The 15th Clinical Trials on Alzheimer's Disease conference (CTAD), held in San Francisco, California, in late 2022, delivered on its promise to provide global leaders of Alzheimer's disease (AD) with a rigorous forum for discussing the future of AD clinical research. The event garnered an exceptional amount of attention leading up to it, as two leading pharma companies, Eisai and Roche, were slated to share results from their highly anticipated Phase 3 studies investigating new treatments to reduce amyloid beta plaques in the brains of patients with AD.
In attendance were Unlearn's Director of Clinical Affairs, Luis Olmos, and Head of Business Development, Andrew Stelzer, who shared their perspective on the conference's key takeaways and how AI can play a major role in accelerating clinical drug development going forward.
What was the general feeling like at CTAD this year?
Luis: Overall, I think that the atmosphere at the Clinical Trials on Alzheimer's Disease (CTAD) was very exciting this year compared to prior ones. There was a lot of buzz being generated over the Phase 3 readouts for Eisai's lecanemab and Roche's gantenerumab competing drugs using disease-modifying monoclonal antibodies against amyloid plaques. It was also great to see a panel discussion after these presentations and time for questions from the audience. This definitely supports the efforts towards more transparency in better understanding the safety profile and the efficacy of these investigational products and fostering more trust between the pharmaceutical industry, medical providers, and patients alike. Additionally, the CTAD program was outstanding and contained substantial results from Phase 1 to Phase 3 clinical trials.
What were some of the key takeaways from the conference?
Andrew: For at least a decade, there have been many challenges to the amyloid-beta hypothesis, which posits that defects in the processes governing the production, accumulation, or disposal of beta-amyloid in the brain are the primary drivers of AD. I think one of the key takeaways from the conference was that the results from the Eisai and Roche Phase 3 trials supported the hypothesis. We're now seeing that for AD patients with certain threshold levels of residual amyloid, amyloid-clearing treatments do provide some clinically meaningful benefit. As an extension of this, focusing on treatments for patients with Preclinical AD may be a viable approach, of course, assuming we can diagnose patients before they present with symptoms. At this point, it's unclear how much AŒ≤ or tau biomarkers will factor into this diagnosis.
Luis: However, even without the appearance of clinical symptoms, some changes in the brain, such as elevated levels of amyloid plaques, can be detected. The development of AD-associated neuropathology begins as far back as 15 to 20 years before clinical symptoms even appear. That's why the earlier we can detect changes to the brain, the earlier we can intervene and hopefully improve outcomes. There's a study led by Reisa Sperling called the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study that used PET amyloid imaging to identify brain amyloid levels in clinically normal older individuals. 1,169 adults with elevated amyloid levels were enrolled in a clinical trial testing the efficacy of a new investigational product (anti-amyloid antibody) in slowing AD-associated memory loss. The results are expected in early spring of this year.
I think one of the other key takeaways from CTAD is the potential of diagnostic blood-based biomarkers for detecting AD, the advantage of which is that the tests are less invasive than current ones. Michelle Mielke's presentation at CTAD provided a great overview of how blood-based biomarkers should best be used in the clinic and some of the key steps to take before doing so. For instance, peripheral factors such as demographics and comorbidities could influence the levels of biomarkers in the blood. Randall Bateman's presentation was another standout from CTAD. He spoke about the relationship between plasma biomarkers and CSF measures of AŒ≤ 42/40, tau, and NfL species for tracking drug effects in clinical trials of Alzheimer's disease.
Lastly, the other interesting question remaining in the field is that not everyone who has elevated amyloid levels ends up developing clinical symptoms. We don't know why that is.
What did you learn about where the future of AD clinical drug development is headed?
Andrew: Given the results showing the efficacy of amyloid-clearing drugs, it follows that we‚Äôll likely see a push to broaden other aspects of the amyloid-beta pathway cascade. I expect we'll see clinical trials investigating combination therapies focused on multiple targets.
I also expect to see more companies in the neuroscience space (AD in particular) wanting to use next-generation tech, such as artificial intelligence, to run more efficient clinical trials and accelerate drug development. The Eisai and Roche clinical programs in AD reiterated the fact that running successful late-phase trials requires enrolling and retaining thousands of patients.
This is where a company like Unlearn can make a big impact. We partner with pharma and biotech companies to run smaller, faster trials called TwinRCTs™ that increase patient participation and retention.
How can TwinRCTs accelerate AD clinical drug development?
Andrew: TwinRCTs use AI-generated prognostic digital twins to run trials with smaller control arms that generate regulatory-suitable evidence. Unlearn's scientists recently conducted a retrospective analysis of a Phase 2 crenezumab study using prognostic digital twins to reduce the control arm size up to 35% while controlling for type-1 error. The results of this study were presented at the Alzheimer's Association International Conference. Future AD trials would benefit from using TwinRCTs to provide a greater number of trial patients with the chance to take the experimental treatment while also significantly accelerating clinical trial timelines.
Overall, this is an extremely exciting time for AD clinical drug development. All of us at Unlearn look forward to continuing our work with pharma and biotech companies helping to expedite bringing novel treatments to patients in need.
To learn more about how Unlearn's TwinRCT solution leverages AI-generated prognostic digital twins to produce regulatory suitable evidence and accelerate clinical drug development in AD, reach out to us at email@example.com.