Panel discussion on...
Distruptive Technology
Cosmetic testing evolution: navigating obstacles, embracing alternatives, and the AI frontier
Ten years ago, a ban on animal testing for cosmetic products came into force in Europe. Ethics and regulatory requirements inspired cosmetic industry to develop alternative models. In vitro approaches are widely used in cosmetic safety tests. Skin/eye irritation, sensitization, phototoxicity, genotoxicity tests have been validated by the European Centre for the Validation of Alternative Methods (ECVAM) and approved by the Organization for Economic Co-operation and Development (OECD). It seems like major obstacles have been successfully overtaken. Nevertheless, questions about systemic toxicity, developmental and reproductive toxicity are still open. Two-dimensional cell culture models are not able to precisely describe the whole complexity of in vivo skin health responses on a toxic ingredient. Single cell type cannot represent a multicellular organ, let alone an organism. That is why an integrated testing strategy is a popular choice to cover the variety of possible negative effects of test products. Development of three-dimensional models of human skin, like Reconstructed human epidermis, was an important breakthrough. They have a close structural and physiological resemblance with human epidermis, proposing also an open-air surface for topical applications. Still, absence of vascularization (epithelial cells), innervation (neurons), immune response (Langerhans cells), pigmentation (melanocytes), skin appendages (hairs and glands) distances this model from reality. Despite the mentioned limits, the currently available alternative models and validated tests have enough predictive power to reliably describe toxic effects of cosmetic products and ingredients.
New generation of 3D-skin models and “organ-on-chip” approach look promising. They may guide us to a new level of safety tests or, at least, expand our investigative capabilities. Advanced tissue engineering allows to produce full-skin models incorporating different cell types. These models are close the real human skin, both structurally and physiologically. Microfluidic-based organ-on-chip device is a promising choice when a simultaneous reply to a treatment from various cell types is needed. A compact chip allows to cultivate various cells in different compartments, mimicking function of an organ, while microfluidics provides precise control of the environment. However, these approaches are not accepted yet by the official authorities.
Ban on animal tests impacted also efficacy tests. In vitro cell-based approaches are not adapted for final cosmetic formulations, while popularity of 3D-models in evaluation of comprehensive biological effects of final cosmetic products or even active ingredients is low. Efficacy tests are mainly performed on living human skin explants or on volunteers in clinical studies.
In silico approaches became popular for safety prediction in cosmetic industry. Large amount of data about toxicity of chemicals is available now. A recent publication identified over 900 databases, covering biology, drug discovery, chemistry, omics, clinical trials, toxicology etc (1). Only in silico tools are able to compile this data and use it for predictive toxicology of cosmetics.
Prediction is based on the structure of target molecule. Logically, similar chemicals are expected to cause similar effects. However, reality is more sophisticated and tiny changes in chemical structure may change its properties.
Discussion about perspectives of AI in cosmetic testing would not be complete without participation of AI itself. Here is the answer of the most popular AI nowadays to the same questions:
The cosmetics industry faces several obstacles in testing, particularly with the growing bans on animal testing. However, companies are actively exploring alternative methods to address these challenges. Here are some key obstacles and the corresponding alternatives being explored:
1. Regulatory Compliance:
- Obstacle: Navigating and complying with diverse global regulations.
- Alternatives: Industry efforts are focused on advocating for and adopting standardized non-animal testing methods that meet regulatory requirements. Collaborative initiatives work toward harmonizing regulations across different regions.
2. Global Market Access:
- Obstacle: Ensuring compliance with animal testing bans in various markets.
- Alternatives: Companies are investing in the development and implementation of alternative testing methods that align with the strictest regulatory standards globally. This may involve adjusting formulations or testing protocols to meet the criteria of multiple jurisdictions.
3. Testing Alternatives:
- Obstacle: Finding reliable alternatives to animal testing.
- Alternatives: Advances in in vitro testing, organ-on-a-chip technology, 3D cell cultures, and computer modeling are being explored. These alternatives aim to provide more accurate predictions of human responses without the use of animals.
4. Scientific Validity:
- Obstacle: Ensuring the reliability and accuracy of alternative testing methods.
- Alternatives: Ongoing research focuses on refining and validating non-animal testing methods to ensure they are scientifically rigorous and capable of predicting human responses accurately. Collaboration between industry, academia, and regulatory bodies plays a crucial role in this validation process.
5. Costs and Investment:
- Obstacle: The expense of developing and implementing alternative testing methods.
- Alternatives: Companies are investing in research and development to make alternative methods more cost-effective and scalable. Collaboration and information-sharing within the industry can help reduce individual R&D costs and accelerate the adoption of alternative testing practices.
6. Consumer Perception:
- Obstacle: Meeting consumer demands for cruelty-free products.
- Alternatives: Companies are increasingly focusing on transparent communication about their commitment to cruelty-free testing. The rise of certifications such as Leaping Bunny and Choose Cruelty-Free helps consumers identify and support products that adhere to cruelty-free standards.
7. Testing for Specific Ingredients:
- Obstacle: Some ingredients lack alternative testing methods.
- Alternatives: Research is ongoing to develop alternative methods for testing specific ingredients. In the meantime, companies may choose to reformulate products to exclude ingredients for which non-animal testing alternatives are not yet available.
8. Collaboration and Information Sharing:
- Obstacle: Industry collaboration and information-sharing challenges.
- Alternatives: Collaborative initiatives, industry consortiums, and partnerships between companies, research institutions, and regulatory bodies aim to facilitate the sharing of knowledge and resources. This collaboration accelerates the development, validation, and adoption of alternative testing methods.
9. Updating Industry Standards:
- Obstacle: Evolving industry standards to incorporate new testing methods.
- Alternatives: Industry associations and regulatory bodies are working together to update and standardize testing protocols to include alternative methods. This ensures that the industry stays aligned with the latest scientific advancements.
10. Global Harmonization:
- Obstacle: Achieving global harmony in regulations and standards.
- Alternatives: Industry advocacy efforts seek to harmonize global regulations on cosmetic testing. Encouraging consistent standards helps companies navigate regulatory landscapes more efficiently and promotes the widespread adoption of alternative testing methods.
Despite these obstacles, the cosmetics industry has been making strides in adopting alternative testing methods, driven by consumer demand for cruelty-free products and increasing regulatory restrictions on animal testing. Advances in technology and a commitment to ethical practices are gradually reshaping the landscape of cosmetic testing.
Role of AI in Cosmetic Testing:
AI has the potential to significantly impact cosmetic testing by:
1. In Silico Testing:
- AI can model biological systems and predict the safety and efficacy of cosmetic ingredients. In silico testing reduces the need for animal testing by simulating complex interactions at a molecular level.
2. Data Analysis:
- AI algorithms can analyze vast amounts of data from various sources, including clinical studies, in vitro tests, and existing literature. This accelerates the identification of potential risks and enhances the understanding of ingredient safety.
3. Predictive Toxicology:
- AI can contribute to predictive toxicology models, aiding in the early identification of potential hazards associated with cosmetic ingredients. This can streamline the testing process and reduce the reliance on animal studies.
4. Personalized Cosmetics:
- AI-driven personalized cosmetics can analyze individual characteristics to recommend products tailored to specific skin types and preferences. This minimizes the need for generalized testing on a wide range of consumers.
5. Automation of Testing Processes:
- AI-powered automation can streamline testing procedures, making them more efficient and reducing the time and resources required. This can contribute to the faster development and release of cosmetic products.
6. Image Analysis:
- AI can be utilized in image analysis for cosmetic testing, such as assessing skin reactions or analyzing cosmetic formulations. This can provide objective and standardized measurements, reducing reliance on subjective assessments.
7. Identification of Alternatives:
- AI can assist in identifying and optimizing alternative testing methods by analyzing vast datasets. This can contribute to the development of more reliable and effective non-animal testing approaches.
While AI holds promise in revolutionizing cosmetic testing, it is essential to ensure that these technologies are validated, transparent, and aligned with regulatory requirements. The integration of AI can enhance the efficiency and ethical standards of cosmetic testing, aligning with the industry's move towards cruelty-free and sustainable practices.
References and notes
Panelists
ELISABETH WILLEIT
Product Development and Regulatory
Affairs Manager, BDI-BioLife Science
THERESA CALLAGHAN
Callaghan Consulting International
ELLA CERAULO
Innovation Chemist, Cornelius Group
MARIE MAGNAN
Regulatory Affairs Manager, COSMED -
the French cosmetic Association for SMEs
ANGELINA GOSSEN
Technical Marketing Manager, Croda
HOWARD EPSTEIN
EMD Electronics, an affiliate of Merck KGaA
NIKITA RADIONOV
Head of sales, Eurofins BIO-EC
JOHAN JANSEN-STORBACKA
Director Personal Care Ingredients, IFF
BELINDA CARLI
Director & Senior Cosmetic Chemist, Institute of Personal Care Science
MARK SMITH
Director General, NATRUE - The International Natural and Organic Cosmetic Association
NEIL BURNS
Managing Partner, Neil A Burns
CHIARA DEGL’INNOCENTI
Product Manager Hair Care Cosmetic Actives, RAHN
ELISA ALTIERI
Market Manager Personal care, ROELMI HPC
LAURIE VERZEAUX
Scientific communication project leader, SILAB
MAURA ANGELILLO
Marketing Director, Vitalab
DR. ÒSCAR EXPÓSITO
CEO, CSO and co-founder, Vytrus Biotech
References and notes
- Pawar G, Madden JC, Ebbrell D, et al. In silico toxicology data resources to support read-across and (Q)SAR. Front Pharmacol 2019; 10: 561. PubMed link : https://www.ncbi.nlm.nih.gov/pubmed/31244651.