Genetic Polymorphism of<i>CYP2D6</i>and Clomiphene Concentrations in Infertile Patients with Ovulatory Dysfunction Treated with Clomiphene Citrate

Ji, Misuk; Kim, Kwang Rae; Lee, Woochang; Choe, Wonho; Chun, Sail; Min, Won‐Ki

doi:10.3346/jkms.2016.31.2.310

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…In contrast, high BMI is a consistent predictor of positive metformin response, as OCT proteins are expressed in adipose tissue ( 106 ). Women with PCOS, who have elevated Anti-Müllerian hormone (AMH) levels are CC resistant or they may require higher CC starting dosage ( 107 ).…”

Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

“…Besides, ovulation predictors such as high FSH levels and low BMI range were identified ( 97 ). A pharmacokinetics exploration of CC proved that women who did not ovulate show lower concentrations of the active metabolites ( 107 ).…”

Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

“…Parameters to assess treatment effectiveness are various such as increase ovulation ( 97 , 103 , 108 ), number of oocytes retrieved ( 104 ) or clinical responses (weight loss, lipids profiles or oral glucose tolerance tests) ( 110 ). Some studies combined PCOS patients with other women sharing similar traits as hyperinsulinemia, androgen excess, ovulatory dysfunction, to increase statistical power ( 90 , 103 , 107 ). Confounding factors such as diet or lifestyles were not cautiously supervised in all studies, except for two studies that added a low calorie diet as part of the treatment ( 95 , 109 ).…”

Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

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Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

et al. 2020

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Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Epidemiological findings revealed that women with PCOS are prone to develop certain cancer types due to their shared metabolic and endocrine abnormalities. However, the mechanism that relates PCOS and oncogenesis has not been addressed. Herein, in this review article the genomic status, transcriptional and protein profiles of 264 strongly PCOS related genes (PRG) were evaluated in endometrial cancer (EC), ovarian cancer (OV) and breast cancer (BC) exploring oncogenic databases. The genomic alterations of PRG were significantly higher when compared with a set of non-diseases genes in all cancer types. PTEN had the highest number of mutations in EC, TP53, in OC, and FSHR, in BC. Based on clinical data, women older than 50 years and Black or African American females carried the highest ratio of genomic alterations among all cancer types. The most altered signaling pathways were p53 in EC and OC, while Fc epsilon RI in BC. After evaluating PRG in normal and cancer tissue, downregulation of the differentially expressed genes was a common feature. Less than 30 proteins were up and downregulated in all cancer contexts. We identified 36 highly altered genes, among them 10 were shared between the three cancer types analyzed, which are involved in the cell proliferation regulation, response to hormone and to endogenous stimulus. Despite limited PCOS pharmacogenomics studies, 10 SNPs are reported to be associated with drug response. All were missense mutations, except for rs8111699, an intronic variant characterized as a regulatory element and presumably binding site for transcription factors. In conclusion, in silico analysis revealed key genes that might participate in PCOS and oncogenesis, which could aid in early cancer diagnosis. Pharmacogenomics efforts have implicated SNPs in drug response, yet still remain to be found.

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Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

Section: Pharmacogenomics In Pcosmentioning

confidence: 99%

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Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

et al. 2020

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“…Clomiphene resistance may be linked with cytochrome CYP2D6 variants, as the drug is mainly metabolized into its active components by the enzyme 78 . Small studies have however produced conflicting results 79,80 . Larger datasets may be a step towards personalized medicine in the field of ovulation induction for women with PCOS and in order to decrease the risk of cycle cancellation.…”

Section: Machine Learning Is Aiding Art Treatmentsmentioning

confidence: 99%

Big Data and Artificial Intelligence (AI) are Poised to Transform Infertility Healthcare

Santiago¹,

Polański²

2020

Preprint

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Advances in machine learning (ML) and artificial intelligence (AI) are transforming the way we treat patients in ways not even imagined a few years ago. Cancer research is at the forefront of this movement. Infertility, though not a life-threatening condition, affects around 15% of couples trying for a pregnancy. Increasing availability of large datasets from various sources creates an opportunity to introduce ML and AI into infertility prevention and treatment. At present in the field of assisted reproduction, very little is done in order to prevent infertility from arising, with the main focus put on treatment when often advanced maternal age and low ovarian reserve make it very difficult to conceive. A shift from this disease-centric model to a health centric model in infertility is already taking place with more emphasis on the patient as an active participator in the process. Poor quality and incomplete data as well as biological variability remain the main limitations in the widespread and reliable implementation of AI in the field of reproductive medicine. That said, one of the areas where this technology managed to find a foothold is identification of developmentally competent embryos. More work is required however to learn about ways to improve natural conception, the detection and diagnosis of infertility, and improve assisted reproduction treatments (ART) and ultimately, develop clinically useful algorithms able to adjust treatment regimens in order to assure a successful outcome of either fertility preservation or infertility treatment. Progress in genomics, digital technologies and advances in integrative biology has had a tremendousimpact on research and clinical medicine. With the rise of ‘big data’, artificial intelligence, and the advances in molecular profiling, there is an enormous potential to transform not only scientific research progress, but also clinical decision making towards predictive, preventive, and personalized medicine. In the field of reproductive health, there is now an exciting opportunity to leverage these technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. In this review, we present a comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector. Here we discuss recent influential work and explore the limitations of the use of Machine Learning models in this rapidly developing area.

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“…Until now, only four teams have studied the link between the response to CC and the CYP2D6 genotype, and their results have been contradictory (35,(37)(38)(39).…”

Section: Introductionmentioning

confidence: 99%

Could Cytochrome P450 2D6, 3A4 and 3A5 Polymorphisms Explain the Variability in Clinical Response to Clomiphene Citrate of Anovulatory PCOS Women?

et al. 2021

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IntroductionCytochrome P450 2D6, 3A4 and 3A5 are involved in the metabolism of many drugs. These enzymes have a genetic polymorphism responsible for different metabolic phenotypes. They play a role in the metabolism of clomiphene citrate (CC), which is used to induce ovulation. Response to CC treatment is variable, and no predictive factors have thus far been identified.ObjectiveTo study a possible link between the cytochrome P450 2D6, 3A4 and 3A5 polymorphisms and clinical response to CC.Study DesignSeventy-seven women with anovulatory Polycystic Ovarian Syndrome (PCOS) treated with CC were included which determined their cytochrome P450 2D6, 3A4 and 3A5 genotypes and used the results to predict ovarian response to this drug. Predicted responses based on the cytochrome genotypes were compared with the observed clinical responses using the calculation of a weighted Kappa coefficient.Main Outcome MeasuresNumber of dominant follicles assessed by ultrasound at the end of the follicular phase and confirmation of ovulation by blood progesterone assay in the luteal phase.ResultsConcordance between the predicted and observed responses for the combination of the three cytochromes was 36.71%, with a negative Kappa coefficient (K = -0.0240), which corresponds to a major disagreement. Similarly, for predictions based on the cytochrome P450 2D6 genotype alone, only 39.24% of predictions were verified (coefficient K = -0.0609).ConclusionThe genetic polymorphism of cytochromes P450 2D6, 3A4 and 3A5 does not appear to influence clinical response to CC used to induce ovulation in anovulatory PCOS women.

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Genetic Polymorphism ofCYP2D6and Clomiphene Concentrations in Infertile Patients with Ovulatory Dysfunction Treated with Clomiphene Citrate

Cited by 12 publications

References 18 publications

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Big Data and Artificial Intelligence (AI) are Poised to Transform Infertility Healthcare

Could Cytochrome P450 2D6, 3A4 and 3A5 Polymorphisms Explain the Variability in Clinical Response to Clomiphene Citrate of Anovulatory PCOS Women?

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