Advancing Parkinson's research through imaging innovation and AI

Parkinson's disease presents unique challenges for clinical researchers. Its heterogeneous progression, fluctuating symptoms, and the subjective nature of traditional clinical assessments create significant obstacles in trial design and patient selection. In our recent two-part podcast series with Ixico , Leone Atkinson, Executive Medical Director of our Neuroscience and Ophthalmology, Joanie Brown, Head of Operations at our Rapid Development Studio AI/ML and host Amber Burg explored these challenges alongside breakthrough imaging technologies and artificial intelligence that looks to transform how we design, conduct, and deliver clinical trials.

The complexity of tracking Parkinson's in clinical trials

Traditional clinical rating scales like UPDRS can be subjective and variable, sometimes plateauing over time and providing only a snapshot of a patient's presentation while in the clinic. This variability makes it difficult to establish steady patient populations and accurately measure disease progression. Imaging biomarkers offer the objective, quantifiable measurements needed to overcome these challenges, providing better population definition and more precise tracking of treatment response¹.

We're incorporating imaging into almost every Parkinson's disease study these days. The type of imaging really depends upon the phase of the study, the objectives, and the target population.
— Leone Atkinson, Executive Medical Director of Neuroscience and Ophthalmology, Fortrea

Strategic imaging approaches across trial phases

The strategic use of imaging varies significantly depending on the clinical development phase and study objectives:

Early phase studies focus on foundational questions:

Target engagement and mechanism of action
Proof of concept validation
Faster pathway to critical decision points

Late phase development emphasizes regulatory readiness:

Complementing clinical endpoints
Enhancing diagnostic confidence
Supporting regulatory submissions with objective evidence

This phased approach makes sure t imaging biomarkers deliver maximum value at each stage of development while building the evidence base needed for regulatory acceptance.

Established and emerging biomarker technologies

The gold standard: SPECT imaging Dopamine SPECT remains the established approach for confirming nigrostriatal dopaminergic deficit, with strong regulatory endorsement through FDA and EMA letters of support^2,3. Led by the Critical Path for Parkinson's initiative, this measurement serves as an agnostic enrichment biomarker in clinical trials, though questions remain around its longitudinal sensitivity for tracking non-linear disease changes.

Next-generation MRI techniques Several advanced imaging approaches are emerging as exploratory endpoints with promising early results:

Neuromelanin-sensitive imaging: Recently deployed in anti-alpha-synuclein trials, showing treatment effects in phase 2 studies^4,5
Quantitative susceptibility mapping: Tracks iron accumulation and shows increasing use as a potential measure for disease progression⁶
Advanced diffusion techniques: Enable understanding of microstructural changes in early disease stages⁷

"We see some quite exciting novel MRI sequences, like those that are sensitive to melanin changes, and quantitative susceptibility mapping, which tracks iron accumulation and is being increasingly used as a potential measure for PD progression."
— Robin Wolz, Chief Scientific Officer, Ixico

Implementation strategies for sponsors

Early engagement is critical Begin discussions about imaging strategies during the earliest phases of clinical development planning, even before protocol synopsis development. This early collaboration allows for proper site qualification and capability expansion, ensuring imaging biomarkers don't become critical path constraints.

Lead time and site preparation Success requires adequate lead time for site qualification and expansion into new regions. Working closely with imaging collaboration assures alignment on biomarker selection and site capability mapping, enabling the transition from exploratory endpoints to validated secondary and primary endpoints.

Validation pathways Moving novel imaging biomarkers from exploratory use requires robust evidence generation through natural history studies and academic collaborations. This involves developing standardized acquisition protocols and building both technical accuracy and clinical meaningfulness evidence.

The AI advantage in Parkinson's research

Artificial intelligence is emerging as a transformative force in Parkinson's research, offering new possibilities for patient stratification, imaging analysis, and trial efficiency. Speaking during one of Fortrea’s podcast episodes, Ixico, a global leader in neuroscience imaging and biomarker analytics⁸, is developing deep learning models which can integrate multimodal data—combining structural imaging with blood-based biomarkers and cognitive assessments—to build sophisticated accurate analysis. This approach allows the AI to detect subtle patterns and relationships between brain structure, blood chemistry, and cognitive performance—patterns that might be invisible when each data type is examined separately.

However, the most promising applications maintain a human-centred approach, where AI enhances rather than replaces human expert ise. This philosophy is explored in the podcast episode, to address critical concerns around model explainability, regulatory compliance, and ensuring that AI outputs remain biologically meaningful and clinically relevant.

Moving research forward together

The integration of advanced imaging biomarkers represents a pivotal moment in Parkinson's research. Success requires strategic collaboration between CROs, technology companies, and sponsors who share a commitment to scientific rigor and patient-focused innovation. By leveraging these emerging technologies while maintaining robust validation standards, we can help predict disease progression, identify patients for clinical trials, or evaluate the effects of new treatments with greater precision than ever before.

Ready to explore how these innovations can accelerate your Parkinson's research? Listen to our full two-part podcast series with Ixico to dive deeper into the strategies and technologies shaping the future of neurodegenerative disease trials.

References

1. Gelb, D. J., Oliver, E., & Gilman, S. (1999). Diagnostic criteria for Parkinson’s disease. Archives of Neurology, 56(1), 33–39.
2. FDA Letter of Support, issued to: Coalition Against Major Diseases (CAMD), Critical Path Institute. https://www.fda.gov/media/112637/download Created on: March 16, 2015. Last accessed: September, 2025
3. EMA Letter of Support, issued to: Critical Path for Parkinson’s (CPP) Consortium. https://www.ema.europa.eu/en/documents/other/letter-support-molecular-imaging-dopamine-transporter-biomarker-enrichment-biomarker-clinical-trials-early-parkinsons-disease_en.pdf Created on: September 2, 2016. Last accessed: September, 2025
4. Sulzer, D., Cassidy, C., Horga, G., et al. (2018). Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson’s disease. npj Parkinson’s Disease, 4(11).
5. University of Texas Health Science Center, Houston. (2020). A Phase IIa Study of Mesenchymal Stem Cells in Idiopathic Parkinson’s Disease [Clinical trial]. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT04506073 . Updated on: July 2024. Last accessed: September, 2025
6. Li, K. R., Avecillas-Chasin, J., Nguyen, T. D., et al. (2022). Quantitative evaluation of brain iron accumulation in different stages of Parkinson's disease. Journal of Neuroimaging, 32(2), 363–371.
7. Bingbing G., Yujing Z., Yanwei M., et al. Diffusion Kurtosis Imaging of Microstructural Changes in Gray Matter Nucleus in Parkinson Disease. Front Neurol. 2020 Apr 17;11:252.
8. https://ixico.com/therapeutic-areas/#parkinsons-disease-pd Last accessed: September 2025