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Obesity significantly alters the pharmacokinetic processes of drug absorption and distribution, presenting unique challenges in medical treatment. The increased fat tissue and decreased lean muscle in obese individuals can significantly affect how drugs are absorbed into the body and distributed across different tissues. This alteration can lead to variances in the effectiveness and safety of medications, necessitating adjustments in dosing or drug selection for obese patients.One notable...
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Updated: Nov 5, 2025

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
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Agent-Based Learning Model for the Obesity Paradox in RCC.

Matteo Belenchia1, Giacomo Rocchetti1, Stefano Maestri1,2

  • 1Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy.

Frontiers in Bioengineering and Biotechnology
|May 17, 2021
PubMed
Summary
This summary is machine-generated.

Obesity paradox in renal cell carcinoma immunotherapy shows improved outcomes in obese patients. This study uses a computational framework to model cell-cell interactions influencing the tumor microenvironment and immune response.

Keywords:
bioagentcell-cell interaction networkcomputational biologydynamical networksimmunotherapyinteraction-as-perception paradigmmultiagent systemrenal cell carcinoma

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Area of Science:

  • Oncology
  • Computational Biology
  • Immunology

Background:

  • Renal cell carcinoma (RCC) immunotherapy outcomes appear counterintuitively better in obese patients than lean individuals.
  • This phenomenon, known as the obesity paradox, is hypothesized to relate to the tumor microenvironment's cell-cell interaction network during immune responses.

Purpose of the Study:

  • To develop a computational framework for the in silico study of tumor behavior.
  • To investigate the role of cell-cell interaction networks in the tumor microenvironment during immunotherapy for renal cell carcinoma.

Main Methods:

  • A multiagent system model was developed to represent the tumor based on its cell-cell interaction network.
  • Simulations were performed to generate hypothesized effects of this network on the tumor microenvironment.

Main Results:

  • The multiagent system simulation successfully reproduced the dynamics of the cell-cell interaction network.
  • The model demonstrated how these interactions can influence the tumor microenvironment in response to simulated environmental changes.

Conclusions:

  • The computational framework provides a novel approach to studying tumor behavior in silico.
  • This approach can help elucidate the mechanisms behind the obesity paradox in cancer immunotherapy and inform future treatment designs.