Pharmacogenetic Algorithm for Individualized Controlled Ovarian Stimulation

Study Overview
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Brief Summary
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In this study, the investigators will evaluate genetic variation or polymorphisms between individuals submitted to IVF/ICSI cycles, and how these variations influence response to COS and pregnancy outcomes. Thus, this project has the aim to develop a pharmacogenetic algorithm associating gene polymorphisms, the patient clinical information, and functional and hormonal biomarker to: 1) predict the patient response to gonadotropin; 2) develop individualized gonadotropin regimens; 3) improve the accuracy of determining appropriate dosages of gonadotrophins; 4) improve efficacy and patient compliance to COS; and finally 5) reduce the time-to-pregnancy.

Detailed Description
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Controlled ovarian stimulation (COS) is crucial for optimizing in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) success. Multiple factors influence the ovarian response to COS, making predictions about oocyte yields not so straightforward. As a result, ovarian response may be poor or suboptimal, or even excessive, all of which has negative consequences to the affected patient. The study of gene polymorphisms regulating the female reproductive function may help to clarify the mechanisms responsible for gonadal function and fertility, and also the inter-individual variability in ovarian response to COS. The main objective of COS is obtaining an adequate number of good quality oocytes, and also minimize the adverse drug reactions and cycle cancellations due to a high or poor response. The ideal number of oocytes to optimize treatment outcomes has been subject of debate for many years. However, based on recent published data, it seems reasonable to define the optimal number of oocytes to be retrieved after an adequate COS as 10-15 oocytes. To perform an adequate COS, it has been suggested that the antral follicle count (AFC) and anti-müllerian hormone (AMH) are the best biomarkers to predict the ovarian response. Recently, two nomograms have been proposed for the calculation of the follicle stimulating hormone (FSH) starting dose for COS, taking into account different parameters, namely: 1) Age, AFC and day 3 serum FSH; and 2) Age, serum AMH and FSH. Meanwhile, these prediction models are not able to predict an unexpected poor response and also a higher and undesirable ovarian response to FSH monotherapy. Considering the number of retrieved oocytes, it has been recently purposed a new patient stratification during ART treatments: poor response (1-3 oocytes); sub-optimal response (4-9 oocytes); normal response (10-15 oocytes); high response (>15 oocytes). The reason to create this new category of sub-optimal responder is that they have poorer outcomes during ART treatments when compared to patients that have 10-15 oocytes retrieved. This category of sub-optimal responder has led to the introduction of new concept in reproductive medicine with the objective to stratify low prognosis patients undergoing ART based on the combination of quantitative and qualitative parameters. There is a group of patients that although present normal biomarkers of ovarian reserve, such as AFC and AMH, they have a sub-optimal response to COS. They have 9 retrieved oocytes, showing the inadequacy of using only the traditional ovarian reserve biomarkers to predict the ovarian response. This sub-optimal response should be related to ovarian sensitivity to exogenous gonadotrophins modulated by genetic factors. Hence, adoption of a pharmacogenetic approach in assisted reproduction seems attractive as it may help understanding the relationship between genetic variants and ovarian response to exogenous gonadotropins. The patient´s genetic profile could be used to select the most appropriate gonadotropin type, predict the optimal dosage for each drug, develop cost-effective treatment plan, maximize the success rates, and finally decrease the time-to-pregnancy. The study of genetic polymorphisms is undoubtedly a promising field in reproductive medicine. The candidate genes that play a role in the ovarian response to COS are as follows: i) genes that affect follicular function by exerting a hormonal effect - FSH, FSHR, V-LH, AMH, AMHR2, ERα, ERβ, CYP17, CYP19, COMT, MTHFR, GnRH1, KISS1, and KISS1R; ii) genes that affect the rate of initial primordial follicle recruitment in relation to the pool of growing follicles - BMP15, GDF9, and FOXL2; and iii) genes that encode DNA binding proteins and transcription factors, such as LHX8 and NANOS3. Variations in these genes can determine the variability of the follicular pool and explain the variance of the COS response and the results of ART. In modern reproductive medicine, where the individualization and personalized treatments should be the norm, optimizing and obtaining the best result in each COS would be of great interest to both clinicians and patients alike. Considering the variability of patients´ response to COS, it is clear inadequate to select the type of gonadotropin and its initial dose empirically or based only on clinical and hormonal parameters. In this study, the investigators will evaluate genetic variation or polymorphisms between individuals submitted to IVF/ICSI cycles, and how these variations influence response to COS and pregnancy outcomes. Thus, this project has the aim to develop a pharmacogenetic algorithm associating gene polymorphisms, the patient clinical information, and functional and hormonal biomarker to: 1) predict the patient response to gonadotropin; 2) develop individualized gonadotropin regimens; 3) improve the accuracy of determining appropriate dosages of gonadotrophins; 4) improve efficacy and patient compliance to COS; and finally 5) reduce the time-to-pregnancy.

Official Title
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Pharmacogenetic Algorithm for Individualized Controlled Ovarian Stimulation (iCOS) in Assisted Reproductive Technology Cycles

Conditions
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Infertility

Intervention / Treatment
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* Diagnostic Test: Pharmacogenetic test


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Females aged ≤38 years AMH 1.2 ng/mL and AFC 5 Normal TSH and prolactin levels The presence of both ovaries without morphological abnormalities Ovulatory cycles with a mean duration between 25-35 days BMI ≤30 No evidence of endocrine diseases such as hyperprolactinemia, thyroid dysfunction, or PCOS Exclusion Criteria: Females aged >38 years PCOS patients Moderate/severe endometriosis (grade III and IV) Previous ovarian surgery Previous radio/chemotherapy Severe male factor infertility (severe oligozoospermia and nonobstructive azoospermia).

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 38 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Group 1<br>recombinant FSH starting dose: 100 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|
| Active Comparator: Group 2<br>recombinant FSH starting dose: 125 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|
| Active Comparator: Group 3<br>recombinant FSH starting dose: 150 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|
| Active Comparator: Group 4<br>recombinant FSH starting dose: 175 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|
| Active Comparator: Group 5<br>recombinant FSH starting dose: 200 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|
| Active Comparator: Group 6<br>recombinant FSH starting dose: 225 IU to develop a pharmacogenetic test | Diagnostic Test: Pharmacogenetic test<br>* To develop a pharmacogenetic prediction test<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of retrieved oocytes | The number of retrieved oocytes during an IVF treatment | It will be assessed on the 22th month after the first patient recruitment | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of mature oocytes | The number of mature during an IVF treatment | It will be assessed on the 22th month after the first patient recruitment | 
| Embryo quality - morphological classification | Assessment of morphological embryo quality in each group of patients | It will be assessed on the 22th month after the first patient recruitment | 
| Ovarian Hyperstimulation Syndrome | The incidence of OHSS in each group of patients | It will be assessed on the 22th month after the first patient recruitment | 
| Pregnancy Rate | The ratio between the number of positive pregnancy blood test and the number of patients submitted to and IVF cycle | It will be assessed on the 22th month after the first patient recruitment | 
| Clinical Pregnancy Rate | The ratio between the number of patients with at least one gestational sac identified in an ultrasound scan and the number of patients submitted to and IVF cycle | It will be assessed on the 22th month after the first patient recruitment | 
| Miscarriage Rate | The ratio between ongoing pregnancy and clinical pregnancy | It will be assessed on the 22th month after the first patient recruitment | 
| Ongoing Pregnancy Rate | The ratio between the number of ongoing pregnancies in 12th week and the number of patients submitted to and IVF cycle | It will be assessed on the 22th month after the first patient recruitment | 
| Live Birth Rate | The ratio between the number of live birth and the number of patients that started the treatment | It will be assessed on the 22th month after the first patient recruitment | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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pharmacogenetic, pharmacogenomic, polymorphism, individualized controlled ovarian stimulation, algorithm, assisted reproduction