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.

Drug Discovery Today
|April 29, 2020
PubMed

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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