This article recaps our Microbiome Intelligence Live session with OneSkin, highlighting how microbiome validation supports product development and claims.
Clinical claims are often built through a linear process: run a study → extract a result → translate it into messaging. In practice, microbiome-based skincare validation is rarely linear.
What matters is not the result itself, but whether it can inform formulation, claims, and product direction. At our recent Microbiome Intelligence Live, Elsa Jungman, Ph.D., CEO of HelloBiome, and Alessandra Zonari Scheel, Ph.D., Co-founder and CSO of OneSkin, discussed how microbiome validation supports those decisions.
Microbiome data is only useful when its meaning is clear.
Understanding how signals translate into formulation and claims determines whether that data can be used in practice.
Across applied product development, this becomes visible in how microbiome signals are interpreted — and where that interpretation breaks:
1. The Myth of Microbiome Diversity in Skincare
Understanding what microbiome changes mean is what makes the data actionable. Measuring change alone is not enough.
Does a “younger” microbiome always mean it is more diverse? Not necessarily.
As discussed during the our session, skin aging is often associated with a decline in beneficial bacteria and an increase in opportunistic species — leading to higher diversity but lower stability.
In the microbiome study conducted with OneSkin, the reverse was observed: the microbiome shifted toward a more stable state, with stronger representation of bacteria associated with healthier skin and lower overall diversity.
Without context, diversity can be misread as improvement when it may reflect imbalance. This remains a core challenge for microbiome claims: interpretation.
2. Microbiome in Product Development: Starting Point or Supporting Layer?
Microbiome validation does not typically define the starting point of product development. Instead, it becomes relevant once a formulation direction is already defined.
It is used to clarify how a full formula interacts with the skin ecosystem — not to replace underlying biological rationale.
This reflects how product pipelines operate in practice. Without this layer of validation, signals can be misinterpreted and claims may not hold.
Used this way, the microbiome is not an add-on — it directly informs product decisions and performance evaluation.
3. Early Validation: Direction or Final Proof?
Early microbiome testing is not intended to prove efficacy, but to determine the next steps in product development.
Before formal clinical studies, an early-stage test was used to answer a simple question: Are there sufficient signals to continue development?
That signal was enough to justify further validation. This stage is often overlooked, but it helps clarify early decisions and strengthen skincare claims substantiation before major resources are committed.
4. Strong Validation Sometimes Means Stopping
Validation is not only about confirming what works. It’s necessary to identify what should not move forward. When results do not support a formulation, they guide changes, not progression.
With a clear picture of what needs to be corrected and improved in formulation, microbiome validation strengthens the final product and the credibility of its claims.
5. Layers of Microbiome Validation
Microbiome validation functions as a layered system, where each stage supports a different type of decision:
- Early-stage microbiome testing determines whether a formulation shows enough signal to move forward
- Microbiome claims validation confirms whether the product acts on the intended biological mechanism
- Microbiome data shows how the full formula affects the skin ecosystem over time
- Clinical claim studies confirm whether these effects translate into measurable outcomes
Together, these layers prevent weak signals from progressing and ensure that formulation and claims are supported at each stage.
From Longevity Science to Microbiome Proof
What matters in microbiome validation is not the data itself, but how early it shapes product direction. The difference comes from identifying which signals are reliable enough to act on and which should not guide decisions.
That’s what determines whether validation strengthens a product — or reinforces the wrong conclusions.