Project

Personalized Ovarian Stimulation Protocols: A Translational Medicine Approach for Optimizing Fertility Outcomes (PROSPECT- study)

Short Summary

We propose to develop a computational personalized medicine approach for oocyte collection during in vitro fertilization (IVF). The project will complement and extend a previously approved PHRT iDoc grant. In particular, additional hormones will be measured, and we will complement clinical measurements with measurements in cattle, a large animal model, to obtain data that cannot be collected from patients, and enlarge the set of hormones to be assessed. Our project represents an opportunity to pave the way for mechanistic modeling in personalized medicine.

Goals

The main goal of this project is to arrive at a mathematical model that is sufficiently accurate that it can be used for the planning of personalized clinical treatment protocols. By personalising treatments, we expect to greatly reduce the burden for patients, while enhancing success rates, in particular for challenging cases. This is important to reduce costs and to shorten treatment duration for patients. The latter is of critical importance for patients at an advanced reproductive age and those who seek to freeze eggs prior to undergoing medical procedures that affect their fertility. We will base our mathematical models both on clinical data and on data from a large animal model. We expect that the detailed, comprehensive, and consistent measurements in cattle will allow us to overcome current short-comings. These will enable us to greatly reduce the amount of data that has to be obtained from patients prior to treatment as we will be able to infer optimal treatment strategies based on a small number of prior measurements in the patient.

Significance

Most research in personalized medicine is based on statistical correlations because of limited knowledge of the underlying molecular mechanisms of disease. Reproduction is one of the few areas in medicine where mechanistic modeling is feasible to date, because reproductive processes are sufficiently reproducible and the overarching regulatory feedbacks are largely known. As in all clinical projects, data generation remains a challenging bottleneck. Therefore, we propose to leverage human studies with comprehensive studies in a large animal to obtain the required amount of data to build robust predictive computational models. We believe that the modelling-based translation of data from animal models to humans is key to the development of mechanistic, predictive models in personalized medicine.

Background

First introduced more than 40 years ago, in vitro fertilization (IVF) is now routinely used to address infertility in the clinic. Even though the cumulative likelihood of life birth reaches 80% by the fifth IVF embryo transfer cycle, an urgent need to further improve hormonal stimulation protocols remains, mainly to enhance the success rate for challenging cases, to shorten the duration of the process for all, and to reduce the burden on affected women.