43
(*) MD2-TLR4-IN-1138454798−9.9x = 11.213size_z = 44N/AGlu110,Ala114
y = 33.474size_z = 45
z = 11.162size_z = 46
(*) Aprepitant135413536−9.6x = 11.213size_z = 47N/ATyr31,Asp34,Gly35
y = 33.474size_z = 48 Gln111
z = 11.162size_z = 49
Mulberroside A6443484−9.5x = 11.213size_z = 50Glu110,Ser37,Asp34Ala114,Gln111
y = 33.474size_z = 51Tyr31
z = 11.162size_z = 52
Homoplantaginin5318083−9.5x = 11.213size_z = 53Asp34,Gln111Ala38
y = 33.474size_z = 54
z = 11.162size_z = 55
(*) NE 52-QQ5768379135−9.4x = 11.213size_z = 56Ser37Asp34,Tyr31,Ala114
y = 33.474size_z = 57 Gln111
z = 11.162size_z = 58
(*) Madecassic acid73412−9.4x = 11.213size_z = 59Glu110Ala114,Tyr31
y = 33.474size_z = 60
z = 11.162size_z = 61
(*) GSK58367469084−9.0x = 11.213size_z = 62N/AGln111,Ala38
y = 33.474size_z = 63
z = 11.162size_z = 64
(*) IQ 3777728−9.0x = 11.213size_z = 65N/ATyr31,Glu110
y = 33.474size_z = 66
z = 11.162size_z = 67
(*) Methylprednisolone6741−9.0x = 11.213size_z = 68N/ATyr31,Glu110
y = 33.474size_z = 69
z = 11.162size_z = 70
(*) Hydrocortisone hemisuccinate16623−8.9x = 11.213size_z = 71N/AGlu110,Ala114,Gln111
y = 33.474size_z = 72
z = 11.162size_z = 73
20(S)-Ginsenoside Rh112855920−8.8x = 11.213size_z = 74Gln111Asp34,Tyr31
y = 33.474size_z = 75
z = 11.162size_z = 76
Stylopine6770−8.8x = 11.213size_z = 77N/AGln111,Ala114,Glu110
y = 33.474size_z = 78
z = 11.162size_z = 79
(*) Methylprednisolone Acetate5877−8.6x = 11.213size_z = 80N/AGln111,Tyr31,Ala114
y = 33.474size_z = 81 Glu110
z = 11.162size_z = 82
Gardenoside24721095−7.8x = 11.213size_z = 83Tyr31,Asp34,Gln111N/A
y = 33.474size_z = 84
z = 11.162size_z = 85
(*) 4-Methylesculetin5319502−7.6x = 11.213size_z = 86Arg24,Arg16Pro18
y = 33.474size_z = 87
z = 11.162size_z = 88
(*) Auraptene1550607−7.6x = 11.213size_z = 89N/AAsp34,Glu110,Ala114
y = 33.474size_z = 90 Tyr31
z = 11.162size_z = 91
(*) AX-024 HCl129909862−7.5x = 11.213size_z = 92N/AGln111,Tyr31,Ala114
y = 33.474size_z = 93
z = 11.162size_z = 94
(*) APX-115 free base51036475−7.2x = 11.213size_z = 95Tyr31Glu110,Gln111,Asp34
y = 33.474size_z = 96
z = 11.162size_z = 97
(*) Resatorvid11703255−7.1x = 11.213size_z = 98Tyr31,Gln111Glu110,Asp34
y = 33.474size_z = 99
z = 11.162size_z = 100
(*) Myrislignan21636106−7.1x = 11.213size_z = 101Gln111Tyr31,Gly35,Asp34
y = 33.474size_z = 102
z = 11.162size_z = 103
(*) Muscone10947−6.7x = 11.213size_z = 104N/AN/A
y = 33.474size_z = 105
z = 11.162size_z = 106
(*) 2′,5′-Dihydroxyacetophenone10279−6.5x = 11.213size_z = 107N/AGln17,Pro18
y = 33.474size_z = 108
z = 11.162size_z = 109
(*) α-Cyperone6452086−6.3x = 11.213size_z = 110N/AGln111,Glu110
y = 33.474size_z = 111
z = 11.162size_z = 112
(*) Veratric acid7121−6.1x = 11.213size_z = 113Arg16Gln17,Pro18
y = 33.474size_z = 114
z = 11.162size_z = 115
Triolein5497163−5.5x = 11.213size_z = 116N/AN/A
y = 33.474size_z = 117
z = 11.162size_z = 118
Methylthiouracil667493−5.4x = 11.213size_z = 119N/AArg24
y = 33.474size_z = 120
z = 11.162size_z = 121
Falcarindiol5281148−5.2x = 11.213size_z = 122Glu110Ala114,Tyr31,Gln111
y = 33.474size_z = 123Glu110
z = 11.162size_z = 124
Diethyl phosphate654−4.9x = 11.213size_z = 125N/AArg16,Gln17
y = 33.474size_z = 126
z = 11.162size_z = 127
Sodium thiocyanate516871−2.6x = 11.213size_z = 128N/AArg16,Gln17
y = 33.474size_z = 129
z = 11.162size_z = 130
(*): The indication of 19 compounds accepted by the three criteria: (1) Lipinski’s rule violation (≤1), (2) TPSA (<140 Å2), and (3) binding energy (<−6.0 kcal/mol).
Table
Table 4. A prediction of toxicological propensity of MD2-TLR4-IN-1 (PubChem ID: 138454798).
Table 4. A prediction of toxicological propensity of MD2-TLR4-IN-1 (PubChem ID: 138454798).
ParametersCompound
MD2-TLR4-IN-1
Ames toxicityNAT
CarcinogensNC
Acute oral toxicityⅢ
Rat acute toxicity2.2347
NAT: Non-Ames toxic; NC: non-carcinogenic; Category II: 50 mg/kg > Lethal Dose 50% (LD50) < 500 mg/kg; Category III: 500 mg/kg > Lethal Dose 50% (LD50) < 5000 mg/kg; Category IV: Lethal Dose 50% (LD50) > 5000 mg/kg; Rat acute toxicity: the treatment of 2.2347 mol/kg in rats shows LD50 toxicity.
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Oh, K.-K.; Adnan, M.; Cho, D.-H. Drug Investigation to Dampen the Comorbidity of Rheumatoid Arthritis and Osteoporosis via Molecular Docking Test. Curr. Issues Mol. Biol. 2022, 44, 1046-1061. https://doi.org/10.3390/cimb44030069

AMA Style

Oh K-K, Adnan M, Cho D-H. Drug Investigation to Dampen the Comorbidity of Rheumatoid Arthritis and Osteoporosis via Molecular Docking Test. Current Issues in Molecular Biology. 2022; 44(3):1046-1061. https://doi.org/10.3390/cimb44030069

Chicago/Turabian Style

Oh, Ki-Kwang, Md. Adnan, and Dong-Ha Cho. 2022. "Drug Investigation to Dampen the Comorbidity of Rheumatoid Arthritis and Osteoporosis via Molecular Docking Test" Current Issues in Molecular Biology 44, no. 3: 1046-1061. https://doi.org/10.3390/cimb44030069

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