Modeling energy intake by adding homeostatic feedback and drug intervention

Area of Science:

• Physiology
• Pharmacology
• Computational Biology

Background:

• Energy intake (EI) is crucial for understanding metabolic diseases like obesity and diabetes.
• Eating behavior involves complex short-term and long-term regulatory controls.
• Challenges to energy balance, such as drug treatments or diet restriction, can trigger tolerance and rebound phenomena in EI.

Purpose of the Study:

• To describe a novel model that captures physiological counter-regulatory feedback actions in response to energy imbalances.
• To develop a model capable of handling both increases and decreases in EI across acute and chronic settings.
• To demonstrate the model's utility in explaining drug-induced changes in EI and adaptation phenomena.

Main Methods:

• Development of a mathematical model incorporating a drug mechanism function that modulates EI.
• Inclusion of a non-linear appetite control signal influenced by EI deviation from a set-point.
• Application of the model to simulate EI responses in various scenarios, including drug interventions and dietary challenges.

Main Results:

• The model successfully captures tolerance and rebound phenomena in EI.
• It demonstrates the ability to handle both increases and decreases in EI under different conditions.
• Simulations show the model shifts predicted concentration-response relationships, unlike models without adaptation.
• The model qualitatively explains differences in adaptation rates and extents observed in rodents and humans.

Conclusions:

• The developed model provides a robust framework for understanding EI regulation and adaptation.
• It offers insights into the physiological feedback mechanisms underlying eating behavior.
• The model has potential applications in predicting drug effects on EI and understanding metabolic disease processes.