Systems Modeling for Oncolytic Viruses

By: Drew Marquis, PhD,
Jessica Sinha

In the fight against cancer, gene therapies delivered in viral vectors offer an exciting and challenging approach to targeted therapy. Systems modeling can help developers thread the needle between what is safe and what is effective for single-agent and combination treatments involving oncolytic viruses.

Understanding Viruses

Viruses are submicroscopic (<380 nm) organisms that can only replicate by infecting other living cells. Viruses infect not only humans but also plants, fungi, and bacteria. The two key components of a virus are the genetic material inside the virus and the protein-lipid envelope, which varies based on the specific virus type. To replicate, the virus enters a cell, unpacks its genetic information, and hijacks the cell's molecular machinery to synthesize more viral genomes and proteins assembled into new viruses. Today, many known diseases are caused by viruses such as the flu (influenza virus), COVID (SARS coronavirus), and HIV/AIDS (human immunodeficiency virus). The ability of viruses to insert their genome into a host cell is actively leveraged in modern molecular biology practices, where recombinant viruses are used as vectors to manipulate the genes of a cell or organism.

Oncolytic Viruses in Cancer Treatment

Since the mid-1800s, there have been multiple case reports of cancer patients experiencing tumor regressions that coincided with viral infections. Early clinical trials tested the viability of "virotherapy," where cancer patients were purposefully infected with a virus [3]. Indeed, many patients experienced tumor regressions, however many also experienced undesirable side effects of said viral infection (ex: hepatitis). Today, oncolytic viruses are an emerging class of cancer therapeutics.


Historical Observations of Viral Infections Associated with Tumor Regression

1896 - A woman with “myelogenous leukemia” went into remission after influenza infection, 37 years before influenza was determined to be a viral infection [1]

1953 - Chicken pox led to regression of lymphatic leukemia in a 4-year-old boy [2]

While non-cancerous cells and a competent immune system can arrest/eliminate a viral infection, cancer cells often develop mutations that enable them to evade immune surveillance. However, such mutations make them extremely vulnerable to viral infections [4]. Modern research and development of oncolytic viruses is focused on creating "attenuated" recombinant viruses that can shrink tumors, but have limited to no ability to infect healthy tissue and cause undesirable side effects or toxicity.


There are two key mechanisms of action oncolytic viruses take. Most modern virotherapies use recombinant DNA technologies to attenuate a wild-type virus so that they may preferentially only infect cancer cells and have limited ability to infect non-cancerous cells. The other mechanism is using a gene therapy vector, where recombinant oncolytic viruses are designed to “reprogram” cancer cells to change the tumor microenvironment to drive cancer cell killing. As a vector for gene manipulation, there is a profound diversity of possible strategies an oncolytic virus could use to reprogram the tumor microenvironment. For example, an oncolytic virus could prevent cancerous cells from expressing proteins that enable them to evade the immune system, or they could make infected cells express proteins that recruit immune cells into the tumor.



Imlygic (T-VEC), a recombinant Herpes Simplex Virus (HSV) approved by the FDA to treat melanomas

Imlygic (T-VEC), a recombinant Herpes Simplex Virus (HSV) approved by the FDA to treat melanomas


Challenges with the Development and Modeling of Oncolytic Viruses

Certain challenges are unique to the development of oncolytic viruses. One challenge with different oncolytic viruses is that they can have species-specific permissivity. The same virus can have a different ability to infect and replicate in mouse cells versus in human cells for example. Therefore, it becomes difficult to translate preclinical doses to human applications. At Applied Biomath, we use modeling to bridge species differences and make human predictions, solving questions around toxicity and/or efficacy.

Another consideration is that oncolytic viruses are live, and upon clinical administration, will proliferate within the body, causing variable effective doses. Currently, there is not enough publicly available data to fully understand the relationship between viral replication and clinical outcomes. Understanding this relationship will be essential in establishing safe and effective doses. [5] 

For traditional drugs like small molecules or monoclonal antibodies, we can empirically correlate measurements of the drug in the bloodstream (PK) to the efficacy/safety effects without a detailed understanding of the drug's mechanism of action. However, for gene therapies, it is not obvious how to empirically relate the PK (of the virus or other gene therapy vector) to the efficacy/safety effect. To understand a viral therapy’s mechanism of action we need to understand the mechanism(s) of how the genetic material is taken up by cells and translated into a protein. Systems modeling is well-positioned to capture these subtleties because each step is modeled explicitly with forward and reverse reaction rates and can be parameterized based on species-specific information.

[Poster] Prediction of systemic cytokine exposure in human after IV administration of oncolytic myxoma virus, using quantitative systems pharmacology modeling

We created a QSP model of oncolytic immunotherapies based on the myxoma virus (MYXV) platform to predict systemic cytokine exposure and determine safe doses.

Learn More>> 

Also unlike most traditional drugs, oncolytic viruses are not removed via metabolism but rather subject to the host’s antiviral immune response. Factors such as the neutralizing antibodies circulating in the patient’s body, or the potential for viruses to be disabled by haemaggluttinin binding, or to escape immune detection entirely must be considered. Further research on how oncolytic viruses are processed through a cancer patient’s body will be required to better understand exposure and dose-response. [5]

With many more such factors to be considered when working with immunocompromised populations as such, there is a pressing need to design effective clinical trials and determine appropriate dosing regimens.

Market Outlook

Explore Our Applied BioMath Assess™ Gene Therapy Model Pack:

Includes 8 models covering LNP-encapsulated mRNA and siRNA therapies.

Learn More>> 

The market for oncolytic viruses is currently small compared to other cancer therapeutics with very few approved therapies to date. However, the market is expected to grow rapidly over the next decade, with a projected CAGR of 27% from 2023 to 2033 [6]. 

While antibody-based and cell therapies are still the dominant biotherapeutics for treating cancer, there is growing excitement around oncolytic viruses as monotherapies and in the combination setting. A recent publication has highlighted that the combination of intratumoral oncolytic virus (DNX-240) and a checkpoint PD1 inhibitor, pembrolizumab, “was safe with notable survival benefit in select patients.” [7] In June 2023, Oncolys announced exciting preliminary data from a Phase II trial (NCT03921021) of its oncolytic virus treatment telomelysin in combination with Merck's Keytruda (pembrolizumab) in refractory gastroesophageal adenocarcinoma.

Currently Approved Oncolytic Viruses for Clinical Treatment

Oncorine (Shanghai Sunway Biotech) - approved by NMPA (China) in 2005 for nasopharyngeal carcinoma 

Imlygic (Amgen) - approved by the FDA (USA) in 2015 for melanomas

Delytact (Daiichi Sankyo) - approved by Ministry of Health (Japan) in 2016 for glioblastomas

Applied BioMath’s Experience with Oncolytic Viruses

To date, Applied BioMath has worked on several oncolytic virus collaborations with such partners as OncoMyx, answering questions such as whether the oncolytic virus is expected to cause cytokine-mediated toxicity and what therapeutic properties and dosing schedules will toxicity be observed. Using first principles, mechanistic models built at Applied BioMath can predict how uncertainties in virotherapy design lead to uncertainties in clinical efficacy/toxicity. Applied BioMath Assess™ offers pre-built model packs for gene therapy modalities.

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[1] Dock G. The influence of complicating diseases upon leukaemia. The American Journal of the Medical Sciences (1827-1924). 1904 Apr 1;127(4):563.

[2] Bierman HR, Crile DM, Dod KS, Kelly KH, Petrakis NI, White LP, Shimkin MB. Remissions in leukemia of childhood following acute infectious disease. Staphylococcus and streptococcus, varicella, and feline panleukopenias. Cancer. 1953 May;6(3):591-605.

[3] Kelly E, Russell SJ. History of oncolytic viruses: genesis to genetic engineering. Molecular therapy. 2007 Apr 1;15(4):651-9.

[4] Mondal M, Guo J, He P, Zhou D. Recent advances of oncolytic virus in cancer therapy. Human vaccines & immunotherapeutics. 2020 Oct 2;16(10):2389-402.

[5] Kaufman, H., Kohlhapp, F. & Zloza, A. Oncolytic viruses: a new class of immunotherapy drugs. Nat Rev Drug Discov 14, 642–662 (2015).


[7] Nassiri, F., Patil, V., Yefet, L.S. et al. Oncolytic DNX-2401 virotherapy plus pembrolizumab in recurrent glioblastoma: a phase 1/2 trial. Nat Med 29, 1370–1378 (2023).


About the Authors

Dr. Drew Marquis, Senior Scientist, Modeling

Jessica Sinha, Senior Content Marketing Manager