Multi-Scale Modeling of Drug Absorption in Personalized Medicine: Advancements and Applications

This study explores the application of multi-scale modeling in predicting drug absorption within the framework of personalized medicine. Multi-scale models integrate data from molecular dynamics, cellular transport mechanisms, and physiological parameters to simulate how drugs are absorbed, distributed, metabolized, and eliminated in the human body. The incorporation of patient-specific variables, such as genetic polymorphisms, age, sex, and health conditions, allows for a more accurate prediction of drug absorption and bioavailability. The results demonstrate that multi-scale models outperform traditional pharmacokinetic models by providing a personalized approach that accounts for inter-individual variability. Simulations reveal how genetic variations in drug-metabolizing enzymes and transporters can significantly influence drug absorption, highlighting the importance of considering these factors for personalized drug therapy. Furthermore, the study emphasizes the significance of physiological factors, such as gastrointestinal pH and gastric emptying time, in determining drug absorption rates. While multi-scale modeling shows great potential for enhancing personalized medicine, challenges remain in data collection, model complexity, and computational demands. Future research should focus on improving data acquisition methods, reducing computational burdens, and integrating more complex biological factors, such as the microbiome, to further enhance the accuracy and applicability of multi-scale models. In conclusion, multi-scale modeling offers a promising tool for optimizing drug therapy by providing individualized predictions of drug absorption, thereby improving therapeutic efficacy and minimizing adverse drug reactions.

کلیدواژه ها:

Multi-scale modeling ، Drug absorption ، Personalized medicine ، Pharmacokinetics