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Calculating Margins of Safety (MoS) in Cosmetics: Data, Assumptions and Common Pitfalls

  • 1 day ago
  • 5 min read

Updated: 4 hours ago

Calculating Margins of Safety (MoS) is at the core of cosmetic ingredient safety assessment.

On paper, the principle appears simple: comparing a no-effect dose to an estimated exposure in order to verify that a sufficient safety margin exists. In practice, however, things are often far more complex.


The calculation of a MoS relies on two major parameters:

  • the Point of Departure (POD)

  • the Systemic Exposure Dose (SED)


Each of these parameters depends on assumptions, sometimes incomplete data, and ultimately on expert scientific judgment.


👉 As a result, the same substance may lead to different conclusions depending on:

  • the selected data

  • the exposure assumptions

  • or the choices made regarding dermal absorption.


For cosmetic ingredients, a MoS ≥ 100 is generally expected to conclude on safe use. The mathematical ratio itself is straightforward:

MoS = PODsys / SED



However, significant scientific complexity often lies behind this formula.


Part 1: Point of Departure (POD) – Hazard Assessment

The POD corresponds to the toxicological reference dose used as the starting point for risk assessment.


1.1 From NOAEL to PODsys

Historically, the POD has most often been represented by the NOAEL (No Observed Adverse Effect Level), generally derived from repeated-dose toxicity studies, often conducted in rats.


However, since most studies are performed by the oral route, the administered dose must be converted into a systemic dose in order to be comparable to cosmetic exposure. This is referred to as PODsys.


This conversion notably takes oral bioavailability into account.


In the absence of experimental data, the SCCS generally applies a default oral bioavailability of 50%. In some cases, when very low oral absorption has been demonstrated, a value of 10% may be considered.


1.2 The BMD Approach

The SCCS increasingly favors the Benchmark Dose (BMD) approach.

Unlike the NOAEL, which directly depends on the dose levels selected in the experimental study, the BMD approach uses the entire dose-response relationship to identify a dose associated with a predefined critical effect level.


The associated lower confidence limit (BMDL) is generally retained as the Point of Departure because it incorporates statistical uncertainty.


👉 This approach often allows a more robust use of toxicological data.


1.3 Other Points Requiring Attention

In practice, several elements may strongly influence the relevance of the selected POD.


  • Study Reliability

Not all available studies provide the same level of robustness. Scientific publications that do not follow OECD guidelines require a critical evaluation in order to assess their reliability and suitability.


  • Adjustments and Extrapolations

Certain adjustments may be necessary:

  • correction for non-daily administration

  • extrapolation from short-term studies

  • read-across adjustments


  • Data gaps

Data gaps remain frequent in cosmetic safety assessment.

In the absence of a NOAEL, some alternative approaches may be considered, but they require rigorous scientific justification.


👉 Read-across approaches, for example, should only be used following a structured assessment of the relevance of the selected analogue.


Genotoxic TTC approaches and Cramer classes are only applicable to impurities or non-intentionally added compounds, and not to cosmetic ingredients themselves.


Part 2: Systemic Exposure Dose (SED) – Use Assessment

The Systemic Exposure Dose (SED) represents the amount of ingredient that actually reaches systemic circulation following cosmetic product application.

Its estimation relies on several parameters:

  • the amount of product applied

  • the ingredient concentration

  • frequency of use

  • retention on the skin

  • and dermal absorption.


👉 In practice, exposure assumptions directly influence the resulting Margins of Safety.


Several reference datasets coexist, sometimes leading to significant differences depending on:

  • the studied population

  • the product category

  • or real-life conditions of use.



2.1 Sources of Exposure Data

Toxicologists rely on several major references to document cosmetic exposure.


  • COLIPA Studies (Hall et al. 2007, 2011)

These studies constitute the historical references for the main cosmetic product categories used by adults (face cream, deodorant, shampoo, etc.).

They notably provide P90 exposure values widely used in safety assessments.


  • LERCCo Data (2017; Gomez-Berrada et al. 2017/2018)

These French studies provide particularly useful data for:

  • children’s exposure

  • real-life sunscreen use

  • specific consumer usage habits.


They notably help refine certain assumptions compared with generic SCCS default values.


  • RIVM Tools (ConsExpo)

ConsExpo probabilistic models are particularly useful for inhalation scenarios (sprays, powders).

They notably help estimate the respirable fraction and refine exposure assessments.


2.2 Quantification of Dermal Absorption

Dermal absorption is the key conversion factor between external exposure and the Systemic Exposure Dose (SED).


👉 It is also one of the parameters most frequently estimated, and therefore one of the main sources of uncertainty in Margin of Safety calculations.


  • Experimental Values

In vitro measurements on human skin according to OECD TG 428 currently constitute the reference approach.

The SCCS also requires compliance with several methodological criteria (“basic criteria”, SCCS/1358/10) to ensure the quality and interpretation of results.

For MoS calculations, the retained value generally corresponds to the mean plus one standard deviation (or two in cases of high variability).


  • Default Values

In the absence of experimental data, a conservative default value of 50% is generally applied for cosmetic ingredients.

For impurities, the most conservative scenario (100%) is usually retained.


  • Predictive Models

Mathematical models (Potts & Guy, ten Berge, Ates, etc.) may also be used to estimate dermal absorption based on the physicochemical properties of the substance (molecular weight, logP, solubility, etc.).


👉 As with all models, these approaches have limitations and must be interpreted cautiously.


2.3 Waiving and Specific Cases

In certain situations, the physicochemical properties of a substance may justify negligible systemic absorption, allowing adaptation of the toxicological data requirements for safety assessment.


  • High Molecular Weight Polymers

For certain high molecular weight polymers (e.g., MW > 1000 Da), insoluble and poorly bioavailable substances, dermal, and even oral, absorption may be considered negligible.


In these situations, the assessment mainly focuses on:

  • local effects (irritation, sensitization, etc.)

  • impurities

  • or potentially bioavailable residual monomers.


👉 Nevertheless, these situations require strong scientific justification, particularly regarding physicochemical properties and the actual composition of the material under assessment.


Conclusion

Margin of Safety calculations are often presented as simple mathematical ratios. In reality, they rely on a succession of scientific and regulatory decisions.

The selection of toxicological data, exposure assumptions, and dermal absorption estimates can strongly influence the final conclusion.


In practice, safety assessors combine:

  • default data

  • experimental results

  • predictive models

  • and sometimes structural analogues when scientifically justified.


Exposure is now relatively well documented for many cosmetic product categories. In contrast, dermal absorption data remain frequently unavailable or estimated, often leading to conservative approaches.


👉 Harmonization of practices and traceability of assumptions are therefore essential to obtain robust and consistent Margins of Safety.


In a context where data continuously evolve, structuring toxicological information is also becoming a major challenge for cosmetic safety assessors.


Author: Clarisse Bavoux


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