Europe’s biopharma stakeholders should seek innovation through AI to meet the challenges of treatment resistance, drug affordability, and a creeping patent cliff, channelling the continent’s health data to catch up to more competitive industries in the US and China.
This was the view Montserrat Dabban outlined in her keynote address at the 2025 CPHI conference, which took place on 28–30 October in Frankfurt. Dabban is the director of strategic foresight and international relations at Biocat, a public-private entity to support the healthcare ecosystem in Catalonia, Spain.
She stated that stakeholders in the European biopharma indusattempt could find innovative solutions to challenges in several key areas by applying AI to leverage the vast amounts of data collected by health systems for drug discovery and improved trial design.
Dabban identified several key challenges faced by the global biopharma indusattempt today, including logistical ones like scaling up cell and gene therapies, improving clinical trial speeds, drug affordability, as well as the uncertainties of supply chain resilience, sustainability, and pandemic preparedness.
Meanwhile, she noted the indusattempt faces an oncoming wave of drug patent expiries, risking billions of dollars in total annual revenues: “Pipelines are thinning … pipeline growth slowed.”
Additionally, current research trconcludes have a lopsided focus on development in metabolic, immunological, and oncological diseases at the expense of other indications.
To avoid the pitfalls of what Dabban termed “therapeutic saturation” — too many molecules developed for too few tarreceives — she stated all stakeholders in European biopharma must cooperate in addressing several key domains: aging populations, brain health, cancer, antimicrobial resistance, health equity, and rare disease. To do so, she pointed to upscaling the utilisation of Europe’s extensive health databases.
Dabban stated that improved apply of data from wearable devices can open the door to digital phenotyping, improved biomarkers, and centralised concludepoints in neurological trials. For example, in oncology, she suggested exploring adaptive trials and federated analytics so that data from multiple sources can be analysed without requireding to be centralised.
But Dabban informed attconcludeees that a vast data store was “applyless if it’s not processed, and we required technology for that”. Hence, AI would prove key to effectively processing health data as a means to developing rapider clinical trials with more focussed concludepoints, she stated.
“We are at a crossroads. We have to seize the moment for Europe … we have to expect more competition, but also more breakthroughs,” Dabban concluded.
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