牙周炎是一種由細菌、病毒和真核生物引發的慢性口腔炎症性疾病，是全球所周知的普遍疾病。儘管有有效的牙周炎治療方法，但其管理也存在一些缺點，包括治療選擇有限、復發風險和高昂的治療費用。我們的目標是在臨床試驗之前，為牙周炎開發一種更高效、系統化的藥物設計。我們通過系統生物學和深度學習方法，致力於牙周炎治療的系統性藥物發現和設計。首先，我們應用大型數據庫挖掘來建立候選的全基因組遺傳和表觀遺傳網絡（GWGEN），其中包括牙周炎和健康對照的蛋白質相互作用網絡（PPIN）和基因調控網絡（GRN）。接下來，基於不健康和健康的微陣列數據，我們應用系統識別和系統階數檢測方法來去除候選GWGEN中的假陽性結果，從而分別獲得牙周炎和健康對照的真實GWGEN。獲得真實GWGEN後，我們通過主網絡投影（PNP）方法選出核心GWGEN。最後，參考KEGG pathways，我們建立了牙周炎和健康對照的核心信號通路。我們通過比較它們的核心信號通路來研究牙周炎的致病機制。通過檢查牙周炎的下游核心信號通路和相應的細胞功能異常，我們選擇FOS、TSC2、FOXO1和NF-κB 作為生物標記物。接著，使用基於深度神經網絡的藥物-靶點相互作用（DTI）模型，該模型通過現有的藥物-靶點相互作用數據庫進行訓練，以預測針對重要生物標記物的候選分子藥物。最後，我們根據藥物設計規範，篩選出brucine、disulfiram、verapamil和PK-11195組合成多分子藥物，針對這些重要生物標記物進行靶向治療。總結來說，我們通過運用系統生物學方法研究了牙周炎的致病機制，並通過基於DNN的DTI模型的預測和藥物設計規範徹底地開發了牙周炎治療的治療選項。

Periodontitis, a chronic inflammatory oral condition triggered by bacteria, archaea, viruses, and eukaryotic organisms, is a well-known and widespread disease around the world. While there are effective treatments for periodontitis, there are also several shortcomings associated with its management, including limited treatment options, the risk of recurrence, and the high cost of treatment. Our goal is to develop a more efficient, systematic drug design for periodontitis before clinical trials. We work on systems drug discovery and design for periodontitis treatment via systems biology and deep learning methods. We first applied big database mining to build a candidate genome-wide genetic and epigenetic network (GWGEN), which includes a protein-protein interaction network (PPIN) and a gene regulatory network (GRN) for periodontitis and healthy control. Next, based on the unhealthy and healthy microarray data, we applied system identification and system order detection methods to remove false positives in candidate GWGENs to obtain real GWGENs for periodontitis and healthy control, respectively. After the real GWGENs were obtained, we picked out the core GWGENs based on how significant the proteins and genes were via the principal network projection (PNP) method. Finally, referring to the annotation of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we built up the core signaling pathways of periodontitis and healthy control. Consequently, we investigated the pathogenic mechanism of periodontitis by comparing their core signaling pathways. By checking up on the downstream core signaling pathway and the corresponding cellular dysfunctions of periodontitis, we identified the fos proto-oncogene, AP-1 Transcription Factor Subunit (FOS), TSC Complex Subunit 2 (TSC2), Forkhead Box O1 (FOXO1), and nuclear factor kappa-light chain enhancer of activated B cells (NF-κB) as significant biomarkers on which we could find candidate molecular drugs to target. To achieve our ultimate goal of designing a combination of molecular drugs for periodontitis treatment, a deep neural network (DNN)-based drug-target interaction (DTI) model was employed. The model is trained with the existing drug-target interaction databases for the prediction of candidate molecular drugs for significant biomarkers. Finally, we filter out brucine, disulfiram, verapamil, and PK-11195 as potential molecular drugs to be combined as a multiple-molecular drug to target the significant biomarkers based on drug design specifications, i.e., adequate drug regulation ability, high sensitivity, and low toxicity. In conclusion, we investigated the pathogenic mechanism of periodontitis by leveraging systems biology methods and thoroughly developed a therapeutic option for periodontitis treatment via the prediction of a DNN-based DTI model and drug design specifications.

1. Cieza, A.; Causey, K.; Kamenov, K.; Hanson, S.W.; Chatterji, S.; Vos, T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 2006–2017.
2. Yu, B.; Wang, C.Y. Osteoporosis and periodontal diseases—An update on their association and mechanistic links. Periodontology 2022, 89, 99–113.
3. Boehm, T.K.; Scannapieco, F.A. The epidemiology, consequences and management of periodontal disease in older adults. J. Am. Dent. Assoc. 2007, 138, S26–S33.
4. Kumar, S. Evidence-based update on diagnosis and management of gingivitis and periodontitis. Dent. Clin. 2019, 63, 69–81.
5. Abebe, G.M. Oral biofilm and its impact on oral health, psychological and social interaction. Int. J. Oral Dent. Health 2021, 7, 127–137.
6. National Institute of Dental Research (US). Periodontal (Gum) Disease; National Institute of Dental Research: Washington, DC, USA, 1988.
7. Singh, B.; Singh, R. Gingivitis–A silent disease. J. Dent Med. Sci. 2013, 6, 30–33.
8. G enlargements Periodontitis. Periodontal Pocket. Periodontol. Dent. Hyg. -E-Book 2015, 88.
9. Papapanou, P.N.; Sanz, M.; Buduneli, N.; Dietrich, T.; Feres, M.; Fine, D.H.; Flemmig, T.F.; Garcia, R.; Giannobile, W.V.; Graziani, F. Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J. Periodontol. 2018, 89, S173–S182.
10. Baelum, V.; Lopez, R. Defining and classifying periodontitis: Need for a paradigm shift? Eur. J. Oral Sci. 2003, 111, 2–6.
11. Darby, I. Non-surgical management of periodontal disease. Aust. Dent. J. 2009, 54, S86–S95.
12. Pragati, S.; Ashok, S.; Kuldeep, S. Recent advances in periodontal drug delivery systems. Int. J. Drug Deliv. 2009, 1, 1–14.
13. Slots, J.; Rams, T.E. Antibiotics in periodontal therapy: Advantages and disadvantages. J. Clin. Periodontol. 1990, 17, 479–493.
14. Lu, H.; He, L.; Jin, D.; Zhu, Y.; Meng, H. Effect of adjunctive systemic antibiotics on microbial populations compared with scaling and root planing alone for the treatment of periodontitis: A pilot randomized clinical trial. J. Periodontol. 2022, 93, 570–583.
15. Peter Brooks, M. Use and benefits of nonsteroidal anti-inflammatory drugs. Am. J. Med. 1998, 104, 9S–13S.
16. Puyathorn, N.; Senarat, S.; Lertsuphotvanit, N.; Phaechamud, T. Physicochemical and Bioactivity Characteristics of Doxycycline Hyclate-Loaded Solvent Removal-Induced Ibuprofen-Based In Situ Forming Gel. Gels 2023, 9, 128.
17. Lertsuphotvanit, N.; Santimaleeworagun, W.; Narakornwit, W.; Chuenbarn, T.; Mahadlek, J.; Chantadee, T.; Phaechamud, T. Borneol-based antisolvent-induced in situ forming matrix for crevicular pocket delivery of vancomycin hydrochloride. Int. J. Pharm. 2022, 617, 121603.
18. Lee, F.-Y.; Chen, D.W.; Hu, C.-C.; Hsieh, Y.-T.; Liu, S.-J.; Chan, E.-C. In vitro and in vivo investigation of drug-eluting implants for the treatment of periodontal disease. AAPS PharmSciTech 2011, 12, 1110–1115.
19. Prakasam, A.; Elavarasu, S.S.; Natarajan, R.K. Antibiotics in the management of aggressive periodontitis. J. Pharm. Bioallied Sci. 2012, 4 (Suppl. S2), S252.
20. Hughes, J.P.; Rees, S.; Kalindjian, S.B.; Philpott, K.L. Principles of early drug discovery. Br. J. Pharmacol. 2011, 162, 1239–1249.
21. Sun, D.; Gao, W.; Hu, H.; Zhou, S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm. Sin. B 2022, 12, 3049–3062.
22. Haque, M.M.; Yerex, K.; Kelekis-Cholakis, A.; Duan, K. Advances in novel therapeutic approaches for periodontal diseases. BMC Oral Health 2022, 22, 1–23.
23. Chang, S.; Chen, J.-Y.; Chuang, Y.-J.; Chen, B.-S. Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications. Int. J. Mol. Sci. 2020, 22, 166.
24. Lin, Y.-C.; Chen, B.-S. Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications. Int. J. Mol. Sci. 2022, 23, 10409.
25. Ting, C.-T.; Chen, B.-S. Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications. Int. J. Mol. Sci. 2022, 23, 3649.
26. Yeh, S.-J.; Chung, Y.-C.; Chen, B.-S. Investigating the Role of Obesity in Prostate Cancer and Identifying Biomarkers for Drug Discovery: Systems Biology and Deep Learning Approaches. Molecules 2022, 27, 900.
27. Su, P.-W.; Chen, B.-S. Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications. Int. J. Mol. Sci. 2022, 23, 13869.
28. Wei, S.; Li, Y.l.; Gong, Q.; Liang, H.; Liu, Q.; Bernardi, R.E.; Zhang, H.T.; Chen, F.; Lawrence, A.J.; Liang, J.h. Brucine N-oxide reduces ethanol intake and preference in alcohol-preferring male Fawn-Hooded rats. Alcohol. : Clin. Exp. Res. 2020, 44, 1321–1328.
29. Krampe, H.; Ehrenreich, H. Supervised disulfiram as adjunct to psychotherapy in alcoholism treatment. Curr. Pharm. Des. 2010, 16, 2076–2090.
30. Cooper-DeHoff, R.M.; Handberg, E.M.; Mancia, G.; Zhou, Q.; Champion, A.; Legler, U.F.; Pepine, C.J. INVEST revisited: Review of findings from the International Verapamil SR–Trandolapril Study. Expert Rev. Cardiovasc. Ther. 2009, 7, 1329–1340.
31. Passamonti, L.; Rodríguez, P.V.; Hong, Y.T.; Allinson, K.S.; Bevan-Jones, W.R.; Williamson, D.; Jones, P.S.; Arnold, R.; Borchert, R.J.; Surendranathan, A. [11C] PK11195 binding in Alzheimer disease and progressive supranuclear palsy. Neurology 2018, 90, e1989–e1996.
32. Stark, C.; Breitkreutz, B.-J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; Tyers, M. BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (Suppl. S1), D535–D539.
33. Friard, O.; Re, A.; Taverna, D.; De Bortoli, M.; Corá, D. CircuitsDB: A database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC Bioinform. 2010, 11, 1–10.
34. Salwinski, L.; Miller, C.S.; Smith, A.J.; Pettit, F.K.; Bowie, J.U.; Eisenberg, D. The database of interacting proteins: 2004 update. Nucleic Acids Res. 2004, 32 (Suppl. S1), D449–D451.
35. Bovolenta, L.; Acencio, M.; Lemke, N. HTRIdb: An open-access database for experimentally verified human transcriptional regulation interactions. Nat. Preced. 2012, 1.
36. Orchard, S.; Ammari, M.; Aranda, B.; Breuza, L.; Briganti, L.; Broackes-Carter, F.; Campbell, N.H.; Chavali, G.; Chen, C.; Del-Toro, N. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014, 42, D358–D363.
37. Zheng, G.; Tu, K.; Yang, Q.; Xiong, Y.; Wei, C.; Xie, L.; Zhu, Y.; Li, Y. ITFP: An integrated platform of mammalian transcription factors. Bioinformatics 2008, 24, 2416–2417.
38. Zanzoni, A.; Montecchi-Palazzi, L.; Quondam, M.; Ausiello, G.; Helmer-Citterich, M.; Cesareni, G. MINT: A Molecular INTeraction database. FEBS Lett. 2002, 513, 135–140.
39. Li, J.-H.; Liu, S.; Zhou, H.; Qu, L.-H.; Yang, J.-H. starBase v2. 0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014, 42, D92–D97.
40. Agarwal, V.; Bell, G.W.; Nam, J.-W.; Bartel, D.P. Predicting effective microRNA target sites in mammalian mRNAs. elife 2015, 4, e05005.
41. Wingender, E.; Chen, X.; Hehl, R.; Karas, H.; Liebich, I.; Matys, V.; Meinhardt, T.; Prüß, M.; Reuter, I.; Schacherer, F. TRANSFAC: An integrated system for gene expression regulation. Nucleic Acids Res. 2000, 28, 316–319.
42. Hanes, P.J.; Krishna, R. Characteristics of inflammation common to both diabetes and periodontitis: Are predictive diagnosis and targeted preventive measures possible? EPMA J. 2010, 1, 101–116.
43. Ding, C.; Ji, X.; Chen, X.; Xu, Y.; Zhong, L. TNF-α gene promoter polymorphisms contribute to periodontitis susceptibility: Evidence from 46 studies. J. Clin. Periodontol. 2014, 41, 748–759.
44. Aung, K.T.; Akiyama, K.; Kunitomo, M.; Mun, A.Y.; Tosa, I.; Nguyen, H.T.T.; Zhang, J.; Kohno, T.; Ono, M.; Hara, E.S. Aging-affected MSC functions and severity of periodontal tissue destruction in a ligature-induced mouse periodontitis model. Int. J. Mol. Sci. 2020, 21, 8103.
45. Karki, R.; Sharma, B.R.; Tuladhar, S.; Williams, E.P.; Zalduondo, L.; Samir, P.; Zheng, M.; Sundaram, B.; Banoth, B.; Malireddi, R.S. Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell 2021, 184, 149–168. e117.
46. Kibune, R.; Muraoka, K.; Morishita, M.; Ariyoshi, W.; Awano, S. Relationship between Dynamics of TNF-α and Its Soluble Receptors in Saliva and Periodontal Health State. Dent. J. 2022, 10, 25.
47. Lee, J.-H.; Jung, J.-Y.; Jeong, Y.-J.; Park, J.-H.; Yang, K.-H.; Choi, N.-K.; Kim, S.-H.; Kim, W.-J. Involvement of both mitochondrial-and death receptor-dependent apoptotic pathways regulated by Bcl-2 family in sodium fluoride-induced apoptosis of the human gingival fibroblasts. Toxicology 2008, 243, 340–347.
48. Canakci, C.; Cicek, Y.; Canakci, V. Reactive oxygen species and human inflammatory periodontal diseases. Biochemistry 2005, 70, 619–628.
49. Liu, F.; Wang, Y.; Xu, J.; Liu, F.; Hu, R.; Deng, H. Effects of Porphyromonas gingivalis lipopolysaccharide on the expression of key genes involved in cholesterol metabolism in macrophages. Arch. Med. Sci. 2016, 12, 959–967.
50. Waddington, R.J.; Moseley, R.; Embery, G. Periodontal Disease Mechanisms: Reactive oxygen species: A potential role in the pathogenesis of periodontal diseases. Oral Dis. 2000, 6, 138–151.
51. Dhanasekaran, D.N.; Reddy, E.P. JNK signaling in apoptosis. Oncogene 2008, 27, 6245–6251.
52. Merritt, A.J.; Allen, T.D.; Potten, C.S.; Hickman, J.A. Apoptosis in small intestinal epithelia from p53-null mice: Evidence for a delayed, p53-indepdendent G2/M-associated cell death after γ-irradiation. Oncogene 1997, 14, 2759–2766.
53. Nakano, H.; Nakajima, A.; Sakon-Komazawa, S.; Piao, J.; Xue, X.; Okumura, K. Reactive oxygen species mediate crosstalk between NF-κB and JNK. Cell Death Differ. 2006, 13, 730–737.
54. Soory, M. Actions of Glutathione in Chronic Inflammatory Diseases, Including Periodontitis: Dietary Agonists. In Glutathione: Dietary Sources, Role in Cellular Functions and Therapeutic Effects; Nova Science Publishers, Inc.: Hauppauge, NY, USA, 2014.
55. Ni, Z.; Wang, B.; Dai, X.; Ding, W.; Yang, T.; Li, X.; Lewin, S.; Xu, L.; Lian, J.; He, F. HCC cells with high levels of Bcl-2 are resistant to ABT-737 via activation of the ROS–JNK–autophagy pathway. Free Radic. Biol. Med. 2014, 70, 194–203.
56. Wu, P.; Feng, J.; Wang, W. Expression of miR-155 and miR-146a in the saliva of patients with periodontitis and its clinical value. Am. J. Transl. Res. 2021, 13, 6670.
57. Zhang, J.; Kim, J.; Alexander, A.; Cai, S.; Tripathi, D.N.; Dere, R.; Tee, A.R.; Tait-Mulder, J.; Di Nardo, A.; Han, J.M. A tuberous sclerosis complex signalling node at the peroxisome regulates mTORC1 and autophagy in response to ROS. Nat. Cell Biol. 2013, 15, 1186–1196.
58. Bezamat, M.; Deeley, K.; Khaliq, S.; Letra, A.; Scariot, R.; Silva, R.M.; Weber, M.L.; Bussaneli, D.G.; Trevilatto, P.C.; Almarza, A.J. Are mTOR and endoplasmic reticulum stress pathway genes associated with oral and bone diseases? Caries Res. 2019, 53, 235–241.
59. Gross, D.; Van Den Heuvel, A.; Birnbaum, M. The role of FoxO in the regulation of metabolism. Oncogene 2008, 27, 2320–2336.
60. !!! INVALID CITATION !!! [60–62].
61. Bulut, Ş.; Uslu, H.; Özdemir, B.H.; Bulut, Ö.E. Expression of caspase-3, p53 and Bcl-2 in generalized aggressive periodontitis. Head Face Med. 2006, 2, 1–7.
62. Sattler, M.; Liang, H.; Nettesheim, D.; Meadows, R.P.; Harlan, J.E.; Eberstadt, M.; Yoon, H.S.; Shuker, S.B.; Chang, B.S.; Minn, A.J. Structure of Bcl-xL-Bak peptide complex: Recognition between regulators of apoptosis. Science 1997, 275, 983–986.
63. Liu, C.; Mo, L.; Niu, Y.; Li, X.; Zhou, X.; Xu, X. The role of reactive oxygen species and autophagy in periodontitis and their potential linkage. Front. Physiol. 2017, 8, 439.
64. Ishida, K.; Yuge, Y.; Hanaoka, M.; Yasukawa, M.; Minami, Y.; Ogawa, M.; Masumoto, K.h.; Shigeyoshi, Y.; Saito, M.; Tsuji, T. Gadd45g regulates dental epithelial cell proliferation through p38 MAPK-mediated p21 expression. Genes Cells 2013, 18, 660–671.
65. Li, W.; Xu, L.; Lu, R.; Meng, H. The association of EGF rs2237051 variant, serum EGF levels and generalized aggressive periodontitis: A preliminary study. PeerJ 2020, 8, e9212.
66. Birkedal-Hansen, H. Role of matrix metalloproteinases in human periodontal diseases. J. Periodontol. 1993, 64, 474–484.
67. Pietruska, M.; Pietruski, J.; Stokowska, W. Polypeptide growth factors in the course of surgical periodontal treatment. Rocz. Akad. Med. W Bialymst. (1995) 2000, 45, 199–210.
68. Niu, C.; Yuan, K.; Ma, R.; Gao, L.; Jiang, W.; Hu, X.; Lin, W.; Zhang, X.; Huang, Z. Gold nanoparticles promote osteogenic differentiation of human periodontal ligament stem cells via the p38 MAPK signaling pathway. Mol. Med. Rep. 2017, 16, 4879–4886.
69. Cui, S.; Guo, W.; Chen, C.; Tang, X.; Zhao, J.; Mao, B.; Zhang, H. Metagenomic analysis of the effects of Lactiplantibacillus plantarum and fructooligosaccharides (FOS) on the fecal microbiota structure in mice. Foods 2022, 11, 1187.
70. Xiao, G. Autophagy and NF-κB: Fight for fate. Cytokine Growth Factor Rev. 2007, 18, 233–243.
71. Kizilirmak, C.; Bianchi, M.E.; Zambrano, S. Insights on the NF-κB system using live cell imaging: Recent developments and future perspectives. Front. Immunol. 2022, 13, 886127.
72. Peng, S.; Li, W.; Hou, N.; Huang, N. A review of FoxO1-regulated metabolic diseases and related drug discoveries. Cells 2020, 9, 184.
73. Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 2017, 171, 1437–1452. e1417.
74. Corsello, S.M.; Nagari, R.T.; Spangler, R.D.; Rossen, J.; Kocak, M.; Bryan, J.G.; Humeidi, R.; Peck, D.; Wu, X.; Tang, A.A. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 2020, 1, 235–248.
75. Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021, 49, W5–W14.
76. Wikesjö, U.M.; Nilvéus, R.E.; Selvig, K.A. Significance of early healing events on periodontal repair: A review. J. Periodontol. 1992, 63, 158–165.
77. Akram, Z.; Shafqat, S.S.; Niaz, M.O.; Raza, A.; Naseem, M. Clinical efficacy of photodynamic therapy and laser irradiation as an adjunct to open flap debridement in the treatment of chronic periodontitis: A systematic review and meta-analysis. Photodermatol. Photoimmunol. Photomed. 2020, 36, 3–13.
78. Lee, C.; Huang, H.; Sun, T.; Karimbux, N. Impact of patient compliance on tooth loss during supportive periodontal therapy: A systematic review and meta-analysis. J. Dent. Res. 2015, 94, 777–786.
79. Mohd-Dom, T.; Ayob, R.; Mohd-Nur, A.; Abdul-Manaf, M.R.; Ishak, N.; Abdul-Muttalib, K.; Aljunid, S.M.; Ahmad-Yaziz, Y.; Abdul-Aziz, H.; Kasan, N. Cost analysis of periodontitis management in public sector specialist dental clinics. BMC Oral Health 2014, 14, 1–10.
80. Mehra, N.; Varmeziar, A.; Chen, X.; Kronick, O.; Fisher, R.; Kota, V.; Mitchell, C.S. Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia. Cancers 2022, 14, 4686.
81. Serasanambati, M.; Chilakapati, S.R.; Vangavaragu, J.R.; Chilakapati, D.R. Inhibitory effect of gemcitabine and brucine on MDA MB-231human breast cancer cells. Int. J. Drug Deliv. 2014, 6, 133.
82. Qin, J.-M.; Yin, P.-H.; Li, Q.; Sa, Z.-Q.; Sheng, X.; Yang, L.; Huang, T.; Zhang, M.; Gao, K.-P.; et al. Anti-tumor effects of brucine immuno-nanoparticles on hepatocellular carcinoma. Int. J. Nanomed. 2012, 7, 369–379.
83. Lu, L.; Huang, R.; Wu, Y.; Jin, J.-M.; Chen, H.-Z.; Zhang, L.-J.; Luan, X. Brucine: A review of phytochemistry, pharmacology, and toxicology. Front. Pharmacol. 2020, 11, 377.
84. Yin, W.; Wang, T.-S.; Yin, F.-Z.; Cai, B.-C. Analgesic and anti-inflammatory properties of brucine and brucine N-oxide extracted from seeds of Strychnos nux-vomica. J. Ethnopharmacol. 2003, 88, 205–214.
85. Xie, C.; Yan, J.; Cao, S.; Liu, R.; Sun, B.; Xie, Y.; Qu, K.; Zhang, W.; Weng, Z.; Wang, Z. Bi-layered disulfiram-loaded fiber membranes with antibacterial properties for wound dressing. Appl. Biochem. Biotechnol. 2022, 194, 1359–1372.
86. Stanley, J.; Yamamoto, N. Novel Prospective Treatment Options. Trends Basic Ther. Options HIV Infect. A Funct. Cure 2015.
87. Meneguello, J.E.; Murase, L.S.; de Souza, J.V.P.; de Oliveira, C.G.; Ghiraldi-Lopes, L.D.; Teixeira, J.J.V.; de Lima Scodro, R.B.; Ferracioli, K.R.C.; Siqueira, V.L.D.; Campanerut-Sá, P.A.Z. A systematic review of disulfiram as an antibacterial agent: What is the evidence? Int. J. Antimicrob. Agents 2022, 59, 106578.
88. Yamaguchi, N.; Chiba, M.; Mitani, H. The induction of c-fos mRNA expression by mechanical stress in human periodontal ligament cells. Arch. Oral Biol. 2002, 47, 465–471.
89. Singh, B.N.; Ellrodt, G.; Peter, C.T. Verapamil: A review of its pharmacological properties and therapeutic use. Drugs 1978, 15, 169–197.
90. Surma, S.; Romańczyk, M.; Witalińska-Łabuzek, J.; Czerniuk, M.R.; Łabuzek, K.; Filipiak, K.J. Periodontitis, blood pressure, and the risk and control of arterial hypertension: Epidemiological, clinical, and pathophysiological aspects—Review of the literature and clinical trials. Curr. Hypertens. Rep. 2021, 23, 27.
91. Muñoz Aguilera, E.; Suvan, J.; Orlandi, M.; Miró Catalina, Q.; Nart, J.; D’Aiuto, F. Association between periodontitis and blood pressure highlighted in systemically healthy individuals: Results from a nested case-control study. Hypertension 2021, 77, 1765–1774.
92. Kreisl, W.C.; Fujita, M.; Fujimura, Y.; Kimura, N.; Jenko, K.J.; Kannan, P.; Hong, J.; Morse, C.L.; Zoghbi, S.S.; Gladding, R.L. Comparison of [11C]-(R)-PK 11195 and [11C] PBR28, two radioligands for translocator protein (18 kDa) in human and monkey: Implications for positron emission tomographic imaging of this inflammation biomarker. Neuroimage 2010, 49, 2924–2932.
93. Walter, R.B.; Pirga, J.L.; Cronk, M.R.; Mayer, S.; Appelbaum, F.R.; Banker, D.E. PK11195, a peripheral benzodiazepine receptor (pBR) ligand, broadly blocks drug efflux to chemosensitize leukemia and myeloma cells by a pBR-independent, direct transporter-modulating mechanism. Blood 2005, 106, 3584–3593.
94. Hazini, Z. Prevalence of periodontal disease in patients with leukemia: A systematic review. 2022.
95. Javed, F.; Utreja, A.; Correa, F.O.B.; Al-Askar, M.; Hudieb, M.; Qayyum, F.; Al-Rasheed, A.; Almas, K.; Al-Hezaimi, K. Oral health status in children with acute lymphoblastic leukemia. Crit. Rev. Oncol. /Hematol. 2012, 83, 303–309.
96. Chen, B.-S.; Wu, C.-C. Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. Cells 2013, 2, 635–688.
97. Availabe online: https://github.com/Charles871129/Systems-biology-and-DNN (accessed on).
98. Wang, Y.; Zhang, S.; Li, F.; Zhou, Y.; Zhang, Y.; Wang, Z.; Zhang, R.; Zhu, J.; Ren, Y.; Tan, Y. Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 2020, 48, D1031–D1041.
99. Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J.; Cibrián-Uhalte, E. The ChEMBL database in 2017. Nucleic Acids Res. 2017, 45, D945–D954.
100. Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 2007, 36 (Suppl. S1), D684-D688.
101. Kanehisa, M. The KEGG database. In Proceedings of the ‘In Silico’ Simulation of Biological Processes: Novartis Foundation Symposium 247, Chichester, UK, 2002; John Wiley & Sons: Hoboken, NJ, USA, 2002; pp. 91–103.
102. Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A. PubChem substance and compound databases. Nucleic Acids Res. 2016, 44, D1202–D1213.
103. Consortium, U. UniProt: A hub for protein information. Nucleic Acids Res. 2015, 43, D204–D212.
104. Frazier, K.; Moore, J.; Long, T. Antibacterial activity of disulfiram and its metabolites. J. Appl. Microbiol. 2019, 126, 79–86.
105. Mancia, G.; Messerli, F.; Bakris, G.; Zhou, Q.; Champion, A.; Pepine, C.J. Blood pressure control and improved cardiovascular outcomes in the International Verapamil SR-Trandolapril Study. Hypertension 2007, 50, 299–305.
106. Martin-Cabezas, R.; Seelam, N.; Petit, C.; Agossa, K.; Gaertner, S.; Tenenbaum, H.; Davideau, J.-L.; Huck, O. Association between periodontitis and arterial hypertension: A systematic review and meta-analysis. Am. Heart J. 2016, 180, 98–112.
107. Corsi, F.; Baglini, E.; Barresi, E.; Salerno, S.; Cerri, C.; Martini, C.; Da Settimo Passetti, F.; Taliani, S.; Gargini, C.; Piano, I. Targeting TSPO Reduces Inflammation and Apoptosis in an In Vitro Photoreceptor-Like Model of Retinal Degeneration. ACS Chem. Neurosci. 2022, 13, 3188–3197.
108. Dupuy, O.; Flocard, F.; Vial, C.; Rode, G.; Charles, N.; Boisson, D.; Flechaire, A. Disulfiram (Esperal) toxicity. Apropos of 3 original cases. Rev. Med. Int. 1995, 16, 67–72.
109. Barrow, P.; Houston, P.; Wong, D. Overdose of sustained-release verapamil. BJA Br. J. Anaesth. 1994, 72, 361–365.
110. Nastri, L.; De Rosa, A.; De Gregorio, V.; Grassia, V.; Donnarumma, G. A new controlled-release material containing metronidazole and doxycycline for the treatment of periodontal and peri-implant diseases: Formulation and in vitro testing. Int. J. Dent. 2019, 2019, 9374607.
111. Sgolastra, F.; Gatto, R.; Petrucci, A.; Monaco, A. Effectiveness of systemic amoxicillin/metronidazole as adjunctive therapy to scaling and root planing in the treatment of chronic periodontitis: A systematic review and meta-analysis. J. Periodontol. 2012, 83, 1257–1269.
112. Zhanel, G.; Critchley, I.; Lin, L.-Y.; Alvandi, N. Microbiological profile of sarecycline, a novel targeted spectrum tetracycline for the treatment of acne vulgaris. Antimicrob. Agents Chemother. 2019, 63, e01297–e01218.
113. Caton, J.; Ryan, M.E. Clinical studies on the management of periodontal diseases utilizing subantimicrobial dose doxycycline (SDD). Pharmacol. Res. 2011, 63, 114–120.
114. English, B.K.; Gaur, A.H. The use and abuse of antibiotics and the development of antibiotic resistance. Hot Top. Infect. Immun. Child. VI 2010, 73–82.