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Cancer Therapies

Cancer Therapies

Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

Pharmacodynamic Models: E_max Drug–Concentration Effect Model

The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...

Pharmacodynamic Models: Linear Concentration–Effect Model

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...

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Related Experiment Video

Updated: Jun 26, 2026

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo

Published on: January 7, 2019

A multiscale mathematical model for oncolytic virotherapy.

Leticia R Paiva¹, Christopher Binny, Silvio C Ferreira

¹Departamento de Física, Universidade Federal de Viçosa, Viçosa, MG, Brazil.

Cancer Research

|January 30, 2009

Summary

This study models cancer virotherapy, finding that oncolytic viruses can lead to tumor eradication or growth. It highlights the need for immune suppression and optimal viral cytotoxicity for effective cancer treatment.

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Last Updated: Jun 26, 2026

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo

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Published on: January 7, 2019

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

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Published on: April 3, 2018

Growth, Purification, and Titration of Oncolytic Herpes Simplex Virus

Growth, Purification, and Titration of Oncolytic Herpes Simplex Virus

Published on: May 13, 2021

Area of Science:

Oncology
Virology
Computational Biology

Background:

Cancer virotherapy utilizes oncolytic viruses to selectively destroy tumor cells or stimulate an anti-cancer immune response.
Modeling cancer virotherapy is crucial for understanding treatment dynamics and optimizing outcomes.

Purpose of the Study:

To investigate a multiscale model for cancer virotherapy through computational simulations.
To determine the conditions influencing tumor response to intratumoral virus administration.
To identify key factors for successful single-agent virotherapy.

Main Methods:

Development and simulation of a multiscale mathematical model for cancer virotherapy.
Analysis of tumor cell and virus population dynamics under various conditions.
Determination of probabilities for different therapeutic outcomes.

Main Results:

Intratumoral virus administration can result in complete tumor eradication or persistent growth after initial remission.
The model predicts undamped oscillatory dynamics between tumor cells and virus populations.
Successful single-agent virotherapy requires suppressed host immunity and viruses with high intratumoral mobility.
An optimal range for viral cytotoxicity exists; excessively fast or slow killing is detrimental.

Conclusions:

Cancer virotherapy outcomes are complex and depend on factors like immune response, viral properties, and cytotoxicity.
The findings suggest that targeting viruses with extremely rapid tumor-killing capabilities may not be the most effective strategy.
Further in vivo and in vitro experiments are needed to validate the predicted oscillatory dynamics.