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Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo
Published on: January 7, 2019
Computer-aided designing of oncolytic viruses for overcoming translational challenges of cancer immunotherapy
Anjali Lathwal1, Rajesh Kumar2, Gajendra P S Raghava1
1Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.
Abstract:
Wild-type and genetically engineered oncolytic viruses (OVs) represent powerful therapeutic agents in cancer immunotherapy. Several OV species are in clinical trials for cancer treatment. Preclinical and clinical trials revealed several issues related to OV therapy in terms of viral delivery, spread, antiviral immune response, and tumor resistance. Here, we suggest some promising computational strategies that can overcome these issues. The strategies include predicting and prioritizing tumor-homing peptides, anticancer peptides, neoantigens, and miRNA response elements in the viral genome. The combination of computational approaches with genetic engineering could enhance the safety, delivery, oncolysis, and antitumor immune responses of OVs.
Insights
Computational strategies can enhance oncolytic virus (OV) therapy for cancer. By predicting key viral components, these methods aim to improve OV delivery, spread, and immune response, overcoming common treatment challenges.
Area of Science:
- Oncology
- Virology
- Immunotherapy
- Bioinformatics
Background:
- Oncolytic viruses (OVs) are promising cancer immunotherapy agents currently in clinical trials.
- OV therapy faces challenges including viral delivery, spread, immune response, and tumor resistance.
Purpose of the Study:
- To propose computational strategies for enhancing oncolytic virus (OV) therapy.
- To address limitations in current OV treatment approaches.
Main Methods:
- Predicting and prioritizing tumor-homing peptides.
- Identifying anticancer peptides and neoantigens.
- Analyzing miRNA response elements within the viral genome.
Main Results:
- Computational approaches offer solutions to improve OV safety and efficacy.
- Strategies can enhance viral delivery, spread, and oncolysis.
- Improved prediction of therapeutic targets can boost antitumor immune responses.
Conclusions:
- Combining computational methods with genetic engineering can significantly advance OV therapy.
- These integrated strategies promise safer, more effective oncolytic virotherapy for cancer treatment.
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