Dynamic model formulation of glucose and lipid lowering by blue-green algae extract (spirulina platensis)

Metabolic diseases such as diabetes mellitus and hyperlipidemia are the leading causes of global morbidity, with their prevalence steadily increasing every year. Spirulina platensis, as one of the natural ingredients rich in bioactive compounds, has been empirically proven to have antidiabetic and antihyperlipidemic effects. However, until now, there is no dynamic mathematical model that can model the effect of Spirulina on blood glucose and lipid levels over time. This study aims to develop a dynamic mathematical model based on a system of nonlinear differential equations that models the effect of Spirulina on the decrease in glucose and lipid levels in the body. The model was compiled using the principles of pharmacokinetics-pharmacodynamics and Michaelis-Menten kinetics, then simulated for 72 hours with a daily dose scenario. The simulation results showed that the administration of Spirulina periodically was able to reduce blood glucose levels from 160 mg/dL to 157.79 mg/dL, and lipid levels from 220 mg/dL to 193.85 mg/dL. Spirulina exhibits significant pharmacodynamic effects with faster glucose depreciation than lipids, as well as concentrations of active substances in the body that follow a daily pharmacokinetic pattern of elimination. This model is able to predict the metabolic dynamics of the body against dose and time variations, and can be the basis for the development of personalized therapies based on individual physiological parameters. This research also fills the gap in the quantitative approach in the study of Spirulina, which has been dominated by descriptive experimental studies.