Peptide preservative: Scientists use machine learning to unlock safer cosmetics

13 Feb 2024 --- Chinese researchers have created an antibacterial peptide for cosmetic preservatives using machine learning-assisted rational design. Derived from endogenous human proteins, IK-16-1 has demonstrated antibacterial activity against common spoilage-causing bacteria while adhering to safety regulations.

The study, published in Nature, seeks to present a solution to the safety concerns associated with traditional preservatives in cosmetic products.

Machine learning meets peptide
The study focused on developing a safe and potent antimicrobial peptide with a preservative that is synergetic in cosmetic formulations.

Researchers used computational simulations and machine learning algorithms to design IK-16-1, based on the human antimicrobial peptide class β-defensins.

IK-16-1 showed “significant” antibacterial activity against common microbes linked to cosmetic spoiling, including Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans and Escherichia coli.

Notably, IK-16-1’s safety profile was highlighted to show no hemolytic activity (destruction of red blood cells) and no cytotoxicity (cellular damage) to keratinocytes (skin cells in the epidermis).

Cosmetic safety issues
The researchers highlight the crucial role of preservatives in cosmetic products, as they inhibit microbial growth and prevent spoilage.

Creating new antimicrobial peptides was accelerated by rational design with machine learning support.However, they observe that concerns regarding the safety of conventional preservatives have led to an increasing demand for alternative solutions.

The study claims several preservatives frequently found in cosmetics, like formaldehyde releasers and parabens, have been linked to allergic reactions and skin irritation.

Furthermore, some preservatives can potentially upset the skin microbiota’s natural equilibrium. Because of this, the cosmetics industry is in “urgent” need of broad-spectrum antimicrobial agents that are both safe and effective, underscore the researchers.

Boosting peptide design
The study suggests that IK-16-1 offers a possible substitute for conventional preservatives in cosmetics. The peptide is touted to lower the requirement for allergenic preservatives while preserving the antimicrobial integrity of cosmetic formulations.

The researchers believe the peptide’s broad-spectrum activity and safety make it useful in cosmetic products while addressing consumer concerns about preservative-induced skin irritations and allergies.

Furthermore, IK-16-1 is used to demonstrate the potential of machine learning in accelerating the development of novel antimicrobial peptides. Using AI and computational simulations, researchers can lower development costs and accelerate the screening process.

The study also emphasizes combining machine learning with focused experimentation to completely understand the mechanisms underlying antimicrobial peptide activity.

The researchers state that further investigation is required to clarify its mode of action and its benefits when combined with other preservatives. They suggest using the peptide in formulations will also be easier if alternate dosage forms are studied and safety evaluations are carried out.

By Venya Patel

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