Personalize d me dicine for GnRH antagonist protocol in in vitro fertilization procedure using modeling and optimal control

A B S T R A C T Infertility is an inability of those of reproductive age to become or remain pregnant within five years of exposure to pregnancy WHO . Since its inception in 1978, In vitro fertilization (IVF) has become the most resorted protocol for treating infertility. This article is focused on the first stage of IVF namely, superovulation. Superovulation is the drug-induced ovarian stimulation process to facilitate multiple ovulations per menstrual cycle. Thus, successful superovulation is the foundation for a successful IVF cycle. Despite the existence of general guidelines for dosage prescription, patient customized dosage protocols do not exist, and complications, such as overstimulation, do occur. To solve the limitations of the existing empirical system, a mathematical algorithm is developed to provide a customized model of superovulation owing to the size distribution of eggs (follicles/ oocytes) obtained per cycle as a function of the chemical interactions of the drugs used and the patient conditions during the cycle, to serve as a baseline for forecasting the outcome. A personalized medicine approach was previously presented based on mathematical modeling and optimal control for the agonist Long Lupron protocol of IVF(Nisal et al., 2020). This article is an extension of that work and describes the theory, modeling, and optimal control strategy to improve outcomes of IVF treatment for the antagonist Ganirelix (GnRH) protocol used in clinical practice. The validation of the procedure is performed using clinical data from the patients previously undergone IVF cycles. The data was available for 13 patients and customized patient-specific model parameters were obtained from the data. The model was used to predict follicle size distribution for all 13 patients for the remaining days of the cycle. It was found that the personalized models showed a strong fit with the clinically observed follicle size distribution (FSD). Then, the optimal control method was used for each individual patient to predict optimized drug dosage protocols using the customized patient specific parameters. The validity of modeling and optimization approach is corroborated by the results and show the efficacy of the optimized drug dosage protocols for each patient. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This article discusses the importance of superovulation in the IVF process and the need for customized dosage protocols, as well as the development of a mathematical algorithm to provide personalized superovulation models.