Zeitschriftenartikel: „Chemo-Informatics“

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Vandewiele, Nick M., Kevin M. Van Geem, Marie-Françoise Reyniers und Guy B. Marin. „Genesys: Kinetic model construction using chemo-informatics“. Chemical Engineering Journal 207-208 (Oktober 2012): 526–38. http://dx.doi.org/10.1016/j.cej.2012.07.014.

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Hawash, Mohammed, Nidal Jaradat, Murad Abualhasan, Johnny Amer, Serkan Levent, Shahd Issa, Sameeha Ibrahim, Aseel Ayaseh, Tahrir Shtayeh und Ahmed Mousa. „Synthesis, chemo-informatics, and anticancer evaluation of fluorophenyl-isoxazole derivatives“. Open Chemistry 19, Nr. 1 (01.01.2021): 855–63. http://dx.doi.org/10.1515/chem-2021-0078.

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Abstract The current study aimed to design and synthesize a novel series of fluorophenyl-isoxazole-carboxamide derivatives and evaluate their antiproliferative activities. Anticancer activities of the novel compounds were evaluated by MTS assay against four cancer cell lines, including liver (Hep3B, HepG2), cervical (HeLa), and breast (MCF-7), and α-fetoprotein tumor marker, cell cycle analysis, and annexin V tests. Chemo-informatics analysis showed that all synthesized derivatives 2a–2f obeyed Lipinski’s rule. Compound 2f was the most potent compound against Hep3B and Hep-G2 cancer cell lines with IC50 values of 5.76 and 34.64 µg/mL, respectively. Moreover, compounds 2a–2c and 2e showed potent inhibitory activity against Hep3B with an IC50 value range of 7.66–11.60 µg/mL. Hep3B secretions of α-fetoprotein (α-FP) results showed that compound 2f reduced the secretion of Hep3B to 168.33 ng/mL and compound 2d reduced the secretion to value approximately 598.33 ng/mL, in comparison with untreated cells’ value of 1116.67 ng/mL. Furthermore, cell cycle analysis showed that the 2f compound induced arrest in the G2-M phase in 6.73% of the total cells and that was lower than the activity of the positive control doxorubicin (7.4%). Moreover, 2b and 2f compounds reduced the necrosis rate of Hep3B to 4-folds and shifted the cells to apoptosis.

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Kassab, Mohammed. „DEVELOPMENT OF NOVEL ANTIMICROBIAL TETRACYCLINE ANALOG B (IODOCYCLINE) BY CHEMO-INFORMATICS.“ Ain Shams Medical Journal 74, Nr. 1 (01.03.2023): 303–15. http://dx.doi.org/10.21608/asmj.2022.159722.1042.

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Kassab, Mohammed. „DEVELOPMENT OF NOVEL ANTIMICROBIAL TETRACYCLINE ANALOG B (IODOCYCLINE) BY CHEMO-INFORMATICS.“ Ain Shams Medical Journal 73, Nr. 4 (01.12.2022): 969–81. http://dx.doi.org/10.21608/asmj.2022.285476.

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Bajorath, Jürgen. „Rational drug discovery revisited: interfacing experimental programs with bio- and chemo-informatics“. Drug Discovery Today 6, Nr. 19 (Oktober 2001): 989–95. http://dx.doi.org/10.1016/s1359-6446(01)01961-4.

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Stahura, Florence L., und Jürgen Bajorath. „Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities“. Drug Discovery Today 7, Nr. 11 (Mai 2002): S41—S47. http://dx.doi.org/10.1016/s1359-6446(02)02271-7.

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Tsochatzis, Emmanouil, Joao Alberto Lopes, Fabiano Reniero, Margaret Holland, Jenny Åberg und Claude Guillou. „Identification of 1-Butyl-Lysergic Acid Diethylamide (1B-LSD) in Seized Blotter Paper Using an Integrated Workflow of Analytical Techniques and Chemo-Informatics“. Molecules 25, Nr. 3 (07.02.2020): 712. http://dx.doi.org/10.3390/molecules25030712.

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The rapid dispersion of new psychoactive substances (NPS) presents challenges to customs services and analytical laboratories, which are involved in their detection and characterization. When the seized material is limited in quantity or of a complex nature, or when the target substance is present in very small amounts, the need to use advanced analytical techniques, efficient workflows and chemo-informatics tools is essential for the complete identification and elucidation of these substances. The current work describes the application of such a workflow in the analysis of a single blotter paper, seized by Swedish customs, that led to the identification of a lysergic acid diethylamide (LSD) derivative, 1-butyl-lysergic acid diethylamide (1B-LSD). Such blotter paper generally contains an amount in the range of 30–100 ug. This substance, which is closely related to 1-propionyl-lysergic acid diethylamide (1P-LSD), seems to have only recently reached the drug street market. Its identification was made possible by comprehensively combining gas chromatography with mass spectrometry detection (GC–MS), liquid chromatography coupled with high-resolution tandem MS (LC–HR-MS/MS), Orbitrap-MS and both 1D and 2D nuclear-magnetic-resonance (NMR) spectroscopy. All the obtained data have been managed, assessed, processed and evaluated using a chemo-informatics platform to produce the effective chemical and structural identification of 1B-LSD in the seized material.

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Scherbinina, Sofya I., und Philip V. Toukach. „Three-Dimensional Structures of Carbohydrates and Where to Find Them“. International Journal of Molecular Sciences 21, Nr. 20 (18.10.2020): 7702. http://dx.doi.org/10.3390/ijms21207702.

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Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

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Lafata, Jennifer Elston, Stephen Harris, Megan Fasold, Audrey Holdren und Hanna Kelly Sanoff. „Building a population management informatics infrastructure for oncology care.“ Journal of Clinical Oncology 37, Nr. 27_suppl (20.09.2019): 315. http://dx.doi.org/10.1200/jco.2019.37.27_suppl.315.

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315 Background: While most primary care practices have informatics infrastructures to support population management, such infrastructures are not commonplace in oncology. Yet, value-based care requires that oncology practices be able to identify patients in real time, use risk stratification to target care efficiently, and monitor care quality to identify improvement opportunities. We describe an oncology informatics infrastructure development initiative in a large academic medical center. Methods: We convened a quality improvement team of administrators, analysts, clinicians, health services researchers and performance improvement staff. The team was sponsored by a senior leadership committee convened for a strategic planning initiative. We used PDSA cycles to develop and test ways to leverage data from an electronic health record (EHR) and billing system for oncology patient identification, risk stratification, and routine quality monitoring. We used clinician engagement, medical record review, and tumor registry comparisons to validate query strategies. Results: After considering different query strategies, we opted to identify patients via a new cancer treatment episode (as defined by a cancer diagnosis combined with evidence of pharmaceutical, radiation, and/or surgical treatment for cancer with no evidence of such treatment in the prior six months). This was done using diagnostic and procedural codes for chemo/immunotherapy and radiation treatment, and pathology reports and procedural codes for surgery. Using this approach, we identified over 7800 cancer treatment episodes within the health system in 2018. These episodes corresponded to 4178 chemo/immunotherapy, 1437 radiation, and 3440 surgical treatments. Quality monitoring has identified opportunities to enhance data capture, harmonize documentation processes across practitioners and practices, and initiate quality improvement efforts. Conclusions: Using data from the EHR and billing systems we are able to identify oncology patients as they initiate a cancer treatment episode. In so doing, we are able to track the quality of care delivered to oncology patients as they move across the care continuum from treatment to survivorship.

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Pedretti, Alessandro, Luigi Villa und Giulio Vistoli. „VEGA – An open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming“. Journal of Computer-Aided Molecular Design 18, Nr. 3 (März 2004): 167–73. http://dx.doi.org/10.1023/b:jcam.0000035186.90683.f2.

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Hathout, Rania M., Sherweit H. El-Ahmady und AbdelKader A. Metwally. „Curcumin or bisdemethoxycurcumin for nose-to-brain treatment of Alzheimer disease? A bio/chemo-informatics case study“. Natural Product Research 32, Nr. 24 (12.10.2017): 2873–81. http://dx.doi.org/10.1080/14786419.2017.1385017.

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Nepolraj, Amaladoss, Vasyl I. Shupeniuk, Manisekar Sathiyaseelan und Nagamuthu Prakash. „Synthesis of new 3‐(hydroxymethyl)‐2‐phenyl‐2,3 dihydroquinolinone and in‐silico evaluation of COVID‐19 main protease inhibitor“. Vietnam Journal of Chemistry 59, Nr. 4 (August 2021): 511–21. http://dx.doi.org/10.1002/vjch.202000221.

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AbstractAn exclusive approach towards the synthesis of novel 3‐(hydroxymethyl)‐2‐phenyl‐2,3 dihydroquinolin‐4(1H)‐one and it's in‐silico evaluation as inhibitor of COVID‐19 main protease. The one‐pot synthesis of an established procedure Claisen ester condensation reaction was sodium hydride mediated with intramolecular cyclization with solvent free conditions. The structures of the synthesized compound were confirmed by IR, 1H,13C NMR, and EI‐MS spectral studies. Chemo‐informatics study showed that the compound obeyed the Lipinski's rule, PASS, Swiss ADME. Computational docking analysis was performed using PyRx, AutoDock Vina option based on scoring functions. In‐silico molecular docking study results demonstrated Greater binding energy and affinity to the active pocket the N3 binding site of the Coronavirus primary protease.

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Kuznetsov, Julie Lawrence, Kathryn Bailey, Pacharintra K. Bombach, Stacey Carmichael, Xuemei Chen, Maja Lichstein Herberg, Terri Owen, Jorge Wilson und Douglas W. Blayney. „Real-time extraction of breast cancer treatment process and outcome measures from an EPIC electronic health record (EHR).“ Journal of Clinical Oncology 31, Nr. 31_suppl (01.11.2013): 1. http://dx.doi.org/10.1200/jco.2013.31.31_suppl.1.

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1 Background: In 2011, Stanford Cancer Institute (SCI) clinical leadership began process improvements to enhance patient satisfaction and quality of care. To measure impacts, unnecessary care variation, and outcomes, we recently developed an informatics infrastructure utilizing data from our EHR (epic modules: BEACON, Cadence, OpTime). We report successful initial efforts including real-time cohort identification (comparing with “gold standard” registry data), and improvements in time to treatment. Methods: A cohort of 1,692 patients was defined by 3 criteria: newly evaluated at SCI, AND received treatment [surgery, chemotherapy (chemo), or radiation (XRT)] at SCI, AND had a breast cancer related ICD-9 code. We analyzed data by fiscal year (FY), starting September, 2010 to FY13 year-to-date. “Time to treatment (Rx)” was measured as the interval between first EHR time stamp for SCI patient contact and Rx date. We used discrete data from the BEACON staging module to create sub-cohorts by stage, and used BEACON protocols to identify chemo regimens. Our cohorts were compared with SCI tumor registry data, and presented in a dynamic Qlikview dashboard. Results: 98% of the EHR-defined cohort matched a similarly defined tumor registry cohort. We detected the effect of process improvements (including scheduling a visit on first contact, coordinating among surgical specialties, outside records available pre-visit, etc.), which resulted in an accelerated time to Rx (Table). Discrete BEACON stage is available for 7% of these 1692 patients. The methodology is scalable and has been successfully replicated in GI and thoracic cohorts. Conclusions: Our work demonstrates utility of EHR data to track process improvements. Uniform use of the BEACON staging module will facilitate variation analysis across cancer types and stages, and allow us to explore variation within modalities (chemo, surgery, XRT). We will analyze associated costs and intermediate clinical outcomes (e.g., unplanned emergency visits and hospitalizations) to inform care pathway choice. [Table: see text]

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Singh, Sarvendra Vikram. „Bioinformatics – Supporting modern life science research, applications, and challenges“. Brazilian Journal of Development 10, Nr. 2 (07.02.2024): e67060. http://dx.doi.org/10.34117/bjdv10n2-011.

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Bioinformatics is an interdisciplinary field that develops methods, software tools for understanding biological data and aims to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. As we are moving towards the era of cutting-edge technologies there will be a lot of data to store, process and analyze. It offers analysis software for data studies and comparisons and provides tools for modeling, visualizing, exploring and interpreting data. It includes analysis, structural and functional characterization of biomolecules leading to the development of Genomics, Proteomics, Transcriptomics, and Metabolomics, etc. Drug discovery and development tools, supported by recent advancements in machine learning and cloud computing should shorten the time to find and produce an efficient drug compound with fewer side effects and more results emerge as a branch called Chemo-informatics. Personalized medicine where bioinformatics can help a lot to make drug molecules based on the genetic makeup of individuals for better outcomes is a prime area of research and need of the society at present. The major futures challenge of the scientific community is to create an in-vitro model of whole-cell or organism and further simulating a whole cell or an organism by applying in-silico approaches. To achieve that, reliable tools that utilize those technologies need to be developed and tested. Bioinformatics reduces the search space/size of the problem by thousand times. The main goal is to convert a multitude of complex data into useful information and knowledge. As a consequence of understanding such data, one can basically engineer longer life for society.

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Aziz Unnisa, Sirajuddin Anwar, Chettupalli Ananda K, Nasrin E. Khalifa, Weam M. A. Khojali, Mhdia E. Osman, Kareem M. Younes, Amr S. Abouzied, Suresh B. Jandrajupalli und Swarnalatha Chandolu. „The discovery of the natural compound Boeravinone-C as a potential antiobesity drug candidate targeting pancreatic lipase using chemo-informatics-based approaches“. Cellular and Molecular Biology 69, Nr. 7 (31.07.2023): 57–65. http://dx.doi.org/10.14715/cmb/2023.69.7.10.

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Sato, Ryoma, Makoto Yamada und Hisashi Kashima. „Constant Time Graph Neural Networks“. ACM Transactions on Knowledge Discovery from Data 16, Nr. 5 (31.10.2022): 1–31. http://dx.doi.org/10.1145/3502733.

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The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In particular, the existing methods for accelerating GNNs either are not theoretically guaranteed in terms of the approximation error or incurred at least a linear time computation cost. In this study, we reveal the query complexity of the uniform node sampling scheme for Message Passing Neural Networks, including GraphSAGE, graph attention networks (GATs), and graph convolutional networks (GCNs). Surprisingly, our analysis reveals that the complexity of the node sampling method is completely independent of the number of the nodes, edges, and neighbors of the input and depends only on the error tolerance and confidence probability while providing a theoretical guarantee for the approximation error. To the best of our knowledge, this is the first article to provide a theoretical guarantee of approximation for GNNs within constant time. Through experiments with synthetic and real-world datasets, we investigated the speed and precision of the node sampling scheme and validated our theoretical results.

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Al-Jumaili, Mohammed Hadi Ali, und Muqdad Khairi Yahya Al hdeethi. „Study of Selected Flavonoid Structures and Their Potential Activity as Breast Anticancer Agents“. Cancer Informatics 20 (Januar 2021): 117693512110551. http://dx.doi.org/10.1177/11769351211055160.

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Flavonoids contain pharmacological effects that help to protect cells from damage. However, the anticancer activity of flavonoids is related to their modulation of signal transduction pathways within cancer cells. Natural substances such as flavonoids have immune-stimulating anti-tumor effect that could lower breast cancer risk. However, various diseases included Alzheimer’s and cancer disease are associated with flavonoids intake due to their ability as antioxidant agent to alter essential cellular enzyme’s function. Therefore, through interaction between flavonoids and Cytochrome P450 (CYP) family enzymes led to make them chemopreventive agents for breast cancer. In this analysis, the chemo-informatics properties of 5 selective flavonoid derivatives and their efficiency as anti-breast cancer drugs were evaluated. Flavonoid ligands were docked with the predicted protein, which is human placental aromatase complexes with exemestane, a breast cancer drug (3S7S). Based on various docking energies, the molecular characteristics and bioactivity score of the following components, C15H12O6 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-2,3-dihydro-4H-chromen-4-one and C15H12O5 5,8-dihydroxy-2-(4-hydroxyphenyl)-2,3-dihydro-4H-chromen-4-one showed greatest molecular properties and bioactivity docking scores of −8.633117 and −8.633117 kcal/mol respectively. Therefore, both compounds could be considered antitumor agent.

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Zhu, Jianshen, Chenxi Wang, Aleksandar Shurbevski, Hiroshi Nagamochi und Tatsuya Akutsu. „A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming“. Algorithms 13, Nr. 5 (18.05.2020): 124. http://dx.doi.org/10.3390/a13050124.

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Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

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Singh, Shripriya. „Decoding the sense of smell: understanding the structural organization within the brain“. Biotechnology Kiosk 2, Nr. 7 (22.07.2020): 4–9. http://dx.doi.org/10.37756/bk.20.2.7.1.

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The olfactory sense is a potent sensory tool which helps us perceive our environment much better. However, smells despite being similar have different impacts on individuals. What makes one odor categorically different from the other and why do people have a unique and personalized experience with smell is an answer that needs to be addressed. In the present article we have discussed the research in which neuroscientists have decoded and described how the relationships between different odors are encoded in the brain. How the brain transforms information about odor chemistry into the perception of smell is a major highlight of this publication. Carefully selected odors with defined molecular structures were delivered in mice and the neural activity was analyzed. It was observed that neuronal representations of smell in the cortex reflected chemical similarities between odors, thus allowing the brain to categorize scents. The study has employed chemo informatics and multiphoton imaging in the mouse to demonstrate both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odor relationships through correlated patterns of activity. The research has given us cues in the direction of how the brain translates odor chemistry into neurochemistry and eventually perception of smell.

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Abbasi, Muhammad, Hussain Raza, Aziz Rehman, Sahahat Siddiqui, Majid Nazir, Ayesha Mumtaz, Syed Shah, Sung-Yum Seo und Mubashir Hassan. „Synthesis, Antioxidant and In-Silico Studies of Potent Urease Inhibitors: N-(4-{[(4-Methoxyphenethyl)-(substituted)amino]sulfonyl}phenyl)acetamides“. Drug Research 69, Nr. 02 (07.08.2018): 111–20. http://dx.doi.org/10.1055/a-0654-5074.

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AbstractIn this study, a new series of sulfonamides derivatives was synthesized and their inhibitory effects on DPPH and jack bean urease were evaluated. The in silico studies were also applied to ascertain the interactions of these molecules with active site of the enzyme. Synthesis was initiated by the nucleophilic substitution reaction of 2-(4-methoxyphenyl)-1-ethanamine (1) with 4-(acetylamino)benzenesulfonyl chloride (2) in aqueous sodium carbonate at pH 9. Precipitates collected were washed and dried to obtain the parent molecule, N-(4-{[(4-methoxyphenethyl)amino]sulfonyl}phenyl)acetamide (3). Then, this parent was reacted with different alkyl/aralkyl halides, (4a-m), using dimethylformamide (DMF) as solvent and LiH as an activator to produce a series of new N-(4-{[(4-methoxyphenethyl)-(substituted)amino]sulfonyl}phenyl)acetamides (5a-m). All the synthesized compounds were characterized by IR, EI-MS, 1H-NMR, 13C-NMR and CHN analysis data. All of the synthesized compounds showed higher urease inhibitory activity than the standard thiourea. The compound 5 f exhibited very excellent enzyme inhibitory activity with IC50 value of 0.0171±0.0070 µM relative to standard thiourea having IC50 value of 4.7455±0.0546 µM. Molecular docking studies suggested that ligands have good binding energy values and bind within the active region of taget protein. Chemo-informatics properties were evaluated by computational approaches and it was found that synthesized compounds mostly obeyed the Lipinski’ rule.

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Shams ul Hassan, Syed, Syed Qamar Abbas, Mubashir Hassan und Hui-Zi Jin. „Computational Exploration of Anti-Cancer Potential of GUAIANE Dimers from Xylopia vielana by Targeting B-Raf Kinase Using Chemo-Informatics, Molecular Docking, and MD Simulation Studies“. Anti-Cancer Agents in Medicinal Chemistry 22, Nr. 4 (28.02.2022): 731–46. http://dx.doi.org/10.2174/1871520621666211013115500.

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Background: Natural products from herbs are abundant and display powerful anti-cancer activities. Objectives: In the current study, B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma, was tested against two guaiane-type sesquiterpene dimers, xylopin E-F, obtained from Xylopia vielana. Methods: In this work, a systematic in silico study using ADMET analysis, bioactivity score forecasts, and molecular docking along with its simulations was conducted to understand compounds’ pharmacological properties. Results: During ADMET predictions of both the compounds, xylopin E-F displayed a safer profile in hepatotoxicity and cytochrome inhibition, and only xylopin F was shown to be non-cardiotoxic compared to the FDA-approved drug vemurafenib. Both the compounds were proceeded to molecular docking experiments using Autodock docking software, and both the compounds, xylopin E-F, displayed higher binding potential with -11.5Kcal/mol energy compared to control vemurafenib (-10.2 Kcal/mol). All the compounds were further evaluated for their MD simulations, and their molecular interactions with the B-Raf kinase complex displayed precise interactions with the active gorge of the enzyme by hydrogen bonding. Conclusions: Overall, xylopin F had a better profile relative to xylopin E and vemurafenib, and these findings indicated that this bio-molecule could be used as an anti-melanoma agent and as a possible anti-cancer drug in the future. Therefore, this is a systematically optimized in silico approach for creating an anti-cancer pathway for guaiane dimers against the backdrop of its potential for future drug development.

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Ahmed, Attique, Pervaiz Ali Channar, Aamer Saeed, Markus Kalesse, Mehar Ali Kazi, Fayaz Ali Larik, Qamar Abbas, Mubashir Hassan, Hussain Raza und Sung-Yum Seo. „Synthesis of sulfonamide, amide and amine hybrid pharmacophore, an entry of new class of carbonic anhydrase II inhibitors and evaluation of chemo-informatics and binding analysis“. Bioorganic Chemistry 86 (Mai 2019): 624–30. http://dx.doi.org/10.1016/j.bioorg.2019.01.060.

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Chikhale, Hemant U. „PERSPECTIVE INSIGHT AND APPLICATION OF IN-SILICO TOOL AS VIRTUAL SCREENING METHOD FOR LEAD DESIGNING AND DEVELOPMENT“. Journal of Medical pharmaceutical and allied sciences 11, Nr. 6 (15.11.2021): 16–24. http://dx.doi.org/10.22270/jmpas.v10i6.1908.

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Humans are now in a bioinformatics and chemo informatics century, where we can foresee data across domains like as healthcare, the environmental, technology, and public health. The use of information sharing in silico methodologies has impacted sickness administration by predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) patterns of synthetic compounds and efficient and environmentally succeeding pharmaceuticals upfront. The purpose of lead discovery and design is to create the appearance of novel drug candidates that can attach to a specific illness cause. The lead investigative process starts with the recognition of the lead structure, which is followed by the synthesis of its analogs and their estimation in order to produce a candidate for lead improvement. The finding of the proper lead exact is the fundamental and primary worked in the traditional lead discovery progression, and the use of computer (in silico) approaches is widely used in lead innovation. A medicinal chemist's passion for building lead structure is piqued by biomolecules, which are often made up of DNA, RNA, and proteins (such as enzymes, receptors, transporters, and ion channels). The underlying principle of such nuts and bolts is noteworthy to be acquainted with their pharmacological implication to the disease under examination. The motive of this review piece of writing is to emphasize several of the in silico methods that are used in lead discovery and to express the applications of these computational methods.

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Dufoo-Hurtado, Elisa, Ivan Luzardo, Abraham Wall-Medrano, Guadalupe Loarca-Pina und Rocio Campos-Vega. „Bioaccessibility and Synthesis of Chronobiotics During In Vitro Gastrointestinal Digestion of Pistachio (Pistacia vera L.) to Mitigate Diseases Linked to Chronodisruption“. Current Developments in Nutrition 5, Supplement_2 (Juni 2021): 581. http://dx.doi.org/10.1093/cdn/nzab044_012.

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Abstract Objectives This research aimed to evaluate the in vitro chronobiotic potential of Phyto-melatonin (PTM) during gastrointestinal digestion, its fermentative behavior (phenolic acids and other compounds), and potential production of chronobiotics (short-chain fatty acids or SCFAs production). Methods The chemical and nutraceutical composition of dry roasted and salted pistachios with seed coat (SC) (PN + SC) and without (PN) was evaluated. Both samples were digested under static in vitro simulated physiological conditions comprising oral, gastric, intestinal, and colonic stages. The PTM bioaccessibility during in vitro gastrointestinal digestion and colonic fermentation simulation was quantified. The identification and quantification of SCFAs and other colonic metabolites were conducted using SPME-GC-MS, followed by an untargeted metabolomic analysis. Results PN + SC had significantly (p < 0.05) lower lipids (−7.9) and protein (−1.1), but higher carbohydrate (+8.4) and total dietary fiber (+4.8) content (g/100g) than PN. PN + SC had highest content of total phenols (+42%), total flavonoids (+54%), and PMT (+21%) (p < 0.05) compared to PN. The bioaccessibility was low for both pistachio samples [Oral: 1.92 and 3.41%, PN + SC and PN; gastric: 0.83 and 1.63%; intestinal [60 min]: 1.79 and 2.55; colonic [6 h]: 0.32 and 0.36%). Chemo-informatics and an in silico analysis of PTM suggest that it was absorbed when chewed by the participants. The highest SCFAs were produced at 12 h during in vitro colonic fermentation for both pistachio samples, where PN + SC displayed the highest (p < 0.05) value (51 mmol/L), followed by PN (25.9 mmol/L). SCFAs, derived from bacterial fermentation of dietary fibers, can act as chronobiotics in peripheral clocks. The SCFAs molar ratio remained almost constant for both pistachio samples: butyric > propionic > acetic. Some metabolites with chronobiotic potential (e.g., indole, benzaldehyde, phenolic acids, and aliphatic/aromatic hydrocarbons) were detected, sample-dependent, through the untargeted metabolomics. Conclusions Pistachio's digestion increases the bioaccessibility of PTM and the biosynthesis of colonic metabolites (SCFAs, among others), all with chronobiotic potential to mitigate diseases linked to chronodisruption. Funding Sources The funding received by CONACyT/FOPES is appreciated.

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Madiraju, Charitha, Shu-ichi Matsuzawa, Robert Ardecky, Ian Pass, Tram Ngo, Yasuko Matsuzawa, Carina Wimer et al. „Discovery and Characterization of Chemical Inhibitors of UBC13.“ Blood 120, Nr. 21 (16.11.2012): 2950. http://dx.doi.org/10.1182/blood.v120.21.2950.2950.

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Abstract Abstract 2950 Poly-ubiquitination of signaling proteins via lysine 63 (K63)-linked chains is recognized as a critical post-translational modification involved in activation of NF-kB and stress kinases in the context of signaling by Tumor Necrosis Factor Receptors (TNFRs), Toll-like receptors (TLRs), and antigen receptors. UBC13 is a K63-specific ubiquitin conjugating enzyme that partners with TNFR-associated factors (TRAFs) to mediate K63-linked ubiquitination. Gene ablation studies have shown UBC13 is required for NF-kB signaling induced by a variety of stimuli in specific types of immune cells, making it a potential target for certain cancers, autoimmune and inflammatory diseases. UBC13 operates together with obligatory cofactors, either UEV1A in the cytosol or MMS2 in the nucleus. The nuclear function of UBC13 is evolutionarily conserved, where it plays a critical role in double strand DNA repair, making UBC13 a potential chemo- and radio-sensitizer target for oncology. To identify chemical inhibitors of UBC13, we developed a HTS assay measuring UBC13-UEV1A enzymatic activity by TR-FRET, screening altogether ∼450,000 diverse compounds. Hit compounds were characterized using a rigorous testing funnel consisting of (a) informatics filtering against a database of > 100 HTS campaigns conducted with the same libraries, to eliminate promiscuous compounds; (b) counter-screens against E1, another cysteine-dependent enzyme (caspase-3), and against an irrelevant target formatted as a TR-FRET assay; and (c) ordering compounds from fresh powders and demonstrating reproducible concentration-dependent inhibition of UBC13. The surviving hits were then analyzed by cell-based assays for suppression of TRAF6 ubiquitination but not Mdm2-mediated ubiquitination of p53, resulting in 14 promising hits that included two chemical series. While suppressing TRAF6 ubiquitination (UBC13-dependent) in cells, these compounds did not interfere with either SUMOylation (UBC9-dependent) or NEDDylation (UBC12-dependent) of cellular proteins. UBC13 inhibitors also suppressed NF-kB activity (measured using stably integrated NF-kB-driven luciferase reporter gene) induced by PKC activators (Carma/Bcl-10/MALT pathway) and DNA damaging agent (Doxorubicin) but not by TNF-a. Investigations of the bioactivity of these UBC13 inhibitory compounds and their analogs will be described for a variety of hematolymphoid malignancies. (Supported by NIH R03-MH085677, NIH U54–005033, and by a fellowship grant from International Myeloma Foundation). Disclosures: No relevant conflicts of interest to declare.

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Koc, Soner, Vojislav Varjacic, Miona Rankovic, Marijeta Slavkovic-Ilic, Aleksandar Danicic, Sean Black, Naomi Ohashi et al. „Abstract 5356: Collaborating to ensure data-driven drug discovery on the Cancer Genomics Cloud: Realizing the possibilities for MoDaC and ATOM“. Cancer Research 83, Nr. 7_Supplement (04.04.2023): 5356. http://dx.doi.org/10.1158/1538-7445.am2023-5356.

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Abstract Introduction. The NCI-funded Cancer Genomics Cloud (CGC) and the NCI Predicative Oncology Model and Data Clearinghouse (MoDaC) advance NCI computing infrastructure and tools that aim to reduce the burden of cancer on patients. The CGC provides a collaborative cloud base computation infrastructure that collocates computation, bioinformatics workflows, and 3+ PB data to researchers. MoDaC provides a publicly available resource generated from the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) Program and the Accelerating Therapeutics for Opportunities in Medicine (ATOM Consortium). The NCI-sponsored MoDaC program aims to add machine learning toolsets to identifying novel treatments for Cancer Patients. We present our progress collaborating with MoDaC to make machine learning models available on the Cancer Genomics Cloud. Methods. Our Bioinformatics teams migrated MoDaC tools on the CGC, defined standards for releasing models, and collected recommendations for decreasing the time required to make MoDaC tools available on the Cancer Genomics Cloud. We mirrored the ATOM Modeling Pipeline (AMPL), a drug discovery platform, on the CGC by making the GIT repository cloud accessible through Jupyter Notebook access, supporting interactive analysis. We translated AMPL and JDACS4C models into CWL. Converting these ML models into CWL supports reproducible execution, scalable deployment, and computational portability. Results. The AMPL drug discovery platform on the CGC includes data ingestion & curation, featurization, model training & tuning, prediction generation, visualization & analysis functionality as Jupyter notebooks and CWL workflows. The release consists of chemo-informatics tools for integrating cancer treatment features in deep-learning graph models. JDACS4C ML Models ported to the CGC include classifiers (tumor and normal-tumor pairs), autoencoders (Gene Expression), drug response predictors (single and combination), and Multitask Convolutional Neural Networks (extract information from cancer pathology reports). Conclusion. We optimized the MoDaC Drug Discovery and Machine Learning tools into cloud-native resources on the CGC, supporting interactive and GUI-driven analysis. The release supports technical and newer users to machine learning, allowing access to a broader user base than those who traditionally have access to ML toolsets. These MoDaC toolsets will support pre-clinical study evaluation, treatment identification, and experimental design. Moreover, existing MoDaC-AMPL tutorials on the CGC support distributed ML-Drug Discovery training. Lastly, collaborating with MoDaC teams identified standardization approaches that can reduce the time and effort to make these tools widely available across the NIH-NCI computational infrastructure. Citation Format: Soner Koc, Vojislav Varjacic, Miona Rankovic, Marijeta Slavkovic-Ilic, Aleksandar Danicic, Sean Black, Naomi Ohashi, Titli Sarkar, Zelia Worman, Jack DiGiovanna, Brandi Davis-Dusenbery, Dennis A. Dean. Collaborating to ensure data-driven drug discovery on the Cancer Genomics Cloud: Realizing the possibilities for MoDaC and ATOM. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5356.

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Zini, Elisa Maria, Giordano Lanzola, Silvana Quaglini, Paolo Bossi, Lisa Licitra und Carlo Resteghini. „A pilot study of a smartphone-based monitoring intervention on head and neck cancer patients undergoing concurrent chemo-radiotherapy“. International Journal of Medical Informatics 129 (September 2019): 404–12. http://dx.doi.org/10.1016/j.ijmedinf.2019.06.004.

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Chen, Peixin, Shengyu Wu, Jia Yu, Xuzhen Tang, Chunlei Dai, Hui Qi, Junjie Zhu et al. „mRNA Network: Solution for Tracking Chemotherapy Insensitivity in Small-Cell Lung Cancer“. Journal of Healthcare Engineering 2021 (28.09.2021): 1–11. http://dx.doi.org/10.1155/2021/2105176.

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Background. Small-cell lung cancer (SCLC) has poor prognosis and is prone to drug resistance. It is necessary to search for possible influencing factors for SCLC chemotherapy insensitivity. Therefore, we proposed an mRNA network to track the chemotherapy insensitivity in SCLC. Methods. Six samples of patients with SCLC were recruited for RNA sequencing. TopHat2 and Cufflinks were used to make differential analysis. Functional analysis was applied as well. Finally, multidimensional validation was applied for verifying the results we obtained by experiment. Results. This study was a trial of drug resistance in 6 SCLC patients after first-line chemotherapy. The top 10 downregulated genes differentially expressed in the chemo-insensitive group were SERPING1, DRD5, PARVG, PRAME, NKX1-1, MCTP2, PID1, PLEKHA4, SPP1, and SLN. Cell-cell signaling by Wnt ( p = 6.98 E − 21 ) was the most significantly enriched GO term in biological process, while systemic lupus erythematosus ( p = 6.97 E − 10 ), alcoholism ( p = 1.01 E − 09 ), and transcriptional misregulation in cancer ( p = 0.00227988 ) were the top three ones of KEGG pathways. In multiple public databases, we also highlighted and verified the vital role of glycolysis/gluconeogenesis pathway and corresponding genes in chemo-insensitivity in SCLC. Conclusion. Our study confirmed some SCLC chemotherapy insensitivity-related genes, biological processes, and pathways, thus constructing the chemotherapy-insensitive network for SCLC.

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Collins, Annette, Clare L. Burns, Elizabeth C. Ward, Tracy Comans, Claire Blake, Lizbeth Kenny, Phil Greenup und Daniel Best. „Home-based telehealth service for swallowing and nutrition management following head and neck cancer treatment“. Journal of Telemedicine and Telecare 23, Nr. 10 (28.10.2017): 866–72. http://dx.doi.org/10.1177/1357633x17733020.

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Introduction Following (chemo)radiotherapy (C/RT) for head and neck cancer (HNC), patients return to hospital for regular outpatient reviews with speech pathology (SP) and nutrition and dietetics (ND) for acute symptom monitoring, nutritional management, and swallowing and communication rehabilitation. The aim of the current study was to determine the feasibility of a home-based telehealth model for delivering SP and ND reviews, to provide patients with more convenient access to these appointments. Methods Service outcomes, costs, and consumer satisfaction were examined across 30 matched participants: 15 supported via the standard model of care (SMOC), and 15 via the home-based telehealth model of care (TMOC). Results All patients were successfully managed via telehealth. The TMOC was more efficient, with a reduced number ( p < 0.003) and duration ( p < 0.01) of appointments required until discharge. Significant patient cost savings ( p = 0.002) were reported for the TMOC due to decreased travel requirements. While staff costs were reduced, additional telehealth equipment levies resulted in a lower but non-significant overall cost difference to the health service when using the TMOC. High satisfaction was reported by all participants attending the TMOC. Discussion The findings support the feasibility of a home-based telehealth model for conducting SP and ND reviews post C/RT for HNC.

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Rahman, Muhammed Masudur, Paul N. Watton, Corey P. Neu und David M. Pierce. „A chemo-mechano-biological modeling framework for cartilage evolving in health, disease, injury, and treatment“. Computer Methods and Programs in Biomedicine 231 (April 2023): 107419. http://dx.doi.org/10.1016/j.cmpb.2023.107419.

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Ebrahimi Zade, Amir, Seyedhamidreza Shahabi Haghighi und M. Soltani. „Deep neural networks for neuro-oncology: Towards patient individualized design of chemo-radiation therapy for Glioblastoma patients“. Journal of Biomedical Informatics 127 (März 2022): 104006. http://dx.doi.org/10.1016/j.jbi.2022.104006.

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Stergiou, Ioanna, Konstantinos Kambas, Stavroula Giannouli, Theodora Katsila, Aglaia Dimitrakopoulou, Veroniki Vidali, Evgenia Synolaki et al. „Autophagy in Myelodysplastic Syndromes: The Role of HIF-1a/REDD1 Molecular Pathway“. Blood 132, Supplement 1 (29.11.2018): 1808. http://dx.doi.org/10.1182/blood-2018-99-112813.

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Abstract Background: Hypoxia is a prominent feature of the BM microenvironment, influencing both normal and malignant hematopoiesis. HIF-1α, which is a key regulator of hypoxia responses by mediating the transition to glycolytic metabolism, serves as a cell cycle checkpoint of HSC quiescence and function. It has been proposed that differential HIF-1α protein expression between hypoxic endosteal and less hypoxic vascular niche finely regulates normal hematopoiesis by promoting both quiescence and survival of HSCs, as well as proliferation and differentiation of HPCs. DNA damage response 1 gene (REDD1) is a direct transcriptional target of HIF-1α linking hypoxia to energy regulation and autophagy. Recent evidence suggests that metabolism and autophagy are developmentally programmed and essential for effective hematopoiesis. Aims: To study the implication of HIF-1α/REDD1/autophagy/metabolism axis in differentiation/maturation of hematopoietic BM cells of MDS patients. Methods: BM aspiration and biopsy samples were collected from 15 untreated MDS patients from all subtypes except MDS-RARS and 7 age-matched controls with non-malignant hematologic disorder. Demographic, clinical, laboratory and karyotypic parameters were recorded. BM biopsies were immunohistochemicallly stained by fluorescent-labeled 2-nitroimidazole to assess hypoxic areas in BM. CD34 and myeloid lineage cells were isolated using magnetic beads and ficoll double-layer protocol, respectively. BM cell populations were determined by FACS analysis using standard gating strategies. HIF-1α and REDD1 gene and protein expression was evaluated by qRT-PCR and FACS analysis, respectively. Autophagy was determined by immunofluorescence for LAMP-1/LC3B and immunoblotting for LC3B/p62 (SQSTM1), whereas mitophagy by immunofluorescence for LC3B/TOMM20. Mitochondrial membrane potential (ΔΨ) and mitochondrial mass were analyzed by FACS analysis using mitotrackers. Metabolomic analysis of myeloid lineage cells was performed by liquid chromatography mass spectrometry (LC-MSn). Raw data files were processed using several chemo-informatics tools. Results: We found a preferential strong accumulation of 2-nitroimidazole in intrasinusoidal regions of MDS BM, indicating that hypoxia is a fundamental feature of BM in MDS. We demonstrated a statistically significant REDD1 gene overexpression and an increased intracellular protein co-expression of HIF-1α and REDD1 protein levels in both CD34 and myeloid cells from MDS compared to controls, as determined by RT-qPCR and FACS analysis, respectively. Higher REDD1 protein expression was shown in patients with high grade dysplasia as assessed by the Ogata classification system. Moreover, both CD34 and myeloid cells from MDS demonstrated increased LC3B puncta compared to controls with concurrent staining for CD34 and MPO. The quantitative evaluation of LC3B by Western blot revealed high level of expression of LC3B-II in the MDS myeloid cells compared to controls indicating increased autophagic activity. The observed p62/SQSTM1 degradation along with the colocalization pattern of LC3B/LAMP-1 suggest increased autophagic flux. Metabolomic analysis of MDS myeloid lineage cells compared to controls revealed excessive glycolysis, defective oxidative phosphorylation and increased reductive carboxylation glutaminolysis associated with elevated level of intracellular 2-hydroxyglutarate, all indicative of HIF-1α driven metabolism. The co-localization between TOMM20 marker and autophagosomes in MDS myeloid cells was compatible with increased mitophagy whereas, MDS myeloid cells, were characterized by a reduction of mitochondrial mass and membrane potential in comparison to controls, as determined by FACS analysis. Conclusion: Our results provide evidence for the first time of the hypoxia-driven HIF-1α/REDD1/autophagy axis in the pathophysiology of MDS. Our study suggests that this deregulated pathway is responsible for the production of 2-hydroxyglutarate, an oncometabolite, which is implicated in dysregulated epigenetic homeostasis. All the above may lead to the dysregulated metabolism and differentiation potential of the myeloid cells, thus unraveling a new pathogenetic mechanism for the MDS development. Disclosures No relevant conflicts of interest to declare.

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Starenkiy, Viktor, Sergii Artiukh, Mykhaylo Ugryumov, Viktoriia Strilets, Serhii Chernysh und Dmytro Chumachenko. „A Method for Assessing the Risks of Complications in Chemoradiation Treatment of Squamous Cell Carcinoma of the Head and Neck“. Open Bioinformatics Journal 14, Nr. 1 (19.11.2021): 138–43. http://dx.doi.org/10.2174/18750362021140100138.

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Background: More than 500,000 new cases of squamous cell carcinoma of the head and neck (SCCHN) are registered annually in the world. 7,036 new cases of the disease were registered in Ukraine during 2018, about 35% of patients did not live even a year from the date of diagnosis as a modern standard for the treatment of patients with inoperable locally advanced SCCHN, chemoradiation treatment in the classical dose fractionation mode with chemo modification with cisplatin is used by specialists. Objective: The objective of this study is to analyze the effectiveness of chemoradiation treatment with cisplatin and 5-fluorouracil in the treatment of patients with SCCHN using modern mathematical models. Methods: During the investigation we assessed the effectiveness of treatment in 108 patients with locally advanced SCCHN (stages III, IVa, IVb). The results of calculating the probabilities of complications were obtained using the method of multivariate classification based on the radial basis ANN. Results: Analyzing the groups with different methods of chemo modification, we can conclude that the method of chrono-modulated radiochemotherapy with 5-fluorouracil and the chemoradiation therapy with cisplatin were almost equal in efficiency, namely 77% and 73.5%, respectively (p=0.35). Conclusion: Using the chemoradiation therapy with 5-fluorouracil in the treatment of patients with low somatic status and elderly patients is more expedient in contrast to the methods using cisplatin. The advantage of selection of mentioned treatment method is also confirmed by the results of calculating the average complication risks using the method of multivariate classification based on a radial-basis neural network.

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Cobb, Jared S., Maria A. Seale und Amol V. Janorkar. „Evaluation of machine learning algorithms to predict the hydrodynamic radii and transition temperatures of chemo-biologically synthesized copolymers“. Computers in Biology and Medicine 128 (Januar 2021): 104134. http://dx.doi.org/10.1016/j.compbiomed.2020.104134.

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Kamakura, T., M. Sakamoto, T. Odaka, Y. Nose und K. Akazawa. „Patient Registration and Treatment Allocation in Multicenter Clinical Trials Using a FAX-OCR System“. Methods of Information in Medicine 33, Nr. 05 (1994): 530–34. http://dx.doi.org/10.1055/s-0038-1635059.

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Abstract:This article describes the design and results of implementation of an automated patient registration and freatment allocation system (RETAS) used in multicenter clinical trials. RETAS was developed using a FAX-OCR system by which handwritten Japanese and English characters, as well as numericals and forms with check boxes, are sent from participating institutions by Fax, processed using an optical character reader, and then transmitted to a host computer at a statistical center. Based on the facsimile data, RETAS can automatically review eligibility, collect patient identification data and provide a randomized treatment allocation. RETAS permits uninterrupted, unattended operation at a statistical center, 24 hours a day, 7 days a week. Therefore, it drastically decreases the workload of personnel at the statistical center needed to support central telephone registration coverage. Consequently, staff members are free to focus on patient registration, treatment allocation, and follow-up of patients. The treatment allocation procedure in this system is based on Pocock and Simon’s minimization method combined with Zelen’s method for institution balancing. By this system it was possible to balance treatment numbers for each level of various prognostic factors over an entire trial and, at the same time, balance the allocation of treatments within an institution. The system currently supports the protocol of a clinical trial for Adjuvant Chemo-Endocrine Therapy for Breast Cancer in West Japan.

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de Hond, Anne, Marieke van Buchem, Claudio Fanconi, Mohana Roy, Douglas Blayney, Ilse Kant, Ewout Steyerberg und Tina Hernandez-Boussard. „Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study“. JMIR Medical Informatics 12 (18.01.2024): e51925. http://dx.doi.org/10.2196/51925.

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Background Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. Objective This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. Methods We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. Results Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. Conclusions The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups.

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Taylor, Katherine J., Cecilie D. Amdal, Kristin Bjordal, Guro L. Astrup, Bente B. Herlofson, Fréderic Duprez, Ricardo R. Gama et al. „Serious Long-Term Effects of Head and Neck Cancer from the Survivors’ Point of View“. Healthcare 11, Nr. 6 (21.03.2023): 906. http://dx.doi.org/10.3390/healthcare11060906.

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The long-term problems of head and neck cancer survivors (HNCS) are not well known. In a cross-sectional international study aimed at exploring the long-term quality of life in this population, 1114 HNCS were asked to state their two most serious long-term effects. A clinician recorded the responses during face-to-face appointments. A list of 15 example problems was provided, but a free text field was also available. A total of 1033 survivors responded to the question. The most frequent problems were ‘dry mouth’ (DM) (n = 476; 46%), ‘difficulty swallowing/eating’ (DSE) (n = 408; 40%), ‘hoarseness/difficulty speaking’ (HDS) (n = 169; 16%), and ‘pain in the head and neck’ (PHN) (n = 142; 14%). A total of 5% reported no problems. Logistic regression adjusted for age, gender, treatment, and tumor stage and site showed increased odds of reporting DM and DSE for chemo-radiotherapy (CRT) alone compared to surgery alone (odds ratio (OR): 4.7, 95% confidence interval (CI): 2.5–9.0; OR: 2.1, CI: 1.1–3.9), but decreased odds for HDS and PHN (OR: 0.3, CI: 0.1–0.6; OR: 0.2, CI: 0.1–0.5). Survivors with UICC stage IV at diagnosis compared to stage I had increased odds of reporting HDS (OR: 1.9, CI: 1.2–3.0). Laryngeal cancer survivors had reduced odds compared to oropharynx cancer survivors of reporting DM (OR: 0.4, CI: 0.3–0.6) but increased odds of HDS (OR: 7.2, CI: 4.3–12.3). This study provides evidence of the serious long-term problems among HNCS.

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Hatakeyama-Sato, Kan, Yasuhiko Igarashi, Takahiro Kashikawa, Koichi Kimura und Kenichi Oyaizu. „Quantum circuit learning as a potential algorithm to predict experimental chemical properties“. Digital Discovery, 2022. http://dx.doi.org/10.1039/d2dd00090c.

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We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo- and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth...

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Andújar, Ivan, Daviel Gomez, Lianny Perez und Jose Carlos Lorenzo. „Use of Chemo-Informatics to Identify Molecular Descriptors of Auxins, Cytokinins and Gibberellins“. Journal of Applied Bioinformatics & Computational Biology 07, Nr. 02 (2018). http://dx.doi.org/10.4172/2329-9533.1000151.

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Rai, Sumit Kumar, Rajesh Kumar Pathak, Dev Bukhsh Singh, Arun Bhatt und Mamta Baunthiyal. „Chemo-informatics guided study of natural inhibitors targeting rho GTPase: a lead for treatment of glaucoma“. In Silico Pharmacology 9, Nr. 1 (03.01.2021). http://dx.doi.org/10.1007/s40203-020-00061-y.

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Das, Jyotirekha, Fayaz Shaik Mahammad und Rajanikant Golgodu Krishnamurthy. „An integrated chemo-informatics and in vitro experimental approach repurposes acarbose as a post-ischemic neuro-protectant“. 3 Biotech 12, Nr. 3 (15.02.2022). http://dx.doi.org/10.1007/s13205-022-03130-5.

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Griesenauer, Rebekah H., Constantino Schillebeeckx und Michael S. Kinch. „CDEK: Clinical Drug Experience Knowledgebase“. Database 2019 (01.01.2019). http://dx.doi.org/10.1093/database/baz087.

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Abstract The Clinical Drug Experience Knowledgebase (CDEK) is a database and web platform of active pharmaceutical ingredients with evidence of clinical testing as well as the organizations involved in their research and development. CDEK was curated by disambiguating intervention and organization names from ClinicalTrials.gov and cross-referencing these entries with other prominent drug databases. Approximately 43% of active pharmaceutical ingredients in the CDEK database were sourced from ClinicalTrials.gov and cannot be found in any other prominent compound-oriented database. The contents of CDEK are structured around three pillars: active pharmaceutical ingredients (n = 22 292), clinical trials (n = 127 223) and organizations (n = 24 728). The envisioned use of the CDEK is to support the investigation of many aspects of drug development, including discovery, repurposing opportunities, chemo- and bio-informatics, clinical and translational research and regulatory sciences.

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Anyubaga, Salim Bitrus, Gideon Adamu Shallangwa, Adamu Uzairu und Stephen Eyije Abechi. „Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis“. Heliyon, Dezember 2023, e23115. http://dx.doi.org/10.1016/j.heliyon.2023.e23115.

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Hussein, Baydaa Abed, Isaac Karimi und Namdar Yousofvand. „Chemo- and bio-informatics insight into anti-cholinesterase potentials of berries and leaves of Myrtus communis L., Myrtaceae: an in vitro/in silico study“. BMC Complementary Medicine and Therapies 23, Nr. 1 (21.11.2023). http://dx.doi.org/10.1186/s12906-023-04241-z.

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Abstract Background Myrtus communis L. (MC) has been used in Mesopotamian medicine. Here, the cholinesterase (ChE) inhibitory potential of its methyl alcohol extracts has been investigated and computationally dissected. Method The ChE inhibition has been measured based on usual Ellman’s colorimetric method compared to a canonical ChE inhibitor, eserine. Through a deep text mining, the structures of phytocompounds (= ligands) of MC were curated from ChemSpider, PubChem, and ZINC databases and docked into protein targets, AChE (PDB 1EVE) and BChE (PDB 1P0I) after initial in silico preparedness and binding affinity (BA; kcal/mol) reported as an endpoint. The calculation of ADMET (absorption, distribution, metabolism, excretion, and toxicity) features of phytocompounds were retrieved from SwissADME (http://www.swissadme.ch/) and admetSAR software to predict the drug-likeness or lead-likeness fitness. The Toxtree v2.5.1, software platforms (http://toxtree.sourceforge.net/) have been used to predict the class of toxicity of phytocompounds. The STITCH platform (http://stitch.embl.de) has been employed to predict ChE-chemicals interactions. Results The possible inhibitory activities of AChE of extracts of leaves and berries were 37.33 and 70.00%, respectively as compared to that of eserine while inhibitory BChE activities of extracts of leaves and berries of MC were 19.00 and 50.67%, respectively as compared to that of eserine. Phytochemicals of MC had BA towards AChE ranging from -7.1 (carvacrol) to -9.9 (ellagic acid) kcal/mol. In this regard, alpha-bulnesene, (Z)-gamma-Bisabolene, and beta-bourbonene were top-listed low toxic binders of AChE, and (Z)-gamma-bisabolene was a more specific AChE binder. Alpha-cadinol, estragole, humulene epoxide II, (a)esculin, ellagic acid, patuletin, juniper camphor, linalyl anthranilate, and spathulenol were high class (Class III) toxic substances which among others, patuletin and alpha-cadinol were more specific AChE binders. Among intermediate class (Class II) toxic substances, beta-chamigrene was a more specific AChE binder while semimyrtucommulone and myrtucommulone A were more specific BChE binders. Conclusion In sum, the AChE binders derived from MC were categorized mostly as antiinsectants (e.g., patuletin and alpha-cardinal) due to their predicted toxic classes. It seems that structural amendment and stereoselective synthesis like adding sulphonate or sulphamate groups to these phytocompounds may make them more suitable candidates for considering in preclinical investigations of Alzheimer’s disease.

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Embarez, Donia H., Ahmed S. Abdel Razek, Emad B. Basalious, Magdi Mahmoud und Nadia M. Hamdy. „Acetaminophen-traces bioremediation with novel phenotypically and genotypically characterized 2 Streptomyces strains using chemo-informatics, in vivo, and in vitro experiments for cytotoxicity and biological activity“. Journal of Genetic Engineering and Biotechnology 21, Nr. 1 (19.12.2023). http://dx.doi.org/10.1186/s43141-023-00602-w.

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AbstractWe isolated two novel bacterial strains, active against the environmental pollutant acetaminophen/Paracetamol®. Streptomyces chrestomyceticus (symbol RS2) and Flavofuscus (symbol M33) collected from El-Natrun Valley, Egypt—water, sediment, and sand samples, taxonomically characterized using a transmission electron microscope (TEM). Genotypic identification, based on 16S rRNA gene sequence analysis followed by BLAST alignment, were deposited on the NCBI as 2 novel strains https://www.ncbi.nlm.nih.gov/nuccore/OM665324 and https://www.ncbi.nlm.nih.gov/nuccore/OM665325. The phylogenetic tree was constructed. Acetaminophen secondary or intermediate product’s chemical structure was identified by GC/LC MS. Some selected acetaminophen secondary-product extracts and derived compounds were examined against a panel of test micro-organisms and fortunately showed a good anti-microbial effect. In silico chemo-informatics Swiss ADMET evaluation was used in the selected bio-degradation extracts for absorption (gastric), distribution (to CNS), metabolism (hepatic), excretion (renal), and finally not toxic, being non-mutagenic/teratogenic or genotoxic, virtually. Moreover, in vitro cytotoxic activity of these selected bio-degradation secondary products was examined against HepG2 and MCF7 cancer cell lines, where M33 and RS2 extract effects on acetaminophen/paracetamol bio-degradation products were safe, with higher IC50 on HepG2 and MCF7 than the acetaminophen/paracetamol IC50 of 108.5 μg/ml. Moreover, an in vivo oral acute single-dose toxicity experiment was conducted, to confirm these in vitro and in silico lower toxicity (better safety) than acetaminophen/paracetamol.

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Papadakis, Marios, Arabinda Ghosh, Debanjana Ghosh, Nobendu Mukerjee, Swastika Maitra, Padmashree Das, Abhijit Dey et al. „The Efficient Activity of Glabridin and its Derivatives Against EGFR-mediated Inhibition of Breast Cancer“. Current Medicinal Chemistry 30 (03.03.2023). http://dx.doi.org/10.2174/0929867330666230303120942.

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Background: Breast cancer (BC) is one of the most typical causes of cancer death in women worldwide. Activated epidermal growth factor receptor (EGFR) signaling has been increasingly associated with BC development and resistance to cytotoxic drugs. Due to its significant association with tumour metastasis and poor prognosis, EGFR-mediated signaling has emerged as an attractive therapeutic target in BC. Mainly in all BC cases, mutant cells over-expresses EGFR. Certain synthetic drugs are already used to inhibit the EGFR-mediated pathway to cease metastasis, with several phytocompounds also revealing great chemopreventive activities Methods: This study used chemo-informatics to predict an effective drug from some selected phytocompounds. The synthetic drugs and the organic compounds were individually screened for their binding affinities, with EGFR being the target protein using molecular docking techniques. Results: The binding energies were compared to those of synthetic drugs. Among phytocompounds, Glabridin (phytocompound of Glycyrrhiza glabra) manifested the best dock value of -7.63 Kcal/mol, comparable to that of the highly effective anti-cancer drug Afatinib. The glabridin derivatives also exhibited comparable dock values. Conclusion: The AMES properties deciphered the non-toxic features of the predicted compound. Pharmacophore modeling and in silico cytotoxicity predictions also exhibited a superior result assuring their drug likeliness. Therefore, Glabridin can be conceived as a promising therapeutic method to inhibit EGFR-mediated BC.

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„Computational Exploration of Anti-Cancer Potential of GUAIANE Dimers from Xylopia vielana by Targeting B-Raf Kinase Using Chemo-Informatics, Molecular Docking, and MD Simulation Studies“. Anti-Cancer Agents in Medicinal Chemistry 22, Nr. 4 (2022). http://dx.doi.org/10.2174/18755992mte4dndqi0.

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UChikhale, Hemant. „PERSPECTIVE INSIGHT AND APPLICATION OF IN-SILICO TOOL AS VIRTUAL SCREENING METHOD FOR LEAD DESIGNING AND DEVELOPMENT“. Journal of Medical pharmaceutical and allied sciences, 15.11.2021, 16–24. http://dx.doi.org/10.22270/jmpas.vic1i1.1908.

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Humans are now in a bioinformatics and chemo informatics century, where we can foresee data across domains like as healthcare, the environmental, technology, and public health. The use of information sharing in silico methodologies has impacted sickness administration by predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) patterns of synthetic compounds and efficient and environmentally succeeding pharmaceuticals upfront. The purpose of lead discovery and design is to create the appearance of novel drug candidates that can attach to a specific illness cause. The lead investigative process starts with the recognition of the lead structure, which is followed by the synthesis of its analogs and their estimation in order to produce a candidate for lead improvement. The finding of the proper lead exact is the fundamental and primary worked in the traditional lead discovery progression, and the use of computer (in silico) approaches is widely used in lead innovation. A medicinal chemist's passion for building lead structure is piqued by biomolecules, which are often made up of DNA, RNA, and proteins (such as enzymes, receptors, transporters, and ion channels). The underlying principle of such nuts and bolts is noteworthy to be acquainted with their pharmacological implication to the disease under examination. The motive of this review piece of writing is to emphasize several of the in silico methods that are used in lead discovery and to express the applications of these computational methods.

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Abdullahi, Sagiru Hamza, Adamu Uzairu, Muhammad Tukur Ibrahim und Abdullahi Bello Umar. „Chemo-informatics activity prediction, ligand based drug design, Molecular docking and pharmacokinetics studies of some series of 4, 6-diaryl-2-pyrimidinamine derivatives as anti-cancer agents“. Bulletin of the National Research Centre 45, Nr. 1 (09.10.2021). http://dx.doi.org/10.1186/s42269-021-00631-w.

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Abstract Background The most well-known cause of cancer deaths identified in female is breast cancer. Several drugs approved by the food and drug administration (FDA) for the treatment of breast cancer may have adverse health effects. This research is aimed at developing a QSAR model and utilize it to predict the inhibitive activities of newly designed novel compounds, examine their ADMET and drug-likeness properties and carry out molecular docking studies between the designed compounds and the VEGFR-2 receptors in order to identify the essential amino acid residues involved in protein–ligand interactions and possible mechanism of action of the designed compounds. Results The first model was selected as the best because of its fitness statistically with the following assessment parameters: R2train = 0.832, R2adj = 0.79, R2ext = 0.62, Q2 = 0.68, and LOF = 0.14509. Compound 11 was selected as a template to design new powerful compounds based on its low residual and high pIC50 values. Majority of the designed compounds has predicted pIC50 greater than that of the lead compound and the standard drug (Sunitinib) used as reference. Molecular docking studies results of the designed compounds revealed that they have higher docking scores than the template and the reference drug (Sunitinib) and are found to bind to the VEGFR-2 receptor in a similar manner to the reference drug. Pharmacokinetics and ADMET properties revealed that the designed compounds passed drug-likeness criteria because they did not violate more than 1 Lipinski’s rule of Five, They are uniformly distributed to the brain and are assumed to penetrate the central nervous system and finally they are all found to non-toxic and orally bioavailable. Conclusion The developed model was therefore found to be efficient in predicting the pIC50 of Anti breast cancer compounds that are yet to be synthesized and it also help in reducing the cost and synthetic duration the compounds. The result of this research confirmed that the designed compounds may be developed as novel VEGFR-2 inhibitors.

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Olajide, Monsurat, Misbaudeen Abdul-Hammed, Isah Adewale Bello, Ibrahim Olaide Adedotun und Tolulope Irapada Afolabi. „Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic drugs against colorectal cancer (CRC)“. Physical Sciences Reviews, 21.03.2023. http://dx.doi.org/10.1515/psr-2022-0281.

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Abstract Colorectal cancer is the third most deadly cancer globally. Drug resistance and attendant side effects make the available standard anti-colorectal cancer drugs against target receptors inefficient. Phytochemicals from medicinal plants are safer, cheaper, effective, and heal diseases from the cellular level. This study is aimed at identifying potential inhibitors of thymidylate synthase (TS) and nuclear factor kappa-B (NF–κB) target receptors from Capsicum annuum towards the development of new therapeutic drugs against colorectal cancer via in silico approach. One hundred and fifty (150) ligands previously reported from Capsicum annuum were downloaded from the PubChem database and were subjected to chemo-informatics analyses such as ADMET, drug-likeness, oral bioavailability, bioactivity, and PASS prediction to ascertain their therapeutic and safety profile before docking. The ligands that passed the analyses were docked against TS and NF–κB in duplicate using a creditable docking tool (PyRx). Raltitrexed and emetine were used as the standard drug inhibitors for TS and NF–κB, respectively. The results obtained from this study showed that feruloyl-beta-D-glucose (8.45 kcal/mol), 5-O-caffeoylquinic acid (−8.40 kcal/mol), 5-O-caffeoylquinic acid methyl ester (−7.89 kcal/mol), feruloyl hexoside (−7.40 kcal/mol), O-glucopyranoside (−7.55 kcal/mol), and quercetin (−7.00 kcal/mol) shared the same binding pocket with TS while feruloyl-beta-D-glucose (−7.00 kcal/mol), chlorogenic acid (−6.90 kcal/mol), 5-O-caffeoylquinic acid (−6.90 kcal/mol) and feruloyl hexoside (−6.50 kcal/mol) shared the same pocket with NF–κB. These compounds were selected as best hits due to their excellent inhibitory efficiency and chemoinformatic profiles. Thus, the compounds may function as prospective lead compounds for developing a new anti-colorectal cancer drug.

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