Editorial update — September 28, 2026. Revised by Averin Capital to reflect subsequent clinical results and developments in diagnostics. The original publication date and author attribution are retained.
Progress in Alzheimer’s disease depends on more than identifying a promising biological target. It requires finding the right patients, measuring meaningful changes, and showing that a treatment helps people in well-designed clinical trials. Better tools can improve that process, but they do not remove the uncertainty inherent in drug development.
Clinical evidence must lead
Our original article highlighted interest in GLP‑1 therapies as a potential approach to Alzheimer’s disease. That hypothesis has since faced an important clinical test. In November 2025, Novo Nordisk reported that its evoke and evoke+ phase 3 trials of oral semaglutide did not demonstrate superiority to placebo in slowing disease progression. Improvements in biomarkers did not translate into a delay in clinical progression. The company discontinued the planned extension period.
The distinction matters for founders and investors: an association in observational data, a plausible mechanism, or a change in a biomarker is a reason to investigate. It is not evidence that a therapy improves clinical outcomes.
Diagnostics are changing the development process
In May 2025, the FDA cleared the Lumipulse G pTau217/β‑Amyloid 1–42 Plasma Ratio blood test to help diagnose Alzheimer’s disease in symptomatic adults aged 55 and older. It must be interpreted alongside other clinical information and is not a screening or standalone diagnostic test.
More accessible diagnostic tools could help researchers identify appropriate trial participants and characterize disease earlier. Their usefulness still depends on validation in the intended population, a clear clinical workflow, and evidence that the information changes a decision.
What we look for
At Averin, we are interested in companies connecting rigorous biology with better tools for diagnosis, patient selection, and clinical development. We ask whether an approach measures what matters, how it will be tested, and what a negative result would teach the team.
AI may help teams analyze complex data and develop hypotheses. The investment case ultimately depends on reproducible evidence and a credible path to clinical use. A more capable development toolkit is a reason for sustained work, not a guarantee of therapeutic success or a predictable approval timeline.
