Despite the threat of tariffs, increased competitive pressure, a significant innovation gap, and uncertainty regarding future medicine prices, there are many positive prospects for Danish and European biotech and pharmaceutical companies.

It’s no secret that the European life science sector's innovation capacity and competitiveness—particularly compared to the U.S. and China—are under pressure.

Upcoming life science strategy, EU presidency, and Biotech Act bring renewed hope for pharmaceutical biotech companies.

We would like to exploit the Danish life science research potential, we would like to have many viable startups, and we would like to strengthen Denmark's role as a life science export industry.

There is a recognition from the government of the crucial role of the life science sector in the Danish economy, including through the export of pharmaceuticals, and there is a strong appetite to create more growth stories like Novo Nordisk.

If the pattern of the majority of life science funding in Denmark going to early stages continues, alternative financing options need to be established for more mature companies that require long-term investments to scale up and realize their potential in Denmark.

The reasons for the disparity between the extent of clinical drug research and the disease burden likely vary for different diseases. However, there are probably some common factors, including the ability and willingness to pay or lack thereof, and thus the expected return associated with investment. In these cases, it makes sense to establish commercial incentives for research and development in under-prioritized disease areas.

The ongoing discussion about initiatives that can counteract and reduce unnecessary treatments and treatments with low value is also relevant for new medicines. One of the dilemmas here is the weighing of the relationship between the clinical significance of a drug and the costs of treatment with the drug.

There are many questions related to the development, limitations and validity of AI and machine learning applied to healthcare solutions and products. Despite various challenges such as dataset shifts and automation bias, one can envision a future where AI-generated data is used to document clinical safety and efficacy, thereby replacing the randomized controlled trials as we know them today. This would lead to a massive acceleration in the development of new healthcare solutions and products. However, there is still a way to go. Another and perhaps more realistic scenario is broader use of AI-supported clinical research and drug development, where AI and machine learning become an integrated tool for faster and less expensive development of safer and more efficacious healthcare solutions and products. It could be for accurate and comprehensive prediction of desired and unwanted effects, individual disease development, risk profile and prognosis, as well as for identifying which patients will benefit the most from new therapies, and allocation to the optimal treatment offer.