ISSN: 2451-0637
Archives of Medical Science - Civilization Diseases
Current volume Archive About the journal Abstracting and indexing Contact Instructions for authors Ethical standards and procedures
vol. 7
Experimental research

Newer congeners of doxycycline – do they hold promise for periodontal therapy?

Prabhu Manickam Natarajan
Vidhya Rekha
Anita Murali
Bhuminathan Swamikannu

Department of Clinical Sciences, College of Dentistry, Center of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
Sree Balaji Dental College and Hospital, Bharath University, Chennai, India
Arch Med Sci Civil Dis 2022; 7: e16–e23
Online publish date: 2022/09/19
Article file
- Newer congeners.pdf  [0.40 MB]
Get citation
JabRef, Mendeley
Papers, Reference Manager, RefWorks, Zotero
PlumX metrics:


Periodontitis is a very common infection of the oral cavity and has profound systemic implications [1, 2]. It has been observed that approximately 47% of adults aged above 30 years harbor periodontal infection of some sort. Investigators have also related the prevalence to age and have reported that the prevalence is directly proportional to age [3]. Additionally, the incidence and prevalence of periodontitis are higher in the lower socioeconomic strata. Other risk factors, such as smoking and alcoholism, are strong contributors to periodontitis [4].
Periodontitis is a polymicrobial infection, and its pathogenesis has been studied intensively. On a clean dental surface, the formation of a gel-like layer called the acquired pellicle marks the beginning of periodontal floral colonization on the tooth surface. The initial aerobic floral predominance is later given up to anaerobes. In this ecologic succession, every bacterium that adheres becomes a part of a biofilm, which is a well-equipped system for nutrition, signaling and resistance to antibacterial agents. Of the thousands of unculturable and culturable bacteria of the periodontal biofilm, major species implicated in the aetiology of periodontitis are Porphyromonas gingivalis and Actinobacillus actinomycetemcomitans [5, 6]. P. gingivalis is a major contributor to periodontal pathogenesis. It is a gram-negative and anaerobic organism called a keystone pathogen. It has developed many characteristics to evade the local immune response. Furthermore, it has certain biochemical mechanisms responsible for its virulence against the periodontium. This increase in virulence is exhibited by enzymes called gingipains, which are trypsin-like cysteine proteinases that facilitate infection by various microbes in the biofilm [7]. It is worth noting that even nonpathogenic flora can cause infection once P. gingivalis reduces local immunity. Hence, there is a change in the microbial population, leading to dysbiosis [8]. Here, the mechanism becomes highly complicated by the involvement of various species in the process.
Furthermore, bacteria that adhere to both biofilm and host cells have specialized structures called fimbriae. These fimbriae are shown to be important factors that facilitate bacterial interactions with host tissues, leading to invasion [9]. An increase in these fimbrial molecules, both in number and in type, therefore contributes directly to the virulence of the bacteria [10]. P. gingivalis is known to express two such fimbria molecules: one is long, and the other is short [11]. They are expressed on the cell surface, leading to increased host invasion and local tissue destruction.
As the disease progresses, there is a clear increase in inflammation mediated by various biochemical factors, such as interleukins and tumor necrosis factor  (TNF-). Interleukin 1 (IL-1) is a proinflammatory cytokine and is involved in inflammation and bone resorption. Various studies provide great evidence about its role in this process. It is initially synthesized as pro-IL-1  and must be proteolytically cleaved by caspase 1 to attain the active form [12]. Additionally, periodontal pathogens upregulate the synthesis of IL-1. The inflammatory effect is dependent upon the local concentration, and its increase leads to an upward shift in the inflammatory process. After it assumes the functional form, it triggers a cascade of inflammatory reactions leading to raised local blood flow, increased leucocyte recruitment and enhanced neutrophil infiltration. Most importantly, IL-1 is a strong stimulator of bone resorption, which worsens periodontitis. Hence, therapeutic modalities to block IL-1 are constantly being searched for [13].
As a result of periodontal inflammation, there is increased bone resorption due to the shift of bone homeostasis to the negative side. It has been reported that a wide range of host factors (inflammatory mediators, genetic factors, etc.), as well as microbial factors (virulence factors, enzymes, lipopolysaccharides, etc.) contribute strongly to alveolar bone loss in periodontitis [14]. It is hence understood that upregulation of osteoblasts will lead to a shift of this homeostasis to the anabolic or positive side.
Hence, periodontitis must be addressed from four perspectives: gingipain control, inhibition of fimbrial proteins (FimA), inhibition of interleukin 1, and promotion of osteoblast differentiation and subsequent osteogenesis [15–17] In the literature, various drugs have been reported to have inhibitory action on the bacterial population, and the spectrum of activity varies widely among antibacterial drugs. Commonly, tetracyclines are used to kill or inhibit periodontal pathogens. In this regard, there is a lacuna in the literature regarding the binding of various congeners of tetracycline with various parts of gingipain, IL-1, FimA protein and estrogen receptor . The molecular interaction of residual amino acids with the core functional groups determines the efficacy of the drug molecules. In this study, molecular docking methods were applied to determine the binding efficacy of various commonly used drugs to these targets, resulting in the control and eradication of periodontal pathology.
Molecular docking is a powerful and capable tool for in silico screening. It is becoming increasingly important in rational drug design. Docking is a computational procedure for finding a suitable ligand that fits the protein’s binding site both energetically and geometrically. In other words, it is the study of how two or more molecules, such as a ligand and a protein, interact with one another. The problem is similar to putting together a 3D puzzle. The application of computational methods in this field has been subjected to intensive research over the last decade to understand the formation of intermolecular complexes [18].
It is well understood that accurate drug activity is caused by the molecular binding of one molecule (the ligand) with the pocket of another molecule (the receptor), which is typically a protein. Molecular docking has proven to be an extremely effective tool for discovering new drugs that target proteins. Because of its application in the pharmaceutical industry, protein-ligand docking is of particular interest among the various types of docking [19]. When the structure of a protein is known, protein-ligand docking refers to the search for accurate ligand conformations within that protein [20].
Docking procedures are essentially an integration of search algorithms and a scoring function. Search algorithms predict the ligand binding orientation and conformations known as posing. The scoring functions, which predict the binding free energies, are used to distinguish between active and random compounds [21–27].
In the current scenario, systemic drug administration is also used as an adjunct to local therapy. Doxycycline is observed to have a good impact on clearing the pathogenic flora of periodontitis, as it is well secreted through the gingival crevicular fluid. However, there is a lacuna in the literature regarding the comparative efficacy of newer congeners of doxycycline, such as tigecycline, eravacycline and sarecycline. Hence, this study was performed to objectively compare the binding capacity of these drugs with clinically significant targets relevant to periodontitis.

Material and methods

Details of lead compounds

A total of 5 drugs were selected, of which doxycycline was used as a control. The chemical structures of drugs used were retrieved from PubChem, and the development of 2d and 3d structures was performed using chem draw software.

Protein preparation

The crystal structure of the targets shown in Table I was downloaded from the RCSB Protein Data Bank (PDB) using AutoDock version 4 software, and a protein clean-up process was performed. The protein structures used for docking are shown in Figures 1–4.

Molecular docking analysis

In silico docking simulations were also performed using Auto Dock version 4. Three-dimensional pharmacophores of these lead molecules (Table II) were subjected to virtual screening against the selected protein targets mentioned in Table I. Docking grids were set with pocket size measuring maps of 70 × 70 × 70 Å grid points and with 0.375 Å. Each docking calculation was set to run with 10 different cycles after a maximum of 250 000 energy evaluations. The population size was set to 150. During the search, a translational step of 0.2 Å and quaternion and torsion steps of 5 were applied [28, 29]. Different orientations of the lead molecules with regard to the target proteins were evaluated, and the best dock pose was selected based on the interaction study analysis. The docking tool provides binding free energy, inhibition constant, electrostatic energy, intermolecular energy and total interaction surface. The best pose of docking for each target is selected and displayed in this paper.


A total of 5 lead compounds were investigated. Of them, omadacycline, tigecycline, sarecycline, and doxycycline revealed 100% binding efficacy by occupying the core amino acid residues (444 HIS, 477 CYS and 388 ASP) over the target protein. However, eravacycline ranked second with the maximum of 3 interactions with the active site of the target enzyme gingipain K. Based on the results of the computational analysis, there was significant binding affinity with the amino acids present on the active site of the target protein gingipain K; therefore it was concluded that these compounds may exert promising inhibition against gingipain K and thus may be expected to halt the progression of periodontitis.
A total of 5 lead compounds were investigated with the FimA protein. Nearly 24 amino acids are present in sequences 339–363 present in the active site of FimA of Porphyromonas gingivalis. In our present investigation, it was observed that out of 5 compounds, tigecycline and eravacycline revealed 12–16% binding efficacy by occupying some of the active amino acid residues with the sequence 339–363 present on the target protein. The binding efficacy was lower in omadacycline, sarecycline and doxycycline, with a maximum of 1 or 2 interactions that accumulated 4–8% affinity with the active site of the target protein FimA. Based on the results of the computational analysis, it was concluded that these compounds may exert some productive efficacy in hindering biofilm formation by the organism Porphyromonas gingivalis.
With regard to interleukin 1, out of 5 compounds, tigecycline revealed 40–50% binding efficacy by occupying the core amino acid residues (Arg11, Asp12, Ser13, Gln14, Gln15, Lys27, Gln32, Gly33, Gln34, Glu128) over the target protein. The binding efficacy was lower in omadacycline, sarecycline, eravacycline, and doxycycline, with a maximum of 2–3 interactions with the active site of the target protein IL-1. Based on the results of the computational analysis, there was significant binding affinity with the amino acids present on the active site of the target protein IL-1; hence it was concluded that these compounds may exert promising inhibition against IL-1 and thus may be expected to halt the progression of pain and inflammation associated with periodontitis.
With regard to ER, omadacycline, tigecycline, sarecycline, eravacycline and doxycycline revealed 100% binding efficacy by occupying all core amino acid residues (Glu305, Arg346, His475,Met336, Ile373) over the target protein – estrogen receptor . Based on the results of the computational analysis it was concluded that all the tested drugs revealed significant binding affinity with the amino acids present on the active site of the target protein estrogen receptor ; therefore it was concluded that these compounds may be expected to upswing the mechanism of osteoblast induction by regulating the signaling process mediated via estrogen receptor .
The best docking poses of all five drugs with all four targets are attached in Tables IV–VI.


Periodontitis has always been a complex puzzle to solve for clinicians due to its multiple pathways of pathogenesis. Since it is a polymicrobial infection, antibiotics are the first-line drugs to treat the infection. Usually, doxycycline is commonly used both topically and systemically to treat the infection [30]. However, in the current scenario of drug research, new congeners are being constantly developed and marketed for various uses. Therefore, there is a need to compare these new congeners with doxycycline in terms of their efficacy in reducing periodontitis.
Considering the cost of developing new drugs and testing for toxicity, designating an existing drug for new use is seen as an attractive preposition with lower risk. To make this a possibility, molecular docking has been seen as a primary tool of screening [31].
Various relevant targets are tested in this study with a specific purpose as follows. Gingipains are trypsin-like proteinases that cause tissue destruction and worsen periodontal integrity. Therefore, any mechanism that inhibits this enzyme can potentially slow or halt the progression of periodontal disease. Biofilm formation is facilitated by the FimA group of proteins, whose inhibition can inhibit biofilm formation, thereby reducing the pathogenesis of periodontitis. Inhibition of interleukins can lead to a reduction in inflammation, and binding to ER can increase bone formation. In this background, there is a lacuna in the literature regarding the capacity of the latest congeners of tetracyclines, such as omadacycline, tigecycline, sarecycline, and eravacycline, to bind to the abovementioned targets compared with doxycycline. Hence, this study was performed. The study revealed binding free energy, inhibition constant, electrostatic energy, intermolecular energy, total interaction surface and interactions/affinity.
The binding free energy, or Gibbs free energy, is positive when there is no binding detected between the host molecule and its guest. When it is negative, it implies a spontaneous interaction within the physiological system. The inhibition constant (Ki) was calculated from the binding energy by the formula Ki = exp(G/RT). (R is the universal gas constant, and T is the temperature). Hence, these two quantities are mathematically related. To completely understand the interaction, in addition to shape and structure, electrostatic interactions and the energetics of intermolecular attraction have to be discussed, in addition to exploring the affinity or the number of sites of interaction. This extensive analysis can reduce the sensitivity of the prediction procedure to structural errors. The intermolecular interaction energy is of two types: van der Waals forces and electrostatic interactions [32–34].
Comparing the binding free energies of various drugs with gingipain (Table III), doxycycline had the highest (–7.31 kcal/mol), and omadacycline had the lowest binding energy (–9.57 kcal/mol), making it the best in the category. In contrast, doxycycline had the lowest binding free energy with FimA, making it the best in the category. Other congeners had similar binding free energies ranging from –4.1 to –5.8 kcal/mol. With regard to binding free energy with IL-1, tigecycline had exceptionally low bond energy, making it the best in the category, followed by omadacycline. Furthermore, other congeners were more or less similar. With respect to the binding free energy with ER-, doxycycline had the highest bond energy (–2.6 kcal/mol). Omadacycline and tigecycline had the most favorable binding free energies. Therefore, while comparing the overall performance, omadacycline can be proposed as the best among the congeners. Since the inhibition constant is mathematically related to binding free energy, they are not separately discussed.
Omadacycline had the least energy of interaction with both gingipain and IL-1. Eravacycline had the least energy of interaction with FimA, and sarecycline had the least energy of interaction with ER-. However, with FimA and ER-, omadacycline had insignificant interactions but not repulsive interactions. With regard to intermolecular energy, omadacycline had the most favorable interaction with gingipain, FimA and ER-. It had good interaction with IL-1. Therefore, overall, omadacycline is the best selection with regard to electrostatic and intermolecular interactions.
With respect to the total interaction surface, omadacycline had the maximum surface area of interaction with FimA, IL-1 and ER-. It had the second highest surface area of interaction with gingipain, next to tigecycline. With respect to affinity or sites of interaction with gingipain, all drugs were more or less similar. However, with FimA, eravacycline had maximum affinity (4), and doxycycline had the least affinity. With respect to IL-1, all drugs had higher affinity than doxycycline. However, with regard to interaction with ER-, all drugs were essentially similar and had a high level of affinity.

Clinical relevance

Doxycycline has been extensively used in periodontology to control the infection and, thereby, the inflammation. However, newer congeners have been compared in this study and the results show that omadacycline could potentially be a much better replacement for doxycycline. This may have far-reaching implications in clinical periodontology. The use of newer drug may modify the course of periodontitis in a highly desirable way.
In conclusion, Therefore, with respect to all four targets, omadacycline is found to have better binding capacity compared to doxycycline; hence, doxycycline can be replaced with omadacycline. However, with molecular docking studies alone, it is premature to declare the efficacy for this purpose. This result warrants future clinical investigations on omadacycline for periodontal therapy in both local and systemic administration.

Conflict of interest

The authors declare no conflict of interest.


1. Vitkov L, Muñoz LE, Knopf J, et al. Connection between periodontitis-induced low-grade endotoxemia and systemic diseases: neutrophils as protagonists and targets. Int J Mol Sci 2021; 22: 4647.
2. Hajishengallis G, Chavakis T. Local and systemic mechanisms linking periodontal disease and inflammatory comorbidities. Nat Rev Immunol 2021; 21: 426-40.
3. Nazir MA. Prevalence of periodontal disease, its association with systemic diseases and prevention. Int J Health Sci 2017; 11: 72-80.
4. Almerich-Silla JM, Almiñana-Pastor PJ, Boronat-Catalá M, Bellot-Arcís C, Montiel-Company JM. Socioeconomic factors and severity of periodontal disease in adults (35-44 years). A cross sectional study. J Clin Exp Dent 2017; 9: e988-94.
5. Xu W, Zhou W, Wang H, Liang S. Roles of Porphyromonas gingivalis and its virulence factors in periodontitis. Adv Protein Chem Struct Biol 2020; 120: 45-84.
6. Sang-Ngoen T, Czumbel LM, Sadaeng W, et al. Orally administered probiotics decrease aggregatibacter actinomycetemcomitans but not other periodontal pathogenic bacteria counts in the oral cavity: a systematic review and meta-analysis. Front Pharmacol 2021; 12: 682656.
7. He Z, Zhang X, Song Z, et al. Quercetin inhibits virulence properties of Porphyromas gingivalis in periodontal disease. Sci Rep 2020; 10: 18313.
8. Suárez LJ, Garzón H, Arboleda S, Rodríguez A. Oral dysbiosis and autoimmunity: from local periodontal responses to an imbalanced systemic immunity. A review. Front Immunol 2020; 11: 591255.
9. Rodrigues RS, Silveira VR, Rego RO. Analysis of Porphyromonas gingivalis fimA genotypes in severe periodontitis patients. Braz Oral Res 2020; 34: e090.
10. Mahalakshmi K, Krishnan P, Chandrasekaran SC. Detection of Aggregatibacter actinomycetemcomitans leukotoxin and fimbria-associated protein gene genotypes among periodontitis patients and healthy controls: a case-control study. Dent Res J 2018; 15: 185-90.
11. Moreno S, Contreras A. Functional differences of Porphyromonas gingivalis Fimbriae in determining periodontal disease pathogenesis: a literature review. Colomb Med 2013; 44: 48-56.
12. Kwak A, Lee Y, Kim H, Kim S. Intracellular interleukin (IL)-1 family cytokine processing enzyme. Arch Pharm Res 2016; 39: 1556-64.
13. Cheng R, Wu Z, Li M, Shao M, Hu T. Interleukin-1 is a potential therapeutic target for periodontitis: a narrative review. Int J Oral Sci 2020; 12: 2.
14. Hienz SA, Paliwal S, Ivanovski S. Mechanisms of bone resorption in periodontitis. J Immunol Res 2015; 2015: 615486.
15. Olsen I, Potempa J. Strategies for the inhibition of gingipains for the potential treatment of periodontitis and associated systemic diseases. J Oral Microbiol 2014; 6. DOI: 10.3402/jom.v6.24800.
16. Wang H, Zhang W, Wang W, Zhang L. The prevalence of fimA genotypes of Porphyromonas gingivalis in patients with chronic periodontitis: a meta-analysis. PLoS One 2020; 15: e0240251.
17. Enersen M, Nakano K, Amano A. Porphyromonas gingivalis fimbriae. J Oral Microbiol 2013; 5. DOI: 10.3402/jom.v5i0.20265.
18. Azam SS, Abbasi SW. Molecular docking studies for the identification of novel melatoninergic inhibitors for acetylserotonin-O-methyltransferase using different docking routines. Theor Biol Med Model 2013; 10: 63.
19. Muegge I, Rarey M. Small molecule docking and scoring. Rev Comp Chem 2001; 17: 1-60.
20. Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: current status and future challenges. Proteins Struct Funct Bioinf 2006, 65: 15-26.
21. Abrão F, Silva T, Moura C, et al. Oleoresins and naturally occurring compounds of Copaifera genus as antibacterial and antivirulence agents against periodontal pathogens. Sci Rep 2021; 11: 4953.
22. Guevara T, Rodríguez-Banqueri A, Lasica AM, et al. Structural determinants of inhibition of Porphyromonas gingivalis gingipain K by KYT-36, a potent, selective, and bioavailable peptidase inhibitor. Sci Rep 2019; 9: 4935.
23. Qingping X, Mikio S, Satoshi S. A distinct type of pilus from the human microbiome. Cell 2016; 165: 690-703.
24. Shibata S, Shoji M, Okada K, et al. Structure of polymerized type V pilin reveals assembly mechanism involving protease-mediated strand exchange. Nat Microbiol 2020; 5: 830-7.
25. Halim SA, Jawad M. Attempt to explore the binding mechanism of IL1 inhibitors via molecular docking studies. Med Chem 2015; 5: 10.
26. Balaji B, Ramanathan M. Prediction of estrogen receptor  ligands potency and selectivity by docking and MM-GBSA scoring methods using three different scaffolds. J Enzyme Inhib Med Chem 2012; 27: 832-44.
27. Grande F, Rizzuti B, Occhiuzzi MA, et al. Identification by molecular docking of homoisoflavones from leopoldia comosa as ligands of estrogen receptors. Molecules 2018; 23: 894.
28. Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 2009; 30: 2785-91.
29. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31: 455-61.
30. Spasovski S, Belazelkoska Z, Popovska M, et al. Clinical therapeutic effects of the application of doxycycline in the treatment of periodontal disease. Open Access Maced J Med Sci 2016; 4: 152-7.
31. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 2019; 18: 41-58.
32. Duan G, Ji C, Zhang JZ. Developing an effective polarizable bond method for small molecules with application to optimized molecular docking. RSC Advances 2020; 10: 15530-40.
33. Heifetz A, Katchalski-Katzir E, Eisenstein M. Electrostatics in protein-protein docking. Protein Sci 2002; 11: 571-87.
34. Bitencourt-Ferreira G, Veit-Acosta M, de Azevedo WF Jr. Electrostatic Energy in protein-ligand complexes. Methods Mol Biol 2019; 2053: 67-77.
Copyright: © 2022 Termedia & Banach. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
Quick links
© 2023 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.