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  1. Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations
    Chongjun Y, Nasr AMS, Latif MAM, Rahman MBA, Marlisah E, Tejo BA
    SAR QSAR Environ Res, 2024 Aug;35(8):707-728.
    PMID: 39210743 DOI: 10.1080/1062936X.2024.2392677
    Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.
  2. Fulltext Analysis of the Origin of Emiratis as Inferred from a Family Study Based on HLA-A, -C, -B, -DRB1, and -DQB1 Genes
    Al Yafei Z, Hajjej A, Alvares M, Al Mahri A, Nasr A, Mirghani R, et al.
    Genes (Basel), 2023 May 26;14(6).
    PMID: 37372339 DOI: 10.3390/genes14061159
    In this study, we investigated HLA class I and class II allele and haplotype frequencies in Emiratis and compared them to those of Asian, Mediterranean, and Sub-Saharan African populations.

    METHODS: Two-hundred unrelated Emirati parents of patients selected for bone marrow transplantation were genotyped for HLA class I (A, B, C) and class II (DRB1, DQB1) genes using reverse sequence specific oligonucleotide bead-based multiplexing. HLA haplotypes were assigned with certainty by segregation (pedigree) analysis, and haplotype frequencies were obtained by direct counting. HLA class I and class II frequencies in Emiratis were compared to data from other populations using standard genetic distances (SGD), Neighbor-Joining (NJ) phylogenetic dendrograms, and correspondence analysis.

    RESULTS: The studied HLA loci were in Hardy-Weinberg Equilibrium. We identified 17 HLA-A, 28 HLA-B, 14 HLA-C, 13 HLA-DRB1, and 5 HLA-DQB1 alleles, of which HLA-A*02 (22.2%), -B*51 (19.5%), -C*07 (20.0%), -DRB1*03 (22.2%), and -DQB1*02 (32.8%) were the most frequent allele lineages. DRB1*03~DQB1*02 (21.2%), DRB1*16~DQB1*05 (17.3%), B*35~C*04 (11.7%), B*08~DRB1*03 (9.7%), A*02~B*51 (7.5%), and A*26~C*07~B*08~DRB1*03~DQB1*02 (4.2%) were the most frequent two- and five-locus HLA haplotypes. Correspondence analysis and dendrograms showed that Emiratis were clustered with the Arabian Peninsula populations (Saudis, Omanis and Kuwaitis), West Mediterranean populations (North Africans, Iberians) and Pakistanis, but were distant from East Mediterranean (Turks, Albanians, Greek), Levantine (Syrians, Palestinians, Lebanese), Iranian, Iraqi Kurdish, and Sub-Saharan populations.

    CONCLUSIONS: Emiratis were closely related to Arabian Peninsula populations, West Mediterranean populations and Pakistanis. However, the contribution of East Mediterranean, Levantine Arab, Iranian, and Sub-Saharan populations to the Emiratis' gene pool appears to be minor.

  3. The first BGICC consensus and recommendations for breast cancer awareness, early detection and risk reduction in low- and middle-income countries and the MENA region
    Elghazaly H, Aref AT, Anderson BO, Arun B, Yip CH, Abdelaziz H, et al.
    Int J Cancer, 2021 08 01;149(3):505-513.
    PMID: 33559295 DOI: 10.1002/ijc.33506
    In low-middle income countries (LMICs) and the Middle East and North Africa (MENA) region, there is an unmet need to establish and improve breast cancer (BC) awareness, early diagnosis and risk reduction programs. During the 12th Breast, Gynecological & Immuno-oncology International Cancer Conference - Egypt 2020, 26 experts from 7 countries worldwide voted to establish the first consensus for BC awareness, early detection and risk reduction in LMICs/MENA region. The panel advised that there is an extreme necessity for a well-developed BC data registries and prospective clinical studies that address alternative modalities/modified BC screening programs in areas of limited resources. The most important recommendations of the panel were: (a) BC awareness campaigns should be promoted to public and all adult age groups; (b) early detection programs should combine geographically distributed mammographic facilities with clinical breast examination (CBE); (c) breast awareness should be encouraged; and (d) intensive surveillance and chemoprevention strategies should be fostered for high-risk women. The panel defined some areas for future clinical research, which included the role of CBE and breast self-examination as an alternative to radiological screening in areas of limited resources, the interval and methodology of BC surveillance in women with increased risk of BC and the use of low dose tamoxifen in BC risk reduction. In LMICs/MENA region, BC awareness and early detection campaigns should take into consideration the specific disease criteria and the socioeconomic status of the target population. The statements with no consensus reached should serve as potential catalyst for future clinical research.
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