The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Abstract

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect—especially with a clear relationship to dose—can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to “de-risk” the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer’s disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

PET as a Translational Tool in Drug Development for Neuroscience Compounds

“…C T ∕C P = f P ∕f ND (5) V ND = f P ∕f ND slope of the regression line provides an estimate of the occupancy and the intercept on the x axis provides an estimate of V ND . The revised Lassen plot can also be used to validate the use of cerebellum as reference region, in case V ND cannot be estimated from full blocking studies.…”

“…PET radioligands developed for binding to specific targets associated with CNS disorders are developed as biomarkers for disease diagnosis, patient stratification, and assessment of disease progression ( Figure ). One example is the development of amyloid and tau PET radioligands for in vivo identification of Alzheimer pathology, Braak staging (tau), and assessment of a biological effect of a compound (reduction of amyloid plaques or tau accumulation) 5 …”

Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials

“…The regions listed are the minimum set we recommend reporting on (Table 1) but reporting results from more regions is encouraged; we include the hippocampus because it is generally only minimally affected in HD, but algorithms are well-established for its measurement due to its prominence in AD research. The rationale for reporting regions atrophied by different degrees in HD is to help disambiguate treatment effects that are consistent with a slowing of the disease process from non-specific changes that might reflect confounding effects such as fluid shifts or inflammation (11). It is also important to be explicit on the directionality of treatment-induced changes (favoring treatment or favoring placebo) as the natural directionality is different for outcomes such as ventricular volume (increase is worse) and those measuring parenchymal volumes or thicknesses (decrease is worse).…”

Brain susceptibility imaging provides valuable in vivo insights into cerebellar diseases, but biological interpretations remain elusive

“…Such analysis, in parallel with efforts to improve automation of dentate nucleus segmentation, may offer exciting possibilities for translation of QSM to multi-centre and clinical trial contexts. 7 …”