A prediction method for plasma concentration by using a nonlinear grey Bernoulli combined model based on a...

Area of Science:

• Pharmacokinetics
• Mathematical Modeling
• Biostatistics

Background:

• Accurate modeling of drug plasma concentration is crucial for pharmacokinetics.
• Existing models struggle with the stochastic fluctuations of single-peaked concentration-time data.
• The need for robust forecasting methods with limited data points is significant.

Purpose of the Study:

• To introduce and evaluate a novel nonlinear grey Bernoulli combined model with a self-memory algorithm (SA-NGBM).
• To assess the SA-NGBM's efficacy in modeling and forecasting acetylsalicylate plasma concentration data.
• To compare the SA-NGBM's performance against traditional modeling techniques.

Main Methods:

• Development of the SA-NGBM incorporating a self-memory routine for initial state estimation.
• Application of SA-NGBM to model single-peaked time-sample sequences of acetylsalicylate plasma concentration.
• Comparative analysis using optimized NGBM (ONGBM), exponential smoothing (ES), and simple moving average (SMA) models.
• Validation through four established accuracy and significance tests.

Main Results:

• The SA-NGBM demonstrated superior accuracy and efficiency in capturing nonlinear, stochastic fluctuations compared to ONGBM, ES, and SMA.
• The model's self-memory feature reduced reliance on single initial values, improving robustness.
• SA-NGBM effectively extrapolated predicted plasma concentration data against later time samples.

Conclusions:

• The SA-NGBM is a highly accurate and efficient tool for modeling single-peaked plasma concentration series.
• The findings suggest potential applications in signal matching for similar small-sample, nonlinear biological data.
• This novel approach enhances pharmacokinetic modeling capabilities.