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KC2021 - A machine learning platform to estimate anti-SARS-CoV-2 activities

ABSTRACT: Predictor of several endpoints related to Sars-CoV-2. It provides predictions for Live Virus Infectivity, Viral Entry, Viral Replication, In Vitro Infectivity and Human Cell Toxicity using a combination of three models. Consensus results are obtained by averaging the prediction for the three different models for each activity and toxicity models. The models have been built using NCATS COVID19 data. Model Type: Predictive machine learning model. Model Relevance: Predicts activity of compound in COVID assay. Model Encoded by: Pradnya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos8fth

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405130004 | BioModels | 2024-05-13

REPOSITORIES: BioModels

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			Action	DRS
	MODEL2405130004?filename=BioModelsMetadata%20-%20eos8fth.csv	Csv

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Project description:Previously we reported that a recombinant vaccinia virus (VACV) carrying a light-emitting fusion gene enters, replicates in, and reveals the locations of tumors in mice. A new recombinant VACV, GLV-1h68, as a simultaneous diagnostic and therapeutic agent, was constructed by inserting three expression cassettes (encoding Renilla luciferase-green fluorescent protein (RUC-GFP) fusion, b-galactosidase, and b-glucuronidase) into the F14.5L, J2R (encoding thymidine kinase, TK), and A56R (encoding hemagglutinin, HA) loci of the viral genome, respectively. Intravenous (i.v.) injections of GLV-1h68 (1 Ã 107 pfu/mouse) into nude mice with established (500 mm3) subcutaneous (s.c.) GI-101A human breast tumors were used to evaluate its toxicity, tumor targeting specificity and oncolytic efficacy. GLV-1h68 demonstrated an enhanced tumor targeting specificity and much reduced toxicity compared to its parental LIVP strains. The tumors colonized by GLV-1h68 exhibited growth, inhibition, and regression phases followed by tumor eradication within 130 days in 95% of the mice tested. Tumor regression in live animals was monitored in real time based on decreasing light emission, hence demonstrating the concept of a combined oncolytic virus-mediated tumor diagnosis and therapy system. Transcriptional profiling of regressing tumors based on a mouse-specific platform revealed gene expression signatures consistent with immune defense activation, inclusive of interferon stimulated genes (STAT-1 and IRF-7), cytokines, chemokines and innate immune effector function. These findings suggest that immune activation may combine with viral oncolysis to induce tumor eradication in this model, providing a novel perspective for the design of oncolytic viral therapies for human cancers. Objective: To determine the gene expression changes induced by GLV-1h68 vaccinia virus injection in mouse carrying human breast cancer implant and leading to tumor eradication. Methods: Gene expression was analyzed using oligonucleotide microarrays. Responsiveness to vaccina virus injection was assessed by toxicity and survival study, gene expression anaysis and tumor volume change. Result: The tumors colonized by GLV-1h68 exhibited growth, inhibition, and regression phases followed by tumor eradication within 130 days in 95% of the mice tested. Tumor regression in live animals was monitored in real time based on decreasing light emission, hence demonstrating the concept of a combined oncolytic virus-mediated tumor diagnosis and therapy system. Transcriptional profiling of regressing tumors based on a mouse-specific platform revealed gene expression signatures consistent with immune defense activation, inclusive of interferon stimulated genes (STAT-1 and IRF-7), cytokines, chemokines and innate immune effector function. Conclusion: Our findings suggest that immune activation may combine with viral oncolysis to induce tumor eradication in this model, providing a novel perspective for the design of oncolytic viral therapies for human cancers. Experiment Overall Design: tumor tissues 3 and 6 weeks post virus injection

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KC2021 - A machine learning platform to estimate anti-SARS-CoV-2 activities

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