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Updated: Jul 17, 2025
Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
Quantifying assays: inhibition of signalling pathways of cancer
Roumen Anguelov1,2, G Manjunath1, Avulundiah E Phiri1
1Department of Mathematics and Applied Mathematics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa.
Abstract:
Inhibiting a signalling pathway concerns controlling the cellular processes of a cancer cell's viability, cell division and death. Assay protocols created to see if the molecular structures of the drugs being tested have the desired inhibition qualities often show great variability across experiments, and it is imperative to diminish the effects of such variability while inferences are drawn. In this paper, we propose the study of experimental data through the lenses of a mathematical model depicting the inhibition mechanism and the activation-inhibition dynamics. The method is exemplified through assay data obtained from an experimental study of the inhibition of the chemokine receptor 4 (CXCR4) and chemokine ligand 12 (CXCL12) signalling pathway of melanoma cells. The quantitative analysis is conducted as a two step process: (i) deriving theoretically from the model the cell viability as a function of time depending on several parameters; (ii) estimating the values of the parameters by using the experimental data. The cell viability is obtained as a function of concentration of the inhibitor and time, thus providing a comprehensive characterization of the potential therapeutic effect of the considered inhibitor, e.g. $IC_{50}$ can be computed for any time point.
Insights
This study introduces a mathematical model to analyze cancer cell signaling pathway inhibition, reducing experimental variability. The model quantizes drug effects over time, aiding in evaluating therapeutic potential for melanoma treatments targeting the CXCR4/CXCL12 pathway.
Area of Science:
- Biochemistry
- Mathematical Biology
- Cancer Research
Background:
- Cancer cell viability, division, and death are regulated by signaling pathways.
- Assay protocols for drug inhibition testing exhibit significant experimental variability.
- Reducing variability is crucial for accurate interpretation of drug efficacy.
Purpose of the Study:
- To develop a mathematical model for analyzing experimental data on signaling pathway inhibition.
- To quantify the dynamics of activation-inhibition in cellular processes.
- To characterize the therapeutic effects of inhibitors by modeling cell viability over time.
Main Methods:
- A two-step quantitative analysis combining theoretical derivation and experimental data estimation.
- Deriving cell viability as a function of time and model parameters.
- Estimating model parameters using experimental assay data.
Main Results:
- A mathematical model was developed to depict signaling pathway inhibition mechanisms.
- Cell viability was successfully modeled as a function of inhibitor concentration and time.
- The model allows for comprehensive characterization of inhibitor effects, including calculation of IC50 at any time point.
Conclusions:
- The proposed mathematical model effectively reduces experimental variability in drug inhibition assays.
- This approach provides a robust method for characterizing the therapeutic potential of inhibitors.
- The study demonstrates the model's application using data from the CXCR4/CXCL12 pathway in melanoma cells.
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