Key takeaways
- Sequential has raised $3.5m (€3m) in its first equity round, bringing total funding to $7.5m (€6.5m)
- New capital will support development of an AI discovery engine built on a large real-world clinical dataset
- The platform aims to accelerate ingredient discovery, active complex design and next‑generation skin health innovations
- CEO Oliver Worsley says large-scale biological datasets can help predict product performance and reveal new ingredient interactions
- Sequential continues to expand testing infrastructure and work with global brands and suppliers
UK-based genomic testing company Sequential has closed its first equity round, securing $3.5m to further grow the business and bringing its total funding to $7.5m to date.
The round was co-led by Sparkfood and Corundum Systems Biology (CSB), with participation from Dermazone Holdings, SOSV, Scrum Ventures, an ex-General Partner at Index Ventures, and continued support from Innovate UK.
Sequential focuses on skin health innovation through its proprietary non-invasive testing platform, which transforms complicated biological data into actionable insights. Its dataset now comprises more than 50,000 samples, 4,000-plus ingredients and 10,000 participants worldwide.
The newly raised capital will support the development of an AI-powered discovery engine built on Sequential’s proprietary real-world clinical dataset. This aims to enhance the discovery of next-generation active complexes and novel bioactive ingredients.
AI discovery platform for large-scale skin biology analysis
We spoke to Sequential’s CEO and co-founder Oliver Worsley to find out more.
CosmeticsDesign-Europe (CDE): Hi Oliver, can you tell us any more about the AI discovery platform? How does it work and how could it impact the beauty and personal care industry in the future?
Oliver Worsley (OW): “Our core focus for the past several years has been building one of the most detailed clinical datasets on how skin biology responds to products and ingredients. Through our testing platform we’ve generated data from more than 50,000 samples across thousands of ingredients and participants.
“The AI discovery platform is essentially a way to analyse that dataset at scale. We’re using machine learning approaches to look for patterns between ingredients, microbiome changes, molecular biomarkers, and observed skin outcomes.
“Historically, product development in personal care has often relied on relatively small studies or incremental formulation changes. By analysing large-scale real-world clinical datasets, we believe it becomes possible to identify biological signals and ingredient interactions that would otherwise be very difficult to detect.”
CDE: You are using this dataset to predict and design next-generation active complexes. Can you share more about this and what it means for future beauty NPD?
OW: “What we mean by that is that we now have a dataset that links ingredients, formulations, multi-omics (including microbial and human biomarkers) measured directly from human skin.
“When you analyse those relationships at scale, you can begin to see which combinations of ingredients tend to influence certain human gene pathways or microbial communities.
“In the future, that type of analysis could help guide the development of new ingredient combinations or active complexes that are designed with a clearer biological rationale from the start.
“So rather than relying purely on trial-and-error formulation, there is an opportunity to use large clinical datasets to inform which ingredient strategies are most likely to be effective.”
CDE: What are you currently working on?
OW: “Right now, a lot of our work continues to focus on expanding and refining our clinical testing infrastructure.
“We’re continuing to run studies with global brands and ingredient suppliers to understand how products influence both microbial and molecular biomarkers on the skin.
“At the same time, we’re beginning to apply more computational approaches to the dataset we’ve built over the past several years. That includes exploring how microbiome patterns relate to skin conditions such as acne, atopic dermatitis and rosacea, and how ingredients influence those systems.
“So, the immediate focus is really on growing the dataset and extracting deeper insights from it.”
CDE: Is there anything else you’d like to talk about on this topic?
OW: “One of the biggest challenges in personal care innovation has historically been connecting biological mechanisms with real-world product performance.
“What we’ve tried to do at Sequential is build the infrastructure to measure those relationships directly from human skin at scale. That means linking microbiome data, molecular biomarkers, and controlled product testing in real-world clinical settings.
“As the dataset grows, it becomes an increasingly valuable resource for understanding skin biology and helping guide the next generation of scientifically validated products.”
