LEO Pharma bets on AI to find the next skin-care breakthrough
Company looks to algorithms and open innovation to secure the future
Lars Erik Kristensen, VP and Head of Innovation at LEO Pharma, tells us how artificial intelligence and a new wave of data-driven collaborations are reshaping the company’s approach to drug discovery.
In most sectors, AI is changing how organisations compete. In pharmaceuticals, it may soon decide who can do so.
In Denmark, dermatology specialist LEO Pharma is betting that algorithms and open innovation – rather than closed, siloed research – will define the future of its business.
The 116-year-old company is rethinking drug development through what it calls a search-and-develop model: instead of relying solely on internal labs, it actively scouts for promising discoveries from biotech firms, universities and patient organisations, then applies its own expertise to bring those innovations to market.
Now, LEO Pharma is adding a digital dimension to that strategy. AI tools are being woven into how the company identifies new research opportunities, analyses complex biological data and decides which external partnerships to pursue.
“We primarily use analytical AI to analyse large and complex data sets in medical dermatology,” says Lars Erik Kristensen, VP and Head of Innovation at LEO Pharma. “These tools systematically accelerate how we scan the external environment for new assets and help us keep track of scientific and clinical trends.”
While most of the company’s focus remains on analytical AI rather than generative models, Kristensen says LEO Pharma is monitoring rapid developments in generative tools, from molecule simulation to automated literature reviews, to assess where they might create value.
From noise to insight
AI’s real power, Kristensen argues, lies in its ability to spot what human researchers might overlook. By detecting subtle correlations across diverse data sets – from biomarkers and cell behaviour to patient-reported outcomes – algorithms can flag new therapeutic angles and untapped market opportunities.
“AI helps us quickly and efficiently identify and assess new opportunities and trends in dermatology,” he says. “Our enabling tools make it easier to make informed decisions about which external innovations to pursue, so we can bring new treatments to patients faster and more effectively.”
Dermatology, being a highly visual discipline, offers fertile ground for AI. Analysing skin images, biopsy data and patient self-assessments all demand models capable of handling large, heterogeneous data sets, including different skin tones, conditions and cell types.
Academic AI meets industrial R&D
That data challenge underpins LEO Pharma’s newly announced three-year partnership with the Parker Institute at Copenhagen University Hospital, an academic collaboration that blends clinical trial design, AI and advanced analytics.
The partnership will apply the Parker Institute’s expertise in artificial intelligence and single-cell RNA sequencing to deepen understanding of how immune cells behave in skin diseases. The findings could guide LEO Pharma’s next generation of dermatological treatments – and potentially feed into wider pharmaceutical R&D.
For Kristensen, who previously worked at the Parker Institute, the collaboration exemplifies LEO Pharma’s “world-as-our-lab” mindset: a deliberate move to make innovation porous and data-driven.
“By partnering with the Parker Institute, we see potential for unlocking new insights and strengthening our ability to advance the standard of care within medical dermatology,” he says.
Managing the data dilemma
Like many IT leaders, Kristensen is quick to acknowledge the challenges in AI adoption. Data quality, patient privacy and regulatory compliance are major constraints in healthcare R&D. And of course, explainability - understanding why an algorithm produces a given result - remains a sticking point, especially in a domain where human health is at stake.
“It requires close collaboration between AI specialists, dermatology experts and our external partners to ensure the solutions are relevant and effective,” he says.
R&D in the age of algorithms
For LEO Pharma, AI has already changed how teams operate, automating repetitive processes, accelerating literature reviews and improving coordination across research sites and external collaborators. Over time, Kristensen expects the technology to become central to decision-making, pushing the company towards a more data-driven and globally networked model of R&D.
For IT and innovation leaders beyond life sciences, LEO Pharma’s move offers a glimpse of what’s coming: industries where competitive advantage depends less on owning the data, and more on how intelligently – and collaboratively – it’s used.