Personalized medicine for in vitro fertilization procedure using modeling and optimal control

Area of Science:

• Reproductive Medicine and Endocrinology
• Mathematical Modeling and Control Theory
• Biomedical Engineering

Background:

• In vitro fertilization (IVF) is a primary assisted reproductive technology for infertility.
• Superovulation, a key IVF stage, uses drug-induced multiple ovulation but lacks patient-specific dosage optimization.
• Current protocols risk complications like overstimulation due to generalized dosing.

Purpose of the Study:

• To develop a customized mathematical model for optimizing superovulation drug dosage in IVF.
• To predict follicle size distribution (FSD) and improve IVF treatment outcomes.
• To reduce uncertainty and risk in drug dosage decisions for personalized IVF protocols.

Main Methods:

• A mathematical procedure was developed to model superovulation based on drug interactions and patient conditions.
• Patient-specific models were created using initial two-day clinical data to predict FSD.
• Optimal control theory was applied to determine customized drug dosages for improved IVF outcomes.

Main Results:

• Customized models closely matched observed FSD in 49 patients, validating the approach.
• A clinical trial showed model-predicted dosages were 40% lower than clinician suggestions.
• Optimized dosage reduced patient testing and monitoring requirements by 72%.

Conclusions:

• The developed mathematical modeling approach enables personalized drug dosage optimization for IVF superovulation.
• This customized strategy significantly improves IVF treatment efficiency and patient safety.
• Further research is underway for broader application across different protocols and patient populations.