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

In vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, fertilization, and embryo transfer. Superovulation is a drug-induced method to enable multiple ovulation per menstrual cycle and key component towards a successful IVF cycle. Although there are the general guidelines for dosage, the dose is not optimized for each patient, and complications, such as overstimulation, can occur. To overcome the shortcomings of this general system, a mathematical procedure is developed which can provide a customized model of this stage regarding the size distribution of eggs (follicles/ oocytes) obtained per cycle as a function of the chemical interactions of the drugs used and the conditions imposed on the patient during the cycle, which provide a basis for predicting the possible outcome. Uncertainty and risk are modeled and included in optimal drug dosage decisions. This paper describes the theory, model, and the optimal control procedure for improving outcomes of IVF treatment for one of the four protocols used in real practice. The validation of the procedure is performed using clinical data from the patients previously undergone IVF cycles. Customized patient-specific model parameters are obtained by using initial two-day data for each patient. Subsequently, this model is used to predict the FSD for the remaining days of the cycle. This procedure was conducted for 49 patients. The results of the customized models are found to be closely matching with the observed FSD. These results thus validate the modeling approach and consequently its use for predicting the customized optimal drug dosage for each patient. Using the customized model and the optimized dosage, the FSD at the end of the cycle was determined. A small double-blind clinical trial was also conducted in India. The results from the trial show that the dosage predicted by using the model is 40% less than the suggestion made by the IVF clinicians. The testing and monitoring requirements for patients using optimized drug dosage is reduced by 72%. Work on the other three protocols and for patients in the USA is started and is showing promising results.

Modeling the superovulation stage in in vitro fertilization.

In vitro fertilization (IVF) is the most common technique in assisted reproductive technology and in most cases the last resort for infertility treatment. It has four basic stages: superovulation, egg retrieval, insemination/fertilization, and embryo transfer. Superovulation is a drug-induced method to enable multiple ovulation per menstrual cycle. The success of IVF majorly depends upon successful superovulation, defined by the number and similar quality of eggs retrieved in a cycle. Modeling the superovulation stage can help in predicting the outcomes of IVF before the cycle is complete. In this paper, we developed a model for superovulation stage. The model is adapted from the theory of batch crystallization. The aim of crystallization is to get maximum crystals of similar size and purity, while superovulation aims at eggs of similar quality and size. The rate of crystallization and superovulation are both dependent on the process conditions and varies with time. The kinetics of follicle growth is modeled as a function of injected hormones and the follicle properties are represented in terms of the moments. The results from the model prediction were verified with the known data from Jijamata Hospital, Nanded, India. The predictions were found to be in agreement with the actual observations.