Reply to Al Obaidi et al

Truong, Clover N; Nailor, Michael D; Walia, Rajat; Cherrier, Lauren; Nasar, Aasya; Goodlet, Kellie J.

doi:10.1093/cid/ciab879

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Inhibitors MONNA (pIC 50 7.1) [1228] Selective inhibitors Ani9 (pIC 50 7) [1090], crofelemer (pIC 50 5.2) [1222] Endogenous channel blockers Ins(3,4,5,6)P 4…”

Section: Endogenous Activators Intracellular Ca 2+mentioning

confidence: 99%

The Concise Guide to PHARMACOLOGY 2023/24: Ion channels

Alexander,

Mathie,

Peters

et al. 2023

British J Pharmacology

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The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and over 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16178. Ion channels are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.

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“…Inhibitors MONNA (pIC 50 7.1) [1228] Selective inhibitors Ani9 (pIC 50 7) [1090], crofelemer (pIC 50 5.2) [1222] Endogenous channel blockers Ins(3,4,5,6)P 4…”

Section: Endogenous Activators Intracellular Ca 2+mentioning

confidence: 99%

The Concise Guide to PHARMACOLOGY 2023/24: Ion channels

Alexander,

Mathie,

Peters

et al. 2023

British J Pharmacology

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“…Unfortunately, a number of patients develop resistance to PARPi after long-term administration 58 . The combination of PARPi and histone deacetylase inhibitors (HDACi) is a promising strategy and has been proved to sensitize PARPi-resistant cells to treatment [59][60][61][62][63][64] .…”

Section: Identification Of Novel Drug Combinationsmentioning

confidence: 99%

Drug-Target Graph based Recurrent Network for Drug Combination Prediction

Wen¹,

Cao

Zhang³

et al. 2022

Preprint

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Compared with monotherapy, drug combination therapy has demonstrated more effective and powerful therapeutic effects in cancer treatment. However, due to the large size of the combinatorial space, experimental identification of synergistic drug combinations is cost- and time-consuming, even with high-throughput screens. With the accumulation of various types of drug informatics data, artificial intelligence techniques have become applicable for the prediction of synergistic drug combinations, thereby reducing the experimental workload and accelerating the discovery of combination therapy. Currently, the emerging graph neural network (GNN) has been applied to drug combination prediction and brings new insight on the discovery of combination therapy. In this study, we proposed Drug-Target Graph based Recurrent Network (DTGRN) to predict synergistic drug combinations in various cancer cell lines. Specifically, we proposed a Graph Recurrent Network-based encoder to learn a low-dimensional representation of features for each node and then predicted the synergy score of novel drug combinations based on these representations via a cell line-specific projection matrix. In DTGRN, gating mechanism and cell-line-level states are employed to ensure each node representation more comprehensively absorbs local and global information and potential association information. In the application of the cell line-specific drug-target heterogeneous graph, DTGRN significantly outperforms the state-of-the-arts and several classical machine learning algorithms. More prominently, the novel drug combinations predicted by DTGRN have been validated by computational analysis and in vitro experiments, confirming the high predictive performance of DTGRN. These drug combinations could be further evaluated as potential combination therapy strategy. In summary, these results indicate that DTGRN can serve as a powerful tool for integrating heterogeneous graph information to predict drug combinations and provide a promising way to discover novel synergistic drug combination for cancer treatment.

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Coccidioidomycosis: A Contemporary Review

Crum

2022

Infect Dis Ther

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Coccidioidomycosis, colloquially known as Valley Fever, is an invasive dimorphic fungal infection caused by Coccidioides immitis and C. posadasii. The fungi are found in the arid desert soils of the southwestern US, as well as in parts of Mexico and Central and South America. Acquisition is typically via inhalation of arthroconidia which become airborne after both natural (e.g., earthquakes, dust storms, and fires) and human-related events (e.g., military maneuvers, recreational activities, agriculture, and construction). The incidence of infection in increasing likely a result of both climatic and populational changes. Further, the recognized geographic distribution of Coccidioides spp. is expanding, as cases are being diagnosed in new areas (e.g., eastern Washington, Oregon, and Utah). Most coccidioidal infections are asymptomatic (60%); however, approximately one-third develop a pulmonary illness which is a leading cause of community-acquired pneumonia in highly endemic areas. Uncommonly (0.5–2% of cases), the infection disseminates to extrapulmonary locations (e.g., skin, bones/joints, and the central nervous system), and is most commonly seen among persons with cellular immunodeficiencies (e.g., transplant recipients, HIV, and pregnancy) and non-Caucasian races (especially African Americans and Filipinos). The diagnosis of coccidioidomycosis requires astute clinical suspicion and laboratory findings, including positive serology, cultures, and/or histopathology results. Treatment is warranted among persons with pneumonia who have risk factors for complicated disease and among those with extrapulmonary disease. Novel antifungals with improved fungicidal activity and rapidity of action with fewer side effects and drug interactions are needed. Preventive strategies (e.g., education regarding the disease, dust avoidance, mask wearing, including among select groups, antifungal prophylaxis, and surveillance laboratory testing) are advised for residents and travelers to endemic areas. Currently, no preventive vaccine is available. Coccidioidomycosis has been recognized for over a century, and an expanding wealth of knowledge has been gained regarding this emerging infectious disease which will be reviewed here.

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Reply to Al Obaidi et al

Cited by 3 publications

References 7 publications

The Concise Guide to PHARMACOLOGY 2023/24: Ion channels

The Concise Guide to PHARMACOLOGY 2023/24: Ion channels

Drug-Target Graph based Recurrent Network for Drug Combination Prediction

Coccidioidomycosis: A Contemporary Review

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