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Fulltext In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

Boswell Z, Verga JU, Mackle J, Guerrero-Vazquez K, Thomas OP, Cray J, et al.

Infect Drug Resist, 2023;16:2321-2338.
PMID: 37155475 DOI: 10.2147/IDR.S395203

The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.
Fulltext Contemporary Approaches to the Discovery and Development of Broad-Spectrum Natural Product Prototypes for the Control of Coronaviruses

Hanna GS, Choo YM, Harbit R, Paeth H, Wilde S, Mackle J, et al.

J Nat Prod, 2021 Nov 26;84(11):3001-3007.
PMID: 34677966 DOI: 10.1021/acs.jnatprod.1c00625

The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.
Fulltext Collective Action for Wellness in the Malaysian Workplace: Protocol for a Feasibility Study

Thai JY, McCaffrey T, Ramadas A, Chandrasekara D, Koh SGM, Choi TST, et al.

JMIR Res Protoc, 2022 Dec 05;11(12):e39238.
PMID: 36469407 DOI: 10.2196/39238

BACKGROUND: Chronic diseases and the associated risk factors are preventable with lifestyle changes such as eating a healthier diet and being more physically active. In Malaysia, the prevalence of chronic diseases, including diabetes, hypertension, and heart diseases, has risen. In the present study, we explore the potential of co-designing and implementing a digital wellness intervention to promote socially-driven health knowledge and practices in the workplace in Malaysia, drawing on social cognitive theory, social impact theory, and social influence theory.
OBJECTIVE: This study aims to co-design and assess the feasibility of a socially-driven digital health intervention to promote healthy behavior and prevent chronic diseases in a workplace in Malaysia.
METHODS: This study involves two phases: (i) identifying the barriers and facilitators to healthy behaviors at work and co-designing the intervention activities with the employees, (ii) implementing and evaluating the intervention's feasibility. Phase 1 will involve qualitative data collection and analysis through semi-structured, in-depth interviews and co-design workshops with the employees, while Phase 2 will consist of a feasibility study employing quantitative measurements of health behaviors through accelerometers and questionnaires.
RESULTS: This study was funded in June 2021 and ethics approval for Phase 1 was obtained from the Monash University Human Research Ethics Committee in January 2022. As of August 2022, qualitative interviews with 12 employees have been completed and the data has been transcribed and analyzed. These results will be published in a future paper with results from all Phase 1 activities.
CONCLUSIONS: The study will help us to better understand the mechanisms through which digital technologies can promote socially-driven health knowledge and behaviors. This research will also result in a scalable wellness intervention that could be further tailored and expanded to other employers and social groups across the region.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39238.

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