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<p>Assessing the safety and efficacy of skincare products has become increasingly important, with the rise of alternative methods to animal testing, due to ethical and regulatory demands. We reviewed the integration of classical statistical techniques with modern <i>in Silico</i> approaches, providing a structured guide for researchers. Following PRISMA 2020 guidelines, we conducted a systematic PubMed search for studies applying statistical or computational methodologies to assess cosmetic product safety and efficacy, and published between 2013 and 2023. Papers lacking methodological rigor or clear application to cosmetics were excluded. Two independent reviewers screened studies to minimize bias, and only peer-reviewed articles were included. Tables and figures were prepared to synthesize the main results. A total of 195 studies met the inclusion criteria. Our findings highlight the increasing role of <i>in Silico</i> approaches and machine learning techniques in cosmetic safety evaluation, alongside traditional statistical methods such as regression analysis, hypothesis testing, and multivariate techniques. The review provides practical guidance on selecting methodologies based on data availability and research objectives, along with a critical analysis of their strengths and limitations. The findings are shaped by search keywords, which may have excluded some related studies. <i>In Silico</i> methods emerge as promising alternatives to animal testing, though their reliability depends on robust validation and high-quality datasets. Standardization and regulatory integration are crucial for broader adoption in cosmetic science.</p>

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From animal testing to in Silico models: a systematic review and practical guide to cosmetic assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Tamara G. Vasiljev,
  • Lucia Salvioni,
  • Miriam Colombo,
  • Paolo Galli,
  • Francesca Greselin

摘要

Assessing the safety and efficacy of skincare products has become increasingly important, with the rise of alternative methods to animal testing, due to ethical and regulatory demands. We reviewed the integration of classical statistical techniques with modern in Silico approaches, providing a structured guide for researchers. Following PRISMA 2020 guidelines, we conducted a systematic PubMed search for studies applying statistical or computational methodologies to assess cosmetic product safety and efficacy, and published between 2013 and 2023. Papers lacking methodological rigor or clear application to cosmetics were excluded. Two independent reviewers screened studies to minimize bias, and only peer-reviewed articles were included. Tables and figures were prepared to synthesize the main results. A total of 195 studies met the inclusion criteria. Our findings highlight the increasing role of in Silico approaches and machine learning techniques in cosmetic safety evaluation, alongside traditional statistical methods such as regression analysis, hypothesis testing, and multivariate techniques. The review provides practical guidance on selecting methodologies based on data availability and research objectives, along with a critical analysis of their strengths and limitations. The findings are shaped by search keywords, which may have excluded some related studies. In Silico methods emerge as promising alternatives to animal testing, though their reliability depends on robust validation and high-quality datasets. Standardization and regulatory integration are crucial for broader adoption in cosmetic science.