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Record W4377968875 · doi:10.1093/jsxmed/qdad061.036

(036) Characterizing flibanserin’s safety profile among postmenopausal women with HSDD: An Exploratory Analysis

2023· article· en· W4377968875 on OpenAlexaboutno aff
S Kingsberg, A Faragasso, S Donatucci, S Patel, Louise Brown

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
Fundersnot available
KeywordsHypoactive sexual desire disorderMedicineAdverse effectPostmenopausal womenNauseaSexual desireInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Flibanserin is approved in the United States (US) for premenopausal women and in Canada for pre and naturally postmenopausal women, ≤60 years of age, to treat acquired, generalized hypoactive sexual desire disorder (HSDD). Clinical trials suggest that flibanserin's safety profile is similar among pre-and postmenopausal women. During a Phase 3 postmenopausal study, the most frequently reported adverse events (AEs) were dizziness (10%), somnolence (9%), nausea (8%), and headache (6%). This study explores postmarketing AEs received from postmenopausal women and their clinicians during flibanserin treatment. Objective The primary objective of this safety study is to further characterize flibanserin's safety profile among postmenopausal women with HSDD treated in a clinical setting. Methods This study is a retrospective exploratory analysis of AEs captured in a global safety database (August 15, 2015, to May 12, 2022). The database was queried for US reports of AEs in postmenopausal women using the Medical Dictionary for Regulatory Activities Search Strategy Version 21.1. Preferred Term (PT) “Product use in unapproved indication.” This PT was selected as postmenopausal use is an unapproved indication in the US. Reports were filtered to identify and exclude non-postmenopausal unapproved usages, such as use in males and premenopausal women with non-HSDD conditions. All reports stating postmenopausal use are included in this analysis. If reproductive status was not specified, age was used as an indicator. If age was unknown, the report was excluded. The age range used in this study was 51-75 years. Adverse events were categorized using PTs, and descriptive statistics were used to summarize the results. Dispensing data from a sample of 30,000 scripts filled over the past year at one pharmacy were used to estimate the percentage of flibanserin prescriptions filled by women ages >50 years. No incidence data were calculated as AE reporting is voluntary and true exposure is unknown. Results We identified 195 unique reports of postmenopausal use with at least one AE during the study period. There were 656 reported AEs, 16 (2.4%) were serious, and no fatalities. Adverse events occurring in ≥5 postmenopausal women excluding events not related to a body system are summarized in the Figure. Approximately 18% of scripts filled over the past year at one pharmacy were for women ages >50 years. It is important to acknowledge that this pharmacy’s findings may not represent all pharmacies. Conclusions The postmarketing safety profile of flibanserin among postmenopausal women is similar to that observed with premenopausal women and during clinical trials. Excluding PTs not related to a body system such as unapproved use and drug ineffective, the most frequently reported postmarketing AEs included dizziness, nausea, and insomnia. No safety signals specific to postmenopausal women have been identified to date. Although informative, the lack of information on medical history, concomitant medications, and other potential interacting products limit the ability to make inferences on causality in most reported cases. Disclosure Yes, this is sponsored by industry/sponsor: Sprout Pharmaceuticals, Inc Clarification: Industry initiated, executed and funded study Any of the authors act as a consultant, employee or shareholder of an industry for: Sprout Pharmaceuticals, Inc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.293
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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