Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation

Squeezing the eggs to grow: The mechanobiology of mammalian folliculogenesis

The formation of functional eggs (oocyte) in ovarian follicles is arguably one of the most important events in early mammalian development since the oocytes provide the bulk genetic and cytoplasmic materials for successful reproduction. While past studies have identified many genes that are critical to normal ovarian development and function, recent studies have highlighted the role of mechanical force in shaping folliculogenesis. In this review, we discuss the underlying mechanobiological principles and the force-generating cellular structures and extracellular matrix that control the various stages of follicle development. We also highlight emerging techniques that allow for the quantification of mechanical interactions and follicular dynamics during development, and propose new directions for future studies in the field. We hope this review will provide a timely and useful framework for future understanding of mechano-signalling pathways in reproductive biology and diseases.

Real-world evidence analysis of the follicle-stimulating hormone use in male idiopathic infertility

Male idiopathic infertility remains a therapeutic challenge in the couple infertility management. In this setting, an empirical treatment with follicle-stimulating hormone (FSH) is allowed, although not recommended. Twenty-one clinical trials and four meta-analyses highlighted an overall increased pregnancy rate in case of FSH administration, but the indiscriminate FSH prescription is still unsupported by clinical evidence in idiopathic infertility. This context could represent an example in which real-world data (RWD) could add useful information. From a nationwide clinical practice survey performed in Italy, emerged the clinicians' attitude to prescribe FSH in the case of impaired semen with a significant improvement of semen parameters, identifying FSH treatment as a therapeutic card in the real-life management. Although more robust data are still needed to optimize FSH treatment in male idiopathic infertility, RWD should be included in the body of evidence considered in healthcare decision-making.

Hormonal regulation of ovarian follicle growth in humans: Model-based exploration of cycle variability and parameter sensitivities

We present a modelling and simulation framework for the dynamics of ovarian follicles and key hormones along the hypothalamic-pituitary-gonadal axis throughout consecutive human menstrual cycles. All simulation results (hormone concentrations and ovarian follicle sizes) are in biological units and can easily be compared to clinical data. The model takes into account variability in follicles' response to stimulating hormones, which introduces variability between cycles. The growth of ovarian follicles in waves is an emergent property in our model simulations and further supports the hypothesis that follicular waves are also present in humans. We use Approximate Bayesian Computation and cluster analysis to construct a population of virtual subjects and to study parameter distributions and sensitivities. The model can be used to compare and optimize treatment protocols for ovarian hyperstimulation, thus potentially forming the integral part of a clinical decision support system in reproductive endocrinology.

The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique

PURPOSE

Several mathematical models have been developed to estimate individualized chances of assisted reproduction techniques (ART) success, although with limited clinical application. Our study aimed to develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS) phase, helping the physician to decide whether to perform oocytes pick-up continuing the ongoing ART path.

METHODS

A single-center retrospective analysis of real-world data was carried out including all fresh ART cycles performed in 1998-2020. Baseline characteristics, ART parameters and biochemical/clinical pregnancies and live birth rates were collected. A seven-steps systematic approach for model development, combining linear regression analyses and decision trees (DT), was applied for biochemical, clinical pregnancy, and live birth rates.

RESULTS

Of fresh ART cycles, 12,275 were included. Linear regression analyses highlighted a relationship between number of ovarian follicles > 17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates (p < 0.001), but not live birth rate. DT were created for biochemical pregnancy (statistical power-SP:80.8%), clinical pregnancy (SP:85.4%), and live birth (SP:87.2%). Thresholds for OF17 entered in all DT, while sperm motility entered the biochemical pregnancy's model, and female age entered the clinical pregnancy and live birth DT. In case of OF17 < 3, the chance of conceiving was < 6% for all DT.

CONCLUSION

A systematic approach allows to identify OF17, female age, and sperm motility as pre-retrieval predictors of ART outcome, possibly reducing the socio-economic burden of ART failure, allowing the clinician to perform or not the oocytes pick-up.