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Replens is a personal lubricant for vaginal application, intended to moisturize and lubricate, to enhance the ease and comfort of intimate sexual activity and supplement the body's natural lubrication. This product is compatible with natural rubber latex condoms and synthetic (polyurethane and polyisoprene) condoms.

Replens Vaqinal Moisturizer is a non-sterile, water-based, white to off-white, non-irritating, non-greasy, non-staining vaginal gel delivered in a tube with a reusable applicator as a long-lasting moisturizer for vaginal dryness. The use of the reusable applicator provides less mess in application. Replens Vaginal Moisturizer is intended for ongoing use, not exclusively for use during intimate sexual activity.

Replens Vaginal Moisturizer contains ingredients commonly used in other products for vaginal use sold as medical devices and cosmetics. All ingredients are either NF, USP, or are considered "generally recognized as safe for their intended use". The quantitative formulation is considered confidential commercial information.

The Replens Vaginal Moisturizer, a personal lubricant for vaginal application, underwent various tests and clinical studies to demonstrate its safety and effectiveness. The acceptance criteria and performance data are summarized below.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document refers to a series of eight clinical studies as the "test set" for Replens Long-Lasting Vaginal Moisturizer.

• Sample Sizes: The number of patients enrolled in the Replens arm across these studies varied:
  • Bachmann et al (1991): 89 patients
  • Bachmann et al (1992): 54 patients
  • Zinny and Lee (1991): 26 patients
  • Young et al (1991): 30 patients
  • Nakamura (1991): 10 patients
  • Whitehead (1991): 32 patients
  • Nachtigall (1994): 15 patients
  • Gelfand and Wendman (1994): 25 patients
• Data Provenance: The provenance of the data is not explicitly stated in terms of country of origin but is indicated by the publication of several studies in peer-reviewed journals (e.g., Clinical Practice in Sexuality, Today's Therapeutic Trends, Fertility and Sterility, J. Women's Health). Some studies were "Unpublished." The studies appear to be prospective clinical trials given their design (e.g., double-blind, cross-over, open-label) and specific dosage regimens and durations.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The concept of "ground truth" established by experts, as typically understood in the context of AI/ML device performance evaluation, is not directly applicable to this submission. This is a 510(k) for a medical device (vaginal moisturizer), not an AI/ML algorithm.

The "ground truth" for the clinical efficacy and safety was established:

• Clinically by the direct observation, assessment (e.g., vaginal pH, vaginal dryness index), and reported symptoms of the patients participating in the clinical trials.
• Statistically by the analysis of these clinical outcomes by the researchers who conducted and published these studies.
• Biologically through the results of the various biocompatibility tests.

The "experts" involved would be the clinical investigators (physicians, researchers) who designed and executed these studies, interpreted the data, and published the findings. Their qualifications are not explicitly detailed but are implied by their authorship in scientific papers.

4. Adjudication Method for the Test Set

Adjudication methods (like 2+1, 3+1) are typically used in AI/ML studies to resolve discrepancies in expert labeling or diagnoses. This concept is not applicable to this 510(k) submission as it is not for an AI/ML device. The clinical studies followed standard trial protocols for data collection and analysis, which inherently include methods for managing and interpreting patient data and outcomes. For double-blind studies, the blinding helps ensure impartial assessment.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned. This type of study comparing human readers with and without AI assistance is specific to AI/ML devices and is not relevant to this product submission.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No standalone (algorithm only) performance study was conducted or is relevant. This device is a topical moisturizer, not an AI algorithm.

7. Type of Ground Truth Used

The "ground truth" for this device's performance was based on:

• Clinical Endpoints: Patient-reported symptoms (e.g., relief of vaginal dryness), objective clinical measurements (e.g., vaginal pH, vaginal dryness index), and physician assessments of the vaginal mucosa.
• Biocompatibility Standards: Pre-defined safety standards for cytotoxicity, irritation, sensitization, and toxicity.
• Physical/Chemical Testing: Laboratory assessments for stability, preservative effectiveness, and condom compatibility.

8. Sample Size for the Training Set

The concept of a "training set" is specific to AI/ML models. This device is a traditional medical product (vaginal moisturizer), not an AI/ML algorithm. Therefore, there is no training set in the AI/ML sense. The product development and testing relied on chemical formulation, manufacturing best practices, and the clinical and non-clinical studies summarized.

9. How the Ground Truth for the Training Set Was Established

As there is no training set for an AI/ML algorithm, this question is not applicable.

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