Displaying publications 1 - 20 of 40 in total

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  1. Unveiling dynamics of nitrogen content and selected nitrogen heterocycles in thrombin inhibitors: a ceteris paribus approach
    Masand VH, Al-Hussain S, Alzahrani AY, Al-Mutairi AA, Sultan Alqahtani A, Samad A, et al.
    Expert Opin Drug Discov, 2024 Aug;19(8):991-1009.
    PMID: 38898679 DOI: 10.1080/17460441.2024.2368743
    BACKGROUND: Despite the progress in comprehending molecular design principles and biochemical processes associated with thrombin inhibition, there is a crucial need to optimize efforts and curtail the recurrence of synthesis-testing cycles. Nitrogen and N-heterocycles are key features of many anti-thrombin drugs. Hence, a pragmatic analysis of nitrogen and N-heterocycles in thrombin inhibitors is important throughout the drug discovery pipeline. In the present work, the authors present an analysis with a specific focus on understanding the occurrence and distribution of nitrogen and selected N-heterocycles in the realm of thrombin inhibitors.

    RESEARCH DESIGN AND METHODS: A dataset comprising 4359 thrombin inhibitors is used to scrutinize various categories of nitrogen atoms such as ring, non-ring, aromatic, and non-aromatic. In addition, selected aromatic and aliphatic N-heterocycles have been analyzed.

    RESULTS: The analysis indicates that ~62% of thrombin inhibitors possess five or fewer nitrogen atoms. Substituted N-heterocycles have a high occurrence, like pyrrolidine (23.24%), pyridine (20.56%), piperidine (16.10%), thiazole (9.61%), imidazole (7.36%), etc. in thrombin inhibitors.

    CONCLUSIONS: The majority of active thrombin inhibitors contain nitrogen atoms close to 5 and a combination of N-heterocycles like pyrrolidine, pyridine, piperidine, etc. This analysis provides crucial insights to optimize the transformation of lead compounds into potential anti-thrombin inhibitors.

    Matched MeSH terms: Drug Discovery/methods
  2. Fulltext Bioactive Molecule Prediction Using Extreme Gradient Boosting
    Babajide Mustapha I, Saeed F
    Molecules, 2016 Jul 28;21(8).
    PMID: 27483216 DOI: 10.3390/molecules21080983
    Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
    Matched MeSH terms: Drug Discovery/methods*
  3. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors
    Shiammala PN, Duraimutharasan NKB, Vaseeharan B, Alothaim AS, Al-Malki ES, Snekaa B, et al.
    Methods, 2023 Nov;219:82-94.
    PMID: 37778659 DOI: 10.1016/j.ymeth.2023.09.010
    Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
    Matched MeSH terms: Drug Discovery/methods
  4. Fulltext Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors
    Husin MN, Khan AR, Awan NUH, Campena FJH, Tchier F, Hussain S
    PLoS One, 2024;19(5):e0302276.
    PMID: 38713692 DOI: 10.1371/journal.pone.0302276
    Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.
    Matched MeSH terms: Drug Discovery/methods
  5. Advances in artificial intelligence for drug delivery and development: A comprehensive review
    Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M
    Comput Biol Med, 2024 Aug;178:108702.
    PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702
    Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
    Matched MeSH terms: Drug Discovery/methods
  6. Peptidyl-prolyl isomerases: functionality and potential therapeutic targets in cardiovascular disease
    Rostam MA, Piva TJ, Rezaei HB, Kamato D, Little PJ, Zheng W, et al.
    Clin Exp Pharmacol Physiol, 2015 Feb;42(2):117-24.
    PMID: 25377120 DOI: 10.1111/1440-1681.12335
    Peptidyl-prolyl cis/trans isomerases (PPIases) are a conserved group of enzymes that catalyse the conversion between cis and trans conformations of proline imidic peptide bonds. These enzymes play critical roles in regulatory mechanisms of cellular function and pathophysiology of disease. There are three different classes of PPIases and increasing interest in the development of specific PPIase inhibitors. Cyclosporine A, FK506, rapamycin and juglone are known PPIase inhibitors. Herein, we review recent advances in elucidating the role and regulation of the PPIase family in vascular disease. We focus on peptidyl-prolyl cis/trans isomerase NIMA-interacting 1 (Pin1), an important member of the PPIase family that plays a role in cell cycle progression, gene expression, cell signalling and cell proliferation. In addition, Pin1 may be involved in atherosclerosis. The unique role of Pin1 as a molecular switch that impacts on multiple downstream pathways necessitates the evaluation of a highly specific Pin1 inhibitor to aid in potential therapeutic drug discovery.
    Matched MeSH terms: Drug Discovery/methods
  7. Whole-genome multiparametric screening to identify modulators of epithelial-to-mesenchymal transition
    Said NA, Gould CM, Lackovic K, Simpson KJ, Williams ED
    Assay Drug Dev Technol, 2014 Sep;12(7):385-94.
    PMID: 25181411 DOI: 10.1089/adt.2014.593
    Metastasis accounts for the poor prognosis of the majority of solid tumors. The phenotypic transition of nonmotile epithelial tumor cells to migratory and invasive "mesenchymal" cells (epithelial-to-mesenchymal transition [EMT]) enables the transit of cancer cells from the primary tumor to distant sites. There is no single marker of EMT; rather, multiple measures are required to define cell state. Thus, the multiparametric capability of high-content screening is ideally suited for the comprehensive analysis of EMT regulators. The aim of this study was to generate a platform to systematically identify functional modulators of tumor cell plasticity using the bladder cancer cell line TSU-Pr1-B1 as a model system. A platform enabling the quantification of key EMT characteristics, cell morphology and mesenchymal intermediate filament vimentin, was developed using the fluorescent whole-cell-tracking reagent CMFDA and a fluorescent promoter reporter construct, respectively. The functional effect of genome-wide modulation of protein-coding genes and miRNAs coupled with those of a collection of small-molecule kinase inhibitors on EMT was assessed using the Target Activation Bioapplication integrated in the Cellomics ArrayScan platform. Data from each of the three screens were integrated to identify a cohort of targets that were subsequently examined in a validation assay using siRNA duplexes. Identification of established regulators of EMT supports the utility of this screening approach and indicated capacity to identify novel regulators of this plasticity program. Pathway analysis coupled with interrogation of cancer-related expression profile databases and other EMT-related screens provided key evidence to prioritize further experimental investigation into the molecular regulators of EMT in cancer cells.
    Matched MeSH terms: Drug Discovery/methods*
  8. Extraction, isolation and characterization of bioactive compounds from plants' extracts
    Sasidharan S, Chen Y, Saravanan D, Sundram KM, Yoga Latha L
    Afr J Tradit Complement Altern Med, 2011;8(1):1-10.
    PMID: 22238476
    Natural products from medicinal plants, either as pure compounds or as standardized extracts, provide unlimited opportunities for new drug leads because of the unmatched availability of chemical diversity. Due to an increasing demand for chemical diversity in screening programs, seeking therapeutic drugs from natural products, interest particularly in edible plants has grown throughout the world. Botanicals and herbal preparations for medicinal usage contain various types of bioactive compounds. The focus of this paper is on the analytical methodologies, which include the extraction, isolation and characterization of active ingredients in botanicals and herbal preparations. The common problems and key challenges in the extraction, isolation and characterization of active ingredients in botanicals and herbal preparations are discussed. As extraction is the most important step in the analysis of constituents present in botanicals and herbal preparations, the strengths and weaknesses of different extraction techniques are discussed. The analysis of bioactive compounds present in the plant extracts involving the applications of common phytochemical screening assays, chromatographic techniques such as HPLC and, TLC as well as non-chromatographic techniques such as immunoassay and Fourier Transform Infra Red (FTIR) are discussed.
    Matched MeSH terms: Drug Discovery/methods*
  9. Therapeutic potential of selenium and tellurium compounds: opportunities yet unrealised
    Tiekink ER
    Dalton Trans, 2012 Jun 7;41(21):6390-5.
    PMID: 22252404 DOI: 10.1039/c2dt12225a
    Despite being disparaged for their malodorous and toxic demeanour, compounds of selenium, a bio-essential element, and tellurium, offer possibilities as therapeutic agents. Herein, their potential use as drugs, for example, as anti-viral, anti-microbial, anti-inflammatory agents, etc., will be surveyed along with a summary of the established biological functions of selenium. The natural biological functions of tellurium remain to be discovered.
    Matched MeSH terms: Drug Discovery/methods*
  10. Utilizing Zebrafish to Identify Anti-(Lymph)Angiogenic Compounds for Cancer Treatment: Promise and Future Challenges
    Okuda KS, Lee HM, Velaithan V, Ng MF, Patel V
    Microcirculation, 2016 08;23(6):389-405.
    PMID: 27177346 DOI: 10.1111/micc.12289
    Cancer metastasis which predominantly occurs through blood and lymphatic vessels, is the leading cause of death in cancer patients. Consequently, several anti-angiogenic agents have been approved as therapeutic agents for human cancers such as metastatic renal cell carcinoma. Also, anti-lymphangiogenic drugs such as monoclonal antibodies VGX-100 and IMC-3C5 have undergone phase I clinical trials for advanced and metastatic solid tumors. Although anti-tumor-associated angiogenesis has proven to be a promising therapeutic strategy for human cancers, this approach is fraught with toxicities and development of drug resistance. This emphasizes the need for alternative anti-(lymph)angiogenic drugs. The use of zebrafish has become accepted as an established model for high-throughput screening, vascular biology, and cancer research. Importantly, various zebrafish transgenic lines have now been generated that can readily discriminate different vascular compartments. This now enables detailed in vivo studies that are relevant to both human physiological and tumor (lymph)angiogenesis to be conducted in zebrafish. This review highlights recent advancements in the zebrafish anti-vascular screening platform and showcases promising new anti-(lymph)angiogenic compounds that have been derived from this model. In addition, this review discusses the promises and challenges of the zebrafish model in the context of anti-(lymph)angiogenic compound discovery for cancer treatment.
    Matched MeSH terms: Drug Discovery/methods
  11. Gut bacteria of animals/pests living in polluted environments are a potential source of antibacterials
    Akbar N, Siddiqui R, Sagathevan KA, Khan NA
    Appl Microbiol Biotechnol, 2019 May;103(10):3955-3964.
    PMID: 30941460 DOI: 10.1007/s00253-019-09783-2
    The morbidity and mortality associated with bacterial infections have remained significant despite chemotherapeutic advances. With the emergence of drug-resistant bacterial strains, the situation has become a serious threat to the public health. Thus, there is an urgent need to identify novel antibacterials. The majority of antibiotics available in the market are produced by bacteria isolated from soil. However, the low-hanging fruit has been picked; hence, there is a need to mine bacteria from unusual sources. With this in mind, it is important to note that animals and pests such as cockroaches, snake, crocodiles, and water monitor lizard come across pathogenic bacteria regularly, yet flourish in contaminated environments. These species must have developed methods to defend themselves to counter pathogens. Although the immune system is known to possess antiinfective properties, gut bacteria of animals/pests may also offer a potential source of novel antibacterial agents, and it is the subject of this study. This paper discusses our current knowledge of bacteria isolated from land and marine animals with antibacterial properties and to propose untapped sources for the isolation of bacteria to mine potentially novel antibiotic molecules.
    Matched MeSH terms: Drug Discovery/methods
  12. Could bacteriophages isolated from the sewage be the solution to methicillin-resistant Staphylococcus aureus?
    Tan CS, Aqiludeen NA, Tan R, Gowbei A, Mijen AB, Santhana Raj L, et al.
    Med J Malaysia, 2020 03;75(2):110-116.
    PMID: 32281590
    INTRODUCTIONS: The emergence of multidrug-resistant bacteria such as Methicillin-Resistant Staphylococcus aureus (MRSA) complicates the treatment of the simplest infection. Although glycopeptides such as vancomycin still proves to be effective in treating MRSA infections, the emergence of vancomycin-resistant strains limits the long term use of this antibiotic. Bacteriophages are ubiquitous bacterial viruses which is capable of infecting and killing bacteria including its antibiotic-resistant strains. Bactericidal bacteriophages use mechanisms that is distinct from antibiotics and is not affected by the antibioticresistant phenotypes.

    OBJECTIVES: The study was undertaken to evaluate the possibility to isolate bacteriolytic bacteriophages against S.aureus from raw sewage water and examine their efficacy as antimicrobial agents in vitro.

    METHODS: Bacteriophages were isolated from the raw sewage using the agar overlay method. Isolated bacteriophages were plaque purified to obtain homogenous bacteriophage isolates. The host range of the bacteriophages was determined using the spot test assay against the 25 MRSA and 36 MSSA isolates obtained from the Sarawak General Hospital. Staphylococcus saprophyticus, Staphylococcus sciuri and Staphylococcus xylosus were included as non-SA controls. The identity of the bacteriophages was identified via Transmission Electron Microscopy and genomic size analysis. Their stability at different pH and temperature were elucidated.

    RESULTS: A total of 10 lytic bacteriophages infecting S.aureus were isolated and two of them namely ΦNUSA-1 and ΦNUSA-10 from the family of Myoviridae and Siphoviridae respectively exhibited exceptionally broad host range against >80% of MRSA and MSSA tested. Both bacteriophages were specific to S.aureus and stable at both physiologic pH and temperature.

    CONCLUSION: This study demonstrated the abundance of S.aureus specific bacteriophages in raw sewage. Their high virulence against both MSSA and MRSA is an excellent antimicrobial characteristic which can be exploited for bacteriophage therapy against MRSA.

    Matched MeSH terms: Drug Discovery/methods
  13. Quantum probability ranking principle for ligand-based virtual screening
    Al-Dabbagh MM, Salim N, Himmat M, Ahmed A, Saeed F
    J Comput Aided Mol Des, 2017 Apr;31(4):365-378.
    PMID: 28220440 DOI: 10.1007/s10822-016-0003-4
    Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
    Matched MeSH terms: Drug Discovery/methods*
  14. Emerging frontiers of deep eutectic solvents in drug discovery and drug delivery systems
    Zainal-Abidin MH, Hayyan M, Ngoh GC, Wong WF, Looi CY
    J Control Release, 2019 12 28;316:168-195.
    PMID: 31669211 DOI: 10.1016/j.jconrel.2019.09.019
    The applications of eutectic systems, including deep eutectic solvents (DESs), in diverse sectors have drawn significant interest from researchers, academicians, engineers, medical scientists, and pharmacists. Eutecticity increases drug dissolution, improves drug penetration, and acts as a synthesis route for drug carriers. To date, DESs have been extensively explored as potential drug delivery systems on account of their unique properties such as tunability and chemical and thermal stability. This review discusses two major topics: first, the application of eutectic mixtures (before and after the introduction of DES) in the field of drug delivery systems, and second, the most promising examples of DES pharmaceutical activity. It also considers future prospects in the medical and biotechnological fields. In addition to the application of DESs in drug delivery systems, they show greatly promising pharmaceutical activities, including anti-fungal, anti-bacterial, anti-viral, and anti-cancer activities. Eutecticity is a valid strategy for overcoming many obstacles inherently associated with either introducing new drugs or enhancing drug delivery systems.
    Matched MeSH terms: Drug Discovery/methods
  15. Current Technology in the Discovery and Development of Novel Antibacterials
    Chung PY
    Curr Drug Targets, 2018;19(7):832-840.
    PMID: 28891454 DOI: 10.2174/1389450118666170911114604
    BACKGROUND: Bacterial resistance to antibiotics is one of the most serious challenge to global public health. The introduction of new antibiotics in clinical settings, i.e. agents that belong to a new class of antibacterials, act on new targets or has a novel mechanisms of action, may not be sufficient to cope with the emergence of multidrug-resistant pathogens such as Staphylococcus aureus, Streptococcus pneumoniae, Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii and Escherichia coli, which are increasingly prevalent in healthcare settings in Europe, the USA and Asia. Hence, coordinated efforts in minimizing the risk of spread of resistant bacteria and renewing research efforts in the search for novel antibacterial agents are urgently needed to manage this global crisis.

    OBJECTIVE: This review highlights the challenges and potential in using current technologies in the discovery and development of novel antibacterial agents to keep up with the constantly evolving resistance in bacteria.

    CONCLUSION: With the explosion of bacterial genomic data and rapid development of new sequencing technologies, the understanding of bacterial pathogenesis and identification of novel antibiotic targets have significantly improved.

    Matched MeSH terms: Drug Discovery/methods
  16. Fulltext In vitro and in silico Approaches to Study Cytochrome P450-Mediated Interactions
    Tan BH, Pan Y, Dong AN, Ong CE
    J Pharm Pharm Sci, 2017;20(1):319-328.
    PMID: 29145931 DOI: 10.18433/J3434R
    In vitro and in silico models of drug metabolism are utilized regularly in the drug research and development as tools for assessing pharmacokinetic variability and drug-drug interaction risk. The use of in vitro and in silico predictive approaches offers advantages including guiding rational design of clinical drug-drug interaction studies, minimization of human risk in the clinical trials, as well as cost and time savings due to lesser attrition during compound development process. This article gives a review of some of the current in vitro and in silico methods used to characterize cytochrome P450(CYP)-mediated drug metabolism for estimating pharmacokinetic variability and the magnitude of drug-drug interactions. Examples demonstrating the predictive applicability of specific in vitro and in silico approaches are described. Commonly encountered confounding factors and sources of bias and error in these approaches are presented. With the advent of technological advancement in high throughput screening and computer power, the in vitro and in silico methods are becoming more efficient and reliable and will continue to contribute to the process of drug discovery, development and ultimately safer and more effective pharmacotherapy. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
    Matched MeSH terms: Drug Discovery/methods*
  17. Fulltext Combating Acanthamoeba spp. cysts: what are the options?
    Anwar A, Khan NA, Siddiqui R
    Parasit Vectors, 2018 01 09;11(1):26.
    PMID: 29316961 DOI: 10.1186/s13071-017-2572-z
    Acanthamoeba spp. are protist pathogens and causative agents of serious infections including keratitis and granulomatous amoebic encephalitis. Its ability to convert into dormant and highly resistant cysts form limits effectiveness of available therapeutic agents and presents a pivotal challenge for drug development. During the cyst stage, Acanthamoeba is protected by the presence of hardy cyst walls, comprised primarily of carbohydrates and cyst-specific proteins, hence synthesis inhibition and/or degradation of cyst walls is of major interest. This review focuses on targeting of Acanthamoeba cysts by identifying viable therapeutic targets.
    Matched MeSH terms: Drug Discovery/methods*
  18. Fulltext Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design
    Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY
    Pathog Glob Health, 2018 05;112(3):123-131.
    PMID: 29528265 DOI: 10.1080/20477724.2018.1446773
    Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.
    Matched MeSH terms: Drug Discovery/methods*
  19. Fulltext Nano based drug delivery systems: recent developments and future prospects
    Patra JK, Das G, Fraceto LF, Campos EVR, Rodriguez-Torres MDP, Acosta-Torres LS, et al.
    J Nanobiotechnology, 2018 Sep 19;16(1):71.
    PMID: 30231877 DOI: 10.1186/s12951-018-0392-8
    Nanomedicine and nano delivery systems are a relatively new but rapidly developing science where materials in the nanoscale range are employed to serve as means of diagnostic tools or to deliver therapeutic agents to specific targeted sites in a controlled manner. Nanotechnology offers multiple benefits in treating chronic human diseases by site-specific, and target-oriented delivery of precise medicines. Recently, there are a number of outstanding applications of the nanomedicine (chemotherapeutic agents, biological agents, immunotherapeutic agents etc.) in the treatment of various diseases. The current review, presents an updated summary of recent advances in the field of nanomedicines and nano based drug delivery systems through comprehensive scrutiny of the discovery and application of nanomaterials in improving both the efficacy of novel and old drugs (e.g., natural products) and selective diagnosis through disease marker molecules. The opportunities and challenges of nanomedicines in drug delivery from synthetic/natural sources to their clinical applications are also discussed. In addition, we have included information regarding the trends and perspectives in nanomedicine area.
    Matched MeSH terms: Drug Discovery/methods
  20. Anti- COVID-19 drug discovery by flavonoid derivatives: an extensive computational drug design approach
    Banerjee S, Mukherjee S, Mohsin Kazi, Sen KK, Das A, Hasan R, et al.
    Cell Mol Biol (Noisy-le-grand), 2024 Sep 08;70(8):39-49.
    PMID: 39262264 DOI: 10.14715/cmb/2024.70.8.5
    The present study deals with the in-silico analyses of several flavonoid derivatives to explore COVID-19 through pharmacophore modelling, molecular docking, molecular dynamics, drug-likeness, and ADME properties. The initial literature study revealed that many flavonoids, including luteolin, quercetin, kaempferol, and baicalin may be useful against SARS β-coronaviruses, prompting the selection of their potential derivatives to investigate their abilities as inhibitors of COVID-19. The findings were streamlined using in silico molecular docking, which revealed promising energy-binding interactions between all flavonoid derivatives and the targeted protein. Notably, compounds 8, 9, 13, and 15 demonstrated higher potency against the coronavirus Mpro protein (PDB ID 6M2N). Compound 8 has a -7.2 Kcal/mol affinity for the protein and binds to it by hydrogen bonding with Gln192 and π-sulfur bonding with Met-165. Compound 9 exhibited a significant interaction with the main protease, demonstrating an affinity of -7.9 kcal/mol. Gln-192, Glu-189, Pro-168, and His-41 were the principle amino acid residues involved in this interaction. The docking score for compound 13 is -7.5 Kcal/mol, and it binds to the protease enzyme by making interactions with Leu-41, π-sigma, and Gln-189. These interactions include hydrogen bonding and π-sulfur. The major protease and compound 15 were found to bind with a favourable affinity of -6.8 Kcal/mol. This finding was further validated through molecular dynamic simulation for 1ns, analysing parameters such as RMSD, RMSF, and RoG profiles. The RoG values for all four of the compounds varied significantly (35.2-36.4). The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADME and drug-likeness properties, fulfilling all the necessary criteria. The findings of the study may support further efforts for the discovery and development of safe drugs to treat COVID-19.
    Matched MeSH terms: Drug Discovery/methods
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