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.

Uncertainty in clinical data and stochastic model for in vitro fertilization

In vitro fertilization (IVF) is the most widely used technique in assisted reproductive technologies (ART). It has been divided into four stages; (i) superovulation, (ii) egg retrieval, (iii) insemination/fertilization and (iv) embryo transfer. The first stage of superovulation is a drug induced method to enable multiple ovulation, i.e., multiple follicle growth to oocytes or matured follicles in a single menstrual cycle. IVF being a medical procedure that aims at manipulating the biological functions in the human body is subjected to inherent sources of uncertainty and variability. Also, the interplay of hormones with the natural functioning of the ovaries to stimulate multiple ovulation as against single ovulation in a normal menstrual cycle makes the procedure dependent on several factors like the patient's condition in terms of cause of infertility, actual ovarian function, responsiveness to the medication, etc. The treatment requires continuous monitoring and testing and this can give rise to errors in observations and reports. These uncertainties are present in the form of measurement noise in the clinical data. Thus, it becomes essential to look at the process noise and account for it to build better representative models for follicle growth. The purpose of this work is to come up with a robust model which can project the superovulation cycle outcome based on the hormonal doses and patient response in a better way in presence of uncertainty. The stochastic model results in better projection of the cycle outcomes for the patients where the deterministic model has some deviations from the clinical observations and the growth term value is not within the range of '0.3-0.6'. It was found that the prediction accuracy was enhanced by more than 70% for two patients by using the stochastic model projections. Also, in patients where the prediction accuracy did not increase significantly, a better match with the trend of the clinical data was observed in case of the stochastic model projections as compared to their deterministic counterparts.