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ID® Millennium® is a personal lubricant, for penile and /or 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, polyisoprene, and polyurethane condoms.

ID® Moments® Silicone is a personal lubricant, for penile and /or 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, polyisoprene, and polyurethane condoms.

ID® Millennium® is non-sterile, an over-the-counter silicone based personal lubricant, formulated to be clear, non-irritating, non-greasy natural and odorless. This device is silicone soluble liquid for use as a personal lubricant. ID® Millennium® Personal Lubricant contains a blend of silicone fluid ingredients similar to ingredients found in the predicate device. ID® Millennium® Personal Lubricant is neither a contraceptive nor a spermicide.

The device is available in the following variant personal lubricant formulas: ID® Millennium® and ID Moments® Silicone.

ID® Millennium® Personal Lubricant is provided in plastic/PET bottles with screw-on cap, flip top closure, pump dispensers, and foils.

This document is a 510(k) Premarket Notification for a personal lubricant, ID® Millennium® and ID® Moments® Silicone, classified as a Class II device under Condoms (21 CFR 884.5300). The submission aims to demonstrate substantial equivalence to a legally marketed predicate device, Wet Platinum Premium Lubricant® (K130012).

Here's an analysis of the provided information regarding acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes several tests performed to demonstrate the safety and effectiveness of the ID® Millennium® and ID® Moments® Silicone lubricants. The acceptance criteria are implicitly or explicitly stated as the desired outcome of these tests (e.g., "not cytotoxic," "non-sensitizing," "not an irritant and not systemically toxic," compatible with condoms).

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

• Sample Size: The document does not explicitly state the specific numerical sample sizes for each test (e.g., how many rabbits for irritation, how many condoms for compatibility testing, how many batches for specifications). It mentions "retains" for shelf-life testing and "major brand name" condoms, which implies a representative selection.
• Data Provenance: The studies appear to be conducted by or for Westridge Laboratories, Inc. The standards referenced (ISO, USP, ASTM) are international and national standards, but the physical location where the tests were performed is not specified. The studies are prospective in nature, as they involve testing the actual device to generate performance data.

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

This type of product does not involve "ground truth" established by human experts in the way an AI diagnostic device would. Instead, the "ground truth" or acceptance criteria are derived from:

• Standardized Test Methods: ISO, USP, and ASTM standards provide predefined methodologies and acceptable limits for chemical, biological, and physical properties.
• Scientific Principles: Biocompatibility, sterility, and material compatibility are judged against established scientific and regulatory principles for medical devices.

Therefore, the concept of "number of experts" and their "qualifications" for establishing a diagnostic ground truth is not applicable here. The expertise lies in the certified labs and personnel who conduct these tests according to the specified standards.

4. Adjudication Method for the Test Set

Adjudication methods like 2+1 or 3+1 are typically used in clinical studies or AI performance evaluations where subjective expert interpretations are involved. For the objective laboratory tests described for this lubricant, such adjudication is not applicable. The results are based on objective measurements and observations according to the test protocols.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. MRMC studies are specific to evaluating human reader performance, often in diagnostic imaging, and comparing it with or without AI assistance. This device is a personal lubricant, not a diagnostic or imaging device, so MRMC studies are not relevant.

6. Standalone (Algorithm Only) Performance Study

No, a standalone (algorithm only) performance study was not done. This device is a physical product (a personal lubricant), not an algorithm or AI system. Therefore, the concept of "standalone performance" for an algorithm is not applicable. The performance studies evaluate the physical and chemical properties of the lubricant itself.

7. Type of Ground Truth Used

The "ground truth" in this context refers to the defined acceptable biological, chemical, and physical properties as determined by:

• International Standards: ISO 10993 for biocompatibility.
• National Pharmacopeia: USP , , , for microbiological and certain physical properties.
• Industry Standards: ASTM 7661-10 for condom compatibility.
• Manufacturer's Internal Specifications: For viscosity, specific gravity, appearance, color, and odor, as well as for defining the shelf-life testing parameters.

8. Sample Size for the Training Set

This product is not an AI/ML device, so there is no "training set" in the computational sense. The product development would involve formulations, pilot batches, and iterative testing, but not a "training set" for an algorithm.

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

As there is no training set for an algorithm, this question is not applicable. The formulation and development of the lubricant would be based on chemical engineering principles, material science, and regulatory requirements, rather than a data-driven "ground truth" for machine learning.

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