Virtual Screening to Identify the Protein Network Interaction of Hypericin with Red Complex Pathogens

Introduction: Hypericin is the anthraquinone derivative and has many properties like antiviral, antifungal and antibacterial. The red complex pathogens which include Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia in association with other microbes found in the periodontal pockets, cause severe inflammation resulting in periodontitis. Novel bioactive agents from several sources have been tested against the microbial pathogens to deduce antimicrobial activity. Aim: The aim of the study is to virtually screen and identify the protein network interaction of hypericin in red complex pathogens. Methodology: The STITCH v5.0 pipeline was primarily used to identify the drug-protein interactions. The VirulentPred and VICMPred software were used for elucidating the functional class of the proteins and virulence property. The sub cellular localization of virulent proteins was analysed with pSORTb v3.0 software. Further, the epitopes in virulent proteins were identified using BepiPred v1.0 linear epitope prediction tool. Results: Heat shock protein 90 of Porphyromonas gingivalis were found to involve in the cellular process and DNA topoisomerase IV subunit B, heat shock protein 90, DNA gyrase subunit A and DNA gyrase subunit B of Treponema denticola were found to be the virulent factors. The virulent proteins were located in the cytoplasm, which would further increase the potential effect of the drug to serve as antimicrobial agents. Finally, epitopes were predicted on the virulent proteins which can be specifically docked to further ascertain their interactions with the phytocompound. Conclusion: Hypericin with all its potential and biological benefits can be addressed, can be used as an antimicrobial agent to eradicate dental pathogens which are recalcitrant to treatment. The mode of action of hypericin is, it is targeting crucial proteins in red complex pathogens. Further in vitro studies should be performed on a wide range of pathogens to substantiate the true interactions between the drugs and the protein repertoire of pathogens.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Immune Modulation As a Key Pathway for Predicting Azacitidine (AZA) Response in Myelodysplastic Syndromes (MDS)

Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance. Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells. Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients. Figure 1 Figure 1. Disclosures Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Predicted Novel Biomarkers for Hyper-CVAD (CVAD) Treatment Response and Combination of Rituximab and Cladribine (RC) in CVAD Resistant Cases of Mantle Cell Lymphoma (MCL)

Background: Mantle Cell Lymphoma (MCL) accounts for 3-10% of all non-Hodgkin lymphomas with a median overall survival of 3-4 years. Hyper-CVAD (CVAD) with or without Rituximab constitutes first line therapy for treatment of MCL, yet the use of this combination is associated with high toxicity and only modest efficacy. On the other hand, impressive clinical efficacy has been reported in relapsed MCL patients treated with rituximab and cladribine (RC). Prediction of response based on cancer genomics heterogeneity creates an opportunity to personalize treatment and avoid toxic therapy which has little chance of response. We conducted a study using the Cellworks Biosimulation Platform to identify novel genomic biomarkers associated with response to CVAD and RC among MCL patients. Method: Newly-diagnosed MCL patients were selected for this study based largely on genomic data (i.e. aberrations and copy number variations) published in PubMed and TCGA. The Cellworks Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Genomic data from each patient served as input for the CBM. Biomarkers unique to each patient were identified within protein network-maps. Drug impact on the disease network was biosimulated using the Cellworks Biosimulation Platform to determine a treatment efficacy value by measuring the treatment effects on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was mapped to each patient's CBM and the predicted biological consequences were used to determine response. Biosimulation of CVAD was applied to the patients in this cohort. RC was biosimulated on all CVAD non-responders. Results: Among the 94 MCL patients treated with CVAD, the Cellworks Biosimulation Platform identified novel biomarkers (Table 1) to predict treatment response or failure. The biosimulation also identified unique drug combinations for patients that were non-responders (NR) to both treatments. Of the 94 patients, 57 were deemed responders (R) and 37 non-responders (NR). ATM LOF/del, RAD51 del, LIG4A del, RB1 del, ERCC5 del, CARD11 amp, IKZF1 amp, and FANCC del were major predictors of CVAD response. These genes contributed to drug efficacy by impacting various pathways, including DNA repair, oxidative-stress, NFKB activation, spindle formation and mitotic-catastrophe. The frequency of aberration affecting these genes was high among the R group and was low in the NR group. Biosimulation was used to assess response to RC, and predicted, that 41 of 94 patients would respond and 53 would not respond. KMT2D LOF and SMAD4 del were associated with response to RC. Epigenetic dysregulation caused by KMT2D LOF decreased MSH6-mediated mismatch repair required for futile DNA repair leading to replication fork arrest and apoptosis. Interestingly, KMT2D LOF was identified in 20/41 R to RC. MYC amp, NOTCH 1 GOF, and NT5C2 amp were identified as key non-response markers for RC. In considering both regimens, 27 patients were predicted R to both CVAD and RC, 14 to RC but not to CVAD, 30 to CVAD but not RC, and 23 NR to both regimens. In the latter group, biosimulation predicted that a venetoclax-based combination would be effective in many cases due to the high incidence of TP53 GOF mutation within this subgroup. Conclusions: This pilot study highlights how the Cellworks Biosimulation Platform applied to the patient-specific CBM can identify treatment alternatives for patients with low likelihood of response to standard therapy or who may be ineligible for CVAD because of co-morbidities. RC responsiveness was either an equivalent but much less toxic option to CVAD or superior to CVAD. By using novel biomarkers derived from comprehensive mutational and copy number analysis, the CBM identified pathway-based, polygenic biomarkers that can be employed to determine optimal drug combinations for MCL patients. This biosimulation approach warrants prospective validation in a larger patient cohort. Figure 1 Figure 1. Disclosures Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Exact sciences Inc.: Consultancy. Khandelwal: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Raju: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Prakash: Cellworks Group Inc.: Current Employment. Mitra: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Mohanty: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Sanofi: Consultancy, Other: Speaker fees; Servier: Consultancy; Cellworks Group Inc.: Consultancy.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Novel Biomarkers to Inform Mitoxantrone, Etoposide, and Cytarabine (MEC)-Based Combination Therapy in Refractory & Relapsed Acute Myeloid Leukemia (AML) Patients

Background: The optimal treatment strategy for managing Acute Myeloid Leukemia (AML) and the use of reliable and predictive biomarkers to guide selection of cytotoxic chemotherapy regimens among patients with diverse genomic profiles remain unmet needs in the clinic. The combination of MEC [mitoxantrone (MIT), etoposide (VP16), and cytarabine (ARA-C)] is a commonly used regimen for relapsed or refractory AML patients. Unfortunately, many patients do not respond to MEC, and which of the three drug agents matters most for each individual patient is not known. Predictors of response are needed urgently. Methods: The Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The aberration and copy number variations from individual cases served as input into the CBM. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Biosimulations were conducted on the Cellworks Biosimulation Platform by measuring the effect of chemotherapy on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drugs was conducted by mapping the interaction of various drug combinations with the patient's genomic and pathway alterations based on signaling pathway mechanisms and their phenotypic consequences. The Cellworks Biosimulation Platform identified unique chromosomal signatures that permit a stratification of patients that are most likely to respond to MIT, VP16, or ARA-C as well as their combinations. 65 AML patients were selected for this study largely based on genomic data published in TCGA and PubMed: ARA-C [N=12, 7 responders (R) & 5 non-responders (NR)]ARA-C + MIT [N=30, 29 R & 1 NR]ARA-C + MIT + VP16 [N=23, 12 R & 11 NR] Results: Of the12 patients treated with ARA-C alone, 5 were predicted to be NR and 7 were predicted to be R. Of the 5 NR, 4 had 5q del which resulted in loss of APC, CSNK1A1 and SLC22A4 (nucleoside carrier) forming the non-response biomarkers for ARA-C. Notably, the biosimulation predicted lenalidomide to be beneficial for these patients. Out of 7 R, 4 patients also had 5q del, but were predicted to be R because of co-occurring aberrations involving CLSPN del, DHODH del, MSH2 del, EP300 del, CREBBP del, MSH6 Del, and RRM2 del. These genes were exclusively present in ARA-C responders. Of 53 patients who received ARA-C + VP16 + MIT or ARA-C + MIT, 41 patients were predicted to be R and 12 patients were predicted to be NR. The genomic aberrations predicted by biosimulation to be associated with response to this regimen include: NPM1-mut, TET2-mut, IDH1-mut, IDH2-mut, RAD17-del, NRAS-mut (Table 1). Notably, CBM predicted 19 of the 41 R had no genomic biomarkers of response to VP16 or MIT, suggesting these patients might have benefited equally from ARA-C alone with less toxicity and cost. Finally, 11/65 patients were predicted NR to MEC treatment. In the biosimulation, treatment failure was associated with high aberration frequencies of KMT2C-mut/del, FLT3 mut, TWIST1 del, LIMK1 del, SNAI2 amp, FNTA amp, and KAT6A amp. Of note, these genomic markers suggested a likelihood of benefit from other therapies, including vincristine, JQ1 and rigosertib. Conclusions: The Cellworks Biosimulation Platform identified novel polygenic biomarkers of response that can be employed to determine the optimal therapy for relapsed AML patients. Biosimulation permits avoidance of cytotoxic drugs with little chance of efficacy and reveals vulnerabilities in each patient's cancer that can be exploited to improve disease control. In AML, biosimulation promises to improve intensive therapy regimens by tailoring chemotherapy to optimize disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Caris Life Sciences Inc.: Consultancy; Exact sciences Inc.: Consultancy; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Chauhan: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Mandal: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Biosimulation Using the Cellworks Computational Omics Biology Model (CBM)-Based Assessment of Cytarabine (ARA-C) and Anthracycline Response and Novel Biomarker Response Criteria for the Addition of Etoposide (VP16) in Acute Myeloid Leukemia (AML)

Background: Genomic heterogeneity in leukemic blasts characterizes Acute Myeloid Leukemia (AML) patients and is associated to variable drug response. However, use of genomics to guide therapy has generally been restricted to a single-gene approach, which rarely has sufficient predictive power to be clinically useful. Comprehensive DNA sequencing and biosimulation of the Computational Omics Biology Model (CBM) provide the opportunity and means of predicting treatment outcome in advance of treatment. Methods: The Cellworks CBM is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. The CBM permits mapping of biological pathways associated with tumorigenesis and drug resistance using mathematical principles to yield a virtual tumor model that can be used in biosimulation. Aberration and copy number variations from each case served as input to the CBM to generate individual patient-specific protein network maps. We used the Cellworks Biosimulation Platform to identify novel genomic biomarkers associated with response among AML patients treated with Cytarabine (ARA-C) + idarubicin or daunorubicin (anthracycline) with or without Etoposide (VP16). 539 AML patients were selected for this study based largely on genomic data published in TCGA and PubMed: ARA-C + daunorubicin [N=111, 92 responders (R) & 19 non-responders (NR)]ARA-C + idarubicin [N=109, 94 R & 15 NR]ARA-C + daunorubicin + VP16 [N=6, 4 R & 2 NR]ARA-C + idarubicin + VP16 [N=313, 261 R & 52 NR] Drug impact on individual disease networks was simulated to determine efficacy value by measuring the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was used to map its biological consequences to each patient's cancer genome to predict treatment response. Results: Biosimulation of ARA-C + anthracycline with and without VP16 identified biomarkers responsible for therapy response. Additionally, the Cellworks Biosimulation Platform identified novel drug combinations for NR to these standard combinations. There were 186/220 patients treated with ARA-C + anthracycline that had clinical responses. Major biomarkers predictive of response included: IDH2 mut, TOPBP1 del, ATR del, NPM1 mut, IDH1 mut, XRCC2 del, CDK5 del, AKR1B1 del and other genes (Table 1). The frequency of these genes was significantly higher (exact binomial: p-value < 0.0001) in R (N=186) vs. NR (N=34). Notably, 7/34 NR to the two-drug combination had favorable biomarkers for VP16 response, which included RAD52 del, FANCD2 del, STAG2 mut, MPO amp, and NHEJ del. On the other hand, among 265 R treated with triplet therapy (ARA-C + anthracycline + VP16), 30 patients were unlikely to have derived incremental benefit from the addition of VP16. In these patients, the biosimulation predicted that they would have benefited equally from doublet therapy (ARA-C + anthracycline without VP16). In this subgroup of R, ARID1A del, FLT3-ITD mut, GSTA1 amp, KEAP1 del, or RNF1 del generated resistance to VP16 in the biosimulation. Among 54 NR to triplet therapy, 40/54 had genomic alterations predicting a benefit from JQ1, BRD2/4 inhibitors, including KMT2C del, FLT3 GOF, NPM1 del, DNMT3A LOF, and TP53 del, while 12 patients had 5q del highlighting a potential benefit from lenalidomide. Altogether, 89/539 (16.5%) could have been managed with a potentially superior treatment approach based on the biosimulation by either adding or omitting VP16 or being treated with an alternative therapy. Conclusions: Cellworks Biosimulation Platform applied to the patient-specific CBM identifies novel biomarkers of response and can be employed to determine the optimal therapy for AML patients. This study highlights patients for whom triplet therapy promises potentially superior benefit, others who would benefit equally from doublet therapy without VP16, and others unlikely to respond to standard or triplet therapy for whom an alternative personalized approach might offer better outcomes. In AML, biosimulation offers the possibility to tailor the chemotherapy regimen to each patient to improve disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Castro: Caris Life Sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Bugworks: Consultancy; Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Exact sciences Inc.: Consultancy. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Khandelwal: Cellworks Group Inc.: Current Employment. Watson: BioAi Health: Consultancy, Membership on an entity's Board of Directors or advisory committees; AlloVir: Consultancy, Membership on an entity's Board of Directors or advisory committees; CellMax Life: Consultancy, Other: Advisor; Cellworks Group Inc.: Consultancy, Other: Advisor. Kapoor: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Gupta: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Mitra: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Kumar: Cellworks Group Inc.: Current Employment. Choudhury: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Choudhary: Cellworks Group Inc.: Current Employment. Prakash: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Amara: Cellworks Group Inc.: Current Employment. Yuvavani: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Virtual Screening to Identify the Protein Network Interaction of Berberine with Red Complex Pathogens

Introduction: Periodontal disease is an infection of the tissues that hold your teeth in place. It's typically caused by poor brushing and flossing habits that allow plaque, a sticky film of bacteria, to build up on the teeth and harden. Elimination of these pathogens from the site of infection remains a perplexing task, which demands the use of antibiotics. The emergence of drug resistant forms has spurred interest into identifying novel therapeutic targets against these pathogens. Aim: The present study employs virtual screening method to identify the protein network interaction of berberine with red complex pathogens. Materials and Methods: Computational tools were used to identify the targets, assess their functional role and virulence property. Further, the peptide epitopes present in the virulence factors were identified using the BepiPred tool. The subcellular location of the virulence proteins was also elucidated using PSORTb. Results: Berberine was found to target vital protein transporters such as TetR family transcriptional regulator and MerR family transcriptional regulator, which is known to play a crucial role in the survival of bacterial cells. Conclusion: Hence the present study provides preliminary data on the protein targets of berberine against red complex pathogens. However, in vitro studies using the compound is warranted to further confirm the efficacy of the compound.

Virtual Screening to Identify the Protein Network Interactions of Triclosan with Streptococcus mutans and Enterococcus faecalis

Introduction: Triclosan is considered to be an important ingredient in toothpastes and mouth rinses. Several studies have reported contradictory results regarding the antimicrobial effect of triclosan. Hence, the present in silico study intends to identify the potential targets of triclosan in two common dental pathogens Streptococcus mutans and Enterococcus faecalis. Aim: To identify the protein network interactions of triclosan in Streptococcus mutans and Enterococcus faecalis by virtual screening method. Materials and Methods: The STITCH v5.0 database was initially used for identifying drug-protein interactions followed by VICMPred and VirulentPred which was employed to identify functional class of the proteins and its virulence property. Finally, BepiPred v1.0 Linear Epitope Prediction tool was used to identify the potential epitopes of the virulent proteins. Results: Triclosan was found to interact with crucial proteins in S. mutans and E. faecalis which could contribute to severe forms of periodontitis and endodontic diseases. Conclusion: Taken together, the present study provides the preliminary data on the potential targets of triclosan in common dental pathogens. Further experimental validation is warranted to provide concrete evidence on the molecular targets of dental pathogens.