Follitropin Alpha versus Follitropin Beta in IVF/ICSI Cycle: A Retrospective Cohort Study.

IF 4.7 2区医学 Q1 CHEMISTRY, MEDICINAL Drug Design, Development and Therapy Pub Date : 2024-09-26 eCollection Date: 2024-01-01 DOI:10.2147/DDDT.S479700

Jing-Xian Cao, Jing-Yan Song

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Abstract

Purpose: The purpose of this study was to compare the efficacy of Follitropin alpha (Gonal-F) and Follitropin beta (Puregon) on cumulative live birth rate (CLBR), defined as the percentage of the number of patients who delivered for the first time in a single ovarian stimulation cycle and the number of patients in all oocyte retrieval cycles.

Methods: A retrospective cohort study including 2864 infertile patients who underwent ovarian stimulation with Puregon (group A, n=1313) and Gonal-F (group B, n=1551) was conducted between July 2015 and June 2021 at a university-affiliated reproductive medicine center. Reduce potential confounding factors between groups, propensity scores and multivariable logistic regression analyses were estimated to obtain unbiased estimates of outcomes. The primary outcome was the difference in CLBR between the two groups.

Results: Each group identified 1160 individuals after propensity score matching (PSM). Baseline characteristics were similar between groups after PSM. The total gonadotrophin (Gn) dose (2400 vs 2325), p=0.038) and cost of Gn usage (5327.9¥ vs 7547.2¥, p<0.001) between the Puregon and Gonal-F groups were statistically significant. Nevertheless, the pregnancy outcomes between the two groups were comparable after fresh embryo transfer and subsequent frozen-thawed embryo transfer. Additionally, there was also no difference observed in the primary outcome of CLBR (52.8% vs 55.7%, p=0.169). Multivariable regression analysis revealed that the type of Gn was not associated with CLBR (p = 0.912).

Conclusion: Gonal-F may be a reasonable option for infertile patients who are hesitant to receive more Gn dosage injections. Furthermore, Puregon can eliminate unneeded anxiety and expenses while also administering more flexibility. Taken together, these findings could well be utilized in everyday clinical practice to better inform patients when deciding on an ovarian stimulation strategy.

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体外受精/卵胞浆内单精子显微注射周期中的促性腺激素α与促性腺激素β：回顾性队列研究

目的：本研究旨在比较α型促性腺激素（Gonal-F）和β型促性腺激素（Puregon）对累积活产率（CLBR）的疗效，累积活产率是指在单个卵巢刺激周期中首次分娩的患者人数与所有取卵周期中患者人数的百分比：2015年7月至2021年6月期间，某大学附属生殖医学中心开展了一项回顾性队列研究，纳入了2864名接受Puregon（A组，人数=1313）和Gonal-F（B组，人数=1551）卵巢刺激的不孕患者。为减少组间潜在的混杂因素，对倾向评分和多变量逻辑回归分析进行了估计，以获得无偏的结果估计值。主要结果是两组之间的CLBR差异：经过倾向得分匹配（PSM）后，每组确定了 1160 人。经过倾向得分匹配后，两组的基线特征相似。促性腺激素（Gn）的总剂量（2400 vs 2325，P=0.038）和Gn的使用成本（5327.9¥ vs 7547.2¥，P=0.038）：对于不愿接受更多 Gn 剂量注射的不孕患者来说，Gonal-F 可能是一个合理的选择。此外，Puregon 还能消除不必要的焦虑和费用，同时提供更大的灵活性。综上所述，这些研究结果完全可以用于日常临床实践，以便在患者决定卵巢刺激策略时提供更好的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Drug Design, Development and Therapy CHEMISTRY, MEDICINAL-PHARMACOLOGY & PHARMACY

CiteScore

9.00

自引率

0.00%

发文量

382

审稿时长

>12 weeks

期刊介绍： Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications. The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas. Specific topics covered by the journal include: Drug target identification and validation Phenotypic screening and target deconvolution Biochemical analyses of drug targets and their pathways New methods or relevant applications in molecular/drug design and computer-aided drug discovery* Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes) Structural or molecular biological studies elucidating molecular recognition processes Fragment-based drug discovery Pharmaceutical/red biotechnology Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products** Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing) Preclinical development studies Translational animal models Mechanisms of action and signalling pathways Toxicology Gene therapy, cell therapy and immunotherapy Personalized medicine and pharmacogenomics Clinical drug evaluation Patient safety and sustained use of medicines.