The influence of the pharmaceutical industry on the development of gonadotrophins and ovarian stimulation protocols in assisted reproductive technologies
Bibliographic record
Abstract
Introduction: fertilisation (IVF) in the late 1970s, the pharmaceutical industry has played a pivotal role in advancing gonadotrophin production, improving drug purity and optimising delivery methods. Despite significant progress, questions remain about the robustness of the evidence supporting the use of different gonadotrophins and the impact of industry-driven research on clinical practice. The review critically examines the evolution, evidence and future directions of gonadotrophin use in ART. Methods: A comprehensive literature search was carried out in multiple databases to select articles/reviews on historical developments, manufacturing and analytical techniques, regulatory frameworks and clinical trials undertaken to assess gonadotrophin production, formulation processes and their integration into clinical practice. The analysis included mainly evidence from pharmaceutical sponsored randomised controlled trials (RCTs) as well as single arm, registration or post approval studies. Studies on new molecular entities were reviewed. Systematic reviews and meta-analyses, national registries were consulted. Laboratory developments, regulatory challenges, economic constraints, were considered. Results: Over the past four decades, ART has seen remarkable improvements, including increased live birth rates in women of advanced ovarian age, reduced multiple births, and the advent of patient-friendly pen devices. Innovations such as recombinant FSH (rFSH) and biosimilars have expanded treatment options. However, the high cost of drug development as well as the complexity of the ART process have contributed to underpowered trials and reliance on meta-analyses, which often fail to account for confounding factors. Discussion: While gonadotrophins have been shown to be effective for OS, unresolved issues, such as the role of supplementing LH activity in OS protocols, highlight the need for more robust trials. Collaboration between stakeholders is essential to standardise trial designs, define key outcomes and minimise bias. Emerging technologies, including AI and genetic testing, offer opportunities to refine embryo assessment and implantation outcomes, thus improving trial design. A renewed focus on rigorous, transparent trials and interdisciplinary collaboration is essential to advance patient care and address unmet challenges in ART treatment. Beyond gonadotrophins, alternative therapeutic avenues to improve oocyte competence and implantation success warrant exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".