Compartment Model and Neural Network-Based Analysis of Combination Medication Ratios.

Abstract

Background: Combination medication strategies often involve complex interactions, making determining the appropriate pharmacodynamic component ratios challenging. Methods: This study established a time-dose relationship model through the compartment model, deriving the in vivo drug quantity ratios corresponding to the blood concentrations of the pharmacodynamic components. A neural network was then employed to establish a dose-effect relationship model between the blood concentrations of the pharmacodynamic components and the overall body response. Utilizing the feedback adjustment mechanism of neural networks continuously adjusts the network to achieve the desired drug efficacy, thereby deriving the corresponding dose ratio of the pharmacodynamic components. Empirical studies were conducted on combining Cynanchum otophyllum saponins M₁ and M₂ with phenobarbital for epilepsy treatment, as well as the anti-ischemic stroke activity of the prototype and metabolites of Erigeron breviscapus. Results: After adjusting the efficacy, the model recalculated the new ratio proportions for each combination, validated by the reduced Combination Index (CI). Conclusions: This model provides a new approach to combination medication strategies.