Modeling Endocrine Control of the Pituitary–Ovarian Axis: Androgenic Influence and Chaotic Dynamics

Efficacy of optimal nutraceutical combination in treating PCOS characteristics: an in-silico assessment

BACKGROUND

Polycystic ovary syndrome (PCOS) is a serious health condition affecting women of reproductive age. High prevalence of PCOS and associated metabolic complications needs effective treatment and management. This study evaluated the efficacy of optimal nutraceutical combinations in improving PCOS characteristics using system biology-based mathematical modelling and simulation.

METHODS

A shortlisting of eight potent nutraceuticals was carried out with literature search. Menstrual cycle model was used to perform simulations on an in-silico population of 2000 individuals to test individual and combined effects of shortlisted nutraceuticals on five PCOS characteristics [oligomenorrhea, anovulation, hirsutism, infertility, and polycystic ovarian morphology (PCOM)] for a duration of 6 months. Efficacy was tested across lean and obese phenotypes and age groups.

RESULTS

Individual assessment of nutraceuticals revealed seven most potent compounds. Myo-inositol among them was observed to be the most effective in alleviating the PCOS characteristics. The in-silico population analysis showed that the combination of melatonin and ALA along with myo-inositol was efficacious in restoring the hormonal balance across age-groups and Body Mass Index (BMI) categories.

CONCLUSION

Supplementation with the combination of myo-inositol, melatonin, and ALA demonstrated potential in managing PCOS symptoms in our in-silico analysis of a heterogeneous population, including lean and obese phenotypes across various severities and age groups, over a 6-month period. Future clinical studies are recommended to validate these findings.

Reduced model for female endocrine dynamics: Validation and functional variations

A normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype-displaying relatively normal hormone levels and cycle dynamics-that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone.

FSHR antagonists can trigger a PCOS-like state

Over the recent years, FSHR has become an important target for development of fertility regulating agents, as impairment of FSH-FSHR interaction can lead to subfertility or infertility. In our previous study, we identified a 9-mer peptide (FSHβ (89-97)) that exhibited FSHR antagonist activity. The histopathological and biochemical observations indicated, in addition to FSHR antagonism, a striking resemblance to a PCOS-like state. These observations led us to hypothesize that use of FSHR antagonists can trigger a PCOS-like state. In the present study, to validate this hypothesis, we performed qRT-PCR validation using ovarian tissue samples from our previous study. Expression of three genes known to be differentially expressed in PCOS was evaluated and found to be similar to the PCOS state. To further test the hypothesis, theoretical simulations were carried out by using the human menstrual cycle model available in the literature. Model simulations for FSHR antagonism were indicative of increased testosterone levels, increased ratio of luteinizing hormone/follicle stimulating hormone, and stockpiling of secondary follicles, which are typical characteristics of PCOS. The findings of this study will be relevant while reviewing the utility of FSHR antagonists for fertility regulation and reproductive medicine.Abbreviations: FSH: Follicle-stimulating hormone; FSHR: Follicle-stimulating hormone receptor; cAMP: Cyclic adenosine 3'5' monophosphate; PKA: Protein kinase A; PI3K: Phosphoinositide 3-kinase; PKB: protein kinase B; ERK1/2: Extracellular signal-regulated protein kinase 1/2; MAPK: Mitogen-activated protein kinases; T: testosterone; E2: estradiol; PCOS: Polycystic ovarian syndrome; LH: luteinizing hormone; Lhcgr: luteinizing hormone/choriogonadotropin receptor; CYP17A1: cytochrome P450 family 17 subfamily A member 1; Inhba: inhibin subunit beta A; qRT-PCR: Real-Time quantitative reverse transcription polymerase chain reaction; FSHβ: Follicle-stimulating hormone β subunit; Ct: Cycle threshold; Rn18s: Rattus norvegicus 18S ribosomal RNA.

Monitoring sexual steroids and cortisol at different stages of the ovarian cycle from two capuchin monkey species: use of non- or less invasive methods than blood sampling

Endocrine monitoring of non-human primates (NHP) via faecal metabolites of steroid hormones appears as a useful non-invasive alternative to evaluate the reproductive status of free living NHP, as well as of those kept in captivity but of difficult handling. However, validation is needed with plasma values before its application in the field. The aim of the present study was to monitor the different phases of the menstrual cycle from the new world NHP Sapajus apella and S. libidinosus. For this, hormonal and faecal plasma levels of E₂, P₄ and cortisol were assessed during different days of the menstrual cycle, together with colpocitology. The mean duration of the menstrual cycle according colpocitology was of 21.7 and 21.0 days for S. apella and S. libidinosus, respectively. These values were similar to those observed via plasma analysis, i.e. 22.7 and 20.3 days for S. apella and S. libidinosus, respectively. The day of plasmatic E₂ peak was set as Day -1 and the estimated day of ovulation was set as Day 0 and occurred two days earlier in S. libidinosus than in S. apella females. In both species, it was observed a delay in faecal E₂ peak of six days for S. apella and of 11 days for S. libidinosus when compared with the plasma peak. A maximum P₄ plasma concentration was observed in the middle of luteal phase in S. apella and in S. libidinosus, both at around day 5. However, faecal P₄ peaks were detected at days 9 and 8 in S. apella and S. libidinosus, respectively. Mean plasma and faecal cortisol levels were variable during all ovulatory cycle of S. apella and S. libidinosus females. Although no exact correlation was observed between plasmatic and faecal profile of steroid hormone, faecal samples were able to indicate ovarian cycle phase, being important to assess the reproductive status of the females applying a non-invasive method.

High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors

BACKGROUND

Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures.

OBJECTIVES

We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles.

METHODS

We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast™ aromatase assay provided concentration-inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus-pituitary-ovarian control of ovulation in women.

RESULTS

Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%.

CONCLUSIONS

These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment. https://doi.org/10.1289/EHP742.

A model of ovulatory regulation examining the effects of insulin-mediated testosterone production on ovulatory function

Polycystic ovary syndrome (PCOS), a common cause of infertility in women, is often accompanied by abnormal reproductive and metabolic hormone levels. Specifically, androgens such as testosterone are elevated in many PCOS women, and the syndrome itself is frequently associated with insulin resistance, which leads to hyperinsulinemia, i.e., elevated insulin. Although the precise role of insulin in ovulatory function is unclear, its role in ovulatory dysfunction is often linked to the effects of increased ovarian androgen production. We present a mathematical model of the menstrual cycle that incorporates regulation by the pituitary-ovarian axis and mechanisms of ovarian testosterone production. We determine a physiological role for testosterone in the normal ovulatory cycle and study the role of hyperinsulinemia in pathological regulation of the cycle. Model results indicate increased ovulatory disruption with elevated insulin-mediated testosterone production and suggest that variations in the response of ovarian follicles to essential signals can alter the degree to which hyperinsulinemia disrupts the ovulatory cycle. The model also provides insight into the various PCOS phenotypes and the severity of ovulatory dysfunction.

MatVPC: A User-Friendly MATLAB-Based Tool for the Simulation and Evaluation of Systems Pharmacology Models

Quantitative systems pharmacology (QSP) models are progressively entering the arena of contemporary pharmacology. The efficient implementation and evaluation of complex QSP models necessitates the development of flexible computational tools that are built into QSP mainstream software. To this end, we present MatVPC, a versatile MATLAB-based tool that accommodates QSP models of any complexity level. MatVPC executes Monte Carlo simulations as well as automatic construction of visual predictive checks (VPCs) and quantified VPCs (QVPCs).

Modeling endocrine regulation of the menstrual cycle using delay differential equations

This article reviews an effective mathematical procedure for modeling hormonal regulation of the menstrual cycle of adult women. The procedure captures the effects of hormones secreted by several glands over multiple time scales. The specific model described here consists of 13 nonlinear, delay, differential equations with 44 parameters and correctly predicts blood levels of ovarian and pituitary hormones found in the biological literature for normally cycling women. In addition to this normal cycle, the model exhibits another stable cycle which may describe a biologically feasible "abnormal" condition such as polycystic ovarian syndrome. Model simulations illustrate how one cycle can be perturbed to the other cycle. Perturbations due to the exogenous administration of each ovarian hormone are examined. This model may be used to test the effects of hormone therapies on abnormally cycling women as well as the effects of exogenous compounds on normally cycling women. Sensitive parameters are identified and bifurcations in model behavior with respect to parameter changes are discussed. Modeling various aspects of menstrual cycle regulation should be helpful in predicting successful hormone therapies, in studying the phenomenon of cycle synchronization and in understanding many factors affecting the aging of the female reproductive endocrine system.

Bifurcation analysis of a menstrual cycle model reveals multiple mechanisms linking testosterone and classical PCOS

A system of 16 differential equations is described which models hormonal regulation of the menstrual cycle focusing on the effects of the androgen testosterone (T) on follicular development and on the synthesis of luteinizing hormone (LH) in the pituitary. Model simulations indicate two stable menstrual cycles - one cycle fitting data in the literature for normal women and the other cycle being anovulatory because of no LH surge. Bifurcations with respect to sensitive model parameters illustrate various characteristics of polycystic ovarian syndrome (PCOS), a leading cause of female infertility. For example, varying one parameter retards the growth of preantral follicles and produces a "stockpiling" of these small follicles as observed in the literature for some PCOS women. Modifying another parameter increases the stimulatory effect of T on LH synthesis resulting in reduced follicular development and anovulation. In addition, the model illustrates how anovulatory and hyperandrogenic cycles which are characteristic of PCOS can be reproduced by perturbing both pituitary sensitivity to T and the follicular production of T. Thus, this model suggests that for some women androgenic activity at the levels of both the pituitary and the ovaries may contribute to the etiology of PCOS.