Early, precise and efficient; the methods and technologies advancing Alzheimer’s and Parkinson’s R&D

The role of innovative new techniques in neurodegenerative trials. 

The attrition rate for phase 2 and 3 Alzheimer’s trials was nearly 100% between 2003 and 2022. These high failure rates in neurodegenerative drug development have been attributed to disease complexity, coupled with limitations in trial design and execution. Sponsors may be willing to accept the significant risks involved in R&D for neurodegenerative therapies due to the urgent and growing need for effective treatments and an expected return for a successful development.

However, recent approval of three disease-modifying treatments for Alzheimer’s disease indicates the tide is beginning to turn. Successful Alzheimer's trials were differentiated by their use of new tools and methods for correctly diagnosing patients, treating them earlier in their disease, and assessing the effects of treatment with improved sensitivity.

Because the breakthroughs in Alzheimer’s can be partially attributed to improvements in trial design and execution, the entire neurodegenerative field stands to benefit from these recent advancements. This whitepaper offers a roadmap for pragmatic adoption of the technologies and methodologies most poised to accelerate CNS research and development. We provide guidance based on ICON’s experience and a survey of over 120 CNS therapeutic developers focused on neurodegenerative disease, who provide exclusive insights into the present adoption and impact of innovative methods and technologies, including, biomarkers, digital health technology, AI, diversity plans and decentralised elements. 

• Hone in on therapeutic approaches that better account for the complexity of CNS disorders and heterogeneity of patient populations
• Diagnose patients earlier and more precisely, enabling more effective treatment
• Accelerate patient enrolment, improve diversity, and enhance the feasibility of clinical development
• Enable sensitive, real-time data collection